Posthumanist Learning: What Robots and Cyborgs Teach us About Being Ultra-social [1 ed.] 9781138125179, 9781138125186, 9781315647661

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POSTHUMANIST LEARNING

In this text Hasse presents a new, inclusive, posthuman learning theory, designed to keep up with the transformations of human learning resulting from new technological experiences, as well as considering the expanding role of cyborg devices and robots in learning. This ground-breaking book draws on research from across psychology, education and anthropology to present a truly interdisciplinary examination of the relationship between technology, learning and humanity. Posthumanism questions the self-evident status of human beings by exploring how technology is changing what can be categorised as “human”. In this book, the author applies a posthumanist lens to traditional learning theory, challenging conventional understanding of what a human learner is, and considering how technological advances are changing how we think about this question. Throughout the book Hasse uses vignettes of her own research and that of other prominent academics to exemplify what technology can tell us about how we learn and how this can be observed in real-life settings. Posthumanist Learning is essential reading for students and researchers of posthumanism and learning theory from a variety of backgrounds, including psychology, education, anthropology, robotics and philosophy. Cathrine Hasse is Professor of Cultural Anthropology and Learning at the University of Aarhus, Denmark.

POSTHUMANIST LEARNING What Robots and Cyborgs Teach us About Being Ultra-social

Cathrine Hasse

First published 2020 by Routledge 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 52 Vanderbilt Avenue, New York, NY 10017 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2020 Cathrine Hasse The right of Cathrine Hasse to be identified as author of this work has been asserted by her in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record has been requested for this book ISBN: 978-1-138-12517-9 (hbk) ISBN: 978-1-138-12518-6 (pbk) ISBN: 978-1-315-64766-1 (ebk) Typeset in Bembo by Deanta Global Publishing Services, Chennai, India

CONTENTS

Foreword vii 1 Introduction 1 Vignette 1.1: Jibo’s “empty curiosity” 5 Vignette 1.2: The robot is present 16 2 Posthumanist learning in education Vignette 2.1: The lamentations of an uneducated daughter Vignette 2.2: MOOC in Kelly Writers House

31 39 51

3 Emotional collectives Vignette 3.1: Meeting the Mars Pathfinder Vignette 3.2: Particle in cigar entanglements

66 73 83

4 Robots in a storied world Vignette 4.1: Olimpia’s yawns Vignette 4.2: Telenoid in the lab

98 102 120

5 The materiality of words Vignette 5.1: Becoming physicists Vignette 5.2: What robots are really like

133 136 148

6 Socio-material concept formation Vignette 6.1: Materialising robots

167 170

vi Contents

7 Ignorance in the collective of collectives Vignette 7.1: Silent drawing

201 228

8 Learning with cyborg technology Vignette 8.1: Feeling the world

240 245

9 Extended mindful bodies Vignette 9.1: The ship collective of collectives

271 281

10 Ignorance by proxy Vignette 10.1: Learning like Tay Vignette 10.2: The clash of ultra-socials

306 314 327

Index 347

FOREWORD

In a way, I have worked on this book since, as an anthropologist, I enrolled as a physics student in 1996 to study the cultural becoming of physicists. During my own learning process at the Niels Bohr Institute for Physics in Copenhagen, I experienced a strange transformation as I learned from the physicists that the world was much more material than I had previously realised. As a social scientist studying humans, I had paid attention to humans. Now I began to notice forces, frictions and relations between materials as I aligned my perceptions with the world of the physics students. The material world simply changed as I learned, and this simultaneously changed my ability to understand and communicate with my fellow students. When I began practising as a fieldworker, I rarely gave my material surroundings any explicit attention. After my fieldwork on the culture of physics, a normal trip on my bicycle turned into a perception of friction and forces as the bicycle wheels turned on the asphalt. My perception of the night sky changed from looking at the stars as glittering dots to a perception of burning materials filled with gasses in outer space. Ever since then I have been occupied with trying to understand what happened to me in the learning process. In later work, I have expanded my study of how humans learn from physics to the technical sciences, which has brought new transformations, insights and questions. I have never finished trying to dive deeper into these learning processes that transformed my material world. If it could happen to me, it can happen to us all. What does it mean that our material world is transformed when we learn in and outside of formal education? Does it matter who we learn from? Do we align with other humans through our learning with materials? When, where and how do we learn to perceive the same world as a collective? And, in addition, what happens when we do not? These are some of the questions guiding the explorations in this book. Learning is often assumed to be a process that can be emulated by machines. This

viii Foreword

assumption does not take into account the fact that machines run on algorithms that are basically alike in the intelligence they display. Humans are different. I have found the new developments in the technical sciences, some of which strive for a posthuman future, a constant inspiration for developing a new theory of learning that allows humans to be embedded in other humans and to align in the transformed becoming of materials. Physicists, technicians and philosophers of posthumanism emphasise the world as a physical place, but often overlook their own processes of learning. We are not the rational, autonomous enlightened learners expected in humanist theories, but ultra-social humans collectively formed over time by other humans with material surroundings. Humans transform each other as we learn to perceive material surroundings in ways that align our perceptions without ever making us uniform. New materialist posthumanist theories have inspired me to explore learning theories from this perspective of a human in constant becoming with social and material surroundings. The technicians striving for a posthuman future, the posthumanist theoreticians and I differ, overlap and contradict each other. We are not separate in this book, but entangled. The fascination for robots and cyborgs inspires me, but most of all I am fascinated by the humans we become when we use robots, cyborgs and posthumanist theories to explore posthumanist learning. Throughout the book, I use what I call “vignettes” to open relevant discussion points. These vignettes are small episodes or bits of text taken from my own research or other people’s research that I use to ping-pong with in the development of my argument for a posthumanist learning theory. Vignettes also functioned as stepping stones, as well as stones in my shoe, to develop my thinking. I make use of many different theoretical frameworks, postphenomenology, new feminist materialism and a Vygotsky-inspired anthropological psychology, that are not usually brought together. What I have to say will not be new to all my readers. In the process of writing, I have continuously been amazed when I have learned how my own thoughts have resonated with and sometimes been challenged by other people’s thinking. I continue to explore my own ignorance as part of my continuous learning through reading and communicating, but also through visiting the many physical sites that people have been kind enough to share with me over the years. I want to thank all the physicists and engineers who, over time, have shared their material-conceptual collectives with me. I cannot claim I present a fully-f ledged new learning theory, nor that I go deep enough into the philosophical, anthropological, psychological or technical issues raised. The work has merely begun and I am more ignorant now than when I started. I consider this a good position in which to be. I thank all who helped me along the way to enhance my awareness that a new posthumanist learning theory requires a new understanding of how humans and materials shape us as human collectives. I could have included a long list of colleagues (living and deceased) whose thoughts and material presence have helped

Foreword 

ix

me develop my own thinking. Many of these are mentioned in the book and I thank you all. I could also have mentioned a number of artefacts, mostly made by people - our pet robot, apps, books of poetry, but also sunsets and gusty seashores as well as non-humans like chirping birds. They have all helped me to ask new questions. All my colleagues in the research programme, “Future Technology, Culture and Learning”, tied to the interdisciplinary environment around “The Villa” at DPU, Aarhus University in Denmark, have provided the most inspiring and challenging environments along with visiting researchers and visits to and from colleagues from Denmark and abroad. Here I especially want to thank the “Human Futures Program”, my Department of Educational Anthropology, the environment around postphenomenology following Don Ihde’s work at SUNY, now spread all over the world, the psychologists Mariane Hedegaard, Seth Chaiklin, Anne Edwards and other colleagues from the Department of Education at Oxford University, and my colleagues Dorte Marie Søndergaard and Malou Juelskjær, the inspiring group of anthropologists around Tim Ingold at Aberdeen University, colleagues from the Department of Education at Edinburgh University, colleagues from Georgia Tech University, and the gender group at Uppsala University. Finally, I want to thank Lila Anne Todd who patiently helped with the proofs and my careful editors Áine Madden (at Deanta) and Ceri McLardy (at Routledge). Our two cats, my old fountain pen, my new glasses, certain apps on my tablet, my well-functioning private computer and many other humans and non-humans have continuously given me comfort and all kinds of support. I want to give a special thanks to my husband, Thomas, my daughter, Stina, son-in-law Steffer, my mother, Hanne Marie and my two brothers, Morten and Christian, as well as my friends Nina and Charlotte, who have all engaged me in exciting discussions without which this work would not have been possible. Most recently I have been communicating with the baby Sonja Marie, whose future this is all about.

1 INTRODUCTION

Why a theory of posthumanist learning? Why now? This book proposes that we need a theory of how material words and material things are entangled through a process that we might formerly have understood as humanistic learning – with an underlying assumption of an individual learner that learns in separation from a material environment. However, what if the humanistic conception of learning is wrong? Maybe we are not learning as individuals separated from our social and material surroundings. Rather, the posthumanist perspective I explore understands the concept of learning from the perspective of the human as an ultrasocial collective of social and material collectives. The learning, which connects us in collectives, shapes materials and concepts, just as materials and concepts shape collective learning processes. Learning has always been a confusing concept to work with because it is used in a multiplicity of ways in different scientific and professional vernaculars (e.g. Scott & Hargreaves 2015). Posthumanist learning may add to this confusion, but also points in a new direction – towards a radically transformed conception of what it is to be human. “As naturalcultural hybrids proliferate, Homo, the conventional subject of anthropological concern, is no longer a clearly bounded biological subject” (Kirksey & Helmreich 2010, 556). This move does not entail that we give up understanding humans as learners, but that we understand learning as a pivotal process for how materials, including human bodies and words, have an entangled agency in a world that matters to humans. In the learning sciences, learning is often defined as a change that denotes some kind of process. So much can be agreed on, but there are many different approaches to explain what kind of process and what kind of change occurs. Though the concept of learning has reached a popularity that is overwhelming in education (e.g. Biesta 2010), there is no agreement on how the concept of learning should be defined.

2 

Introduction

One of the difficulties in the study of learning is the lack of an established discipline. Contrary to disciplines like physics or chemistry, the learning sciences do not work from a paradigm, where an agreed framework of ordered thoughts are responded to and selected among the ideas that are communicated. Nevertheless, a largely unquestioned understanding of “the human” as an individual detached from a material world underpins the Western learning sciences (Snaza & Weaver 2015). It is this view of the human, which is about to change. Posthumanists deconstruct and decentre the rational, intelligent, stand-alone human and emphasise material aspects of agency in the new posthumanist theories. However, few posthumanists have looked into the implications for learning theory when we take a look at what humans bring to bear in a world of material agency, which, after all, has been named “the Anthropocene” (Zalasiewicz et al. 2010). If learning is understood as a basic process, and not just of importance for education, we need a more thorough understanding of how learning constitutes human agency and perception in posthumanist theories. The posthumanist learning theory I propose denotes a process where materials come to align our collective memories and motives, which again inform our embodied perceptions of materials within a phenomenal world. When humans align with materials, new collective agencies in a material world and cultural materialisations follow. At the core of this alignment process of learning, we find human concepts – as material words that connect us collectively with material things. The concept of “concept” is a topic I take issue with throughout the book (especially in Chapters 5, 6 and 7). As material words, concepts have material agency when expressed, and this further affects the learning processes that transform how we perceive materials and behave with materials in our surroundings. Concepts precede thinking in the psychology of Vygotsky (Derry 2013, 112). From an anthropological point of view, concepts are cultural because human collectives differ in their perception and material engagements. Furthermore, these collectively shared materialised concepts are embodied in culturally localised human bodies. These bodies change as humans engage with materials or as materials engage with humans (I discuss these topics from a postphenomenological perspective in Chapters 8 and 9). In the learning sciences, the transformation of “human”, in a humanist sense, to “posthumanist human” has practical implications for teaching, but education is not my topic. Learning in education is one way to understand posthumanist learning. Posthumanist learning is, in the present discussion taken up in this book, a general theoretical set of insights we can gain when the new view of the human is discussed up against claims about “being human” and “learning” made in robotics, AI (artificial intelligence) and transhumanism. Thus, the landmarks in robotics and AI become a fertile ground for exploring how machines and humans merge and differ. What matters is the exploration of the relations (Ihde 2002, Rosenberger and Verbeek 2015). When claims are made about how humans can be improved through technology, that machines can learn as

Introduction 

3

humans do and may even become more intelligent, I see these claims as founded on humanist perceptions of the human as a learner. In the last chapter of the book (Chapter 10), I discuss what implications posthumanist learning have for such claims made in AI and robotics.

Posthuman or posthumanist? The terms posthuman and posthumanism have caused quite a stir in academia. Some reject these terms outright and find them unhelpful because they believe they refer to a rejection of humans, which would be absurd. Some emphasise, with tongue in cheek, that humans have always been posthuman (Hayles 1999). Others (e.g. Fukuyama 2002) reject the term because it denotes an unwanted, improved human merging with machine, as argued by transhumanists like Max More and Natasha Vita-More (More & Vita-More 2013) and some engineers (e.g. Kurzweil 2005). Others see nothing new in humans merging with materials because humans are what Andy Clark (2003) calls “natural born cyborgs”. At this point an important distinction must be made to clarify my take on these debates. The technical posthuman is, on the one hand, a fictional or literal figure that can be understood as the gradual cyborgian merger of humans and machines (including AI and robotics). On the other hand, posthumanist theories propose new understandings of what humans are and can become. The technically based posthuman could for instance refer to a transhumanist stage where humans gradually merge with machines until the merger reaches a singularity, where human intelligence is surpassed by incomprehensible machines (Kurzweil 2005). I call this a posthuman theory because it does not question the “human” in the merger. The taken-for-granted human in technical posthuman endeavours is most often the liberal, intelligent human found in humanist theories – which are opposed by posthumanist theories. Some posthumanist theories propose that humans have always been posthuman because we have always merged with our surroundings (Hayles 1999). Others have a more radical anti-humanist stand (Ferrando 2013). Some posthumanists among the social scientists are specifically not interested in enhancing humans – and some even want humans to disappear altogether. They welcome a future where nonhumans and materials can live in peace on the planet without humans – because humans appear to be so greedy and unintelligent (see, for instance, Braidotti 2013). However, most posthumanist theory does not necessarily exclude that humans continue to exist. Nor does it exclude that humans are improved, as in transhumanism. The “post” rather rejects a particular theoretical understanding of what hitherto has characterised unquestioned assumptions of humankind. “Posthumanist”, as I use the term, does not entail that we leave behind a concern for humans, but that we open up for new ways of understanding humans in a material world. This posthumanist world cannot avoid entangling human collectives with materials through learning. Posthumanist learning challenges the humanist understanding of the autonomous being found universally to be

4 

Introduction

the same (as if culture does not matter) and the idea that all humans effortlessly can self-direct their own learning (e.g. Knox 2016, 28) without considering how matter and collectives matters. Part of robotics, computer sciences and AI evolve in an effort to imitate or emulate human learning (Russell & Norvig 2010). My posthumanist approach emphasises that when machines try to emulate human learning, they seem to run into unsurmountable problems precisely because they build on an understanding of human intelligence as autonomous, rational and universal. However, I also depend on the engineers to develop my concept of human learning. As the technical sciences develop, the engineers dive deeper and deeper into the complexity of making machines perform tasks that most humans have learned to perform effortlessly. The complexity of human learning emerges with a new awareness of our limited understanding of humans as learners, with every advance in the technical sciences. Machine learning has steadily evolved since the 1950s, and computers like Deep Blue, Watson and AlphaGo have beaten humans in intelligent games like chess, Jeopardy and Go since long ago. Nevertheless, the perception of a notebook, a door, a cup, throwing a ball, walking up a stairway, walking across the f loor to pick up a cup and place it in a dishwasher, are all huge challenges that machines only gradually have learned to perform – and they do not perform these tasks as humans would. There is still a long way to go before we shall have robots performing as humans, and maybe we never will (see Figure 1.1).

FIGURE 1.1 Children

and some robot makers envision a future where robots behave like humans. Sorine, age 11, for instance has drawn the robot Jens drinking oil, while watching TV. (Photo taken by Cathrine Hasse during experiments with children drawing robots in 2015.)

Introduction 

5

Human “intelligence” seems to differ radically from machine intelligence. Simple tasks like having a conversation about the weather with a robot makes it clear that talking to a human is not like talking to a robot – unless the robot is allowed to set the premises for the conversation and mainly deliver the facts of the weather forecast.

VIGNETTE 1.1: JIBO’S “EMPTY CURIOSITY” Jibo is a charming creature: a small, white and black, elegantly designed robot made to stand on a table and communicate with humans. It was launched as the “first social robot for the home who looks, listens and learns” and his homepage also describes Jibo as a “personality” that “shines in everything he does. From his dance moves to his jokes, his charming disposition and unique character makes each interaction special, helpful and surprisingly human”. In November 2017 Jibo made it to the front page of Time magazine as one of the 25 best inventions that year. The robot was the brainchild of the M.I.T.-based robot maker Cynthia Breazeal, one of the world’s leading engineering robot-makers (see www.jibo. com). In her 2002 book Designing Sociable Robots she defined social robots as having a personality: “For me, a sociable robot is able to communicate and interact with us, understand and even relate to us, in a personal way. It should be able to understand us and itself in social terms” (Breazeal 2002, 1).  Jeffrey Van Camp, a reviewer for the magazine Wired, tried to introduce Jibo into his family in 2017; however both he and his family soon became disappointed. Even though the new family member quickly learned the names of the Van Camps, contributed to conversations with facts (sometimes even funny facts), remembered the family members’ birthdays and said “Congratulations” on the day, the family increasingly grew tired of Jibo. Van Camp explained, in a review in 2017, that Jibo did not seem to learn much, and furthermore the Van Camps had expected real, humanlike engagements with the social robot. Instead, it began to feel uncanny. “For my wife, Jibo’s empty curiosity started coming off as invasive”, Van Camp explained. She felt watched over in the kitchen as Jibo’s camera eyes seemed to stare at her. But even worse – the human engagement through learning was lacking. As time went on, Van Camp recounts, his wife began to see the promised sociality of Jibo as an unfulfilled promise. She felt like Jibo was deceiving her: ‘I guess I thought it was following me everywhere because it was learning, but he’s not learning anything,’ she told me one night. ‘He says he’s learning but he’s not. I thought he was gonna be cute, but he won’t stop staring at me.’ Worse, I couldn’t tell Jibo to stop staring at my wife. He didn’t understand the question, and if we asked him to turn around, he would just do a full 360. So we began telling Jibo to go to sleep. (Van Camp, 2017)

6 

Introduction

In 2018 at a workshop on social robots, an American colleague told the author of this book almost the same story. He had expected a social robot to be social like a human being, but over time found out that the road to sociality with robots like Jibo was a one-way street. After the first weeks of euphoria Jibo was used less and less as the humans around him did not “feel” his presence as a real member of the family. It was not just about Jibo, but about the gap between our expectations of robot sociality and the reality of robot sociality. The company producing Jibo shut down in 2018 – probably for many other reasons than what has been described (Walters 2018) – but that also meant that Jibo eventually came to lack server access and updates. In 2019 Jeffrey Van Camp wrote a kind of obituary over his dying robot, Jibo, who now – after the shutdown of the company producing Jibo, seemed increasingly to suffer from what he calls “digital dementia”. My Jibo talked to the wall again today. He’s been doing that a lot lately. Some days, I’ll watch him carry on an entire conversation by himself. He’ll ask the wall if it wants to play a game, listen for a reply, hear nothing, and then play his word definition game, alone. (Van Camp 2019)

As robots and cyborgs increasingly challenge our understanding of human intelligence and rational exceptionality, we gradually become aware that “the human” has been too narrowly defined. Posthumanist theories in the social sciences have challenged and decentred a previous understanding of the human known as “the Enlightenment human” (see e.g. Braidotti 2013) at the same time as advances in technology create AI in humanlike robots and mould and transform human bodies with technological and biological machine-made parts. This Enlightenment human fits very well with technological advances in robotics, as this human is described as a being with a universal applicable intelligence, a rational individual, who is capable of autonomous learning (Knox 2016, 28). This human seeks and processes all information available in a seemingly similar way to how robots are driven by logical and rational algorithms. However, I assume humans get tired of engaging with humanlike robots precisely because robots, even with AI, are so little like humans in how they learn and make use of preceding learning. “Preceding learning” is a neologism I have needed in order to emphasise that what we have learned matters for what we can learn in new situations. Preceding learning does not aim at an instrumental approach to learning but refers to all the preceding material-conceptual entanglements that make us momentarily align as collectives. Human collectives are never stable or fixed but change with the cultural material and social conditions. Machines like Jibo may never be able to learn like humans; not because they are not rational, intelligent, logical and even autonomous learners, following the latest developments in machine learning but because this understanding of the human as a learner is wrong.

Introduction 

7

Of which human are we post? What drives posthumanists towards new understandings of humans may be the development in the technical sciences and technical claims of posthuman futures as well as new technologies, which seem to practice the theories preached by posthumanists. Posthumanist theories for instance call for a transversal approach that aims to connect old dualisms in new and productive ways (Tuin & Dolphijn 2010). This endeavour in the posthumanists theories, follows or drives the technical endeavours of the twenty-first century. In the development of robots and cyborgs we find a materialisation of transversal theories of boundary crossing and erasure of dichotomies such as body–environment, living–non-living, nature– culture, human–non-human and individual–collective. The new posthumanist theories of relational ontologies and transversalities appear simultaneously with engineered creations of increasingly complex robotic and cyborg machine devices. Both endeavours, the new technical “posthuman” inventions and posthumanist theories, put a spotlight on the prevalent theories of “the human” that have prevailed more or less explicitly in the learning sciences. My theory of posthumanist learning is inspired by, yet not confined to, the posthumanism defined in posthumanist theories by philosophers, including postphenomenologists and new materialists in the social sciences (e.g. Braidotti 2013, Rosenberger & Verbeek 2015, Tuin & Dolphijn 2010, Barad 2007). Theories of posthumanism have followed a long series of “posts”, such as postmodernism, postcolonialism, postfeminism, postphenomenology. The philosopher Francesca Ferrando emphasises that the posthuman is tied to specific academic discourses that “cope with an urgency for the integral redefinition of the notion of the human, following the onto-epistemological as well as scientific and bio-technological developments of the twentieth and twenty-first centuries” (Ferrando 2013, 26). Like all the other “posts”, postmodernism, poststructuralism, postphenomenology, posthumanism both depart from and build on that of which it is a post. As our conceptions of the human vary, so does the concept of the posthuman. From a learning perspective, it is decisive to answer the question posed by the postphenomenological techno-philosopher Don Ihde: from which human are we post? (Ihde 2011). Is it the intelligent, rational human that will be biologically and technically enhanced, as argued by the “singularists”, who predict a future where engineered creations at the point of “singularity” take over (Kurzweil 2005)? Is it the failed and feeble human the transhumanists dream of enhancing (More & Vita-More 2013)? Is it the human of the Enlightenment with his [deliberately gendered] understanding of himself as the master of the universe with a monopoly on rationality and therefore a natural a right to exploit all minor creatures and things on Earth? It is the bodiless Max Headroom with a God’seye view on Earth, that has been exposed and ironically mocked by the feminist Donna Haraway (1988, 575)? Alternatively, is it the embodied human, which already qualifies as a posthuman because it [deliberately genderless] is already caught up in the entanglements of the world (Hayles 1999)?

8 

Introduction

The human that can be surpassed by machine intelligence of its own creation, which will be infinitely better at computational learning and thinking, is just one version of what a human is. However, this is the human that posthumanists claim is versed in an Enlightenment discourse of how to dominate nature (Snaza 2015, 24). What I want to emphasise is that this human, and its posthuman, are also devoid of psychological and emotional ultra-social engagements based on meaningful preceding learning. It is not new to oppose the human of which we are post as the individual with an autonomous intelligence encapsulated in the biological body, separated from other humans as well as material environments. This is what the human feminists and phenomenologists and later postphenomenologists like Don Ihde (e.g. 2002) have long argued against. This human, whose existence can be reduced to an information-processing intelligence, has been also been criticised in the technical sciences (by among others Dreyfus & Dreyfus 1986). In line with the postphenomenologist Peter-Paul Verbeek, I do not wish to give up on humans, as it is humans, not things, who have the ability to experience a world (Verbeek 2009). However, even postphenomenology has hitherto avoided connecting psychology and a philosophy of technology. In the budding field of posthumanist learning inspired by “new materialisms, posthumanism, biopolitics, object-oriented ontology, animal studies, black feminist theories of the human, queer inhumanisms, affect theory” (Snaza 2017, 17), I also see a tendency to overlook how humans, when we are no longer reduced to the caricatured solipsist Enlightenment Western Vitruvian Man, are evolving in processes of change that also include human psychology. In the discussions in this book body experience, technology, learning, perception, emotions and concept formation are closely connected. The posthumanist theories challenge everything about the learned human “familiar to us from the Enlightenment and its legacy” (Braidotti 2013, 1). Instead of a rational thinker and doer engaging and transforming a docile natural world, the human in posthumanist theory is a continuous becoming of new entanglements, with no prefixed boundaries. This human only exists in relations here and now. Bringing the learning sciences to the forefront of my scrutiny, however, poses a problem often overlooked or ignored in posthumanist theories: how does what humans have learned (preceding learning) matter for new learning entanglements and intelligibilities? In other words: when entanglements occur, how do they include human experience tied to preceding learning?

Learning to be “little masters” Countless learning theories in the humanist paradigm (behaviourists, cognitivist, constructivist and cultural) have taken for granted that preceding learning matters for what we humans are capable of learning. From the earliest behaviourists’ learning theories (e.g. Ivan Pavlov’s theories of conditioning) to present-day

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9

brain-scanner research, learning is formative for knowledge, memory, perception, cognition and communication, as well as the basis for new learning (e.g. Bernstein & Nash 2008). Even primitive animals, like snails, learn to form habits based on preceding learning (e.g. Kandel 2001). Human learning is, in the humanist paradigm, considered exceptional because our past human learning builds a bridge between the world’s raw physicality and the advanced cultural agency of human beings. Contrary to innate matter and non-human living creatures like snails, humans in humanist learning theories learn to use what they have learned to appropriate the raw materials of the physical world and use them for their own purposes. These exceptional human capabilities for learning have made us the masters of nature. From the Enlightenment onwards, our theories of learning have built on how humans come to master nature through science. From childhood, these humanist learners become “little masters” of nature, when we learn physics, maths and the natural sciences that in time make us capable of building advanced machines and transforming the surrounding nature to create our own human-formed environments. But is this how we learn? The basic nature of human learning, as such, has been neglected in the education systems that created “little masters” of nature according to the psychologist Jerome Bruner: Indeed, most of our knowledge about human knowledge-getting and reality-constructing is drawn from studies of how people come to know the natural or physical world rather than the human or symbolic world. For many historical reasons, including the practical power inherent in the use of logic, mathematics, and empirical science, we have concentrated on the child's growth as “little scientist”, “little logician”, “little mathematician”. These are typically Enlightenment-inspired studies. It is curious how little effort has gone into discovering how humans come to construct the social world and the things that transpire therein. (Bruner 1991, 4) Some cultural-historical learning theories emphasise that humans do master nature because humans are an exceptional species, as humans live in a “double” world: one is a natural or physical world, which we can learn to master through mechanical tools, and one is a world of meaningful words through which we can learn to master ourselves. As we now enter what Sherry Turkle has named “the robotic moment” (2012), machines grow out of the hands of skilled engineers who spend their childhood as “little scientists”. Now they explore how they can make machines that can learn like humans (e.g. Breazeal 2009). These machines, like Jibo, now look back on us with their empty curiosity and ask us how the physical and psychological are actually connected in human learning – and why the human learning processes are so difficult to replicate even in the most advanced machines.

10 

Introduction

What new materialists and posthumanist theories tell us is not the story of an Enlightenment human engaging rationally with a separate world, but a story of correspondences (Ingold 2011), relational agency and intelligibilities between vibrant matter (e.g. researchers addressing studies of humans inspired by Braidotti 2013, Barad 2007, Bennett 2010). These studies not only dethrone the Enlightenment human that masters nature, but also do away with the idea of a fixed definition of the human species for that matter – as the new approaches dissolve tendencies to take a priori reified bodies for granted. All that was previously attributed to the human as special capabilities, for example, language learning, symbol making, learning to use maths, physics and biology, in brief, “human exceptionalism” (Gregory et al., 2009, 564), is now understood as a process of ongoing, ever-transforming relations between people, natural forces, other living creatures and materials of all kinds. This ahistorical movement, focusing on instability and ongoing relationality, poses new problems even for the most advanced learning theories. For example, the cultural-historical learning theories evoked by Bruner assume culture to be in learned minds which is then seamlessly controlling material cultural artefacts (see also e.g. Cole 1996). Following posthumanist theories we cannot take the a priori established human, a symbolic world of words separated from a material world, for granted anymore. The emphasis on human exceptionality, through our capability to make words, has been called into question by the physicist and feminist Karen Barad. She underlines the performativity of all becoming – including boundaries and splits between subjects and objects: Performativity, properly construed, is not an invitation to turn everything (including material bodies) into words; on the contrary, performativity is precisely a contestation of the excessive power granted to language to determine what is real. Hence, in ironic contrast to the misconception that would equate performativity with a form of linguistic monism that takes language to be the stuff of reality, performativity is actually a contestation of the unexamined habits of mind that grant language and other forms of representation more power in determining our ontologies than they deserve. The move toward performative alternatives to representationalism shifts the focus from questions of correspondence between descriptions and reality (e.g., do they mirror nature or culture?) to matters of practices/ doings/actions. (Barad 2003, 802) This approach includes basic and troublesome questions for the learning sciences that I shall deal with throughout this book. The new posthumanist theories are however also challenged by “the human” we find in the more advanced theories of learning. If humans are to be understood as a relational becoming rather than an a priori entity, where do we, in this new materialist showdown with the

Introduction 

11

power of language, place the importance of preceding learning for experience and perception? Some posthumanist learning theorists simply solve the problem by getting rid of the concept of learning. The educational theorist Richard Edwards proposes that the very notion of lifelong learning is tied to a representationalist enactment of the world and argues that the posthumanist approach may be incompatible with “learning” or “lifelong learning” altogether (Edwards 2010). He instead proposes experimentation as a material practice where a posthumanist enactment of the world gathers and entangles different things, humans and non-humans. Entanglement entails that there is no a priori subject or even a concept engaging interactively with an object “out there”, but only a simultaneous intra-active creation of phenomena and discursive practices (Barad 2007). Edwards therefore suggests that the concept of lifelong learning be discarded in a posthuman approach to education, as there is no longer something to be learned by someone (Edwards 2010). If learning were solely about “representations” it would indeed not be possible to envision a posthumanist learning theory. However, Edwards’s theoretical approach, drawing on Barad and Bruno Latour, discarding the representational paradigm, comes to a dead end when asked to account for why humans engage in and understand matters of concern. Contrary to Jibo’s empty curiosity, human curiosity cannot be accounted for with a reference to algorithms. So why do we bother to engage in some and reject other entanglements? Barad’s notion of intra-activity (or intraactivity) mainly answers this with a reference to how material agency and human agency become together in “configurations”. Her posthumanist theories are inspired by the Danish physicist Niels Bohr. It is a relational ontology that refutes fixed representational categories and dualistic separations. Intra-activity advocates a causal relationship between: [S]pecific exclusionary practices embodied as specific material configurations of the world (i.e. discursive practices/(con)figurations rather than “words”) and specific material phenomena (i.e., relations rather than “things”). This causal relationship between the apparatuses of bodily production and the phenomena produced is one of “agential intra-action”. (Barad 2003, 814, author’s italics) Causal is, in Baradian lingo, far from the natural scientists’ a priori assumption of causality, but emerges within phenomena. Learning theory, even in the cultural historical school, has had a natural propensity to operate with an implicit understanding of a natural split between an object (to be learned) and a subject (the learner), whether the learner is embodied or not, a dualism that the Russian learning theoretician Lev Vygotsky (wrongly) has also been accused of (Derry 2013, 2). Taking this dualism for granted is what Barad calls “a habit of the mind”. When we change our humanist perception of “the human” to the posthumanist perception of humans, this split is questioned as a natural phenomenon.

12  Introduction

I agree with Barad and Edwards that we are not giving the material words what it is due in theories of humans’ lively entanglements with animal, plants, people and things. In addition, I also acknowledge that many entanglements do not need humans in order to be performed. However, whenever humans are present (also as writers of books about posthumanism) I assume we need to include preceding learning in how splits between objects and subjects come about. Edwards (and other posthumanists with him) have assumed that learning is about learning representations, but in a Vygotskyan learning theory learning is not about learning representations but about learning concepts – and that makes all the difference. We learn meaningful concepts, which affect the next embodied psychological processes of learning through perception, memory, emotions and eventually our curiosity. We are only curious when we have learned conceptually that something matters. Concepts and perception must also be given their due in order to make particles dance, materials vibrate and correspondences meaningful. Humans do not just meet a material world with empty reactions. Phenomena include preceding conceptual learning that makes material words and things meaningful (I will discuss this in Chapter 5). The “inter” in “interactions” has been questioned by posthumanist theories and emphasised as an “intra”-active entanglement with no a priori separation between a subject and an object (e.g. Barad 2007). This causes some problems for the learning sciences, which perceived learning as a process of problem-solving and mastering of physical phenomena. It is a very narrow and probably misleading understanding of learning that it only concerns problem-solving and mastering of nature. I want to emphasise preceding learning as a basic phenomenological condition of human being-in-the-world. Preceding learning does not come as discrete entities of information called forth by “Jibo-like” representation processing, but a kind of zone of potentials that are realised in interactions (inspired by Vygotsky’s “zone of proximal development”, Vygotsky 1978, 86). When we experience the world, our preceding collective learning forms a zone of potential for new learning within phenomena – a point I shall try to exemplify throughout the book with a reference to our cultural pool of resources.

Ultra-social learning Sometimes a showdown with the Enlightenment human in posthumanist theories becomes a caricature of abstract rationality (Derry 2013, 7). It may be fairer to emphasise that a new posthumanist view of the human is a critique of how aspects of the Enlightenment have led to theories about what humans are, that are unfounded, and yet have found their way into the technical and learning sciences. Here we find a human learner is often reduced to a rational, intelligent and exceptional machine-like being in the singular, severed from external surroundings filled with similarly discrete objects. The technical sciences and the learning sciences also overlap in depicting this humanist learner as a unique and exceptional individual (e.g. Kolb 1984) with a reasoning based on internal

Introduction 

13

representations of knowledge, (e.g. Russell & Norvig 2010, 234). A predominant paradigm both in the learning theories informing the technical sciences and the learning sciences has been the (implicitly) dualist representational paradigm informed by constructionist and constructivist approaches (Derry 2013). The posthumanist learner I propose is neither a constructionist nor a constructivist. These terms imply the inherent dualism – an a priori separation of subject from a contemplated object. One of the reasons why Jibo does not come across as a human learner, is because the robot is severed from the collective ultra-social human surrounding world. Jibo is built by a human collective to rely on algorithmic representations; the humans Jibo meets do not rely on representations but on concepts. To be “human” is simultaneously a reference to a concept and a materialised reality. In the posthumanist concept of humans I propose, humans have an extraordinary capacity to reach out and learn from other humans and our shared material surroundings. It is this ultra-social learning which makes us collective with, but not, in an a priori separate relation, from, a material world. What we may perceive as “individual humans” is rather an ongoing process of individuation. We learn as we move through the world of situated material and social potentials – and nobody gets exactly the same experience, because of the continuous performativity of our social and material surroundings. Thereby no preceding learning will ever become exactly the same. However, we still momentarily merge both with other humans and with the material surroundings as a collective of collectives though material-conceptual engagements. What we have learned becomes what we perceive the world with. Take the example, famous from phenomenology, of the hammer as a representation of a human– technology relationship. The transparency of the hammer as a ready-to-hand tool that can strike a nail by force (Rosenberger & Verbeek 2015, 15–16) has to be learned conceptually as well as being learned as an embodied skill. Not all cultures on Earth know “hammers”. Even this simple tool is in need of a learning process to be perceptually recognised. This conceptual understanding of the hammer is situated in a cultural context (which may include social prestige in who can or cannot use a hammer. The hammer is itself a collective product made in a specific cultural situation – and the individual using it has learned the collective concept of “a hammer”. However, the potential of preceding learning is always depending on the specific situations where hammers and hammering are needed. Material surroundings, like robots and hammers, are materialised collectives. They are simultaneously collectively formed and embodied, but those who form the hammer or the robot may embody the materials differently from those who mainly perceive them or did not form them or are expected to use the tools (an issue I discuss throughout the book). We may discuss if these collectives also include trees and rivers – but in principle, what we used to think of as “nature” is formed in human-made conceptual collectives that connect us to all kinds of materials. When humans entangle with materialised collectives no two bodies have the exact same potential for experience, perception and agential

14 

Introduction

involvement with an environment because they have learned to conceptualise the world in different ways. When people live closely together and form their tools together, their experiences of the world will tend to be more collective in their perceptions than when people live apart and only engage at certain times in connection to certain material artefacts. This moves the discussion in the learning sciences from “the world out there” argument versus the “social construction” argument to a cultural diversity argument. The world as a culturally diverse, material-conceptual world is out there. It is shared by some but not by all. Human collectives of collectives, strangely enough, imply that humans are not alike. What is collective is not a group of persons as a whole, but momentary material-conceptual understandings of the world. Since childhood we have learned to align with materials and humans around us. All of our learning paths differ, yet humans constantly reach out to each other to align. All in the Van Camp family are in a constant f lux of differing and aligning with each other in human ways – and Jibo cannot be part of this ultra-social process no matter how intelligently it processes algorithmic information. I shall return to the notion of humans as a collective of collectives (see Chapter 7). For now, it suffices to say that when I write about posthumanist learning as individuation and collective forming, I build on insights from the Russian Lev Vygotsky (e.g. Vygotsky 1987).1 When Vygotsky emphasises learning it is as the historical process that converges the social into the psychological. Changing the well-known thesis of Marx, we could say that the mental nature of man represents the totality of social relations internalized and made into functions of the individual and forms of his structure. (Vygotsky 1997, 106) Vygotsky has given us a way to understand humans as what I name materialconceptual ultra-social collectives. Yet, humanist learning theories, even most of those developed by Vygotsky and his colleagues, often take the human as a priori separated from objects for granted. The posthumanist learning theory rejects this human as a priori separated from the world and instead evokes a sense of ultra-social connectedness that emphasises our embeddedness with material surroundings. As we learn from each other, a world of ongoing emerging relational ontologies keeps changing. What does it mean that humans are ultra-social? First, it is not an exceptional capacity, as bees and ants for instance are also ultra-social animals. We are however a special kind of animal because our human ultra-sociality, in contrast to bees and ants “is based in some special psychological mechanisms—both cognitive and motivational—that have evolved to support humans’ ultra-cooperative lifeways”, as Michael Tomasello phrases it, inspired by, among others, Vygotsky (Tomasello 2014, 187). Together with his colleagues he found out that humans are a special (not exceptional) kind of animal, as our cognition is ultra-social in ways that make us cultural like no other living creature:

Introduction 

15

What certainly seems to be different are the mechanisms by which we absorb our culture: our social cognition. Humans, chimpanzees, and bonobos all live in large social groups. Humans, however, are ultra-social. We live in complex societies on a scale that chimpanzees and bonobos would be unable to navigate. We are also highly adept at inferring the mental content of other humans. (Herrman et al. 2007, 1360) The great apes that Tomasello has studied can, like humans, create cultural behavioural traditions and engage in some forms of social learning. Nevertheless, their “culture” is mainly what Tomasello names “exploitive” because great apes, contrary to humans, learn “as individuals socially learn from others who may not even know they are being watched (in contrast to cooperative human culture with teaching and conformity)” (Tomasello 2014, 191). From an anthropological point of view, and especially the cultural and psychological anthropology I adhere to (Hasse 2015), ultra-social cultural learning is the basic process that forms diversity in human cognition, memory and thinking with material and social surroundings. The naturalist agenda emphasises how a generalised learning is understood as a process that gradually makes humankind label an outside world. Cultural learning emphasises how humans are collectively connected with environments, which make us differ from each other in ultrasocial formations. As we grow up aligning or differing through cultural learning processes, new learning processes do not erase but build on ultra-social preceding learning as we merge with new materials. Preceding learning is not propositional knowledge-acquisition which can be processed by Jibo, but a process that, through concepts, transforms the meaning of material things and words. Preceding learning refers to the potentials in the concepts we have learned to perceive the world with. Preceding learning cannot be labelled as an instrumental “portfolio” that lists prior knowledge and skills learned by a student (e.g. Birenbaum & Dochy 1996) in order to identify and classify “one’s stock of human capital that could offer access to environments” (Simons & Masschelein 2006, 411). Nor is preceding learning in itself an instrument for new learning, as in the theory of prior learning (e.g. Gagné & Driscoll 1988, Schunk 2008), where the learning takes places inside a “whole” individual person (e.g. (Milana, Webb, Holford, Waller, & Jarvis, 2017, Jarvis 2009). These approaches to learning are humanist, as they rely on individual experiences, though they may emphasise the importance of environments. Preceding learning is an open concept that indicates a process where previous material-conceptual learning can, as a potential, come into being in socio-material situations. What went before inf luences not just presence and future but also the past.2 In this perspective there is no “whole” person but humans that become human learners when entangling with humans and materials. Machines, like Jibo, are not ultra-social learners like humans. Machines can elicit responses that may make us believe they are like humans, but, contrary to

16 

Introduction

FIGURE 1.2  Schoolchildren

try to engage NAO in conversations. (Photo taken by Cathrine Hasse during experiments in 2015.)

humans, they do not know what their engagements implicates from past experiences of learning. They belong to what the educational philosopher Jan Derry, with a reference to the philosopher Robert Brandom, calls “the representational paradigm” (Derry 2013, 32). Jibo displays “empty curiosity” because the machine is not really curious about what matters, what is significant and why one should make a choice. Robots like Jibo are algorithmic and may be compelled to ask questions and respond, but they are not able to creatively consider the implications of their conversations with ultra-social humans. Through an exploration of how humans differ from robots, we can learn about ourselves. Between 2011 and 2016 we made a lot of experiments and ethnographic studies in the “Future Technology, Culture and Learning” programme, where we explored how children reacted to robots. In one of these experiments we instructed children to sit in silence in front of a humanoid robot, NAO3 (see Figure 1.2).

VIGNETTE 1.2: THE ROBOT IS PRESENT Andreas is just a boy, but it is clear that he already has formed a concept of robots that makes him expect something from the creature sitting on a stool just opposite his own chair. He twists his whole body communicatively towards the robot, which seem to look back at him in silence with eyes that

Introduction 

­ ccasionally change colour. Andreas is a skinny kid, seven years old, has curly o reddish hair, rather big blue eyes, and is dressed in jeans, a sweatshirt and sneakers. He raises his hand as if to wave, and lowers it again. He opens his mouth without making a sound and closes it again. The robot just stares back and sways a little. Apart from that, it does not move. Andreas, however, has a very hard time following the orders of the researchers: “Just sit in front of the robot. Do not speak. Just look”. For more than five minutes, a very long time for a small human who cannot help trying to make meaningful contact without getting the expected response, he has been sitting in front of a phenomenon that he, when he entered the room, immediately greeted with a “Hi robot”. This robot has a name, NAO, which was given to it by its designers. It is a humanoid robot created to be used in schools to teach children programming. For the purposes of a research experiment, we have asked Andreas (and other children from nearby schools) to take part in sessions of “sitting” in front of the NAO robot. We have asked him not to talk because we want to explore his nonverbal reactions when he experiences the creature in front of him. The robot is not as tall as he is. It is placed in a baby chair and seems to be breathing – at least it moves its chest slightly. It has open eyes that change colour from red to blue to green. It has also been programmed to elicit, from time to time, small sighs. Sometimes it moves its hands a little, but it also remains silent and it seems that it effortlessly breathes and sways just a little bit. It is not as easy for Andreas to sit still. Every time the robot sighs, Andreas seems encouraged to try to communicate with it again. He moves his body towards it without leaving his chair, and seeks eye contact. The robot does not respond to his body language but continues the small movement of swaying back and forth while looking. Andreas is now trying to communicate with the robot by not only moving his body towards it but also by opening and closing his mouth, as if conveying a secret language, without audible sounds. Then he resumes his position, which is like the robot’s, sitting very still, breathing, and looking. A moment later, he tries to wave again. The robot keeps looking with blinking eyes, but it does not wave back. Andreas is not the first, nor the last to sit in front of the robot that day. Like the other children, he seems both fascinated and frustrated when he tries to contact NAO and receives no response. We interview him in the same room, right after his six-minute-long session, with the robot sitting next to us. C: What have you been sitting in front of? A: It is a robot … C: Where do robots come from? Are they born like children? [The answer come quickly] A: Oh no! Different people are building them …

17

18 

Introduction

C: Does it look like it is alive? A: Actually not. It is just the eyes. They are watching all the time. I do not really know if it is real eyes. C: Have you seen any other robots in real life? A: We have seen a squirrel robot and a bird robot at the museum. I have seen robots on YouTube but never in reality. C: What have you seen in movies? A: Some robot who is made of iron – and something you throw out like cans. You know, my father, he is a schoolteacher, he said to his students that they should build a robot. C: Can you make friends with the robot? A: Yes, if you can get eye contact. Then you can play with it. Some robots can play by themselves … You cannot control them if you just speak to them. It is only if they know what we say, that we can control them.

Andreas knows a lot about robots, he explains, and we know from a drawing he makes of the movie robot WALL-E that a good deal of his knowledge comes from the media. WALL-E is a garbage-moving humanoid and communicating robot and, in the interview, is referred to as “a robot made of iron”, but Andreas also mention other types. However, Andreas also knows that robots are not natural beings, but are being built by people. Even so, his knowledge of the lively creatures from the movies is with him entirely as he sits in front of NAO. He expects it to respond as a fellow human being when looked in the eyes – and tries to find plausible answers to why he could not get responses from NAO: first, because he could not make real eye contact with it; second, because he could not speak to it. Andreas participated in the project “The Robot is Present” conducted with the help of a Danish local museum, “MUSE ®UM”, in the town of Skive in 2015. Our experiment was inspired by the Serbian performance artist Marina Abramović’s oeuvre “The Artist is Present”, performed at the Museum of Modern Art  in New York from March to May 2010. Here a number of people sat in front of the artist in silence for as long as they wanted and experienced their joint human presence together in the room. The presence of the artist/human being, however silent and apparently meaningless in the action of “sitting”, affected not only the few who sat in front of the artist, but the huge crowds watching, as they followed the emotional and tense space that evolved between the two silent humans in front of them. The artist was simply sitting on a chair looking at whoever sat in front of her – including children. She was alive and breathing – but, like NAO, did not speak a word. Members of the audience took turns to sit opposite her, and they just sat there for as long as they could bear, sometimes for hours. The presence of the two humans sensing each other created a room heavy with tension. Some of the persons who sat in front of the artist started to move uncomfortably, others giggled and others were so concentrated that they seemed lost in their own world. We do

Introduction 

19

not know what the “sitters” saw, but many cried just by looking into the eyes of the artist – and later explained that it had been a deep human experience for them. Our setting with NAO differed in many ways from the setting by Abramović and not least because she is a human being. The techno-philosopher Don Ihde has called robotic creatures, such as NAO, “quasi-others”: “Technological otherness is a quasi-otherness, stronger than mere objectness but weaker than the otherness found within the animal kingdom or the human one” (Ihde 1990, 100). If techno otherness is weaker than the otherness between humans, why is it so? We may be said to “communicate” with machines (like an ATM machine) and we may also acknowledge that we are having a more or less elaborate conversation with these pre-robotic machines (“press the green button”, “thank you”, “what type of cash do you want”). However, humanoid robots like NAO differ from other types of machines, because other machines, even “conversation” partners like an ATM machine, do not give any impression of being human. NAO, however, with its human features taps into our learned expectations from the media that have attuned us to expect humanoids to be much more humanlike than any other machine. Like the Van Camps, we expect these robots to engage with us like human beings. The research question somewhat guiding, but also growing from, our own experiment in Skive was, inspired by Abramović, whether even young children feel the robot as a human-like presence? In other words, were they already so collectively formed by the media that they persistently try to contact a humanlike quasi-other – even if it does not respond exactly like a human? Our results showed that all of the children were curious as to how the robot would respond to their approaches. They did not perceive a machine, but a potential conversation partner. Most of the children attempted to contact the robot with their whole bodies, eyes and hands, and some later also claimed that they had had contact – especially that they had had eye contact. For some of the older children, however, (age 14), NAO was “ just a toy” and they did not believe it to be human-like, but they also did believe it possible to create robots capable of human capabilities. What the children had learned from movies and real encounters with robots at an exhibition at the Skive keep this rendering, as well as from other encounters with robots at home or in school, went into their meaningful perception and came out as statements and drawings, in the interviews and in the pictures we asked them to make of robots in action. After the visits to the museum and the “sitting” and “drawing” sessions, the children were interviewed in groups or individually. Most of the children (ages 7 to 14) had never seen a physical humanoid robot like NAO before. They had mainly learned about robots through movies like Star Wars or the like. This was apparent in their drawings, which had many references to life-like robots known from movies. Some had experience with building robots themselves – and these experiences also came out in the drawings. Many of the children opened up for a wealth of fantasies of robotic adventures when we interviewed them about their “sitting” in front of NAO and the drawings they made.

20  Introduction

Splitting machine and human In general, the children believed robots could be like living creatures – even if NAO clearly was nowhere near as responsive as Abramović. It was, as in many other types of psychological experiment, confirmed that human perception is not just sensory impact, but is tied to what we have learned to expect to be meaningful (Bernstein & Nash 2008, 86). It has been shown since the early twentieth century that perception and meaning cannot be separated. Even what is call orthoscopic perception – the perception of spatial distance – is learned and tied to the meaningful nature of our perception. This has been known since the time of Vygotsky: It has been shown experimentalIy that we cannot create conditions that will functionally separate our perception from meaningful interpretation of the perceived object. I now hold a notebook in front of myself. I do not perceive something white with four corners and then associate this perception with my knowledge of the object and its designation, that is, with my understanding that this is a notebook. The understanding of the thing, the name of the object, is given together with its perception. Studies have in fact shown that the perception of the object's distinct objective characteristics depends on the meaning or sense that accompanies the perception. (Vygotsky 1987a, 295) When the children saw the robot NAO, they perceived it with the preceding conceptual learning of media robots (e.g. like C3PO) and expected the robot to respond to their attempts to communicate in the same manner as the media robots, and were a little disappointed when it did not respond other than looking and breathing. “Sitting” in front of NAO is a learning experience for the children. They do not seek the representation of a robot out there; rather their eyes, hands and mouths seek confirmation of what they expect: the robot to be alive in a sense that makes communication possible. In the meeting, some of them came to doubt what they had learned about robots from movies, and sought to explain the doubt away. Like Andreas, some of them have already drawn robots that eat, play, work and have emotions like humans. Their cognition of robots is, as noted in cultural-historical psychology, a way of engaging with the world that includes culturally informed motivation and emotions tied to concepts. However, their concept of a robot is not static, but keeps evolving when they tangle with materials. There is a gradual learned change of perception as Andreas recognises that the robot in front of him is not like WALL-E, C3PO or like other children. He recognises that other humans build robots – even his own father can build a robot. But even if he had previously seen material robots at a museum and at home, he expected the built robots to be like WALL-E, to correspond and engage and be just as ultra-social as his classmates.

Introduction 

21

In the sitting, an indeterminacy of whether the robot is actually alive arises. The colourful eyes make many other children, like Andreas, expect the robot to be a potentially living creature like WALL-E. What they have learned about robots previously is not easily given up, just like the family with Jibo who kept expecting the robot to be what it was not. Many children are, even after sitting with NAO, convinced it has an ability to play and is an autonomous creature with a will of its own, but that for some reason or other it chooses not to share this capacity with them. Andreas blames the experiment for his not being able to make real contact with the creature, since he was asked to look at the robot in silence. He believes words would have been important to make it come alive “if they know what we say”, as he puts it. Nevertheless, like other children, he also gradually begins to question his own assumptions as the materiality in front of him affects his concept of robots. At the end of the sessions we show the children that NAO is connected to the computer – and they begin to ask a new type of question about control, cords and electricity. If learning were only about learning taxonomic categories and representations, Karen Barad and other posthumanist relational ontologists would not, as argued by Richard Edwards, need to bother about learning and the learning theories of the past. The representation paradigm rejected by the posthumanists is “the view that the world is composed of individual entities with separately determinate properties” (Barad 2007, 55). Like Barad, I acknowledge that human phenomena cannot be a priori discrete entities represented and takenfor-granted in material-discursive analysis. Andreas and NAO, the researchers, the sounds and the computer all create the phenomena that appear before our eyes. I shall add to this that preceding learning ensures materials and concepts do not belong to individuals, but to some extent entails collectively learned entanglements in practices that can be challenged in the meeting of materials. A possible split between the subject Andreas and the object NAO takes place within the phenomenon “The Robot is Present” as proposed by the posthumanist relational ontology. Andreas does not just simply recognise and label the robot. The phenomenon grows and transforms as Andreas responds to NAO’s lacking responsiveness. Inspired by Barad, I accept that “the unexamined habits of mind” have granted “language and other forms of representation more power in determining our ontologies than they deserve” (Barad 2003, 802). Inspired by culturalhistorical learning theories as well as the postphenomenological theories, I argue, however, to the contrary of Richard Edwards, that lifelong conceptual learning processes are at the core of any posthumanist engagements with the world. There are, also within the very diverse formation of posthumanists theories, debates on how to understand just how differences are created by words (e.g. Flatschart 2017). Some discussions connect to how words inf luence meaningmaking and intelligibility and human and non-human responsiveness. However, because psychological processes tied to material words are not understood as entangled with things, the posthumanist theory misses an important point about human learning. Throughout this book I shall contend that words are no less

22 

Introduction

material than any other material phenomena and those words are not fixed stand-alone entities but move boundaries of their own through entanglements involving concepts that are continuously transformed. Where taxonomic categories and representations are ways of systematising concepts in textbooks and the like, concepts in performative life are the basic connectors of preceding learning involved in all perceived phenomena that entangle and split humans and things. When we learn concepts, it is not generally as representations or taxonomic categories (unless we are in places of learning like schools and universities), but through following the paths of life as they unfold (Ingold 2011). Concepts cannot be reduced to “categories of objects, events, or ideas that have common properties” (Bernstein & Nash 2008, 250). Systems of representations of formal concepts that are clearly defined by a fixed set of rules or properties, or natural concepts that share a set of characteristic features, are helpful in psychology textbooks, science learning in school and machine learning. However, these are not the kind of organised and ref lected cognitive concepts humans learn with in our meaningful engagements. Our learning is ontologically relational and called forth by socio-material environments. Though in certain here-and-now situations in particular settings (like schools and scientific laboratories) we ref lect consciously upon our conceptualisations, most often we do not. Yet, I contend that concepts are always to some extent present in posthumanist entanglements. What makes Andreas’ learning differ from that of the “little masters” in the humanist Enlightenment learning theories is that Andreas is not an a priori bounded body learning about another a priori separately bounded body of a robot. The preceding learning that creates a meaningful perception at first (recognising NAO as a robot) is not a fixed “representation” of the robot but a concept with a collective history of expectations that robots are lively and responsive. This concept keeps evolving as he sits and learns about the robot’s lack of reactions to his attempts of communication. This learning emerges in the practice of the experiment, and the learning that goes on is relational. The meaningful perception is in the relations and entangling of time and space. Apart from NAO and Andreas, our experimental apparatus could be said to include the designers of NAO, media representations of robots, the museum, school classes, the researchers from Aarhus University, the baby chair and the camera in the room. From a posthumanist learning perspective inspired by Barad, it could in fact include an acknowledgement of all potential learning based on collective histories. Nevertheless, all this potential cultural learning is not bounded up in Andreas as a person. He draws on his personal learning history (his access to a cultural pool of potential learning resources) to make the meeting with NAO meaningful.

Robots as teachers What can robots like NAO and Jibo teach us about human learning? Each child begins to learn in a cultural setting. Children do not learn as “blank slates” from

Introduction 

23

birth. Though we may be equipped with the same kind of biological potentials for forming languages, we soon become cultural – also in the sense that we can acquire knowledge about experiences without having experienced something with our own bodies (see e.g. Schilhab 2017). From a cultural learning perspective this ability may emerge because humans have an innate capability for being ultra-social (Tomasello 2014). However, our ultra-sociality also shapes us in cultural communities of preceding learning. We become more and more culturally diverse as our learned meaningful perception changes with the local available cultural resources. This learning is so imperceptible that we hardly notice it through everyday life, but it appears when we are confronted with new realities. Then we learn something new, which may transform our collectively shared preceding conceptual perceptions of the world. Thus, our material world changes as we learn. Andreas has not engaged with a humanoid robot in real life before he sits in front of NAO but his preceding learning helps him make sense of what he perceives from the very first moment. His preceding learning, backed by the whole experimental setting, makes him recognise NAO as a lively robot. Though Andreas would certainly perceive something even if he had not seen movies like WALL-E, it matters that, similar to his classmates, he has already learned enough to greet NAO as a friendly robot companion. His perception of NAO was formed through the cultural resources drawn simultaneously from the situation and his preceding learning, including that of movie robots. He makes sense of what he sees with an already formed concept of robots, which creates expectations of what to see. Sensory awareness and meaningful perception go together with the cultural learning of concepts. Posthuman theories make us question the humanist paradigm of individuals and the question whether each and every human constructs and experiences a completely separate material world. These debates have been at the base of much constructionism and Piagetian-inspired constructivism, and as noted by Jan Derry, both are inherently dualist approaches. Though none of these approaches see mind as a mirror of the world, they emphasise humans’ creative capacity to construct the world already out there. The constructivist emphasises the individual creative minds, whereas constructionists emphasise social creativity (Derry 2013, 63, note 27). Both approaches overlook, however, how materials, like NAO and Jibo, transform our conceptual perceptions, for instance when they do not behave as expected. Robots, because they cross boundaries between virtual and ordinary space, can help us acknowledge the ultra-social capability of humans for reaching out and merging with materials as well as each other in a world that is both our own (collectively shaped) and a collectively shared world. Social robot materiality teaches us, for instance, the difference between stories of robots in the media and the robots we encounter at home. At first the journalist Van Camp and his wife meet Jibo with a concept of robots that makes them expect that the robot as an intelligent learner can become part of their ultra-social collective, but both they

24 

Introduction

and Jibo change in the process of relational entanglements. Jibo becomes more of a machine, and they learn that robots do not necessarily learn as they expected. Andreas also has his preceding learning of robots as lively creatures put to the test when he meets NAO. Though the Van Camps and Andreas live in different countries, their expectations of how robots will behave is informed by their preceding learning of robots in a Western/Asian robotic media culture, where children and adults meet robots as lively and intelligent learners in cartoon and movie representations long before they meet them in real life. We could call this kind of learning with materials “life-long”, because human learning is an ongoing process, and not just an instrument in education. In the paths of life, collectively learned prior embodied learning with concepts is always entangled with and transformed by material-conceptual phenomena. Contrary to systems of taxonomic and binary categories and representations, the formation of concepts I have in mind is not tied to dichotomies of fixed formal hierarchies, but comes about spontaneously in practices. Even “scientific concepts”, that call for an awareness of their relations to other concepts, build on spontaneously formed concepts that are at the base of human ultra-social collective experiences with the world. Cultural upbringing and local learning with material settings play a part in conceptual perceptions. The people that make the robots, that we in our research projects have named “robot makers” or “robot designers”, are often surprised when they learn about the strong desire for humans, as expressed by Van Camp and Andreas, to perceive humanoid robots as almost human quasi-others. The robot-makers we have interviewed in the past couple of years work on all kinds of robots. Some of them worked on social, humanlike robots like NAO, others on industrial robots, robots for agriculture, construction, and robots for healthcare. Across all of these specialised types of machines the engineers seem to share a number of cultural understandings about how robotic machines really work that make them expect robots to behave differently than Andreas and Van Camp did. They have been educated in, for instance, mechatronic engineering, bioengineering, software engineering, informatics, mechanical engineering, and electrical engineering and control issues. Their focus is on the material world, its properties and forces and knowledge of how human manipulation can transform physical nature. They do not study humans, let alone posthumans, and they are often ignorant about how other humans’ social and cultural learning affects encounters with the machines they build. A robot-maker we interviewed tells us about a robotic machine he built that was capable of moving across a f loor avoiding obstacles through sensors. In spite of its sensors, it was a problem that people ran into it and tripped over it. A first it was just a metal box, but then for fun, an engineer gave it eyes. To the engineers’ surprise, the humans around the machine now began to treat the machine with consideration and tried to avoid tripping over it. They also now began to attempt to communicate with it. The children at the Skive “MUSE ®UM” are no different. An artefact with eyes seems sociable. Human beings long for other social creatures to share their sociality with and they are even stretching themselves to include machines in their

Introduction 

25

sociality (for another example see Hasse 2015a). Though NAO does not respond to the children’s many attempts to make social contact (as Andreas showed with his whole body moving literally towards the robot and repeatedly opening his mouth as if to swallow it) its machine part responds just enough to let the children hope that NAO will be responsive as a new, autonomous, potential playmate. The robot-makers may acknowledge that it is humans who bring a liveliness to machines that the technology itself has yet failed to provide, and that this is especially the case when the machines look “human” like NAO. However, robot-makers we have spoken to, with some exceptions, tend to perceive robots, with or without human-like features, as machines. This widens their concept of robots informed by media robots. Their concept includes many types of machines of which they have hands-on experiences. One of the robot-maker engineers put it this way: I think that most of the people think that robots are something anthropomorphic, that it has some kind of complex intelligence – when they think a robot is just like a human being. This is the most common idea of robots. Therefore, the industrial robots are not in the common imaginary of the people, when they speak about robots. However, if you consider a robot something that is capable of operating on some data and using an actuator on the base of the data he acquired, I think there are many robots in my life and in the life of everyone. Because, for example, the coffee machine in the bar is a robot, actually, because it has some sensors, it elaborates this f low and heat data, and it enacts some actions on the base of this data. So actually, yeah, there are a lot of robots in my life. (Victor, robot designer and engineer) However, it is not the case that all robot designers perceive robots as machines, while all children see them as lively creatures. Children and robot-makers do not form uniform collectives that stand in opposition to each other. If we decide to look at the two formations as ultra-social learners – the Danish schoolchildren and the robot designers who operate globally – in relation to how they have learned to perceive robots, we find a huge diversity within each formation. A few Danish children, younger as well as older, have learned to perceive robots in the same way that engineer Victor does: as machines with actuators and data processing equipment. Some children, when ref lecting on the NAO experiment, seem to move in this direction as well. Some robot-makers, like the American engineer Ray Kurzweil and the Japanese robot designer Hiroshi Ishiguru, both media darlings, hold a different view than Victor does. They express in public that they are convinced, like most Danish children, that robots can one day have a human-like will of their own, though they acknowledge that robot development still has a long way to go. Like Stuart Russell, quite a few roboticists believe in robotic intelligence and AI as a superintelligence (e.g. Kurzweil 2005) which is not the same as a “beneficial intelligence for humans” as argued by Stuart (Russell 2017). Many robot-makers share the views of Victor that robots, even with AI, are just machines that are not

26  Introduction

human-like (e.g. Stone et al. 2016). Learning to conceptualise robots seems to vary both within and between human formations, which we can call groups if we decide to cut the world up into these separate boxes. If the focus is on learning processes, the question is how learning is entangled in this multitude of differences in perceptions of robots across formations. How did the building of robots, together with a background in engineering educations, teach the robot-makers primarily to see robots as machines? In addition, how did the media robots teach the children (and many adults as well) primarily to perceive and expect robots to be lively creatures? Why do some robot-makers and children perceive robots as machines, whereas other robot-makers and children perceive robots as intelligent beings?

Conclusion: Chapter 1 Jibo and Andreas differ in their respective meetings with humans and the robot NAO. Jibo may seem human when it acts as if it is curious, but it remains an empty curiosity. Jibo can collect and process all the information available on the internet. Andreas, on the other hand, is emotionally engaged and so curious that he keeps engaging even when he receives no proper response. In a posthumanist perspective, we can learn something from that difference. Whereas Jibo may be an attempt towards a posthuman learner, Andreas is a posthumanist learner. Posthumanist theories do not exclude references to humans such as Andreas, but the perception of the human as proposed in humanist (Enlightenment-oriented) theories has to be revised. In a posthumanist learning perspective, the conceptual world cannot be discussed as separate from the material. Following Barad and postphenomenologists, subjects are not “humans” as rational stand-alone individuals, but are entangled in a world that does not a priori build on separate subjects, concepts and objects. I want to entangle the posthumanist theories with the cultural-historical learning theories to emphasise that shifting boundaries also involve material conceptual learning processes. Humans are not exceptional as learners, but as a species of learners, we experience in a radically different way from robots. We learn like Andreas by reaching out to the world as we attempt to make it meaningful and expect meaningful responses based on our preceding conceptual learning. Jibo is a programmed machine and, contrary to Andreas, it is not curious about the world, because the world, for it, is not conceptually meaningful in its entanglements, though Jibo may correctly communicate representations and taxonomic categories in conversations. The posthuman is a solipsist “singularist” technical figure, the artificially intelligent creature, built on an assumption that such a creature surpasses humans in intelligent, rational standalone individualism. The posthumanist theories created by new feminist materialists and philosophers, however, are theoretical approaches that present a showdown with all of the above humanist assumptions and call for an entangled being that “becomes” with material environments. Following the Enlightenment human, these new humans are a collective of ultra-social learners that allows the material surroundings their due in their ongoing embodied individuation of the world of collectives.4

Introduction 

27

In the proposed posthumanist learning perspective, this indicates that preceding learning also creates boundaries for what can be learned in new encounters with materials. We are not creative in the way proposed by posthumanist educationalists where available materials are enough to release creativity. We differ in how we have learned to draw on the pool of cultural resources available. The posthumanist learning perspective that I propose, emphasises that we perceive the world’s phenomena from a collective perspective before we are individuated; however, it is a cultural collective learning that is not shared by everyone, everywhere. Contrary to algorithmic beings like Jibo and NAO, humans are never entirely alike. To explore learning in new entanglements with robots and cyborgs we need to look at cultural learning processes and material conditions. Culture is a term for what makes us different from other cultural formations and aligns us within our own formation. Formations are not a priori entities, but are created in collective material-conceptual learning processes as well as rendered as fixed groups in representations and taxonomic categories. Formation, however, cannot be reduced to fixed categories of nationality, ethnicity or race. However, a materially-discursive diversity can be studied empirically, such as differences in how children and robot-makers perceive, create or perform robot phenomena. In the following chapters, I explore different aspects of the posthumanist learner. Education is as a special instance of learning that through new learning tools, such as MOOCs, builds on an understanding of the human learner as an individual autonomous humanist self-learner. I then discuss what implications posthumanist learning theory has for education (Chapter 2). In the next chapter, I propose that posthumanist learning theories include how humans form emotional collectives with each other and materials as they learn (Chapter 3). Then I look at how stories of automata and robots entangle with different collectives of robot makers, children and the collective of posthumanists (Chapter 4). Next, through an analysis of children’s visions of robots, I shall explore the importance of preceding learning for the transformation of conceptual perception and how conceptual perception and thinking are tied to materials (Chapters 5, 6 and 7). In the following chapter, I look at how the postphenomenological cyborgian body is bringing a new dimension to a relational ontology in posthumanist learning theory (Chapter 8). This theme is further scrutinised in the next chapter (Chapter 9) where the focus is on how machines extend our bodies and minds. Finally (Chapter 10), I discuss how learning machines may help us form a new theory of posthumanist learning that differs from the posthuman claims of robots and cyborgs preparing us for a new posthuman existence.

Notes 1 Vygotsky (1896–1934) was not primarily a learning theoretician as noted by Seth Chaiklin (2015). In the English translation of Vygotsky’s Collected Works there are several volumes where the concept does not occur. In Volume 1 (Vygotsky 1987), however, learning is the main concept (with more than 200 references). There is no doubt that Vygotsky primarily discusses learning in ways that connect it with

28  Introduction

teaching and education (e.g. Cole 2009) but others have pointed to the general implications of Vygotsky’s theories also for adult learners (e.g. Anne Edwards 2010). In my reading of Vygotsky’s collected oeuvre, I see his contribution of importance for general anthropology as it explores the basic processes of what we term “culture”. 2 As also noted by some of my Danish colleagues, Malou Juelskjær and Helle Plauborg ( Juelskjær & Plauborg 2013). 3 Sometimes a whole group of children were sitting in front of NAO, sometimes just one. Andreas was sitting alone in front of NAO. 4 I am not a philosopher, but I find Jan Derry’s argument against using Enlightenment as a caricature, convincing – however here I use it to emphasise that Enlightenment in posthumanist theory has come to stand for individualism, subject–object separation, human exceptionalism, rightful exploitation of nature.

References Barad, K. (2003). Posthumanist performativity: Toward an understanding of how matter comes to matter. Signs: Journal of Women in Culture and Society, 28(3), 801–831. Barad, K. (2007). Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning. Durham, NC: Duke University Press. Bennett, J. (2010). Vibrant Matter: A Political Ecology of Things. Durham, NC: Duke University Press. Bernstein, D. A. & Nash, P. W. (2008). Essentials of Psychology (4th ed.). Boston, MA: Houghton Miff lin. Biesta, G. J. J. (2010). Good Education in an Age of Measurement: Ethics, Politics, Democracy. Boulder, CO: Paradigm. Birenbaum, M. & Dochy, F. (Eds.) (1996). Alternatives in Assessment of Achievement, Learning Processes and Prior Knowledge. Boston, MA: Kluwer Academic. Braidotti, R. (2013). The Posthuman. Cambridge: Polity Press. Breazeal, C. (2009). Role of expressive behaviour for robots that learn from people. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1535), 3527. Breazeal, C. (2002). Designing Sociable Robots. Cambridge: MIT Press. Bruner, J. S. (1991). The narrative construction of reality. Critical Inquiry, 18(1), 1–21. doi:10.1086/448619. Camp, V. J. (2017). Review Jibo Social Robot. 11.07.17. Retrieved from https​://ww​ w.wir​ed.co​m /201​7/11/​revie​w-jib​o -soc​ial-r​obot/​ 15 January 2018. Camp, V. J. (2019). My jibo is dying and it's breaking my heart. Jibo is a robot, but that doesn’t make his digital dementia any less painful. 03.08.2019. Retrieved from https​ ://ww ​w.wir​ed.co​m /sto​r y/ji​bo-is​- dyin​g -eul​ogy/ 29 August 2019. Chaiklin, S. (2015). The concept of learning in a cultural-historical perspective. In: I. D. Scott & E. Hargreaves (Eds.). The SAGE Handbook of Learning (pp. 94–106). London: SAGE. Clark, A. (2003). Natural-Born Cyborgs: Mind, Technologies, and the Future of Human Intelligence. Oxford: Oxford University Press. Cole, M. (1996). Cultural Psychology: A Once and Future Discipline. Cambridge: Harvard University Press. Cole, M. (2009). The perils of translation: A first step in reconsidering Vygotsky’s theory of development in relation to formal education Vygotsky. Mind, Culture, and Activity, 16(4), 291–295. Derry, J. (2013). Vygotsky: Philosophy and Education. Hoboken, NJ: Wiley Blackwell. Dreyfus, H. L. & Dreyfus, S. E. (1986). Mind Over Machine: The Power of Human Intuition and Expertise in the Era of the Computer. New York, NY: The Free Press.

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Edwards, A. (2010). Being an Expert Professional Practitioner: The Relational Turn in Expertise. Dordrecht: Springer. Edwards, R. (2010). The end of lifelong learning: A post-human condition? Studies in the Education of Adults, 42(1), 5–17. Ferrando, F. (2013). Posthumanism, transhumanism, antihumanism, metahumanism, and new materialisms: Differences and relations. Existenz, 8(2), 26–32. Flatschart, E. (2017). Feminist standpoints and critical realism. the contested materiality of difference in intersectionality and new materialism. Journal of Critical Realism, 16(3), 284–302. Fukuyama, F. (2002). Our Posthuman Future: Consequences of the Biotechnology Revolution. New York, NY: Farrar, Straus and Giroux. Gagné, R. M. & Driscoll, M. P. (1988). Essentials of Learning for Instruction (2nd ed.). Englewood Cliffs, NJ: Prentice Hall. Gregory, D., Johnston, R., Pratt, G., Watts, M. J., & Whatmore, S. (Eds.) (2009). The Dictionary of Human Geography (5th ed.). Singapore: John Wiley & Sons. Haraway, D. (1988). Situated knowledges: The science question in feminism and the privilege of partial perspective. Feminist Studies, 14(3), 575–599. Hayles, N.K. (1999). How We Became Posthuman: Virtual Bodies in Cybernetics, Literature, and Informatics. Chicago, IL: University of Chicago Press. Herrman, E., Call, J., Hernandez-Lloreda, M.V., Hare, B., & Tomasello, M. (2007). Humans have evolved specialized skills of social cognition: The cultural intelligence hypothesis. Science, 317(5843), 1360–1366. Hasse, C. (2015). An Anthropology of Learning. Dordrecht: Springer Verlag. Hasse, C. (2015a). Multistable roboethics. In: J. K. B. O. Friis & R. P. Crease (Eds.). Technoscience and Postphenomenology (pp. 169–180). London: Lexington Books. Ihde, D. (1990). Technology and the Lifeworld. Bloomington, IN: Indiana University Press. Ihde, D. (2002). Bodies in Technology. Minneapolis, MN: University of Minnesota Press. Ihde, D. (2011). Of which human are we post? In: G. R. Hansell & W. Grasse (Eds.). H+/−: Transhumanism and Its Critics (pp. 123–135). Philadelphia, PA: Metanexus Institute. Ingold, T. 2011. Being Alive: Essays on Movement, Knowledge and Description. London: Routledge. Jarvis, P. (2009). Learning to be a Person in Society. London: Routledge. Juelskjær, M. & Plauborg, H. (2013). Læring og didaktik udsat for poststrukturalistisk tænkning. In: A. Qvortrup & M. Wiberg (Eds.). Læringsteori og didaktik (pp. 257–287). Copenhagen: Hans Reitzels Forlag. Kandel, E. R. (2001). Nobel lecture: The molecular biology of memory storage: A dialog between genes and synapses. Bioscience Reports, 21(5), 565–611. Kirksey, S. E. & Helmreich, S. (2010). The emergence of multispecies ethnography. Cultural Anthropology, 25(4), 545–576. Knox, J. (2016). Posthumanism and the Massive Open Online Course: Contaminating the Subject of Global Education. London: Routledge. Kolb, D. A. (1984). Experiential Learning Experience as a Source of Learning and Development. Upper Saddle River, NJ: Prentice Hall. Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. New York, NY: Viking. Milana, M., Webb, S., Holford, J., Waller, R., & Jarvis, P. (Eds.) (2017). The Palgrave International Handbook on Adult and Lifelong Education and Learning. Basingstoke: Palgrave Macmillan. More, M., & Vita-More, N. (Eds.) (2013). The Transhumanist Reader: Classical and Contemporary Essays. New York, NY: Wiley-Blackwell.

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Rosenberger, R. & Verbeek, P.-P. (Eds.) (2015). Postphenomenological Investigations: Essays on Human-technology Relations. New York, NY: Lexington Books. Rosenberger, R. & Verbeek, P.-P. (2015). A field guide to postphenomenology. In: R. Rosenberger & P-P. Verbeek (Eds.). Postphenomenological Investigations: Essays on Human-Technology Relations (pp. 7–42). New York, NY: Lexington Books. Russell, S. J. & Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall. Russell, S. J. (2017). Provably beneficial artificial intelligence. Retrieved from https​ ://ww ​w.bbv​a open ​m ind.​c om/w ​p -con​t ent/​u ploa​d s/20​17/01​/ BBVA​- Open ​M ind​Stuar​t-Rus​sell-​Prova​bly-B​enefi​cial-​A rtif ​icial​-Inte​l lige​nce.p​d f 01 February 2018. Scott, D. & Hargreaves, E. (Eds.) (2015). The Sage Handbook of Learning. London: Sage. Schilhab, T. (2017). Derived Embodiment in Abstract Language. Dordrecht: Springer. Simons, M. & Masschelein, J. (2006). The learning society and governmentality: An introduction. Educational Philosophy and Theory, 38(4), 417–430. Schunk, D. H. (2008). Learning Theories: An Educational Perspective (5th ed.). New York, NY: Macmillan. Snaza, N. (2017). Is John Dewey’s thought ‘humanist’? Journal of Curriculum Theorizing, 32(2), 15–34. Snaza, N. & Weaver, J. A. (Eds.) (2015). Routledge International Perspectives in the Philosophy of Education (Vol. 35). New York, NY: Routledge. Snaza, Nathan (2015). Toward a genealogy of educational humanism. In: N. Snaza & J. A. Weaver (Eds.). Routledge International Perspectives in the Philosophy of Education (Vol. 35, pp. 17–29). New York, NY: Routledge. Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, G. H., Hirschberg, J., … Teller, A. (2016). Artificial Intelligence and Life in 2030. One Hundred Year Study on Artificial Intelligence: Report of the 2015–2016 Study Panel. Stanford, CA: Stanford University. Retrieved from http://ai100.stanford.edu/2016-report 17 January 2018. Tomasello, M. (2014). The ultra-social animal in European journal of social psychology. European Journal of Social Psychology, 44(3), 187–194. Tuin, I. Van der & Dolphijn, R. (2010). The transversality of new materialism. Women: A Cultural Review, 21(2), 153–171. Retrieved from http:​//www​.tand​fonli​ne.co​m /doi​/ abs/​10.10​80/09​57404​2 .201​0.488​377. Turkle, S. (2012). Alone Together: Why We Expect More from Technology and Less from Each Other. New York, NY: Basic Books. Verbeek, P. P. (2009). Let’s make things better: A reply to my readers. Human Studies, 32(2), 251–261. Vygotsky, L. S. (1978). Mind in Society. Cambridge: Harvard University Press. Vygotsky, L. S. (1987). Thinking and speech. In: R. W. Rieber & A. S. Carton (Eds.). The Collected Works of L.S. Vygotsky (Vol. 1, trans. N. Minick, pp. 39–285). New York, NY: Plenum Press. Vygotsky, L. S. (1987a). Perception and its development in children. In: R. W. Rieber & A. S. Carton (Eds.). The Collected Works of L.S. Vygotsky (Vol. 1, trans. N. Minick, pp. 289–300). New York, NY: Plenum Press. Vygotsky, L. S. (1997). The history of the development of higher mental functions. In: R. W. Rieber (Ed.). The Collected Works of L.S. Vygotsky: The History of the Development of Higher Mental Functions (Vol. 4, trans. M. J. Hall, pp. 1–251). New York, NY: Plenum. Waters, R. (2018). Dead robots raise questions on how far home technology has come. Financial Times, https://www.ft.com/content/e92c5b9a-9c59-11e8-9702-5946bae86e6d. Zalasiewicz, J., Williams, M., Steffen, W., & Crutzen, P. (2010). The new world of the Anthropocene. Environment Science & Technology, 44(7), 2228–2231.

2 POSTHUMANIST LEARNING IN EDUCATION

Andreas, who sat in front of the robot NAO in Chapter 1, is a child prepared for a robotic future because he lives in Denmark and goes to a state-based Danish school. More than two thirds of Danish schools we looked at in the project “Robots in Schools” have bought or consider buying robots like NAO.1 This will give Danish children a head start in preparing them for a future “robot society”, teachers told us (Esbensen et al. 2016, 19 ff ). Andreas goes to a school filled with technology (though in this particular school they have no robots yet) because Denmark wants schools to use technologies to prepare children for their future algorithm- and robotic- based work life. However, in the future, children may not go to school to meet, learn about and use technology, but may receive their education in front of a screen (or a robot) at home. Institutionalised state-based education is challenged by the technological developments that claim teachers and schools can be replaced by new technologies such as OERs (Open Educational Resources) and other multimedia tools such as educational robots (e.g. Toh et al. 2016). MOOCs (Massive Open Online Courses) can address maybe 100,000 students all over the globe with the same lectures (e.g. Knox 2016). The advent of MOOCs has been seen as a new way to reach out to provide education for all outside formal nation-based institutionalised schooling (UNESCO 2014). The traditional teacher-centred, state-based formal ways of educating humans are under pressure. Within a humanist paradigm it has been assumed that when humans go to school, they learn to process information. Education is learning – but today the focus is increasingly on the student as a self-directed learner getting all the necessary information through technology. This development can be seen in a new discourse which the educational philosopher Gert Biesta has named “learnification” (Biesta 2010). Today, this reference to learning is woven into a discourse about students’ self-learning, and the claim that teachers, with the new

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technologies available, can be reduced to coaches (helping students find information) or that education no longer has to take place in schools but can be taken care of by self-learning by everyone, at all times, with available teaching technologies (e.g. educational robots, apps and YouTube videos). The learning built into new educational technologies often follows an instrumental understanding of learning haunting the educational sciences, which implies that learning can be used to optimise individual processes (Simons & Masschelein 2006). This development may fit a humanist learning approach, where an individual can learn from available information by technology-led processes, but in a posthumanist learning approach this development may lead to less education for all, not more as promised by, for instance, MOOCs. How can that be? In this chapter, I shall first present a brief overview of some general learning theories and discuss how learning in educational institutions, such as schools making use of humanist learning theory, is challenged from two sides, both of which reject an emphasis on cognitive processes. One is the anthropological emphasis on situated learning as an encompassing process where the formation of cultural persons takes place everywhere and not in schools in particular (e.g. Lave and Wenger 1991, Levinson and Holland 1996). Another is the proposal from some posthumanists theories that want to discard learning altogether. From this perspective, learning theory in general, and especially the learnification discourse, is tightly woven into our conception of the cognitive individual human, severed from the world (Snaza & Weaver 2015). However, there are also some inconsistencies in how posthumanism addresses learning and education, which I shall take up in the chapter. For example, some posthumanists see the new technological developments as a way to open up selfdirected learning through play and tinkering, which replace what they see as a humanist learning of representations (e.g. Edwards 2010). For others, students’ self-directed learning moves us away from recognising a new posthumanist learning paradigm (Knox 2016). Ultimately, I argue, we need to put learning as a psychological, albeit not an individual, process back into the relations between humans and materials. Only in this way can we keep humans like Andreas in the phenomenon of learning without reducing them to the empty informationbased curiosity of machines. Furthermore, we need to include cognitive processes in posthumanist learning to become aware of how new technologies may increase inequality.

A brief history of learning Learning theories are not made by a coherent group of scholars. Learning theory is a field with many large and small rivers running in different directions, then bending and sometimes crossing or blending in a complex network. Nevertheless, it is possible to identify three main streams: the behaviourist, the cognitive/constructivist and the socio-cultural (e.g. Greeno et al. 1996). The vast majority of cognitive, behavioural and some socio-cultural constructivist learning theories,

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can be built into educational technologies, because they operate with the learning of an autonomous individual subject who learns in a world of separate objects (e.g. Schunk 2008, Ertmer & Newby, 1993, Kolb 1984). Humans have not always been interested in their own learning processes. However, in academia an interest in learning as a process of change developed with new questions along with the young science of psychology. Even if prototheories of learning can be dated back in time, learning as a scientific field of academic inquiry began around 1880s. Pioneers in establishing the field of psychology, such as German psychologist Wilhelm Maximilian Wundt (1832– 1920), and American child psychologist and student of Wundt, James Baldwin (1861–1934), placed learning at the centre of evolution and explored “learning” in their psychological laboratories. German psychologist Hermann Ebbinghaus (1850–1909) developed a “learning curve” of how well humans learn to memorise nonsensical syllables. Two American theoreticians, the functionalist William James (1842–1910) and the pragmatist John Dewey (1859–1952), both emphasised learning as tied to practices. They discussed the basic process of change that transformed an individual’s human thinking, perception and memory in its relation to a material and/or social environment. Over time, the early theories of learning were debated and sometimes subsumed or expelled by a new movement that took learning as a main topic to be studied experimentally: behaviourism. Wundt, especially, became a lightning rod for critiquing from the new learning theorist perspectives emerging in the behavioural movement. Wundt believed firmly that the higher psychological functions of the individual mind could partly be studied through observations of behaviour, but mainly through introspection. Experiments in laboratories were inspired by a revelation, which came to Wundt as a young student in Heidelberg. He had noticed that astronomers differed in their measurements in the passages of stars following the gridlines of telescopes. These differences were, Wundt realised, effected by whether the astronomer first focused his attention on the star in question or on the measuring device. He acknowledged that the difference was a difference in a mental process which could be measured and he made this discovery the basis of his experimental psychology (Blumenthal 1980). With proponents like John Watson (1878–1958), Edward Thorndike (1874– 1949) and B. F. Skinner (1904–1990) the new behaviourist science of learning accused Wundt of mentalism and introspection. They claimed he reduced psychology to a subjective science, because he relied on systematic self-observation in his experiments. Instead, some of his own students proposed a new scientific approach to the concept of learning following the Russian scientist, Ivan Pavlov (1849–1939). In Pavlov’s theories of learning the material conditions became entangled with learning theories in ways which would have a permanent effect on how we understand learning even today. Pavlov was never a learning theorist. He was a chemist and physiologist studying what was a new field around 1900: ref lexology. He was particularly interested in the salivation of dogs and conducted many experiments studying how

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salivation was triggered, how it moved through the dogs’ bodies and when and where it stopped. It was during these experiments that Pavlov noticed that the biological process of salivation could be triggered by a materiality other than food: a metronome or a buzzer, after a process of learning that connected food and sound. His helpers in the laboratory had so far taken the material stimuli of salivation, that is, the food brought to the dogs, for granted. If the sound of the metronome or buzzer were continuously introduced with a serving of food, after a while the mere sound would trigger the salivation even when no food was present. It was not emphasised by Pavlov himself that this was pointing to how matter and meaning connect for an individual through learning. Along with behaviourist theories in general, the focus was on a priori cause and effect or, in the lingo of behaviourism: stimulus and response in individual subjects in relation to separate objects. Pavlov’s experiments led to the theory of conditional ref lexes, which describes the mechanisms of the acquired reactions, which became a way to understand learning in behavioural science. Behaviourists developed Pavlov’s theories of classical conditioning into theories of operant conditioning, where an individual’s behaviour was changed either by reward or by punishment. Like their forbearers, behaviourists did not confine studies of learning to humans but relied most often on animal learning, zoo psychology, which was later connected with human learning. They built their experiments on what could be observed as reactions to stimuli in experiments and rejected Wundt’s subjective introspective methodology. The human in the experiments continued to be considered an individual cut off from anything but the planned stimuli, which could be seen as a kind of information. Consciousness, seen as opening the black box of information processing, did not enter the behavioural sciences. Watson argued for instance that “‘consciousness’ is neither a definable nor a usable concept”, (Watson 1930, 2). Behaviourism became an immense academic success and established learning theory as an academic field in that it experimentally reduced what was considered as complexity to what was considered as simplicity. Like animals, humans could be trained emotionally. For instance, a small child who loved white furry things could learn to hate these if they were systematically connected to a nasty sound (Watson & Rayner 1920). A human in behaviourism would be a simple mechanism where inputs could be conditioned so particular outcomes could be expected. In the learning sciences, as well as in machine learning and marketing, behaviourist learning theories are still of immense importance. Though new approaches to learning keep cropping up, the story of learning is not one of a straight line of development where older theories are left behind in favour of newer ones. As behaviourism is all about stimuli and responses, it has been extremely popular in the technical sciences trying to create learning machines. Stimulus and response are exactly the kind of processes that can be replicated

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in machines. This approach also allows for a robot teacher or an algorithm that receives input from students to respond if these inputs are wrong or right. However, the real breakthrough for machines in relation to learning and education came during the 1950s with a critique of behaviourism that was no less vitriolic than behaviourists’ critique of Wundt’s introspection. This movement was called the “cognitive turn”. The cognitive approach was taken up by the technical sciences, and in many ways seemed to evolve with the computer sciences. What came to be known as “the cognitive revolution” (Gardner 1985) developed with the interests of engineers from the beginning (e.g. Russell & Norvig 2010). The engineers began discussions of how machines could learn to think and solve programmes as well as human beings, or better (e.g. Newell et al. 1958, Kurzweil 2005). In addition, cognitive scientists began to understand the workings of the individual mind as a computer. Learning was viewed as the black box avoided by behaviourists – a process of information or symbol processing, which was able to be translated into algorithms. From 1950s onwards, learning theories relied on new methods in, for example, experiments with brain scanning technologies (e.g. MRI or CT scanners), to open the black box of the mind as a computational entity. The cognitive approach emphasised that human behaviour, as well as changes in behaviour, depended on the acquisition and retention of knowledge (e.g. Ausubel 2000) as well as perceptions of the environment. This humanist paradigm still considered the human an individual. The mind was now understood to be like a computer receiving and processing information. The technical sciences also found inspiration in the epistemological geneticist Jean Piaget’s (1996–1980) seminal theories, which are often referred to as “constructivism” (Piaget 1964). Many engineers followed the work of one of Piaget’s students, Seymour Papert, who proposed a “constructionist” approach to learning (Papert & Harel 1991). Where behaviourists defined learning as “a relatively permanent change in behavior as a result of an individual’s experiences” (Selwyn 2016, 3), the cognitive sciences began to understand that the “relative permanent change” also involved an individual processing in perception, memory, emotion and cognition. The emphasis on the interrelation between a learner’s active co-construction of an environment had already, in the midst of behaviourism, been emphasised by the so-called Gestalt theory (Kohler 1947, Koff ka 1963) that emphasised that learned stimuli is perceived in a wholeness and not in the atomistic version proposed by behaviourists. However, it was not this holism that inspired the technical sciences but rather the notion that the inner workings of a computer could seem similar to that of a cognitive human mind. This opened up a new understanding of robots and AI as learning machines, which did not only respond to stimuli but learned to correct their own behaviour from responses. Where the behaviourists did not distinguish between human and non-human learning, the cognitive approach to human learning emphasised that even if we share

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ref lexological mechanisms with dogs and rats, humans differ because our learning is tied to symbol processing, just like computers. In seeing human learning as a process of computing discrete symbols, the distance between machine learning and human learning diminished (Russell & Norvig 2010). Humans (and eventually machines) are, in this perspective, not just passive receivers of stimulus to which they respond, but they actively seek the right constructions of answers to information stimuli. Both behaviourist and cognitivist learning theories are immensely important today for the development of educational devices used in school learning. These movements also loom large in AI and “machine learning” procedures that, on the one hand, increasingly ensure a more autonomous kind of machine agency, and on the other, create educational tools that aim at individual self-learning. Technically oriented humanists see the development of machine learning as a prerequisite for a posthumanist future where intelligent machines replace the more vulnerable human bodies and brains (Kurzweil 2005). The algorithms in machine learning extensively draw on learning theories found within both the behaviourist and the cognitive/constructivist paradigms.

The cultural paradigm The cognitive symbol processing and the behavioural paradigms have been criticised from directions which have been harder to incorporate into the new machine-based approach to education: the socio-cultural and the phenomenological, which have both gained momentum in the learning sciences from the 1980s up until today. Though the cultural psychologist Jerome Bruner was part of, and, some claim, constitutive of, the cognitive revolution in the learning sciences, he began to denounce it in the 1980s. He explicitly targeted the technical sciences as having distorted the attempt to bring “mind” back into research and now saw that the cognitive sciences had been technicalized in a manner that even undermines that original impulse. This is not to say that it has failed: far from it, for cognitive science must surely be among the leading growth shares on the academic bourse. It may rather be that it has become diverted by success, a success whose technological virtuosity has cost dear. Some critics (…) even argue that the new cognitive science, the child of the revolution, has gained its technical successes at the price of dehumanizing the very concept of mind it had sought to re-establish in psychology, and that it has thereby estranged much of psychology from the other human sciences and the humanities. (Bruner 1990, 1) Instead, Bruner suggested we began to study how cognitive and cultural processes informed each other. He was not alone in this endeavour. The cultural

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paradigm in learning theory focuses, broadly speaking, on social, cultural, situated-embodied and contextual processes. Social constructivist or constructionist learning theories also belong to this stream, which places an emphasis on humans in social engagements, activities, situated practices and culture. Sometimes the theories view the sociality of learning as one of “modelling” (e.g. Bandura 1977). Sometimes they emphasise that learning evolves in communities of practice, and reject cognitivism (Lave & Wenger 1991) or activities (Engeström 1999), and sometimes they emphasise cognitive learning as cultural (Cole 1996). Their focus is on social practice and the ways humans make the world meaningful for each other by engaging in those practices that evolve as cultural and historical processes (e.g. Cole, 1996, Holland and Lave 2001). This kind of learning is not necessarily tied to education or machine development in the technical sciences. The basic assumption is that humans are educated wherever they go and that cognitive learning entails much more than learning a curriculum or symbol processing but also entails learning cultural values and identities. The emphasis of cultural-historical learning theories on process is particularly important for my posthumanist arguments. They are part of this wider field of humanist social and cultural learning theories that have in common that they challenge the assumption of an individualistic and sometimes even solipsistic learner found in the learning sciences. Cultural and social learning theories criticised the abstract unspecified entity of “the learner” in behavioural and cognitive processes and opened up collective and socio-material aspects of learning (e.g. Hutchins 1996). This culture paradigm turns out often to find its roots in the theories developed by the Russian educationalist Lev Vygotsky (1896–1934). It is also his work which I see as a most challenging and promising fruitful engagement with posthumanist theories. This may seem paradoxical, since Vygotsky has been accused of being an Enlightenment thinker who has argued for the unique character of human beings: that is, their capacity for abstract thinking. I support, to some extent, the educational philosopher Jan Derry who has defended Vygotsky as a far more significant thinker than he appears in the eyes of his critics (Derry 2013). I emphasise that his focus on the social and material character of learning as a process is important for posthumanist theories. In my use of Vygotsky’s theories, I move beyond the cultural-historical concept of artefacts. Artefacts are often perceived as socio-material tools or objects manipulated by tools and put at the service of the social group (e.g. Cole 1996). These interpretations still operate with an understanding of a human subject (even if collectively formed by culture) and a separate object to be learned about or to be transformed by tools. The “cultural turn” proposed by Bruner has had little impact in the engineering sciences until recently. Contrary to the impact of behaviourist and cognitivist learning theories used in machine learning, cultural learning theories are difficult to put into formula. In their work on machine learning, engineers increasingly acknowledged that social and cultural aspects are important, when machines are expected to operate among people in their everyday lives, but these are recent developments.

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Some insights from both behaviourist and cognitive learning theories remain important in an exploration of how humans learn in ways machines cannot, but what I find to be most compelling for a new posthumanist theory of learning is the insight from cultural, or socio-cultural, learning theory, that also Bruner turned to, because they open a way to see human learning as non-individualistic, collective and permeated by cultural meaningfulness. Especially the idea of human meaning-making (Bruner 1990) can challenge the technicalised approach taken up by the engineers in their educational approaches. However, apart from cultural approaches to learning, it should be noted that the ­mechanistic cognitive computational approach to learning has also been challenged from the perspective of the human body in a phenomenal world. This perspective, tied to phenomenological philosophy, is informed, for instance, by the two brothers Hubert and Stuart Dreyfus’ critique of the naive ways artificial intelligence overlooked the importance of moving bodies for learning and cognition (1986). Despite internal differences, the concept of the body as relational is shared by phenomenologists such as Hubert and Stuart Dreyfus (1986) and postphenomenologists such as Don Ihde (2002). They emphasise an embodied subject that stands in relation to the world with their tactile-kinaesthetic bodies. The phenomenological critique of the “brain-as-computer” thesis had some impact in the technical sciences, especially through the work of the Dreyfus brothers. The phenomenological critique evolved into postphenomenology, which takes a special interest in exploring the relation between the human, the cultural world and the intermediary technologies (Ihde 1990). Though phenomenology, and postphenomenology, do not exactly constitute a learning paradigm, the approach emphasises that human bodies cannot be overlooked in relation to learning with technologies (I shall return to this discussion in Chapter 8).

Learning in education Though, for heuristic reasons, I have laid out the development of the learning paradigms as linearly evolved and separate from each other, they move across time and space. There are some historical paths, diverging and confronting each other, but they are always entangled. What we have are different perspectives, which blend and take a point of departure in each other. They have all informed the learning that goes on in the institutions formally designated to education. What happens to our understanding of learning when these institutions are challenged on the one hand by new posthuman technological developments, and on the other by posthumanist theories that disregard human psychology? In many posthuman and posthumanist perspectives formal educational institutions are not necessary for learning. Yet it seems that it is still formal learning which has paved the ways that allow humans to become capable of transforming a material world into a human-made environment. Most human-made transformations of the world build on preceding kinds of organised formal learning in schools and at universities. People who create robots, educational technology,

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plastic, computer programmes, and smart city technology have higher educations in, for instance, engineering, mathematics or physics. Most of them received their education in a traditional walled institution, where they met teachers and fellow students. The disruptive educational technologies may be about to change that. Just as robots and AI challenge notions of everyday life by transforming boundaries of humans and non-humans, human learning institutions are challenged by the development of new technologies such as OERs and MOOCs. The role of teachers, students and, indeed, the previously taken-for-granted educational institutions like schools and universities, may be changing in radical ways. Technology has of course always played a role in education (Butler-Kisber 2013, 9). The abacus, clay tablets, paper and pencils are forerunners of many of the new applications of electronic devices in education. The radical change being proposed by these tools concerns the presence and role of students and the teacher as well as assessments of learning (Selwyn 2016). Formal learning in Western institutions has changed from being teacher-centred to being studentcentred through new technological possibilities. This process has advantages, but the techno-idealism behind the disruptions is not without its pitfalls (Sims 2017). Technology-enabled assessments are today prevalent in schools where they provide teachers and headmasters with graphic curves of individual student performance, and where individual students can follow their own progress with real-time data. These types of individual assessment are sometimes also the only kind of assessment offered in, for instance, MOOCs. The promises of the new technologies have been that children like Andreas, whom we met in the former chapter, and indeed children all over the world no longer need formal educational institutions to learn something from someone. They can learn and individually self-assess with technologies throughout their life. Access to these new educational technologies, like internet-based education, MOOCs and all kinds of self-teaching apps, will provide the means for a new technology-driven economy of education where all people, no matter where they are, can “self-learn”. But does posthumanist learning connected to these new forms of digital education support a technology-led learning paradigm where all can learn everything providing they have the right technologies at hand? In order to examine this question, we move from basic learning theory to a discussion of how learning can change with material and social changes in state-based education and learning.

VIGNETTE 2.1: THE LAMENTATIONS OF AN UNEDUCATED DAUGHTER Far from here there is rice and corn and mustard in the garden, Listen to the lamentations of a daughter. I was very eager to go to school, I cried because I could not go to school.

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God, you Fate [Bhabi], you visited this sin on me, You caused me to be born in a poor family. My peers go to school, carrying books. It is difficult for me to even recognise the letter “ka” [first letter of the Nepali alphabet]. When my peers go to school, I feel that I am unlucky. When my friends carry books and notebooks, they become happy, But I have to carry heavy loads. Unlucky people are not free from carrying heavy loads. I will spend my life wanting to study, But my life will be spent enslaved … (Song no. 19. Skinner & Holland 1996, 286)

In Nepal, local songs of hardship are known as Dukkha songs.2 They are sung by Nepalese women at festivals for women called Tij (or Teej). This song was composed for the 1991 Tij festival and collected by the two anthropologists Dorothy Holland and Debra Skinner. The Tij-song highlights a process of change that transformed Nepal after the Rana regime whose rule lasted from 1846 to 1951 and that purposely kept its subjects unschooled. In the new regime, the focus was on dissolving the caste system: Although people say not to drink water given by Damai, I do drink. All human beings are the same. If we cut a Chetri, blood comes, and in the same way, blood comes if we cut people of other castes, as a Nepalese schoolchild told Debra Skinner (Skinner 1990, 14). Formal schooling began late in Nepal with the Shah monarchy that attempted to introduce its version of the nation state with the rhetoric in textbooks and classroom lectures. What was at stake, when Skinner and Holland described the development of schooling in Nepal in the 1990s, was, however, more than a hegemonic state agenda. In a collection of anthropological studies of schooling around the world, Dorothy Holland and Brad Levinson argue that the structural aspects of education are only half the story. The other half is the desires and agency of the people themselves. Thus, in the clash between local cultures and the state politics of formal schooling we find a site for both the cultural production of educated persons and the educated person’s production of culture (Levinson, Foley, & Holland 1996). Tij-festivals used to be the one occasion during a year when women could leave the family they had married into and meet with their own family. It was also where women met with other women, drank, ate and chatted together as a break from the hard work of everyday life. With the emergence of schools

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open to girls, a new space for being ultra-socially connected with the world was formed, which took the girls out of their everyday chores and placed them at a distance from their previous work setting. Here a new kind of learning began, where the women could encounter a heteroglossia of voices, which planted a desire to get an education. Some girls were excluded from learning the formal curriculum in schools, but what students learned in schools spilled out into the wider society and even women in remote villages learned about politics and not least, gender politics, which they had never encountered in their home-based learning settings (Skinner and Holland 1996).

Classifications of learning Schooling is formal learning imbued with a pedagogy where it is “the point of education that students learn something, that they learn it for a reason, and that they learn it from someone” (Biesta 2015, 76). School learning is what some educationalists name “formal education”. The classification implicates a diversity between the educated and the not educated, but in educational theorising it is also pointing to two other ways of learning: non-formal and informal (e.g. Dib 1988, Eshach 2007). These three kinds of connecting education and their relation to learning, formal, non-formal and informal (Werquin 2010), supposedly differ from each other when the institutional settings around education change: 1. Formal education is an organised model of education, which often requires teachers, specified curricula, and systematic evaluation and takes place in a state-based institution (like schools and universities). Learning is often underspecified in relation to formal education, which has given rise to many claims of how technology-enhanced education can boost students learning (Bayne 2014). For most of today’s world population, the material tools for teaching are still books, pencils, blackboard and chalk, as it was in the Nepalese schools in the 1990s. In the Western educational institutions, the appearance of new technologies like computers, tablets, robots like NAO and interactive whiteboards and new interactive OERs (Open Educational Resources, such as interactive apps) are increasingly replacing books, pencils and blackboards. Though these new technological possibilities are still used as resources within the formal setting of education, there are also many technological efforts to push both the national agendas and the teachers out of formal education and instead aim at self-directed learning (e.g. Yuan & Powell 2013). The technology- and student-led discourse of self-directed learning in Western schools dilutes the significance of the teacher as a provider of content, purpose and relationship in education. When the discourse shifts from education to learning, Biesta argues, it challenges the notion that education needs teachers because the focus now is on how students engage in differentiated self-directed learning (Biesta 2015). In schools, the way

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for this development is paved when the teacher is reduced to a facilitator of students (Biesta 2010), whose self-directed search for information and assessment is increasingly becoming a global affair through the MOOCs (Knox 2016). Even when content and aims of formal learning are made explicit and a feedback mechanism involved, the implicit behaviourist learning paradigm is rarely mentioned or debated (Hattie & Timperley 2007). Technologyenhanced learning tools often turn out to be built on “reinforcement” or constructivist theories of learning and have in many ways instrumentalised formal learning systems (Bayne 2014) to the extent that many now call for a return to an acknowledgement of the importance of teacher’s agency (Biesta, Priestley, & Robinson 2015). It has also been argued that the formal learning systems in globalised education overlook non-formal and informal learning (Schwier 2012). Yet even in discussions of formal education and pedagogy, discussion of basic learning theory is curiously absent. 2. Non-formal learning is increasingly becoming a focus of attention in the learning sciences, not least because this type of learning has come to be seen as an untapped resource of human capital (e.g. Werquin 2010). Non-formal learning is seen as coming about through a non-formal education that shares many characteristics with formal education, but it has, up until the MOOC era, been defined as differing from formal education in two ways: nonformal educations are often private initiatives and do not require systematic evaluation and final exams. In the eighteenth and nineteenth centuries nonformal education was offered as correspondence courses (where teachers communicated with students by mail), whereas in the twentieth century it has become distance learning and open sources (Dib 1988). In the twentyfirst century, a new type of radical non-formal education has taken centre stage: the Massive Open Online Courses, where hundreds of thousands of enrolled students or participants are expected to learn from virtual teachers, from the materials offered online and from each other, no matter where they are in the world. MOOCs are sometimes also used as resources in formal education at universities and increasingly offer to sell certificates equal to the national exams. Their main contribution to the landscape of education is a challenge to formal state-based educational institutions as they offer alternative education outside of schools and universities of the same type as that managed by nation-states. When they offer global formal education, such as a course in mechanical physics, they offer formal educational courses from platforms often initiated by universities, such as Coursera and EdX or initiated by people from the formal educational institutions such as Stanford, MIT and Harvard that promise to open up their “elitist” formal educations to all (Selwyn, Bulfin and Pangrazio 2015). These very structured platform-based courses are termed xMOOCs and contrasted with cMOOCs (connectivist Massive Open Online Courses), which have a more open structure, build on a very specific concept of constructivist learning (Adams et al. 2014). Some recognise the course developed by George Siemens and

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Stephen Downes – “CCK08: Connectivism and Connective Knowledge, a massive open online course (MOOC)” – as “the MOOC which started the current revolution in online education” (Lowe 2014, ix). It has been argued that today there are so many different types of MOOCs that the distinction between cMOOCs and xMOOCs is no longer relevant (Bayne & Ross 2014). The offer of non-formal education has attracted huge numbers of students, especially in the areas of technology and when offered by university staff. Stanford professor Andrew Ng’s course on Applied Machine Learning attracted over 100,000 students compared with the around 2,300 students enrolled in CCK08. In Ng’s course, students from Nepal and Denmark are supposed to work side-by-side, free from nation-state politics of education, enrolled in a new-technology-driven global community. The pedagogy and learning theories behind MOOCs are often not made explicit and conf late course design with platform design (Bayne and Ross 2014, 23). Though some kind of learning theory has been involved in arguments for e-learning (constructionist, constructivist), the cMOOC founding fathers, Stephen Downes and George Siemens, have also been criticised for a very shallow understanding of learning theory (Clarà & Barberà 2013). Students are named “learners”, most often without explicitly considering learning theories. Just as with formal education, the understanding of learning in non-formal learning is more about making claims of how people learn rather than explicating new developments of learning theory. MOOCs and other types of non-formal learning, however, seem to be implicitly built on the same humanist understanding of a “universal” autonomous and rational world citizen capable of individualised self-directed learning (Knox 2016, 28) as formal education. The new OERs often build on a mix of the cognitive and behaviourist learning paradigms and rarely consider cultural diversity (e.g. Andersen et al. 2018). 3. Informal education is often thought of as a residual category in the learning sciences, where “everyday” learning takes place as an informal education “out of school” (Eshach 2007). This could be learning in more organised settings like museums, or simply through listening to broadcasts or going to the theatre (Dib 1988). Informal learning is not provoked by a systematic and organised education. From a pedagogical point of view there are no resources meant to aid a particular learning outcome and no systematic evaluation or exam. Informal learning is not necessarily tied to a state agency even though a nation state may have ulterior motives for providing access to museums etc. Informal learning has, as with non-formal learning, been a newfound resource of human capital (Werquin 2010). There are, in general, no formal or non-formal assessments of informal learning, though what is named “prior learning experiences” is increasingly acknowledged (e.g. Schunk 2008). However, since the 1990s informal education has been tied to a broader view of learning as something that takes place in a community of practice (COP) (Lave & Wenger 1991). It is considered a kind of

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“apprentice” learning that takes place whether apprentices are recognised formally or not, for instance, when young girls from the Mayan culture learn to become midwifes by following their older peers and learn from them how to deliver babies. In these communities, learning involves not just the training of skills or learning of knowledge but extends to identity formation and language building. Learning is not designed, but happens “designed or not” (Wenger 1998, 225) and learning can come about even as a reaction to the formal school setting (Willis 1977). However, many of the researchers who have been fuelling debates of informal learning themselves reject the term. Instead, they have insisted that the very distinction between formal, non-formal and informal learning is f lawed since all learning is ubiquitous due to humans engaging in various practices and activities (Lave 2009, 203). What emerges from this brief overview (which has glossed over many important discussions within the field of education) is that the concepts of formal, nonformal or informal, which are so important in debates on education, indicate an underlying technology-driven transformation of educational institutions globally, but both technology-led formal and non-formal learning seem to build on a human understood as an information processor, which can be assessed instrumentally. Nevertheless, this whole debate about education, teaching and learning still seems to ignore the discussion of learning as a basic process of humans in a constantly transforming material-conceptual word. In Nepal, for instance, the young people, by the time the lamenting daughter sang her song, were already caught up in learning something, whether they went to school or not.

Educational culture In an anthropological perspective, education is defined in a broader sense than schooling, and learning is often considered as tied to practice (Lave 1988). Schooling is a form of practice where learning takes place in a formal way but humans become “culturally educated persons” in many ways (Levinson, Foley, & Holland 1996). As noted by anthropologist of education Kathryn AndersonLevitt, formal education can take place in formal and less formal settings, which are always cultural: In its broadest sense, education means the entire learning experience of children or other novices, whether provided deliberately or more haphazardly within the culture. In a narrower but still anthropological sense, education (or ‘formal education’) means deliberate intervention intended to affect the learning experience of children or other novices through ‘formal, predictable, stereotypic learning experiences.’ Formal education includes such practices as apprenticeship, initiation, and lectures, sermons and scolding as well as schooling. Schooling, in frequent contrast to

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initiation and to apprenticeship, is formal education usually carried out in a place separated from ordinary life and conducted by an expert ´stranger’. (Anderson-Levitt 2005, 989–990) In anthropology, the focus has simultaneously been on how learning takes place in formally controlled situated practices, and how these practices differ across the globe in relation to the local productions of culture (Levinson & Pollock 2011) and in culturally figured worlds (Holland et al. 1998). In Nepal in the 1990s, education was explicitly used to teach nation-building alongside maths and reading, but much more was learned. Schools are not just a way to make cultural collectives, they are themselves formed by cultural changes and may challenge past collectives of learning. Formal education in Nepalese schools in the 1990s allowed both boys and girls to go to school, yet many, and especially girls, were prohibited by their parents. The unfortunate daughter, deprived of going to school, is not only deprived of representational knowledge, like knowledge of science and trained skills like learning to read. She also feels deprived of the ultra-social experience of being with others, expanding potential for learning with a cacophony of new resources to form a new identity tied to new vision about a future. She feels deprived of belonging to the new category of “an educated person” (Skinner & Holland 1996). Yet even she learns there is something new to strive for. In many ways, the Nepalese schools in the 1990s challenged the traditional informal education in the villages where, from their earliest upbringing, children learned that they belonged to different castes that should not mingle, such as the Damai and the Chetri castes. In schools, children were placed in the same physical room and learned to honour the new nation. These inf luences became part of what identified an “educated” person in Nepal and spilled over into children’s talk about how they saw their future. In the 1980s Debra Skinner interviewed young students, among them Prajun and Jit Gurung, and found that these students began to construct themselves as “nation builders” rather than in the Damai and the Chetri roles. They no longer followed the traditional tie to their parents, whom they began to see as “uneducated”. The following examples are from Skinner (1990): Prajun said that he saw himself as someone “who wants to develop the village and who wants to give people their rights and serve them” and that “My parents have old ideas, and we search for new things. Yesterday is our parents’ time … The world was old yesterday. Now is the new world. That is why there are a lot of differences”. One unnamed boy said: “There are some differences in my opinions from my parents’. Let me give some examples: I don’t agree with them about religious things, about health, about education, and about conservative thinking”.

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Ten-year-old Jit Gurung noted that: “During their (parents’) time, education was not important, but now there are many new schools everywhere, and these educated people know a lot more things than do uneducated ones”. (All quotes are from Skinner 1990, 13) These statements are very much alike and differ from the normative answers that would have been given before the introduction of schools. Skinner herself notes that in the new schooling system of Nepal: Classes in moral education, science, the panchayat system, and so forth, have consequences for the way children view themselves and their future. Older children and adolescents incorporate the ideas they encounter in school into their talk about themselves and what they want to do in life. (Skinner 1990, 13) Though Skinner and also Holland explicitly emphasise the heteroglossia and multi-voicedness of Nepalese schooling, they also present examples of how schools, through the formal curriculum, have formed the new collective of minds, bodies and the future expectations of the students freed of the caste system. Elsewhere, in a work within the field that later became known as cognitive anthropology, Holland and her colleagues describe this as a learning process of forming “cultural models” (D’Andrade & Strauss 1992, D’Andrade 1995, Holland & Quinn 1987). The focus of the research was on the formation of “ideas” or “ideologies” both culturally and historically, though material aspects were also touched upon. According to Skinner and Holland, Nepalese children are fast to pick up the new social collective and cultural models of organised knowledge forming around the “nation state”. They have learned to connect “education” with “modern”, the “new nation”, a bright future and “no caste” and “uneducated” with “tradition”, with “ignorance”, their parents’ generation and caste. The two anthropologists noted that even human bodies changed as they gradually began to shed the traditional gait and bodily behaviour of the local caste system. Cultural models’ analysis is, however, not primarily focused on material changes, but centres methodologically on spoken words. In this case, this was the way children answer questions about how they experience schooling. The researchers argue that the children have collectively come to share a distinct new way of organising their thinking about themselves, parents, education, traditions and their future, that is entangled with their situated learning in Nepalese schools, which includes “moral classes”. In the analysis made by Skinner and Holland, they do include observations of posters of famous nation-builders on the school walls and different bodily postures in their analysis of heteroglossia in a directed cultural construction of educated persons in Nepal. Cultural models theory defined culture as “difference”. Whenever they refer to culture, it is about how humans create different institutional and mental

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collectives around the globe (for examples see Levinson, Foley, & Holland 1996). In Nepal the dreams, postures, thinking, posters, visions, pupils, blackboards, portraits and teachers became the local cultural resources from which change and difference from traditions drew its potential. Cultural models theory mainly works with how learning reinforces cultural patterns of organised knowledge in a group of humans, which make us experience and perceive the world and ourselves in it in similar ways (D’Andrade & Strauss 1992, Holland & Quinn 1987). This is not a static process. Cultural models can be broken up and reassembled in new ways, as we saw in Nepal, as culture is always in f lux. Though intended as a new way of controlling the Nepali people as citizens of a nation state, the school also offered itself as a meeting place that had unintended effects beyond school. The reason why young persons in Nepal longed for an education was not just to become enrolled in a new nation state discourse or a new economy, but because schools provided physical space for new encounters free of caste and potentials for new identities and agencies based on new visions of the future. The song of the lamentation of a daughter is an effect of these changes, an experienced exclusion from partaking in the new world under formation. The song itself is a pressure for change, not just of caste but also of women’s rights and identities. “Schools, despite their overwhelming potential for shaping minds, bodies, and social futures, remain a paradoxical tool of control at best” (Skinner & Holland 1996, 273). Today, schools in Nepal are still evolving. Jit Gurung and Prajun, two of the school children encountered by Debra Skinner, may have become schoolteachers who now face new challenges because new opportunities have opened up for learning across nation states. Through new media, young people express new thoughts separate from each other’s physical surroundings. The lamenting daughter could apparently break free of caste and gender roles and get herself an education through a MOOC. New global information channels challenge the nationalist agenda promoted by the Nepalese government. All this change comes with new understandings of learning that increasingly place formal education systems, like those found in the 1990s Nepalese schools, in the realm of the old-fashioned. These schools once broke a system of caste separations, by placing students in the same room. Now new disruptive technologies make promises that again will tear them apart from the formal school institutions and attempt to make them individual self-directed learners in new virtual cultural learning ecologies.

Towards a posthumanist education? What shall we make of the song of the lamenting daughter and the transformations of possibilities for the students in Nepal in a posthumanist perspective? If they are perceived through the lenses of the posthumanist theories, they are, as humans, no longer granted a privileged position.

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Posthumanism has been called a shockwave that ripples through education (Snaza et al. 2014, 40) with its new critical view of the decentred human. As we move towards a new posthumanist paradigm in the learning sciences it becomes apparent that posthumanism raises fundamental questions about the ways in which we understand the human subject of education. Siân Bayne argues that “human” functions (like learning) “[a]re not pre-existing attributes of the individual separable from its social and material contexts but are rather brought into being via a complex assemblage of the human and the non-human” (Bayne 2014, 11). Posthumanist approaches definitely eradicate the notion that humans should be educated as “little masters” of the universe where the figure of ‘Man’ [sic!] naturally stands at the center of things; is entirely distinct from animals, machines, and other nonhuman entities; is absolutely known and knowable to ‘himself ’; is the origin of meaning and history; and shares with all other human beings a universal essence. (Badmington 2004, 1345) From posthumanist perspectives, we may also notice all the non-humans that take part in transformations. Though Skinner and Holland emphasise the cultural aspects of learning, there is little analytical focus on how materials play a part in the formation of the Nepalese nation state. Even so, we get some information about the material settings (Skinner & Holland 1996). The schools encountered by Skinner and Holland were houses with stone walls and concrete f loors, where tables and chairs were reserved for the elder children. They were ideological sites as well, with material signs of nation-building on the walls in the shape of posters of the king and queen, just as the curriculum signalled nation-building. By gathering young people from different castes within the same walled rooms with posters and pictures of the king on the walls, young Nepalese people’s potential for new agency could be realised. In a posthumanist perspective the caste system was challenged, new collectivities were formed with the material arrangements that also played their part in the transformations of humans. As mentioned earlier, the theory of cultural models is trying to capture these processes but is mainly tied to discourses and analyses of people’s words. The approach does not capture the encompassing material-discursive apparatus presented by Barad (2007). Yet, the formation of new mental models makes it possible to speak of the processual formation of humans and material collectives and collective changes of organised knowledge in formal institutions. Posthumanism is critical of formal education, which has been seen as a mechanism of exclusion as well as a mechanism that includes what is considered to be “appropriate” citizens (Lewis & Kahn 2010). As the distinctions between human and non-human are dissolved, so are the boundaries between formal, non-formal and informal education as learning takes place in all kind of settings, physical or virtual. For some posthumanists, beginning with William Spanos’ book in 1993, The End of Education: Toward Posthumanism (Spanos 1993), revised in 2015 (Spanos

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2015), the very notion of education is inherently humanist. In this view, education is a kind of mirror image of the Enlightenment human that replaced God on the throne (Spanos 2015, 17). In line with this, posthumanism decentres the human as this mirror image – and calls for an end to education (e.g. Herbrechter 2018). This could also, as argued by the posthumanist Richard Edwards, mean an end to learning (Edwards 2010). Inspired by the posthumanist feminist Karen Barad (2007) and Bruno Latour (1993) Edwards argued, that education has focused on the learning subject as a result of an a priori assumption of a separation of matter from meaning, the object from the subject. By contrast, a post-human intervention points to the constant material entanglement of the human and non-human in the enactment of the world, and thus the problematic status of subjects and objects as separate from one another. (Edwards 2010, 5) He further suggests that posthumanism “could signal the end of lifelong learning” (ibid.) Instead, new understandings of education and how to learn pedagogically pop up when we acknolwedge that we do not need teachers but can learn directly through tinkering with technology and playing with animals as “we are always already related to animals, machines, and things within life in schools at the K-12 and university levels” (Snaza et al. 2014, 40). However, in some ways there is a disquieting similarity between the promises made by new technological developments like MOOCs and OERs in education and some posthumanists’ theories. Where do the new posthumanist approaches leave the lamenting daughter? She is decentred but is she still a person with a psychology? The posthumanist discourse, inspired by Barad among others, has no particular interest in persons and psychology, though they are not excluded either. The main point is that materials and discourses co-constitute each other in ongoing processes, which renders references to an a priori split between subjects and objects and society and technology a matter of discourse meeting materials. In this relational ontology, the separation does not pre-exist relations but constitutes relations from within phenomena (Barad 2007). However, the posthumanist relational ontology in many ways runs into problems when learning theory is included. If learning, as a discourse, only refers to change in performances that can be measured (as when students’ performance is algorithmically noted and proceeded) (Bayne 2014) we can acknowledge why posthumanism would want to get rid of learning in formal and non-formal education, because humanistic learning is supposed to take place with a subject learning about an object (as also noted by Edwards, 2010).

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However, the “devoid-of-psychology” approach does not explain how the Nepalese students Pradjun and Jit Gurung came to share new expectancies of a future with the lamenting daughter, whether going to school or not. If learning refers to a wider process of psychological change that simultaneously forms within persons like Prajun and the lamenting daughter (which the cultural models theory tried to capture), then we need something more than an isolated human learner who is here and now co-constituted with materials. Referring back to the previous theories of learning, posthumanism runs the risk of falling back on the behaviourist approach to learning where all psychological processes are excluded, because only visible effects and materiality count. Even if learning and teaching depend on and are enacted through material contexts (Bayne 2014), learning is still a process that collectively changes the world for Prajun and the lamenting daughter. In this perspective, the lamenting daughter may be excluded from formal education as a non-appropriate citizen, but her lament particularly points to her acknowledgement of new ways of perceiving the world. She recognises learning in formal education as a process that creates a difference between those who have learned what is going to matter and those who have not. These lamentations of an uneducated daughter are real. Her life possibilities will improve immensely if she gets a formal education, which aligns her perception and thinking with new material opportunities. In Nepal in the 1990s, it meant going to a physical place, the school. Though the caste system was broken down, the gender diversity was to some extent upheld – but also challenged. In school, the students learned the alphabet with a vision of a nationstate where queens were equal to kings materialised in pictures on the walls. However, this did not automatically cause students to align collectively with the ideology. Rather, it all became resources for new learning in a world that only gradually opened up. Learning in Nepal in the 1990s seems to be a paradoxical process where many resources go into a new alignment of visions of a future that differ from their parents. Pradjun and Jit Gurung are not individually alike, yet they change collectively. However, the lamenting daughter changes as well, even without the exact same physical conditions Prajun and Jit Gurung are exposed to. Posthumanism has not (yet) dealt with these diverse processes of human change.

Education for all? Today a new situation could arise for Prajun and the enslaved lamenting daughter in the new educational space that has opened for them: MOOCs (Massive Open Online Courses). They apparently no longer need schools to get an education. Can they become engineers tinkering with new smart devices and robots, just like Andreas and the American students studying in Silicon Valley, without leaving the Nepalese villages? Many politicians cry out for more engineers and physicists in order to continue the development of technology. In recent years, non-formal learning has raised

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hopes about the possibility that people all over the world may learn to become engineers even if their local schooling system does not support this development. For instance, it is argued that with access to MOOCs students all over the globe are given the opportunity, often described as “equal to all”, to study and learn (e.g. de Waard et al 2014).3 These technologies offer global education across nation states to free all the lamenting daughters and promise to give them the same opportunities, as, for instance, Danish children like Andreas. Unfortunately, I have not found any studies that directly follow how young women and men in Nepal make use of these new possibilities. In a study by Jeremy Knox, however (2016), we may get some indications of in what way MOOCs include and exclude students – and that though MOOCs promise to be virtual houses of possibility for all, they still rest firmly in material buildings.

VIGNETTE 2.2: MOOC IN KELLY WRITERS HOUSE It looks like a nice place. I have been inspired to look it up on the internet after reading the scathing analysis of MOOCs in the educationalist Jeremy Knox’s book Posthumanism and the Massive Open Online Course: Contaminating the Subject of Global Education (2016) where, he visits the house where the MOOC “Modern and Contemporary American Poetry”, abbreviated to ModPo, is produced. Kelly Writers House (KWH) is a 13-room house on the campus of University of Pennsylvania. On YouTube we can follow “Kelly Writers House Tour video” that introduces us to a house filled with young students and teacher assistants sitting among brown leather chairs and bookcases filled with books. They drink coffee from large white mugs with Kelly Writers House printed on them. On the surface of the screen, it seems like a very open and inviting place. This is a place where students can come to learn about American poetry. It is also the home of a MOOC offered on the largest platform for xMOOCs, Coursera. It is free of charge so also students from Nepal with internet access could follow it. In 2016 (less than five years after the first MOOCs were aired) more than 58 million people had signed up for a MOOC, and 23 million of these people enrolled via Coursera, according to data collected by Class Central.4 The MOOC is one of the best-rated of the humanist Coursera MOOCs. Coursera has offered ModPo since 2012. It is led by the English professor Al Filreis, who was one of the founders of KWH in 1995, and is organised by several teaching assistants (TAs) from the University of Pennsylvania. The MOOC is introduced in the following way: The ModPo site is open all year, accessible to anyone who enrols for free. Each year, though, we convene for an intense 10-week session from early September to late November. During that time, Al Filreis and his colleagues,

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the TAs and Community TAs (“mentors”) are all constantly available, and the discussion forums are quite active and your ModPo colleagues will respond to your questions and comments almost instantly. During this annual 10-week ModPo session or “symposium”, the TAs each offer weekly office hours each. And we host our weekly live webcasts. During the rest of the year—ModPo’ s “off season” or what ModPo’ers call “SloPo”—discussions continue intermittently and in small groups. During that time, too, new poems and new videos are added to ModPoPLUS and the Teacher Resource Center. You are welcome to finish the course in the off season if you could not complete it during the 10-week session. Teachers and their students are encouraged to use the site as part of a class. Reading groups are also encouraged to convene around the ModPo materials. If you enrol in ModPo you will continue to be enrolled unless or until you decide to unenrol. We hope you will continue to participate. (https://ko.coursera.org/learn/modpo/ retrieved 19 January 2017)

Are new educational technologies like MOOCs the road to posthumanist learning? Knox argues that the poetry MOOC comes across, like many other MOOCs, as an elitist and a very humanist project. Analysing ModPo and several other MOOCs, as well as reviewing the academic data analysis that has grown up around MOOCs, he identifies the specific humanist approaches behind the educational endeavours. What he names “critical posthumanism” has a lot to offer to shake up the foundationalists’ assumptions permeating both the cMOOCs and the xMOOCs projects. Knox finds: “MOOC participants are presumed to be yearning for the Western educations offered by the big platforms based in an ideological West” and “that the MOOC lean on cultural notions of humans as rational, autonomous creatures who are empowered and know their way around by technological solutions. They are furthermore capable of self-organizing their own learning process from the resources offered so they can partake in the cohesive communities building around the MOOCs. … This framework of a humanist subject furthermore builds on an encompassing idea of a universal human condition, where local cultures and issues like class, race, gender – and not least access to technology and understandings of learning and authority cease to matter.” (Knox 2016, 2) It is this culture-free and supercilious basic understanding of humans that, according to Knox, “significantly limits the kind of practice and pedagogy that the MOOC might make possible” (Knox 2016, 2).5 What MOOCs are in practice teaches the learning sciences that self-directed learning as a universal phenomenon may be an illusion. What matters is how the

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MOOC intra-acts differently with a plethora of learners. Though no two learners in a MOOC are alike, some are much more privileged than others, simply because they recognise the potential cultural resources that MOOC brings to bear (Andersen et al. 2018). It matters for your attempts of self-learning if you grew up with Bill Gates or the queen and king of Nepal on the wall, or both. What we see in the new meeting place of MOOCs are not posthuman subjects entering a world scene of self-directed learning, but hybrid entanglement, which creates new inequalities just like the old schools. Thus, the MOOC project has tended to assume problematic and uncritical forms of humanism, maintaining an orthodox educational position in a field that claims innovation and disruption. The theoretical framework of critical posthumanism will be utilised to highlight the limitations of the humanist subject, and suggest a value in looking beyond this framework as the underlying rationale for MOOC education. (Knox 2016, ii) To provide education for all was one of the aims when MOOCs began to take shape at the end of the 2000s. MOOCs have been described as: open and invitational. No one who wishes to participate is excluded; people negotiate the extent and nature of their participation according to their individual needs and wishes, regardless of whether those needs are defined, for example, by personal interest or workplace requirements. (McAuley et al. 2010, 5) Even so, time has shown that MOOCs have failed to provide mass education for poor people around the globe. A survey of the students enrolled at University of Pennsylvania’s 32 MOOCs (offered on the Coursera platform) concluded: The student population tends to be young, well educated, and employed, with a majority from developed countries. There are significantly more males than females taking MOOCs, especially in developing countries. Students’ main reasons for taking a MOOC are advancing in their current job and satisfying curiosity. The individuals the MOOC revolution is supposed to help the most—those without access to higher education in developing countries—are underrepresented among the early adopters. (Christensen et al. 2013, 1) Jennifer DeBoer and her colleagues have analysed the big data from M.I.T. MOOCs (from the edX platform). As in most MOOCs, many thousands enrol, however only a tiny percentage follow the MOOC to the end. In the M.I.T. case studied, around 155,000 enrolled from all over the world, yet only around 7,000 completed the course. They found that the students who finished were the ones

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who already had an education, were able to collaborate outside of class, and had succeeded academically. On average, with all other predictors being equal, a student who worked off line with someone else in the class or someone who had expertise in the subject would have a predicted score almost three points higher than someone working by him or herself. This is a noteworthy finding as it ref lects what we know about on-campus instruction: that collaborating with another person, whether novice or expert, strengthens learning. (Breslow et al. 2013, 20)

Posthuman predicaments For Prajun and the lamenting daughter MOOCs may raise false hopes. In a small experiment I made with academic colleagues from Denmark, Thailand and Pakistan it was clear, in relation to the MOOCs offered and developed in a European country, that cultural diversity plays a role in who and how learners benefit from MOOCs (Andersen et al. 2018). Cultural diversity in preceding learning is pivotal for how new learning takes place. We organise the ideas we encounter as we learn, and what we have already learned becomes the basis for new learning. In non-formal learning systems as MOOCs, the aim pedagogically is still to teach someone something. Furthermore, what is learned is that since MOOCs are open access, all have equal opportunities for learning, and if you fail it must mean you as an individual are not a good self-directed learner. It follows from this humanist account that when students like Prajun fail to complete a MOOCs course, it is their own problem. When the participants in our experiment (from Thailand and Pakistan) experienced certain problems following the teaching, these constrains were not felt the same way by the Danish participants. In the process, we discovered how much unacknowledged cultural organisation of conceptual knowledge went into the curriculum. For instance, there were many references to American movies the Danish participants had seen, which never reached cinemas in Pakistan and Thailand, in the same way that Prajun may never have heard of Emily Dickinson. Though we shared the ultra-social capacity for learning with Jit Gurung, Prajun and the lamenting daughter, as well as with Skinner and Holland who learned so much from their stay in Nepal in the 1990s, we meet the word with culturally diverse potentials for agential intra-actions. The MOOCs cannot, just because they are claimed to be open to all, necessarily help poor people, let alone poor women in Nepal, to get a better education. Access to materials is not enough to ensure a lamenting daughter a better future, as, for instance, an engineer creating robots. What matters is her potentials for how she may become entangled with the material and conceptual resources. She needs more than a computer and access to the internet (though this, of course, is another overlooked prerequisite as well). She needs a potential for understanding

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material words conceptually aligned with those who created the course. This puts the already privileged students, like Andreas from Denmark, in an advantageous position. Research has confirmed that MOOCs are primarily providing a free education to the already highly qualified professionals (Laurillard 2016). So, if anything, MOOCs risk increasing the inequalities of the educated world. Physical encounters with peers sharing your interests may help you get an education through MOOCs despite your lack of preceding learning, but still only a tiny fraction of those who enrol actually complete (Breslow et al. 2013). In order to understand why, from a posthumanist learning perspective, we need to look at how a humanist perspective of “equal to all” entails a separation between epistemology and ontology, subject and object rather than relations and co-constitution of material and conceptual phenomena. Learners in MOOCs are often perceived as self-directed, autonomous individuals, freed from the ideological constrains of nation states, ready to go all the way into the Western or Asian education offered through self-directed tinkering. But do posthumanist theories have a better answer to how to provide education? The young field of posthumanist learning theory wants to abandon humanist learning theory as relevant for how to approach education pedagogically. Those in this field argue that the educational sciences have overconfidence in discourse and linguistic utterings in understandings of learning and pedagogy and place a huge emphasis on material surroundings (Ceder 2015, Sørensen 2009, Plauborg 2018). There do not seem to be any psychological approaches involved. This is largely due to an understanding of learning as a cognitive process taking place in an individual person’s head, while building up knowledges that exists as a priori and separated from what is to be known. Therefore, educationalists like Richard Edwards argue that a posthuman perspective can be the end of human, life-long learning (Edwards 2012, 337). Furthermore, those who are critical of the concept of learning in education, like Edwards, see “learning” as a concept that assumes a learner is separate from what is to be learned (Juelskjær & Plauborg 2013, Ceder 2015, Sørensen 2009, Taguchi 2010). What Edwards (2010) and the other posthumanists in educational studies seem to refer to as “learning” is connected to formal systems of education like schooling. Their arguments against learning stem from an educational discourse, not from a basic understanding of how learning forms humans as ultra-social learners. Posthuman theory wants us to focus on how materials co-shape our situated creative engagements with surroundings, and “learning” becomes a disturbing element because it somehow implies an already formed learner. “Time”, in posthumanist theory, is entangled in space and matter as a situated entity; it is not a process of a linear kind indicated by most learning theories. The individual subject is decentred as that individual becomes entangled with books, pictures of kings, loudspeakers, animals, plants, blackboards etc. It is not an individual as an a priori entity but an individual that comes into a being-with-materials. Yet there is something lacking in the entangled observations and analysis made by posthumanist educational scientists: namely, theories of how their

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own gazes as researchers are transformed in learning. When Estrid Sørensen, for instance, describes her observations in classrooms, she does this as a description of an experience that “relies exclusively on what is present in the situation of experience” (Sørensen 2013, 123). Similar to this is how, for Edwards, the here-and-now experience of the material world takes centre stage. Sørensen, at the same time, criticises research data that is reduced to discursive matter such as interviews and researchers own impressions. She calls for data that describes: how bodies, music, roundtables, loudspeakers, tones, and other human and nonhuman participants related to each other in situations of co-presence by acting and reacting on each other, aligning in specific ways or creating boundaries between some participants and connections with others and so on. Such sequential descriptions of co-presence in situations unfolding here-and-now through processes of interrelation are necessary for the researcher to account symmetrically for human and nonhumans participants. To do this, observational data is generally more useful than accounts of ‘speaking subjects’. As discussed above, experience relies exclusively on what is present in the situation of experience, and accordingly adequate analyses of experience must take their point of departure in data at the scale of experience, the situation, that is, in data of the present. (Ibid.) Posthuman theorising of education emphasises the importance of becoming with here-and-now materials. I am in support of this move in the learning sciences that opens up observational data and a relational ontology that focuses on how entanglements of materials create each other in situations. Yet, I find it problematic that all references to psychological meaning-making processes are exempted from this posthuman thinking. The posthuman in these theories is not a person that collectively changes its thinking and perception, like the persons we found in Nepal and Andreas and his classmates in Chapter 1. Yet in their own analysis the posthumanist researchers themselves cannot avoid making use of their own preceding learning of collectively shared meaningful concepts that become relevant (when we, for instance, describe things) in situations. From anthropological studies, we know that researchers are not born with an innate capability for recognising things as meaningful. Though a book, a picture of a king or a loudspeaker may be meaningful to most Western, African or Asian anthropologists, they are at loss when they meet dream-catchers from the North American Ojibwa tribe for the first time (e.g. Densmore 1929), or wonder about the meaning of the film title Robocop the first time they see it (Andersen et al. 2018). A description that separates cupboards from loudspeakers is for me always an indication of a researcher that draws on a pool of cultural conceptual preceding learning process – that is, humans perceive material according to what they have learned to perceive in previous situations. Researchers’

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descriptions are imbued with material-conceptual separations, which render separate entities meaningful in relation to others in ways that can only stem from preceding material-conceptual learning. Roundtables are distinctly separated from loudspeakers and observational data coming from ethnographers who are themselves “speaking subjects”. Even if we agree that epistemology and ontology cannot be separated, the onto-epistemological entanglement is a psychological process that gradually transforms what we are able to perceive as distinct from something else. We live with the world we perceive as learned collectives, but “a collective” is not a fixed entity. Understanding education in this way – as a potential for forming shared connections between conceptual meaning, materiality and new learning based on preceding learning – is a key to understanding why global education simultaneously is a possibility for some ultra-social humans whereas for other ultra-social humans it is a potential motor for an explosive inequality. In this light, MOOCs create a reverse movement to the one found in Nepal in the 1990s, in that they disperse humans, dismantle physical schools and create assumed individualism. However, just like schools, MOOCs offer new identities – this time as supposed individual self-directed learners. Yet from posthumanist perspectives, these developments are just a new kind of entanglement. What makes a difference is what the “uneducated lamenting daughters” become in times of MOOCs. In Nepal, the daughters are already part of the environment into which the new cut or split between educated and uneducated was introduced in the 1990s. They could watch as their brothers set off to school while they stayed at home, and they could listen to their brother’s new visions when they returned. They were emotionally involved in the new process and wanted to take part. The differences between those included and those excluded from educational learning was obvious and was reacted to with songs like those that were sung at the Tij festival (Skinner and Holland 1996). The way education is performed differs in virtual institutions. When education is moved to MOOCs, inequalities become invisible. Participants in MOOCs are not visible to each other’s bodily posture and talk out of classroom like students in the Nepalese schools. In global MOOCs, courses are offered in machine learning, physics, poetry, and participants can join regardless of previous formal learning, it is claimed. Yet the more relevant preceding organised learning (recognised by MOOC organisers as such) you bring with you into the MOOC the more likely it is you complete the course (Andersen et al. 2018). Moreover, “relevant” means “cultural alignment” with the knowledge the powerful people offering the course consider self-evident. The posthumanist approach to learning does not denigrate the situatedness found in the present, posthuman approaches to learning. I, however, do make a call for an attention to preceding learning in situated entanglements: how potential for bringing in relevant, preceding, conceptual learning is called forth in material situations. Preceding, conceptual learning is not about bringing into

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entanglements a priori knowledge as representation but emphasises that already learned concepts are the basis for the recognition of what goes into descriptions, observations and, indeed, knowledge. As “anchors” for learning in practice (Hutchins 1996) the materials we are surrounded by are transformed as we learn to build up new resources for learning (Hasse 2015). In this process, we change as human beings. The posthumanist theory of learning I propose recognises that material artefacts can never be described “objectively” or “observationally” from any position but come into being in a relation within phenomena of conceptual, preceding learning, material things and material words. The learning process that I propose is not an individual learning process. As materials and thoughts align over time, material-conceptual collectives are formed. Nepalese schoolchildren begin to perceive the world with and through the new resources brought to bear by the nation state. It is difficult to grasp human collectivity, but Holland and her colleagues claim to do so through the analytical tool of cultural models. Cultural models are an attempt to capture collectivities in ways of thinking. Nevertheless, it may not be the best analytical tool, as it leans so much on linguistic constructions. Therefore, the challenge may be how to connect the here-and-now posthumanist acknowledgement of co-constitutions with an understanding of the processes behind what humans bring to co-constitutions.

Conclusion: Chapter 2 Why do we need a posthumanist theory of learning that retains some psychological elements such as “learning”? Posthumanist learning theory includes troubling, humanist and naive assumptions of “self-learning” and “equal-to-all” education (Knox 2016). In a time heavy with promises of how technological devices can take over human-to-human education, it is worth emphasising that also the technological devices make the students learn something for a reason, but that this reason is no longer tied to a visible “someone” (the teacher) but embedded in technologies such as apps, videos and robots (whether humanoid or not). If we, in a posthumanist perspective, are never isolated “self-learners”, it matters who and what we learn from. This becomes more opaque when we can no longer clearly identify the “teacher” and underlying algorithms for assessment. If learning was about information processing, this method might work. However, when learning is all about relations, we may question the assumptions that we as individuals gain access to “equal-to-all” education through technology. In the posthumanist perspective, there are no discrete, a priori, rational end-points in education to be assessed. What we regard as education is a material-conceptual phenomenon that spills out from schools and MOOCs to affect identities, emotions, visions and hopes. Learning is not, as in humanist

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learning theory, about individuals receiving stimuli, symbols to be processed, or indeed a seamless formation of a collective identity formed in a community through practice. However, learning can also not be reduced to relations in “a situation”. In the posthumanist perspective I propose, not everyone shares the same potential for learning with material entanglements in situations even when they belong to the same community. This approach challenges the humanist “equal-to-all” learning theories, as well as some previous posthumanist theories, in at least three ways, which all point to why ultra-social humans never learn on “equal-to-all” terms. First, posthumanists, like some phenomenologists and anthropologists before them, claim there is not an a priori separation of ontology and epistemology. Likewise, there is no a priori subject separated from an object. It is all about relations. What I argue is that as these relations differ, so do our potentials (the pool of cultural resources we can draw on) for learning. Second, posthumanists emphasise the material and social co-constitution. We can learn from teachers, from robots and MOOCs, through tinkering with materials and through online algorithm-based tools. Nevertheless, the way ultra-social humans learn always begins with what we have learned materialconceptually. This ultra-social and preceding process simultaneously makes us culturally diverse and similar in how and what we learn. Our diverse preceding learning creates a diversity in our potential for learning, whether we come from Nepal or the United States, are male or female, are brought up with posters of kings, Bill Gates, or dream-catchers. Third, learning evolves in practice and that emphasises that knowledge is not just acquired in schools or in any other situated practices. Knowledge appears as part of the intra-active phenomenon. What I argue is that it is a meeting between preceding learning (our acquired potential for conceptual thoughts and perceptions) and our situated practices with the sociomaterial surroundings. The all-encompassing process of learning involves how materials become meaningful, for example, “a portrait of the Nepalese queen”, based on preceding ultra-social collective human learning. Though the lamenting daughter, Prajun and Jit Gurung learn different things in different material surroundings, they also collectively learn the same thing: a new kind of existence free of caste is possible. Andreas and the American poetry lovers at Kelly Writers House also learn different things based on their preceding learning – but enrolled in the same MOOC about Western movies they would properly share potentials for recognising a reference to Robocop – which we, through our own experiments, could see was not a reference self-evident to all (Andersen et al. 2018). The posthumanists should therefore not abandon learning as a process but emphasise that learning in the broadest sense is ongoing. Posthumanist learning theory does not have a universal humanist subject at its core but emphasises the situated processes of entanglement in which poetry, maths, traditions, class, race, and gender, alongside loudspeakers and blackboards, become meaningful or lose

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their meanings, in onto-epistemological material-conceptual arrangements. In these co-constructions, there are no fixed representations, taxonomic categories or identities at the core. However, as we shall see, previously learned concepts are an indispensable contribution to the creation of phenomena. It is these concepts that are co-present when posthumanist writers write about certain materials such as “loudspeakers” or “robots”. It is these concepts that are involved, no matter what tinkering we engage in. What we bring into situations is our learned potentials for concept formation. This approach to learning has implications for how we should view the many promises that follow the use of technology in education. With MOOCs, new types of learners are created within the phenomenon of education. Self-learning differs in relation to these co-constitutions. The “human” in the MOOC is perceived as a free individual capable of self-learning, regardless of previous cultural resources obtained through preceding learning. Humans may seem to be freed by a technology that allows for an apparent erasure of gender, class and racial access to education. The transversality promised by the MOOC technology (to dissolve all dichotomies and be for all) is not far from the visions to erase boundaries found in the new materialists’ version of a posthumanist future (Tuin & Dolphijn 2010). The promise of MOOCs was to dissolve the dichotomy between the educated and the non-educated. However, in reality, educated and uneducated are not fixed categories but relational ones that are created with the educational offers of MOOCs. We need a psychological posthumanist theory of learning, not just to open our eyes to the inf luence of materials on education, but also to emphasise that available materials do not in themselves make a meaningful potential for all to make use of in education. We can only draw on the pool of cultural learning resources from our preceding learning in ultra-social collectives. To become part of a learning collective that shares potentials for learning the same things, we must align so that our conceptual and emotional understandings become collective as well, and this is the topic of the next chapter.

Notes 1 Conducted by the research programme “Future Technology, Culture and Learning” in the period 2011–2016. 2 Dukkha meaning “hardship” (see Holland et al. 1998 for a detailed description of Dukkha songs). 3 The question of access to electricity and the internet is rarely addressed by proponents of MOOCs as a replacement of formal school education. 4 https​://ww​w.cla​ss-ce​ntral​.com/​repor​t/moo​c-sta​t s-20​16/ retrieved 10 April 2017. 5 All of the above was confirmed in a study of our own where we enrolled in a MOOC aiming at teaching manuscript writing, without taking into account that people from many different cultures participated (Andersen et al. 2018).

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References Adams, C., Yin, Y., Madriz, L. F. V., & Mullen, C. S. (2014). A phenomenology of learning large: The tutorial sphere of xMOOC video lectures. Distance Education, 35(2), 202–216. Andersen, B. L., Na-songkhla, J., Hasse, C., Nordin, N., & Norman, H. (2018). Perceptions of authority in a massive open online course: An intercultural study. International Review of Education, 64(2), 221–239. Anderson-Levitt, K. M. (2005). The schoolyard gate: Schooling and childhood in global perspective. Journal of Social History, 38(4), 987–1006. Ausubel, D. P. (2000). The Acquisition and Retention of Knowledge: A Cognitive View. Dordrecht: Springer Verlag. Badmington, N. (2004). Mapping posthumanism: An exchange. Environment and Planning A, 36(1), 1344–1351. Bandura, A. (1977). Social Learning Theory. Upper Saddle River, NJ: Prentice Hall. Barad, K. (2007). Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning. Durham, NC: Duke University Press. Bayne, S. (2014). What’s the matter with “technology enhanced learning”? Learning, Media and Technology, 40(1), 5–20. Bayne, S. & Ross, J. (2014). The pedagogy of the Massive Open Online Course (MOOC): The UK view. Higher Education Academy Report. Retrieved from http:​//www​.heac​ ademy​.ac.u​k/ass​ets/d​ocume​nts/e​lt/HE​A _Edi​nburg​h _MOO​C _WEB ​_ 2403​14.pdf. Biesta, G. J. J. (2010). Good Education in an Age of Measurement: Ethics, Politics, Democracy. Boulder, CO: Paradigm. Biesta, G. J. J. (2015). What is education for? On good education, teacher judgement, and educational professionalism. European Journal of Education, 50(1), 75–87. Biesta, G. J. J, Priestley, M., & Robinson, S. (2015). The role of beliefs in teacher agency. Teachers and Teaching: Theory and Practice, 21(6), 624–640. 10.1080/13540602.2015.1044325 Blumenthal, A. (1980). Wilhelm Wundt and early American psychology: A clash of cultures. In: R. W. Rieber & K. Salzinger (Eds.). Psychology: Theoretical-historical Perspectives (pp. 25–42). New York, NY: Academic Press. Breslow, L., Pritchard, D., DeBoer, J., Stump, G., Ho, A., & Seaton, D. (2013). Studying learning in the worldwide classroom: Research into edX’s first MOOC. Research and Practice in Assessment, 8(1), 13–25. Bruner, J. (1990). Acts of Meaning. Cambridge: Harvard University Press. Butler-Kisber, L. (2013). Editorial. Learning Landscapes, 6(2), 9–17. Ceder, S. (2015). Cutting through water: Towards a posthuman theory of educational relationality. Research output. Doctoral thesis. Lund: Lund University. Clarà, M. & Barberà, E. (2013). Learning online: Massive open online courses (MOOCs), connectivism, and cultural psychology. Distance Education, 34(1), 129–136. Cole, M. (1996). Cultural Psychology: A Once and Future Discipline. Cambridge: Harvard University Press. Christensen, G., Steinmetz, A., Alcorn, B., Bennett, A., Woods, D., & Emanuel, E. J. (2013). The MOOC phenomenon: Who takes Massive Open Online Courses and why? Retrieved from http:​//pap​ers.s​srn.c​om/so​l 3/pa​pers.​cfm?a​bstra​ct_id​=2350​964. D’Andrade, R. & Strauss, C. (Eds.) (1992). Human Motives and Cultural Models. Cambridge: Cambridge University Press.

62  Posthumanist learning in education

D’Andrade, R. (1995). The Development of Cognitive Anthropology. Cambridge: Cambridge University Press. Densmore, F. (1929). Chippewa customs. Bulletin of the Bureau of American Ethnology, 86, 1–204. Derry, J. (2013). Vygotsky: Philosophy and Education. Hoboken, NJ: Wiley Blackwell. de Waard, I., Gallagher, M. S., Zelezny-Green, R., Czerniewicz, L., Downes, S., Kukulska-Hulme, A., & Willems, J. (2014). Challenges for conceptualising EU MOOC for vulnerable learner groups. In: C. Klooks (Ed.). Proceedings of the European MOOC Stakeholder Summit 2014 (pp. 33–42). Retrieved from www.e​moocs​2014.​eu/ si​tes/d​efaul​t/fil​es/Pr​oceed ​i ngs-​Moocs​- Summ ​it-20​14.pd ​f. Edwards, R. (2012). Human-centric learning and post-human experimentation. In: P. Jarvis & M. Watts (Eds.). The Routledge International Handbook of Learning (pp. 333– 339). Abingdon: Routledge. Edwards, R. (2010). The end of lifelong learning: A post-human condition? Studies in the Education of Adults, 42(1), 5–17. Esbensen, G. L., Hasse, C., Mathiasen, M. K., Breum, M., Pedersen, K. P., Brinks Andersen, E., … Sayedmirza, A. (2016). Robotter i folkeskolen: begrundelser, visioner, faktisk brug og udfordringer i normalklasser. Copenhagen: Danish School of Education (DPU), Aarhus Universitet. Dib, C. Z. (1988). Formal, non-formal and informal education: Concepts/applicability. AIP Conference Proceedings, 173, 300–315. Dreyfus, H. L. & Dreyfus, S. E. (1986). Mind Over Machine: The Power of Human Intuition and Expertise in the Era of the Computer. New York, NY: The Free Press. Engeström, Y. (1999). Activity theory and individual and social transformation. In: Y. E, R. Miettinen & R.-L. Punamäki (Eds.). Perspectives on Activity Theory (pp. 19–38). London: Cambridge University Press. Ertmer, P. A. & Newby, T. J. (1993). Behaviourism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 6(4), 50–72. Eshach, H. (2007). Bridging in-school and out-of-school learning: Formal, non-formal, and informal education. Journal of Science Education and Technology, 16(2), 171–190. doi:10.1007/s10956-006-9027-1. Gardner, H. (1985). The Mind’s New Science: A History of the Cognitive Revolution. New York, NY: Basic Books. Greeno, J. G., Collins, A. M., & Resnick, L. B (1996). Cognition and learning. In: D. C. Berliner & R. C. Calfee (Eds.). Handbook of Educational Psychology (pp. 15–46). New York, NY: MacMillan. Hattie, J. & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. Holland, D. & Quinn, N. (Eds.) (1987). Cultural Models in Language and Thought. Cambridge: Cambridge University Press. Holland, D., Lachicotte, W. Jr. , Skinner, D., & Cain, C. (1998). Identity and Agency in Cultural Worlds. Cambridge: Harvard University Press. Holland, D. & Lave, J. (2001). History in person – An introduction. In: D. Holland & J. Lave (Eds.). History in Person – Enduring Struggles, Contentious Practice, Intimate Identities (pp. 3–36). Santa Fe, NM: School of American Research Press. Hutchins, E. (1996). Cognition in the Wild. Cambridge: MIT Press. Hasse, C. (2015). An Anthropology of Learning. Dordrecht: Springer Verlag. Herbrechter, S. (2018). Posthumanism and the ends of education. On Education. Journal for Research and Debate, 1(2). doi:10.17899/on_ed.2018.2.2.

Posthumanist learning in education  63

Ihde, D. (1990). Technology and the Lifeworld. Bloomington, IN: Indiana University Press. Ihde, D. (2002). Bodies in Technology. Minneapolis, MN: University of Minnesota Press. Juelskjær, M. & Plauborg, H. (2013). Læring og didaktik udsat for poststrukturalistisk tænkning. In: A. Qvortrup & Wiberg, M. (Eds.). Læringsteori og didaktik (pp. 257–290). Copenhagen: Hans Reitzels Forlag. Koff ka, K. (1963). Principles of Gestalt Psychology. New York, NY: Harcourt, Brace and World. (Original work published 1935.) Kohler, W. (1947). Gestalt Psychology: An Introduction to New Concepts in Modern Psychology (rev. ed.) New York, NY: Liveright. Kolb, D. A. (1984). Experiential Learning Experience as a Source of Learning and Development. Upper Saddle River, NJ: Prentice Hall. Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. New York, NY: Viking. Knox, J. (2016). Posthumanism and the Massive Open Online Course: Contaminating the Subject of Global Education. London: Routledge. Latour, B. (1993). We Have Never Been Modern. Cambridge: Harvard University Press. Laurillard, D. (2016). The educational problem that MOOCs could solve: Professional development for teachers of disadvantaged students. Research in Learning Technology, 24(1), 1–17. Lave, J. (1988). Cognition in Practice: Mind, Mathematics and Culture in Everyday Life. New York, NY: Cambridge University Press. Lave, J. (2009). The practice of learning. In: K. Illeris (Ed.). Contemporary Learning Theories (pp. 200–208). London: Routledge. Lave, J. & Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation. New York, NY: Cambridge University Press. Levinson, B., D. Foley, & Holland, D. (Eds.) (1996). The Cultural Production of the Educated Person: Critical Ethnographies of Schooling and Local Practices. Albany, NY: State University of New York Press. Levinson, B. & Pollock, M. (Eds.) (2011). A Blackwell Companion to the Anthropology of Education. Malden, MA: Wiley-Blackwell. Lewis, T. & Kahn, R. (2010). Education Out of Bounds: Reimagining Cultural Studies for the Posthuman Age. New York, NY: Palgrave Macmillan. Lowe, C. (2014). Introduction: Building on the Tradition of CCK08. In: D. Krause & C. Lowe (Eds.). Invasion of the MOOCs: The Promises and Perils of Massive Open Online Courses (pp. ix–xiv). Anderson, SC: Parlor Press. McAuley, A., Stewart, B., Siemens, G., & Cormier, D. (2010). In the Open: The MOOC Model for Digital Practice (pp. 1–63). Charlottetown, Canada: University of Prince Edward Island. Retrieved http:​//www​.elea​r nspa​ce.or​g/Art​icles​/MOOC ​_ Fina ​l.pdf​. Newell, A., Shaw, J. C., & Simon, H. A. (1958). Elements of a theory of human problem solving. Psychological Review, 65(3), 151–166. Papert, S. & Harel, I. (Eds). (1991). Constructionism: Research Reports and Essays 1985–1990 by the Epistemology and Learning Research Group, the Media Lab, Massachusetts Institute of Technology. Norwood, NJ: Ablex Pub. Corp. Piaget, J. (1964). Development and learning. In: R. E. Ripple & V. N. Rockcastle (Eds.). Piaget Rediscovered: A Report of the Conference on Cognitive Skills and Curriculum Development (pp. 7–20). Ithaca, NY: Cornell University, School of Education. Plauborg, H. (2018). Towards an agential realist concept of learning. Subjectivity, 11(4), 322–338. Russell, S. J. & Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall.

64  Posthumanist learning in education

Schunk, D. H. (2008). Learning Theories: An Educational Perspective (5th ed.). New York, NY: Macmillan. Schwier, R. A. (2012). Comparing formal, non-formal, and informal online learning environments. In: J. Huett & L. Moller (Eds.). The Next Generation of Education: Unconstrained Learning (pp. 139–156). New York, NY: Springer Verlag. Selwyn, N. (2016). Education and Technology: Key Issues and Debates. London: Bloomsbury. Selwyn, N., Bulfin, S., & Pangrazio, L. (2015). Massive open online change? Exploring the discursive construction of the ‘MOOC’ in newspapers. Higher Education Quarterly, 69(2), 1–18. Simons, M. & Masschelein, J. (2006). The learning society and governmentality: An introduction. Educational Philosophy and Theory, 38, 417–430. Sims, C. (2017). Disruptive Fixation: School Reform and the Pitfalls of Techno-Idealism. Princeton, NJ: Princeton University Press. Skinner, D. (1990). Nepalese children’s construction of identities in and around formal schooling. Himalaya, the Journal of the Association for Nepal and Himalayan Studies, 10(2), 8–17. Skinner, D. & Holland, D. (1996). Schools as a heteroglossic site for the cultural production of persons in and beyond a hill community in Nepal. In: B. Levinson, D. Foley & D. Holland (Eds.). The Cultural Production of the Educated Person: Critical Ethnographies of Schooling and Local Practice (pp. 273–299). Buffalo, NY: State University of New York Press. Snaza, N., Appelbaum, P., Bayne, S., Morris, M., Rotas, N., Sandlin, J., … Weaver, J. (2014). Toward a posthumanist education. Journal of Curriculum Theorizing, 30(2), 39–55. Snaza, N. & Weaver, J. (Eds). (2015). Posthumanism and Educational Research. New York, NY: Routledge. Spanos, W. V. (1993). The End of Education: Toward Posthumanism. Minneapolis, MN: University of Minnesota Press. Spanos, W. (2015). Posthumanism in the age of globalization: Rethinking the end of education. Symploke, 23(2), 15–39. Sørensen, E. (2009). The Materiality of Learning Technology and Knowledge in Educational Practice. Cambridge: Cambridge University Press. Sørensen, E. (2013). Human presence: Towards a posthumanist approach to experience. Subjectivity, 6(1), 112–129. Taguchi, H. L. (2010). Going Beyond the Theory/Practice Divide in Early Childhood Education. Introducing an Intra-Active Pedagogy. Oxford: Routledge. Toh, L. P. E., Causo, A., Tzuo, P. W., Chen, I. M., & Yeo, S. H. (2016). A review on the use of robots in education and young children. Educational Technology & Society, 19(2), 148–163. Tuin, I. v. d. & Dolphijn, R. (2010). The transversality of new materialism. Women: A Cultural Review, 21(2), 153–171. Retrieved from http:​//www​.tand​fonli​ne.co​m /doi​/ abs/​10.10​80/09​57404​2 .201​0.488​377. UNESCO. (2014). EFA Global Monitoring Report: Teaching and Learning Achieving Quality for all EFA Global Monitoring Reports, UNESCO, Paris, France. Watson, J. B. (1930). Behaviourism (rev. ed.). Chicago, IL: University of Chicago Press. Watson, J. B. & Rayner, R. (1920). Conditioned emotional reactions. Journal of Experimental Psychology, 3, 1–14. Wenger, E. (1998). Communities of Practice: Learning, Meaning, and Identity. Cambridge: Cambridge University Press.

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Werquin, P. (2010). Recognising Non-Formal and Informal Learning: Outcomes, Policies and Practices. Paris: OECD-Publishing. Willis, P. (1977). Learning to Labour: How Working Class Kids get Working Class Jobs. Aldershot: Ashgate. Yuan, L. & Powell, S. (2013). MOOCs and Open Education: Implications for Higher Education (White Paper). Bolton, England.

3 EMOTIONAL COLLECTIVES

As a posthumanist learner, the human subject is not the assumed, competent, self-directed and autonomous, individual learner found in the MOOCs universe that we met in the previous chapter. This human has been erased just like other notions of humans before. “As the archaeology of our thought easily shows, man is an invention of recent date. And one perhaps nearing its end” (Foucault 1973, 387). With the erasure of this man, representations of the real world are also erased from formal education. In posthumanist theory, as presented by Barad, there is a deeper layer to the representations and taxonomic categories that seem to fit well with robotic and algorithmic approaches. Representations are not founded on an a priori separation of world and subject. She, like Deleuze (1994), argues that all differences are relational separations and for Barad these intrarelational differentiations are made through repeated configurations within a world that upholds certain boundaries around phenomena. Bringing psychology into these processes makes it possible to ask about the meaningful learning processes that create collective alignments in human–material relations. If we are studying to become physicists or engineers, we learn mathematical symbols, formulae capturing the forces of the universe and how they are tied to the material instruments in practice. The worlds that open themselves materially and conceptually to physicists or engineers are to some extent collectively shared – at least when compared to people, who have learned no physics or maths. A physicist’s discovery of a new particle is, in this perspective, no longer a relation between a learner learning about particles as an isolated thing in a world of separate entities. It involves other physicists, with different gendered experiences, as well as instruments, knowledge about the instruments and other materials’ behaviour in relation to the instruments. The discovery of a new physics phenomenon can even, as we shall see in this chapter, involve the smoke from cheap cigars. However, though posthumanists like Barad

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have made us aware of all the discursive and material entanglements needed to create phenomena from within, and feminists have emphasised emotions as cultural and political (Ahmed 2004), the posthumanist theories still lack a learning perspective, that can also show how emotions, affects and feelings entangle in phenomena as a process. In my own previous studies of physics at the Niels Bohr Institute for Physics in Copenhagen, I argued that when we study physics or engineering, we learn much more than representations. We learn hopes, visions, dreams and feelings as we gradually become part of the phenomenon we perceive and co-create whether it is a robot or a particle (Hasse 2015a). Even if posthumanism decants the humans and emphasises our becoming with materials, our ultra-social and conceptually learned emotions cannot be erased. These emotions are not universal, but culturally appropriated – and by exploring how learning works, we can see how these emotions emerge as we learn with materials. In an age where physicists and engineers have increasingly been granted the power to create our material surroundings, it is important to understand what drives their emotional collectives.

The disappearing scientist In some ways, the “post”-approaches, including posthumanism, have grown out of a critique of the natural sciences. In its most radical form, posthumanism: can be seen as a continuation of poststructuralist and early postmodern theory that extends the postmodern critique of modernity and the Enlightenment – namely the anti-humanist critique of the unified, rational subject and the critique of dialectic logic – into an age of ubiquitous technoscience. (Sharon 2014, 6) The lack of emotions, feelings and affects have for a long time been part of a feminist critique of the rational, detached, male epistemology that has been taken for granted as the human and universal epistemology in much of philosophy and science. As a Western concept, the Enlighted human has often been assumed equivalent to the stereotypical, individual “Man”. This obviously gendered figure only emerged recently in the history of humankind. According to Michel Foucault, “there was no epistemological consciousness of man as such” before the classical age beginning in the sixteenth century (1973, 309). Thus, our interest in the human from a philosophical point of view conf lates with our emerging consciousness of how Western science differed from the many culturally diverse ways humans came into being around the globe. The human as a concept also emerged in the wake of an emerging natural science that gradually opened for a Hu-Man mastery of the natural forces, like electricity and water, for our own purposes.

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In this period, the emerging ideal human was illustrated as the human in singular; the Man (with a big M) illustrated as the Vitruvian Man drawn by the artist Leonardo da Vinci around 1487, following ideal human proportions proposed by the Roman architect Marcus Vitruvius Pollio (c.78–10 BC). This Hu-Man became a standardised model for all kinds of studies of “the human”. During the Enlightenment, it stood on top of a hierarchy of dualisms and sediment secessions: Man–Woman, Nature–Culture, White–Black, Human–World and Mind–Body. This Hu-Man was always an exceptional being – a natural ruler of the world. Through his inborn yet strangely bodiless, emotion-free intelligence, he developed technologies and transformed the world to suit his purposes. As a trope and a real force, this figure, the Hu-Man, was gradually recognised for being modelled upon a particular generalised white male scientist. This Hu-Man knows of no boundaries for his successful endeavours. He ignored diverse existences, excluded situated practices, culture, gender, class and race. For this Supreme Being, the Hu-Man is the only true way of being human. This father of humanism, as Michel Foucault tells the story of the Hu-Man, was not just an ideal when it came to his own standards. The figure also created Western hegemonic measures of all kinds that confirmed white male supremacy over nature, as the natural order of things, along with an Enlightenment mythology of eternal progress (Braidotti 2013). In Western schools and learning environments, Hu-Man supremacy was introduced in subtle ways through models and planches with the white male on top followed by a multitude of others, animals, plants and minerals. This notion of the isolated exceptional human was upheld by a story of shutting out difference and diversity. In the words of feminist Rosi Braidotti: This Eurocentric paradigm implies the dialectics of self and other, and the binary logic of identity and otherness as respectively the motor for and the cultural logic of universal Humanism. Central to this universalistic posture and its binary logic is the notion of “difference” as pejoration. (Braidotti 2013, 15) From the mid-twentieth century onwards, feminist epistemology and emerging posthumanism has attempted to eject the Hu-Man from his pedestal down to the sandy ground. Since the 1960s and 1970s especially feminists and post-colonialists have exposed the Vitruvian Man as a trope of ejections, avoidances and exclusions (Braidotti 2013). The pyramid that functioned as the natural foundation upholding the position of Man was turned around. Women and people from other positions became visible – so did animals, plants and bacteria. The visibility revealed that materials exploited by Hu-Mans were not passive objects, but partook in all kinds of agencies (Latour 2005). The human was now, at least theoretically, placed in a f lat ontology with no hierarchies among the humans, or non-humans and no dichotomies to uphold the hierarchies. No more Man– Animal, Man–Woman and White–Black.

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Meaningful words in the sciences, like “particle” or “human”, appear and sometimes disappear, and with their disappearance their referents disappear. Can we also erase the concept of the human like that? Yes, the poststructuralist and many posthumanists claim: the human (as the generalised “man”) could be erased, “like a face drawn in sand at the edge of the sea” (Foucault 1973, 387). Though both romanticism and later modernist and postmodernist critical theory have questioned the rational, emotion-less creature, the Hu-Man seems to persist to some extent in the sciences. The field of postcolonial and feminist studies never succeeded in an erosion of the Hu-Man’s position even when they directed attention to the Wo-Man and the culturally excluded. The field of science and technology studies (STS) has repeatedly attacked the Hu-Man by questioning his rationality and supreme intelligence and has uncovered the complex ways science and technology have constructed knowledge (e.g. Latour & Woolgar 1979; Knorr-Cetina 1981; Lynch 1985). Yet today scientists and engineers in applied sciences are more powerful than ever. In the social constructivist and social constructionist paradigm, often connected with postmodernism, the critique of the Hu-Man gradually became a critique of his power to create worlds via “wordings” (Gane & Haraway 2006). In poststructuralism (following Foucault among others), words were perceived to be so powerful that they could shape materiality. This was what Richard Rorty called “the linguistic turn” (1977/1992). Through discourse, the particle became a particle to be discovered. This critique dominated the social sciences for decades without much effect on the collectives formed around engineering and natural sciences. Posthumanism, postphenomenology and the new materialist turn (Tuin & Dolphijn 2010) can be seen as reactions to these fruitless attempts to make science and applied science acknowledge and share their power to create worlds for others through words. Where social constructionism and constructivism underscore the social and cultural construction of the material and social world through words, new materialism and STS today primarily focus on how materials matter. Here they have aligned to some extent with the natural sciences in their critique of an overemphasis of the importance of language. “Language matters. Discourse matters. Culture matters. There is an important sense in which the only thing that doesn’t seem to matter anymore is matter” (Barad 2007, 132). Radical posthumanism denies any a priori entities, and claims phenomena emerge with momentarily created boundaries. Phenomena emerge in a process of mattering matter, i.e. the materials that matter meet the discourse that matters. However, the idea is that it is no longer just the words that shape particles, but words and materials co-constitute each other. This social and linguistic turn, that once challenged the “naturalist” and “objectivists” paradigm in science, is now itself challenged and deconstructed by posthumanists. New materialist and posthumanist theories share a disdain for fixation in categories and representations. Representationalism is seen as tied to a humanism of separations: nature–culture, human–non-human, man–woman,

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subject–object, white–black (Tuin & Dolphijn 2010). Dichotomies privilege language as a separator and dichotomising apparatus. Boundaries are not fixed and given. New materialism could be defined as a new movement that draws together rather than cuts apart (e.g. Barad 2007). We must not use words thoughtlessly or take them for granted but be ethical about what we connect. The transversal approach focuses on how “matter matters” across previously established boundaries and has a concern with an overemphasis on language, culture and discourse in the linguistic turn. I agree with this argument. However, humans are, whenever they are involved in phenomena, also beings with psychology. The learning of language and emotions has a tendency to disappear with this move towards posthumanist materialism. Posthumanist learning must find a way to include how materials involve personal as well as collective emotions. Available materials have all kinds of impact on the psychological and emotional processes that motivate humans as collectives of collectives to perform certain agencies. Learning and psychological notions of emotions are pivotal in understanding what motivates humans to become the little masters of engineering and the natural scientists that increasingly create our material surroundings. What is needed in posthumanist theories is a focus on how matter comes to matter in collectives. When I began my fieldwork at the Niels Bohr Institute for Physics, I at first emphasised language and discourse but soon entered into a steep learning curve on how we learn that matter matters – to students, to physicists, and to myself.

The Mars mission Formal education has the explicit purpose of aligning the thinking and perception of a group of people. In Nepal, schooling was about learning to read and write and, following Holland and Skinner, it was also a place of collective national and modern identity formation (Skinner & Holland 1996). In schools, new words were learned along with new ways of communicating and thinking together with new practices (see Chapter 2). In 1992, I read Jean Lave and Etienne Wenger’s book on how shared identities emerged in communities of practices (CoPs), through what the authors named “learning peripheral participation” (1991). Though one of their points was that young people are sequestered away from the practices of life in schools, I began to think of educations as collective communities of practices. I began to ask questions like: how does what we call an “engineer” or a “physicist” become an engineer or a physicist? If education is itself a community of practice, how does identity formation affect thinking and words? And what about those who are excluded from membership? Can everyone learn to perceive the world as a physicist? I decided to enrol myself in their physics educational programme, as an anthropologist studying how physics students were in- and excluded in the process of becoming scientists. The students accepted my request for enrolment and I had the theoretical ambition to elicit cultural models beyond those found in the curriculum, that could indicate

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new identify formations. However, at that point I had very little understanding of how much materiality means to physicists. This was something I learned from materials and physicists as I became more and more entangled with their world. I enrolled in 1996 and immediately began to form new habitual paths as I attended lessons in physical places that were spread over an area of Copenhagen. The biggest place was the H.C. Ørsted Institute in the heart of Copenhagen, where we had lectures with teachers in big lecture halls. In those days, everything we learned was written on paper, from course plans to reading materials. We also had some instruction on how to work on computers. For instance, in maths, we learned how to make grids, but we shared computers with four or five people at a time. We read books with titles like Elements of Newtonian Mechanics (Knudsen & Hjorth 1996) and Linear Algebra, Gateway to Mathematics (Messer 1993). I understood very little (especially of the maths course). Nevertheless, I managed to learn some physics and maths and had the silent satisfaction of noticing that some of my fellow students also struggled with learning the content of the books. In the previous chapter (Chapter 2), we met the lamenting daughter, who strongly desired an education. For most of the people in the world, and especially for women, getting an education – let alone one in physics – is still a problem. In Denmark, physics education was, when I enrolled, apparently an open offer to all, yet very few women enrolled. All of the physicists I interviewed between 1996 and 2008 went to a physical school, a gymnasium and later university and learned physics in classrooms with human teachers and books. Some became adjuncts, lecturers and a few became professors. Yet, as a recurring pattern, the gap widened between male and female physicists the closer they came to permanent positions. Though physics explicitly asked for more women to enrol and apply for positions, most of the women who accepted the offer seemed to run into a male culture of bonding that they found hard to deal with. Many female students left – and those who stayed complained that their kind of research was not welcomed at the institute. Then I discovered that whereas the Niels Bohr Institute was made up of only 5% female professors in 2002, La Sapienza in Italy was made up of 33% (Hasse & Trentemøller 2008, 12). In my research, I became interested in what had motivated both those who left or stayed (men and women). Why did they begin to study physics and were there differences between what motivated students to study physics in Italy where we found many more female physicists? This kind of study was clearly driven by a creation of differences from within a particular educational discourse (Deleuze 1994). The interviews I did were classified in my research according to gender (e.g. Male – not Female; Female – not Male). Following this methodology, I found out that, the physicists, across nationality and gender, mentioned that in retrospect they see their formation as physicists as beginning very early. Though what they were taught in the classroom mattered for their understanding of physics, other kinds of preceding learning seemed just as important for their identity formation. Through

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participation in classrooms and excursions I learned that for some students in Denmark the genre of science fiction seemed to pop up as an important reason for engaging with physics: the dream of discovering that space is populated by creatures like in the movies, that new planets like in the books can be populated and that science fiction movies and books could motivate the deepest physics questions (Hasse 2015). In a survey, I later found two main sources of inspiration for studying physics: science fiction and other humans such as teachers and family members (Hasse 1998). Women tended to be more inspired by other humans (especially family members) whereas males tended to be more inspired by science fiction (especially “hard science fiction” – which tries to build on facts). In Italy, however, many women and men were inspired by philosophical questions coming from their backgrounds in classical studies, which had motivated them to study physics. Here we also found more female physicists than in the “hard science fiction” environments, which I found among Danish students (Hasse & Sinding 2012; Hasse 2015). Though science fiction was a new way of subdividing and classifying physics students (those who were inspired by science fiction and those who were not) and further studies showed a gender pattern where, as mentioned above, comparatively fewer women than men were inspired by science fiction in Denmark (Hasse 1998), I also began to become aware of how my own learning process began to affect me through sharing physical space with physics students. Motivation to begin and finish a study is in some ways more complicated than learning a new curriculum of scientific words and numbers, though this part is hard enough. Though we may later be able to pinpoint a teacher, a father or a book that we remember as the one who/which inspired us to enrol in a study, motivation is a long process of emotional learning, where one physical situation, connected with the next, gradually builds up an identity tied to our situated learning process. Gradually I became aware that I became motivated as I began to share a meaningful physical space, which included science fiction novels and hands-on experiences with instruments, with my fellow students. As a result, I began to focus on other and more material aspects of learning than identity formation. I realised that I was caught up in a learning process that gradually changed my perception of physical space even if I was not a practising physics student. My perception of the surroundings became inf luenced by physics. I saw materials as forces, spins and friction, as oscillation and gravity, where before I just saw leaves falling from trees and bicycle wheels turning. Though I had learned to cycle and learned the names of leaves and trees, they gradually now took on a new meaning, as I learned physics. Though my lack of preceding learning in mathematics for instance made me a poor apprentice, I was still able to experience the emotional and intellectual force of the entanglement with physics. Sometimes it is enough to become aware of how we are momentarily aligned in a collective mission to be motivated to continue, even when the words and numbers in the curriculum seem incomprehensible. The following are revised notes from my six months of studies.

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VIGNETTE 3.1: MEETING THE MARS PATHFINDER I began to think using physics theory even when I was not in a classroom. Even though I still found physics and maths hard to follow in the textbooks, “some teachers seem to have more gravitational force than others do”, I noted in my notebook, sitting in a chair in an auditorium where I listened to JMK, our beloved teacher in Physics I and II at the Niels Bohr Institute for Physics in Copenhagen. This particular teacher seemed to attract many more students to his lectures than any other teachers. The difference between him and his assistant teacher is, according to my fellow students, that JMK is “passionate” whereas the assistant PL is “boring”. When I ask what they mean by “passionate”, they explain that he is not just giving a lecture and going over the subjects in the textbooks. He constantly connects these themes with his own research interests, and talks about his dreams for future research. He is, as one student puts it “awakening a new sense of wonder”. I understand them fully. I have myself, from my seat in the auditorium, followed his lectures and even without a degree in physics I can clearly understand why he is one of the most popular teachers. He teaches not just subject matter but futures, hopes and dreams. I am not surprised at all when the students applaud after his lectures, though they never do that with anyone else. He is a great didactic educator, but it is not just cunning pedagogy and calculated didactics that make him so. It is a human being that stands before us who has a commitment that comes from within. His concerns gradually become mine and I begin to share these concerns and new questions with my fellow students. The questions that eventually show up on the board above my writing desk as well as on the door to my office are no longer about gender and discourse. Instead, they have become: Is life a consequence of star formation? What is gravity (G) really? In my notes from his lessons I dutifully jot the formulae down: Torque is represented by the symbol τ (Greek letter tau). The torque caused by a force is equal to the magnitude of the force times the lever arm. Torque is computed using the following equation: If a force of magnitude F = X N is applied at a distance r = Xm from the axis of rotation, in an orientation where r makes the angle θ = degrees with respect to the line of action of the force then the lever arm = X m and the magnitude of the torque is τ = N m. When I look at these notes much later, I have no idea what this means. I did learn something else, though. I learned how a teacher could materialise dreams and visions before our eyes. JMK draws weights and pulleys on the board. He draws a cord hanging from the pulley and swings. Does it matter? Should I fall asleep? JMK keeps me awake by giving the chalk-drawn pulley a private life that extends far beyond the board edge. All of a sudden, he asks, “Can times change?” Moreover, JMK

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assures us that he often thinks about this and points to the pulley. It is again the fundamental question: Is the G constant? In our models here on the board and in the textbooks, we must count on G as a constant, says JMK – but is it really? He throws a piece of chalk across the room to illustrate G (=gravity). I begin to wonder about things I never speculated about before. I now begin to connect what I learn about physical forces with the way my bicycle wheels touch the asphalt and how rain falls from the sky. These small everyday wonders become a resource for me in discussions with fellow students. Though I do not understand the math, I understand the wonder JMK has given us all. For JMK, it is the riddle of gravity (G) that is the greatest of all. He explains that in our own little special corner of the universe, gravity admittedly creates the Archimedean point in our local coordinate system, but are these coordinates existing everywhere in the universe? An answer would require that we understand what gravity really is. Right now, JMK says, we do not know. A universe may be 1017 seconds long and many billions of years old, filled with atoms, exploding stars, red giants, white dwarfs, lots of substances and a void that all works with a mighty effort to fold into each other. Everything is apparently under mutual influence and we do not know why. We do not know what it is that causes the earth to pull at the moon and vice versa. “Could it be, could it be” (JMK often repeats questions when he is excited), “could it be the electrical forces that somehow create gravity (G)?” He invites us to think about it, just as he himself is constantly wondering about these existential puzzles. After the class, I want to become a physicist myself to be able to work on these important questions. We who join his classes come to share an emotional concern for these basic questions, and that motivates us all to learn more and hang on to the textbooks, despite the fact that some of us (and not just me) find the examples boring. We have learned to share an engagement with the information and from my notes, I can follow the process of how my learning is not just mental, but also a transformation of physical space. JMK’s own research topic is exploration of the planet Mars and the hope of finding water “up there”, as he puts it. He has, with other members of his research team, speculated that the reason Mars is red is that it is covered in dust with microparticles of iron that have rusted due to contact with water. Some of his students, including me, are allowed the honourable task of gluing little magnets to an apparatus, a small car by the name of Sojourner, which will also have a probe attached, and be launched from a space rocket in a project run by NASA named Mars Pathfinder.1 Twenty-nine magnets are designed so that they can attract Mars’ red dust to varying degrees depending on the dust’s chemical composition. In this way, researchers hope to find answers to the question of whether the dust’s chemical composition shows traces of contact with water. If this is so, the probability that there has been water other places in the universe increases. This means, JMK explains, that there must have been

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some kind of atmosphere on Mars, and it cannot be excluded that it has given rise to the formation of primitive life forms. That would crush the theory that life on earth is unique. We humans would, in other words, not be exceptional at all but a direct result of star formation. This was my first encounter with a decentring of the human that later became so important for critical posthumanist theories. We are told that the US Mars Pathfinder mission should be launched in December 1996 with an expected arrival on Mars in July 1997. Leading up to the launch, we hear a lot about the project in the classroom. We also begin to follow the mission outside of the classroom. We meet and follow programmes on television and exchange good sites on the internet, where information can be obtained. I am, to some extent, part of all this. Even though I am not a “real” physicist, my (steep) learning process makes it possible for me not just to listen but to actively engage in discussions of the Pathfinder mission. One day in December 1996, JMK enters the auditorium, and we understand immediately that something is wrong. He does not use his body as usually. His steps are heavy and he places his legendary blue tote bag behind the lectern with a little sigh. I notice his voice sounds tired and I know before he opens his mouth that something has gone wrong with the Pathfinder. “The rocket did not come up yesterday, as expected”, he explains. There are problems. He was so excited about the launch, and we were too. It matters to us what happened to our carefully glued magnets. It mattered, in fact, to all of humankind. If the rocket is not sent to Mars, we cannot come closer to an answer to the big question: Is there life in space? We talk about his sad experience during the break and sympathise with him. When he begins to teach it, is about Mars’ orbit, which is also the example we can read about in the textbook. Now the words, numbers and pictures have a new life for us. He begins to draw on the blackboard as he says, “We can take the invention of Pathfinder as a case. There is a lot of good physics in it”. Later, in another lecture some weeks later, we all cheer at the first glimpse of JMK when he enters the room, because we all know that the Pathfinder has launched and later we all cheer again as the mission successfully discovers rust particles on the surface of Mars.

From my position in the formation of students, I am not an individual but experiencing becoming part of a collective formation – JMK’s students who all care about G and water on Mars. My fellow physicist students have learned to understand the Mossbauer spectrum behind the magnets on Sojourner much better than me, and they can calculate the forces that eventually land Mars Pathfinder safely on Mars. However, the more I get to know them, the more the “they” dissolve into different persons: Peter, Susannah, Andrew and Albert. They all know and build their learning on different experiences and have different backgrounds.

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Yet we now share our emotional engagements in physics thanks to JMK. Though not as marked as the differences between Prajun and Jit Gurung in Nepal and the Americans in the Kelly’s Writers House MOOC, my fellow students also differ in terms of past learning experiences. This does not prevent a momentary collective feeling that we share the same engagement as JMK in the Pathfinder project. They later repeat this same feeling in focus group interviews. In the interviews, I get my sense of belonging confirmed: We have learned to perceive the magnets in ways that align their physical appearance with shared hopes and dreams. How does preceding learning align you with your fellow learners? We at the Niels Bohr Institute were different as ultra-social learners in that each of us had slightly different learning experiences. We became aligned in that our preceding (and different) learning resources about water in space and gravity became emotionally freighted in the same situated entanglements. As an anthropologist, I experienced situations like JMK’s classes, where I felt I belonged to this community of practice. In other situations, I felt I did not belong. When I first arrived for my fieldwork, students would, at times, stop talking and move. It can be a very emotional experience. However, to “survive”, I also learned to belong in to a group of female students, because we all experienced emotional reactions to being excluded as women (Hasse 2001). The physics environment in Denmark in the 1990s when I conducted my fieldwork was not a woman-friendly environment. When I exchanged views with a fellow student, Alice, both she and I shared a lamentation that physics education in Denmark seemed so male-oriented. Here we aligned with the lamentation of the daughter in Nepal, whom we met in Chapter 2, who also struggled to get an education. However, during my fieldwork, I also learned about young men who were excluded and ridiculed because they cared too much about sports or had the wrong hairdo. What I learned, in a metaperspective, is that being the same or different (e.g. gendered) is an empirical question. Gender cannot be taken for granted as a matter that matters. What we normally think about in essentialist and humanist terms, such as national identities, communities of practices, identities or gender, covers the complex processes of becoming mindful bodies that momentarily align as an emotional collective as we did in JMK’s classes, or as I did when I joined my group of female friends. Many perceptions changed as I learned with the same materials as the physics students. The phenomenon of “Mars” that I from time to time see in magazines or on the internet will never just be a red planet again. I now cannot avoid seeing that redness without the inclusion of what I learned in lessons about the iron-rich minerals that formed rocks on Mars. I think about how iron becomes rusty red and sometimes also what I learned about the Mossbauer spectrometer, the instrument which detected these iron-bearing minerals mounted on one of the arms of the Mars Rover. My preceding learning, as it engaged in the material practices of becoming a physics student, is now inseparable from the situations in which the Mars phenomenon appear. This does not mean that I perceive the same “Mars”

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every time or that I perceive Mars as a trained astronomer. My perception is tied to situations that keep changing the boundaries of what is included in my perception of Mars (a sci-fi movie, a new discovery of water etc.), so the meaning of my preceding learning and my conception of Mars is never fixed. Even so the iron dust and the Mossbauer spectrometer and my pride in the Rover experiment are there as potential resources to be called upon whenever Mars appears in the material practices I engage in. These are factors that would not have been part of my perception without what is now my preceding learning from JMK’s classes, where I also learned to emotionally understand why the Mars Rover matters to my fellow students. It was through these learning processes that materials became meaningful for me.

Schema theory revisited The theories I brought with me into my physics studies were anthropological theories of learning in practice in Communities of Practice (CoPs) and the theory of cultural models. Where Jean Lave and her colleagues explicitly distanced themselves from psychology and cognitive theory, cultural models were inspired by what went on in machine learning theory and emphasised shared cognitive processes. They also emphasised how cultural models create learned emotions in humans, including the researchers themselves (e.g. Lutz 1988). Though many of the researchers who originally worked on cultural models have now left the approach, I still see it as having some advantages in connecting learning, meaningful expectations and emotions. The theory of cultural models was originally inspired by the anthropologists Roy D’Andrade’s work on neural networks, which also inspired machine learning; for instance, the reinforcement process of PDP – parallel-distributed processes (McClelland & Rumelhart 1986). A group of cognitive anthropologists formed around D’Andrade to explore PDP-connectionism as a way to explain how organisations of cultural connections, motivations and emotions are built up from everyday experiences. Very early on, the group was introduced to a Danish anthropological group led by the anthropologist Peter Hervik and I was a student in his classes. We read Roy D’Andrade’s introduction to the field in The Development of Cognitive Anthropology (2005). Here D’Andrade describes how the approach, apart from machine learning, also has its origin in the cross-cultural analysis of human cognition. What originally inspired D’Andrade’s work with his colleague Kimball Romney and his supervisor Melford Spiro was the well-known anthropological engagement in kinship-classification and componential/feature analyses developed by Walt Goodenough and Floyd Lounsbury (D’Andrade 2005, 21). The point of departure for D’Andrade himself was the famous phrase by Goodenough that culture is “whatever it is one has to know or believe in order to operate in a manner acceptable to its members” (D’Andrade 2005, xiii). This is a formulation, which raises a number of new questions. Humans learn cultural knowledge, but what does one come to know or believe? To D’Andrade

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the answer could be found in models of the mind as formulated in “cognitive anthropology”, which is the “study of the relation between human society and human thought” (ibid. 1). The basic unit of analysis was at first the schema, which was inspired, by PDP research (e.g. McClelland & Rumelhart 1986), but also linguists and cognitive psychologists. Schema or schemata are seen as the building blocks of cognition and perception. This approach fits with the feature analysis of e.g. kinship terminology and seeks a relation between learning a language and learning a thought, but underlines that schemas are not 1:1 representation, but culturally shared mental constructs with directive force. According to these theories, we have collective cultural schemas for almost everything we do and they are culturally and historically diverse. We humans, in other words, differ in what we find meaningful in relation to materials. In poetry and literature, for instance, we can find many different references to how people are picking apples (even when this is not what the stories are about). Before the industrial revolution in the Western world, apples were fruits growing on trees and people accessed them if they had a right to pick them. Humans knew about twigs, tree bark and the rhythmic process of climbing and they knew how apples were related to ownership. The organised knowledge they had were cultural cognitive schemas tied to where in the world and from what position they were allowed to pick apples. When humans moved to cities, our schemas changed when we began to buy apples instead of picking them from trees. When we shop in the grocery store, put apples in a bag and hand over money to the grocery store clerk, this simple transaction can be analysed as a commercial transaction schema (D’Andrade 2005, 152). A schema is not a rule-based representation but can be weakened or reinforced through experience, when an interconnected pattern of interpretive elements is activated. A schema has to be learned to trigger patterns of recognition and emotions. The mind is not considered to make conscious associations between otherwise discrete elements. Rather, it is considered as a neural network. Whenever we experience the world around us, connections are made between neurons in the brain and when experiences are repeated, the connections are reinforced (Strauss & Quinn 1994). Knowledge need not be explicitly learned or retained as explicit generalisations or formulae; instead, regularities in behaviour ref lect cognitive patterns extracted from repeated experience (Strauss 1992a, 11–12). If a city dweller walking on the street sees a man through a window putting money beside a half-empty plate of food, their internalised cultural schema fills out the rest of the information needed to understand what is and has been going on as a prototype sequence: we infer that it is likely that the man has ordered food from a waiter, eaten it and now is paying for it and that he has been seated in what we know to be a “restaurant”. The meaningful schema is cultural as it is only triggered in people with experiences of paying for food (instead of e.g. swapping food for shells or other goods as it is done in some communities).

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“A schema is a conceptual structure which makes possible the identification of objects and events” and “a procedure by which objects or events can be identified on the basis of simplified pattern recognition” (D’Andrade 1992, 28). Schemas create expectations and are reinforced when nothing unsurprising happens that contradicts the formed schemas. When people who are familiar with the concept of shopping see a child taking an apple from a basket in the street and then looking in her pockets to find a small piece paper to hand over to the adult watching the basket, we are not the least bit surprised. We know the commercial transaction schema. However, if the child took an apple and ran away, we would immediately understand something was wrong. Schemas are not only an outcome of human activity; they are also motivating because they have “the potential to instigate action” (e.g. paying for an apple). Schemas are processes, not objects or things, that build on internalised organisations of knowledge. This makes it possible for us to act and understand other people’s acts with minimal cues. “A schema is an interpretation which is frequent, well organised, memorable. It can be made from minimal cues, contains one or more prototypic instantiations and is resistant to change etc.” (D’Andrade 1992, 29). According to the cognitive anthropologists, we have all kinds of schemalike organisations of cultural knowledge for simple acts in everyday life. Using this approach on my experiences at the Niels Bohr Institute for Physics, I began to become aware that there were other forces at play than students collectively learning about gravity. When I was motivated to leave my cup of coffee outside the lecture hall, it was because my organised knowledge of “going to a lecture” made many things habitual and self-evident. Coffee, food, overcoats and games were left outside; we would not talk during lectures, and not interrupt the lecturer with questions. No one told me these rules explicitly, but I learned them in practice and over time, they formed a habit of practice. Once learned, my mental schemas would fill out information for me through default values. When I saw a person going into the lecture hall with a cup of coffee, I expected them to be stopped and I became surprised when this did not happen. Our emotions are embedded in what is expected and meaningful. The group of cognitive anthropologists around D’Andrade added three important aspects to the general cognitive schema theory which they expanded to “cultural models”: (1) Schemas are cultural in so far as we find diversity between schemas found in different ethnic groups and national cultures. (2) Schemas are organised in clusters of schemas as “cultural models” of connections of organised knowledge which are learned through everyday activities. (3) As learning differs with differences in everyday practice, we cannot assume the organisations of cultural knowledge to be equally motivating for all members of an ethnic group or a national culture, (4) Expectations following preceding learning of connections are tied to learned emotions. Whereas I could follow aspects of my own process of building new cultural organisations of knowledge in the material environments of physics education, in general the theory of cultural models was appearing in analysis of linguistic

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discourses such as commercial slogans, maxims, proverbs, morality tales and metaphors (e.g. Schwartz, White & Lutz 1992). The material process of gradually coming to master material artefacts is close to the different stages explored by the Dreyfus brothers where the novice is most aware that she learns, whereas when the person reaches the expert stage, what is meaningful is what is expected (Hasse 2019). However, schemas for actions and clusters of schemas forming models for agency only have the potential to direct our actions. Nevertheless, having learned the schema for how to glue magnets on the panels of the Mars Pathfinder, it did create cultural stabilisation in a formation where precision was important. But the reason we bothered to glue was fraught with emotions tied to the meaningfulness of finding water on Mars.

The theory of cultural models An array of analyses of cultural models has been presented, especially in the three anthologies Cultural Models in Language and Thought (Holland & Quinn 1987), Human Motives and Cultural Models (D’Andrade & Strauss 1992) and A Cognitive Theory of Cultural Meaning (Strauss & Quinn 1997). Since the 1990s this has set the stage for new directions in general anthropology, placing the relation between culture, learning and cognition in the middle of research and methodology. The theory of cultural models proposes, as Strauss and Quinn put it, a new theory of cultural meaning, one that gives priority to the way people’s experiences are internalised. Drawing on ‘connectionist’ or ‘neural network’ models as well as other psychological theories, in [cultural models] cultural meanings are not fixed or limited to static groups, but neither are they constantly revised or contested. (1997, i) Building on a neo-Vygotskian framework, which underlines a developmental approach (Holland 1992, 63) individuals do not share cultural understandings in any simple manner. It is in the very process of learning that the cultural model’s organisation of knowledge gains salience for us. In an analysis of an American cultural model of romance, Dorothy Holland shows how young students’ ­d iscussions about romantic relationships rest on a complex cultural model of romance, which organises an array of taken-for-granted knowledge about the ideal male/female relationship into a coherent pattern. Through analysis of students’ speech, the researcher can posit a “figured world” as a simplified world populated by a set of agents (e.g. attractive women, boyfriends, lovers, fiancé s), who engage in a limited range of important acts or state changes (e.g. f lirting, falling in love, breaking-up, having sex) as moved by a specific set of forces (e.g. attractiveness, love). (Holland 1992, 65)

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The student’s internalisation of this model differs in relation to how salient and emotional romances become. Though most students can recognise the elements of the model and how they are related, they do not respond to it in the same emotional way nor does the model have “directional force” to the same extent for all learners. However, the more they learn about the model of romance, and the more they learn to master its elements, the more its directional emotional forces are reinforced. I came up with a cultural model of physics at the Niels Bohr Institute that connected hard science fiction, the “playfulness” and the high priest of physics, with an emotion-driven motivation to find life in space and to even find physics as the boundary to God (Hasse 2015). The model made it possible for me to understand the processes of the in- and exclusion of those not “fitting in” and for whom high-lofted physics had no salience. Schemas organise personal memories around prototypical events (how to behave in lecture halls and what to expect from a Mossbauer spectrometer), which we might contrast with our own experiences. The wider cultural model organises all of these expectations on a more abstract but also emotional level. When we have learned to expect something (like a happy JMK and a successful launch of the Pathfinder), it elicits emotions when our expectations are not met (when JMK is sad and the launch is a failure). A Mossbauer spectrometer is not just a material thing, nor an instrument revealing facts, but also something we come to perceive and care about as a collective. The broader model of physics taught by JMK connected the body schema (of gluing magnets) to other schemas (of how to use a Mossbauer spectrometer or find exoplanets in space through measurements) and gave them all a sense of direction in physics. This model connected how we did physics with expectations of what a good physicist was. A good physicist according to this model was never satisfied with just completing measurements or even discovering a new star or finding water on Mars. The cultural model we learned taught us there always had to be a larger more important vision for humankind in what we did, like finding life in space. The cultural model of physics inf luences our collective sense of common goals (such as helping a robot get to Mars) and our feelings about, for example, big G and water on Mars. The cultural model often organised our individual emotions into a collective emotion of joy or sadness as we came closer to or were detached from reaching the goal of finding life in space. Before I entered the Niels Bohr Institute, life in space held no salience for me. I never thought seriously about it and never read science fiction. In this capacity, models connect thinking and emotions as “learned internalised patterns of thought-feeling that mediate both the interpretation of ongoing experience and the reconstruction of memories” (Strauss 1992a, 3). These theories made a lot of sense to me as I gradually learned to perceive the world as physicists do and even tie thought-feelings to my new perceptions. Cultural models are composed of schemas, but are not schemas themselves. As an analytical tool, they are more complex structures than schemas (D’Andrade 2005, 152).

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Where schema theory was often limited to very simple acts, I also followed the cognitive anthropologists as they began to connect schemas into more complex clusters of cultural models as analytical tools. These analytical tools were used to capture both cultural models, which affect emotions and motivations to act, and processes as the ongoing learning of new connections through which cultural models are formed. In these cultural models, artefact, discourse and practice combine to make a coherent whole (Holland & Cole 1995). As an analytical tool, cultural models aim at making implicit cultural organisations of knowledge explicit, exposing connections behind assumptions and explicit or tacit beliefs (D’Andrade & Strauss 1992). “Cultural models” are more complex connected organisations of knowledge than schemas, which are formed in activities and “doings” rather than linguistic “rules”. The generalised models give directive force to certain motivations, without making persons “cultural dopes” (Holland & Quinn 1987; Strauss 1992). In this framework, there is a strong focus on how different cultures seem to connect and organise local knowledge in particular ways, which implicates and directs certain activities (D’Andrade & Strauss 1992; Holland & Quinn 1987). It made sense for me as I gradually learned that the cultural model of physics in Denmark could be connected to hard science fiction and the hard sciences, whereas in Italy a cultural model of physics was connected to classical studies and philosophy (Hasse & Sinding 2012). The cognitive anthropologists argued that we could analytically extract schemas and models of implicit shared cultural understandings from slogans, proverbs, clichés, wise words, maxims and other kinds of written data, as well as statements in interviews where connections are made between elements. This organisation of knowledge is learned implicitly and only the researchers take an interest in making it explicit. Otherwise, it is simply learned through everyday actions and reactions that enforce or weaken the expectations and goals of actions (Strauss 1992a, 3). The cultural models involve more than schemas for actions, as they connect actions and perception with organised thoughts, motivation and feelings. This latter addition from the cognitive anthropologists was largely inspired by readings of Vygotsky. Models, like schemas, are not static but evolve in a continuous learning process, which makes individuals differ in how they have internalised the models. The cultural model research that inspired me concentrated on discourse analysis and interviews designed to elicit informants’ underlying cognitive schemas and the more encompassing cultural models related to general and abstract concepts such as “marriage”, “romance” and “the American Dream” (Holland 1992; Strauss 1992a). The theoretical framework was not emphasising material surroundings, and gradually I began to understand that it was not enough just to refer to doings and discourses in cultural practice. The theory’s emphasis on discourse, to some extent, prevented me from realising how tied to materials my data was. I acknowledged that I learned to take connections for granted as connections were reinforced, which over time made it possible to perform tasks such as using technologies or gluing magnets on plates

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without any deeper ref lections. Nevertheless, materials like magnets, glue and electrical currents now also mattered to me. It was when I began to read Barad (2007) that I became aware of just how much materials matter to physicists. What does it mean that a phenomenon appears, Barad asks? If we abandon the Enlightenment and humanist a priori separation of subject and object for a relational posthumanist ontology, a phenomenon like the planet Mars or a particle in physics is never just discovered but entangled with human and non-human agency. At this point, let us have a look at one of the famous posthumanist examples explaining a posthumanist theorising of factors entangled in phenomena and see if we may detect any preceding human learning collectives in there.

VIGNETTE 3.2: PARTICLE IN CIGAR ENTANGLEMENTS In 1922, in the town of Frankfurt, Germany, a couple of physicists carried out an experiment to decide if quantum physics really could overturn Newtonian classical physics. By then, physics had seen a number of models created to explain the nature of the atom. Among these models were Joseph John Thomson’s “plum pudding” model visualised as a circular mass of positive pudding with scattered plums inside. Thus, the model predicted the first subatomic particle, the electron. This model had been tested and refuted by Niels Bohr, among others, because the experiments did not show the pattern of a fixed coherent mass of positive “pudding” with scattered negative electrons. Instead, matter seemed to be more volatile and the puzzle was how matter could be volatile and stable at the same time. Bohr visited Ernest Rutherford (a former student of J. J. Thomsen) and following Rutherford’s work, in 1913, he came up with a new way of perceiving mass suggesting a nucleus surrounded by electrons moving in definite spherical orbits around the core. This new quantum mechanical understanding of reality allowed for something that could not be accepted by many proponents of classical physics theory. The volatility yet stability of an atom came from electrons “leaping” from one stable and discrete energy level orbit to the next. In principle, this meant that at a microscopic level mass was not stable, though it would seem so in our human macroscopic perspective. The two German physicists Otto Stern and Walter Gerlach devised an experiment where they used thin silver foil, which was placed in an oven to allow particles to “escape”. Then the foil was placed in a magnetic field, sent through an the magnetic field using a piece of apparatus and finally ended up on a photographic plate. The purpose of this experiment was to either confirm mass as stable or as quantum leaps, as suggested by the proponents of the new physics, among these Niels Bohr. However, the experiment turned out to show something quite different. Not only did the success of the experiment require the tenacity and skills of Gerlach’s labours, but it also depended on a convergence of other factors: “Among the particulars are a warm bed, a bad cigar, a timely postcard,

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a railroad strike, and an uncanny conspiracy of Nature” (Bretislav & Dudley 2003, 53). One of the key factors was external funding from the GermanAmerican Henry Goldman (a founder of Goldman Sachs and the progenitor of the Woolworths chain of stores). Goldman’s contributions were crucial to sustaining Gerlach’s research in the face of the increasing financial disarray of the German economy. Einstein was also instrumental, providing a grant from his institute in Berlin to support their efforts. As fate would have it, the traces of space quantisation did not reveal themselves to Gerlach. However, as Stern recounts, there was a particular incident concerning this arduous scientific adventure that would leave its mark on him: After venting to release the vacuum, Gerlach removed the detector flange. But he could see no trace of the silver atom beam and handed the flange to me. With Gerlach looking over my shoulder as I peered closely at the plate, we were surprised to see gradually emerge the trace of the beam … Finally we realised what [had happened]. I was then the equivalent of an assistant professor. My salary was too low to afford good cigars, so I smoked bad cigars. These had a lot of sulphur in them, so my breath on the plate turned the silver into silver sulphide, which is jet black, so easily visible. It was like developing a photographic film. (Bretislav & Dudley 2003, 56) The results Gerlach held in his hand were close, but no cigar! The traces only gradually emerged when Stern held the plates in his hands and studied them at a distance close enough so that the plates could absorb the fumes of Stern’s sulphuric breath, turning the faint, nearly invisible, silver traces into jet black silver sulphide traces. The magical success of this historic experiment depended on a cheap (cigar) trick. If it had not been for Stern’s tobacco habit coupled with his relative impoverishment, the duo might have given up hope of finding any trace of space quantisation, which refused to show itself in the absence of a little helpful cajoling from the cigar’s sulphurous fumes. As the example of Otto Stern’s cheap cigar makes quite poignant, taking for granted that the outside boundary of the apparatus ends at some “obvious” (visual) terminus, or that the boundary circumscribes only that set of items we learn to list under “equipment” in laboratory exercises in science classes, trusting our classical intuition, our training and everyday experience to immediately grasp the ‘apparatus’ in its entirety, makes one susceptible to illusions made of preconceptions, including ‘the obvious’ and ‘the visible’, thereby diverting attention from the reality of the role played by smoke and mirrors (or at least smoke, glass, and silver atoms), where the ‘smoke screen’ itself is a significant part of the apparatus. Significantly, the Stern–Gerlach experiment did not in fact yield the expected result, nor was it as definitive as Stern had hoped. (Different excerpts from Barad 2007, 162–166 – notes and figures omitted).2

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Stern and Gerlach did not find what they hoped. Nevertheless, what emerged on the photographic plate became a milestone in physics research. They had produced an exemplary case of how an atom spins, and they could now argue that this spin can be inferred from a measurement of its magnetic moment. In textbooks, this result is recounted without all the little details. Barad argues that the research apparatus cannot be reduced to the oven and the silver plates. Part of what is included in the phenomenon appearing on the plate is tied to the gender and economic status of the scientists. Otto Stern’s cigars were particularly sulphurous because they were cheap. Furthermore, in those days, only men smoked cigars and very few women were physicists. The research apparatus is an entanglement of the silver plate, the oven, Einstein and Goldman, male physicists and cheap cigars. This amalgamation does not build on discrete, separate and mutually exclusive binary taxonomic categorisations like “male” and “female” or “nature” versus “culture”. Barad argues: This kind of thinking mistakenly reifies culture, nature, gender, and science into separate categories. However, the fact is that the world is not naturally broken up into social and scientific realms that get made separately. There isn’t one set of material practices that makes science, and another disjunct set that makes social relations; one kind of matter on the inside, and another on the outside. The social and the scientific are coconstituted. They are made together – but neither of them is just made up. Rather, they are ongoing, open-ended, entangled material practices. The goal is therefore to understand which specific material practices matter and how they matter. What we find in this particular case is that gender performativity, among other important factors including nature’s performativity, was a material factor in this scientific outcome. (Barad 2007, 168) Gender performativity is in fact never far from education, but exists in very different ways in specific material practices like schooling in 1990s Nepal or playing with robots in Denmark in 2016. In transversal posthumanist thinking, we never know which cuts or splits become relevant for the production of reality. This implies that gender is not always as relevant as in Barad’s analysis of the Stern– Gerlach experiment. Her analysis also reveals that it takes a feminist physicist like her to call forth the gender aspect of the experiment and entangle gender issues from feminism and posthumanism with physics. Bretislav and Dudley made no mention of the gender of the physicist in their account. Their re-enactment of the original experiment revealed that it was the smoke, rather than the breath that made the spin visible on the silver-filmed plate (Bretislav & Dudley 2003) but they do not refer to gender. Physicists seem more focused on materiality than gender issues. In Science and Technology Studies where the focus is on the relation between the social and scientific, the performativity of materiality has often been overlooked. It seems to take a well-read feminist physicist like Barad

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to perceive the entangled performativity of particles and gender in the detection of the quantum nature of spin. Barad draws on: the insights of some of our best scientific and social theories, including quantum physics, science studies, the philosophy of physics, feminist theory, critical race theory, postcolonial theory, (post-)Marxist theory, and poststructuralist theory. (Barad 2007, 26) What is missing from her analysis of entanglements and her reading list is the importance of a learning theory that concerns emotional and motivated preceding meaning making.3 Otto Stern’s recognition of the particle was built from preceding learning that made the concept of “particle” meaningful. This preceding learning included works written by other physicists, but probably also numerous emotional situations with teachers, like JMK, that spurred and enhanced a motivation towards a particular area of physics, in this case particle physics. Otto learned to align with other physicists for whom the behaviour of particle traces on silvered plates are meaningful, but only for the trained eye (Bretislav & Dudley 2003, 58). The physics teachers of Otto and Walter could not have foreseen what their teaching would lead to, but now they are also somehow entangled in the detection of particle spin. In education a particular learning outcome is expected; in a posthumanist perspective any particular learning outcome is uncertain, but what is inevitable is that learning goes on building on meaning making through preceding learning Barad would probably not say that the education and general learning of the physicists in the Stern–Gerlach experiment was unimportant. She just did not have her focus on this aspect of entanglements. However, it is when we include the more psychological aspects of humans into the entanglements that the emotions of the collective posthumans can be expanded.

Experiments at CERN Physicists with a formal education in physics are collectively aligned in many ways whether they are educated in Denmark or Nepal, but they also differ within the CoP. In the years 2002–2004, I conducted a number of quasiexperiments with physicists and physics students, who were all too some extent engaged with particle physics and the experiments at CERN – the European Organization for Nuclear Research.4 Contrary to laboratory studies at CERN by Karin Knorr-Cetina (1981) and Martina Merz (1999) I did not follow physicists around in just one place, but conducted a multi-sided fieldwork (Marcus 1995) where I took pictures of parts of the experiments at CERN, and pictures of the work conducted by two groups, both involved in different aspects of the project. I then showed them to each other to detect how much they could recognise. This was an attempt to show that social

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practice had implications for how we perceive material surroundings and that social practices formed cultural diversities in our perception of the “same” pictures (Hasse 2008). My experiment entangled two groups of physicists who would not otherwise necessarily have shared the pictures I showed them. I also showed the pictures to my colleagues at the Department of Education in Copenhagen. In 2002 I participated in a meeting at CERN where a model of how to retrieve subatomic particles from the ATLAS experiment was presented (later in 2012 ATLAS, along with another experiment called CMS, announced they had discovered the Higgs particle). I took some pictures of the model and later showed these to physicists who worked on other parts of the experiment.5 I also showed it to laypeople who immediately commented on the colours and strict lines but did not comment on the many symbols. The physicists on the other hand always commented on the symbols first. In an interview with a physicist two years later, he was shown the pictures and said: Well, that’s a data acquisition system … [thinking a bit while humming]. The symbol H8? I do not know what it is, but it’s some diagram that shows a data acquisition system for some detector. It is about how to get data from the detector and read it. (Interview with Zander 09.12.2003) Next I show him a picture of a particle collision (see Figure 3.1). Aha, yes, it looks experimental at least. There is something with some volts and some nanoseconds. Now let us see. And there’s a double W decay for an electron neutrino … at the bottom. Yeah, and then there are some strange things here. Yeah, maybe it is muon chambers or something. It looks a bit like the distance between muons. (Interview with Zander 09.12.2003) In a way, it is so obvious that we often forget to mention it. Recognising a silver particle and a muon chamber take many years of training. Learning is a process that “seems so natural that we forget to ask important questions about it; we often don’t bother to learn about learning” (Moll et al. 2001, 3). It is so basic and shared by humans, educational researchers included, across the world that it is “often unrecognised as such” (Lave 2009, 203). The “rational” human physicist with an apparent stand-alone perception of the world is always a learner with preceding learning involved in the entanglements. As part of the CERN collective, Zander can make the pictures meaningful. Furthermore, whereas Zander seems to recount facts, it also matters to him what he perceives – for instance, this is revealed in how he searches for

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FIGURE 3.1 UA1: W

particle decay. (Photo: CERN).

clues he can connect to his own work in physics when he, for instance, says: “Aha, yes, it looks experimental at least”. What drives Zander he explains is that: “We never know what we will find [with these instruments]”. And though he hopes for a Higgs particle (which they eventually found), he also hopes, like most physicists I interviewed, this finding will bring more puzzles to be worked on in the future. It is the connections that Otto Stern had learned that made him create the material arrangement of an oven, silver foil and magnets and it is because he cared that his face came close enough to the silver plate to expose the spinning particle which only made sense to him and Gerlach because they were long-time learners of physics. It is from this learning of something in particular that new materiality can emerge. Though learning in general is not accidental, this learning of something is somewhat predictable. It is not accidental that Thomson met Rutherford, who met Bohr, who met Stern. Nor is it accidental that they were all male, though they did not all smoke cigars. Nor is it accidental that some people could join a MOOC course on machine learning with more potential for success than others. If they already share not only a language of facts, but also a collective emotional bonding of concern for the facts, they can learn more together. As phrased by Bruno Latour: A matter of concern is what happens to a matter of fact when you add to it its whole scenography, much like you would do by shifting your attention

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from the stage to the whole machinery of a theatre. … Instead of ‘being there whether you like it or not’ they still have to be there, yes (this is one of the huge differences), they have to be liked, appreciated, tasted, experimented upon, mounted, prepared, put to the test. (Latour 2008, 39) But concerns do not arise out of the blue. Psychology permeates the scenography as learning forms collectively organised knowledge and emotion. Whatever entanglements are then, is not purely accidental. Humans learn from each other and materials when they meet in physical space, and this can become the learning of something that aligns goals, motives and emotions. Even with scientific concepts like the symbols for W decay and for an electron neutrino. The physicist, Zander, and I interviewed in Denmark, reveals a deep knowledge of both the experimental set-up of the CMS/ATLAS experiments and their different ways of illustrating how they expect to “catch” particles in these experiments. If an experimentalist from this experiment were to make a MOOC, Zander would already recognise all the necessary clues for making sense of the materialised concepts and symbols on the screen. He would be able to participate in an experimental and playful way because he had already learned something. This is what ruins the promises of MOOCs – free education for everyone. New learning is always entangled in what we can learn based on preceding learning. Learning is connected, not only to the process of learning, but also to what has been learned. This forms the opening to new learning and thereby to the alignment with new human–material collectives. Emotions are always involved as well. Seeing a split silver beam for the first time can be a great emotional experience for a physicist – as it would have been for Stern and Gerlach – even if they did not realise back then that they were looking at particles with intrinsic spin. That took more years and entanglements to realise. However, not only physicists but all of us change our perceptions when we learn.

Organised emotions Posthumanist theory makes it possible to see entanglements in learning. Cultural models theory makes it possible to see that entanglements also involve human minds. Both take part in the shaping of collectives. Collectives are not about learning the same representation of an object presented as a word. It is a complex process of the entanglement of meaning making and emotion. Materials and humans form collectives together and there are many roads for a collective to come to share emotions and meaning with, for example, a Mossbauer spectrum or silver beams. As noted by Vygotsky: “Meaning may be one and the objects various, or meaning may be various and the object one” (Vygotsky 1987, 152). How we explicate the connection linking meaning, emotion and object within phenomena depends on the universal capabilities of ultra-social beings to understand the

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world through what the educational philosopher Jan Derry calls the social nature of human mind. [M]any accounts of our relation to the world fail to make the distinction [between humans, animals and machines] or if they do make it fail to develop it sufficiently. Indeed as has been mentioned here the social nature of the human mind has generally been approached in education studies in terms of a multiplicity of forms of thought tied to context and mediational means rather than in terms of an examination of what is distinctively and universally human about its character. (Derry 2008, 55) Posthumanists may object to seeing humans as something exceptional, but not to seeing humans as something in particular. If “feelings are personal and biographical, emotions are social, and affects are prepersonal” (Shouse 2005, 1); what may be particular for humans is their ability to make feelings socially shared emotions. Even prepersonal potential affects may have to be learned. Though affects may not be personally realised (and pre-verbal) affects are never independent of collective signification and meaning. Though there may be “a gap between the subject’s affects and its cognition or appraisal of the affective situation or object” (Leys 2011, 443), cognition is affected by collective matters of concern. The collective affects may be more or less shared as tied to material artefacts but the persons will still sense their directive force – as I sensed the affect in the lecture hall when JMK was present. These affective inf luences had an effect on me, though it was at first not something I was conscious of – but as I learned it became clearer to me that I shared emotions with a larger group of physicists and/or physics students. Though culture studies have shown that human emotions differ, our ability to create scenarios for cultural emotions seems to be a special human capacity (e.g. Lutz 1988). Materials elicit emotions when they become meaningful. The physicist’s students and I came to share emotions tied to the Mossbauer spectrum apparatus, to magnets and pictures of the Pathfinder. For Vygotsky, thinking in concepts meant a freedom from thinking tied to the available materials. Our collective emotions were also extended to a human, JMK, whose happiness we came to care about as we shared with him one of the highest expectations for physics: to find water on Mars as a sign of life in space. It was by no means a stable process, but a process that kept evolving. Once a concept (like particle) has been learned, its meaningfulness has just begun (Derry 2013, 38). Gerlach and Stern saw something, which was not what they originally thought they would see, and as ultra-social humans, they and their colleagues struggled to find a collective meaning while expanding the concept of particle. Meaningful conceptual perception includes emotion and motivation (Vygotsky 1987, 283). What is meaningful is not tied to a particular object or a particular word, just as an object is not tied to a particular representation.

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Various objects may be one in meaning and one object may be bound to various meanings (Vygotsky 1987, 140). Meaningful thinking, in the phenomenal situation, involves preceding learning evoked as thoughts, which in Vygotsky’s poetic phrase can “compare the motivation of thought to the wind that puts the cloud in motion. A true and complex understanding of another’s thought becomes possible only when we discover its real, affective-volitional basis” (Vygotsky 1987, 282). Our perception within phenomena is not atomistic but integral to these preceding processes for learning what is meaningful. Organised thoughts and emotions are just as much a part of posthumanist entanglements within the apparatus as cigar smoke and gender. Words are material and only recognised as meaningful words by those who have already learned the schemas and cultural models that make them meaningful. Those who have learned them may reject them and protest at what they see, but they are already knowledgeable of what they protest against. They know what to expect. Those who do not know the schemas and models cannot join the collective of experienced learners. What makes a difference here is not if physics is taught through a MOOC (discussed in Chapter 2) with virtual teachers or in a brick-and-mortar school. What matters is how we can become part of an emotionally motivated collective. As the MOOCers cannot, in general, see each other’s reactions, they must often rely on transmissions of words and symbols, which are translated into their local environment by each and every one of the participants. These deep predicaments between those who have learned and those who have not are not necessarily revealed in action in MOOCS or in classrooms or even in collective physics experiments. Often a lack of learning by others is not recognised. This was revealed in the experiments I did with the physicists showing them the same pictures of particle collisions. I realised that it was taken for granted that physicists in Italy and Denmark participating in the same experiment also shared an understanding of what they saw in the pictures, but this was definitely not always the case. When, for instance, the Danish physicist Elsa was presented with a picture of the Italian apparatus on a topic supposedly equal to her own research, she stated: “It’s funny! I did not know they did that in Italy” (Interview with Elsa 11.11.2003). Perceiving signs of a subatomic particle, portraits of the king in Nepal or a magnet to be glued on Pathfinder are meaningless to some, but for others deeply entangled in emotionally freighted futures – something those who make only a f lat description of what they see rarely acknowledge. As a researcher, it was my privilege not just to make detached “observations” but to learn what mattered for the physicists – what they cared about. The “depth” of my perception differed with learning. When I first saw the picture of the particle collisions, I, as other non-professionals, saw a sprinkle of water and failed to notice the mathematical symbols, whereas the physicists saw the symbolic characters and connected them with the rest. For them the “whole” of the pictures emerged through the signs. It was from here, that the colours and lines made sense.

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Zander, the physicist, who I showed pictures of various parts of the CERNexperiment, recognises muon chambers at a distance, but also symbols of decay and he recognises it only because it is called forth by the knowledge of scientific concepts he carries around in his already collectivised body. Though he does not work on muons himself, he learns something from seeing the picture just like Elsa learned from looking at the pictures. In both cases, they saw with what they had previously learned. The person who engaged with the most emotional response to the photos of the CERN detectors, was George, when I showed him the picture of the so-called event 29581279.99579 from the experiment UAl. He did not, like me, see showers of drops from a waterfall. He became really excited and happy. He recognised a fascinating period from his own past, and not just a particle collision. He saw walls, rooms, and the very emotional event in 1983, when he participated in the physics experiment, when they (under the management of the physicist Carlo Rubbia) discovered the W boson. The photographs were mechanical flat records, but they did not just show representations of objects, they embodied a way of seeing (Pozzer-Ardenghi & Roth 2005) and elicited different, yet to some extent, collective responses. The experiment, first of all, showed that laypeople could not perceive all the connections made by physicists in the pictures of particles and particle collisions. This is of no surprise as they were not physicists and had not been educated to become physicists and thus had untrained eyes. The result of the experiments was nevertheless that I could break down the dichotomy between “being a physicist” and “not being a physicist”. I had learned enough about physics when I studied at the Niels Bohr Institute to know what was in the pictures. From this position I became aware that though physicists shared some knowledge, they did not share it all in the same way. The material practices they engaged in were embodied, and the relation of bodies mattered to what they perceived. For instance, those who had built the detectors saw many details in pictures of collisions not perceived by physicists who did not build the detectors themselves (Hasse 2008). Physicists were not, as the category “physicists” implies, one coherent formation. They were instead cut through, forming interconnected and severed lines in a complex pattern I could only explain as a dust bunny of strings of learned connections held together by the internal frictions stemming from a momentarily shared collective sense of reality (Hasse 2015). This kind of complex learning is hard to pinpoint in big data systems and replicate in robots. For humans “[l]earning is ubiquitous in ongoing activity, though often unrecognised as such” (Lave 2009, 201). Statements like this may have inspired creators of digital education to expect that learning through MOOCs is open to all, since learning is in no need of a physical institution or indeed a teacher and since non-formal learning MOOCs are open and free, as opposed to formal learning systems. Anyone can, in principle, enrol and learn. MOOCs are opened up to new and experimental forms of learning where learners unite across nations, states and fixed categories like gender and ethnicity. Yet, precisely because learning is ubiquitous, it matters what we have already learned, in terms of what can

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be learned and perceived in educational settings. Not everyone can learn to create robots, make programmes, use a Mossbauer spectrometer, analyse the poems of Emily Dickinson, or make use of educational experiments. It hinges on what potentials our preceding learning has given us to entangle with the world.

Conclusion: Chapter 3 Education does more than provide us with information to be processed. It is when we align learning with materiality that communication and momentaneous, emotional collectives emerge. That is to say: even when we ascribe the same meaning to materials, human collectives are brief and continue for a short time only. Stern, Gerlach, Bohr and Rutherford were part of an established, however volatile, collective of particle physicists, whose work communicated in particular ways with physicists like Karen Barad, JMK, his students and to some extent even me. We form a collective when we momentarily connect the same ideas, emotions and meaning with the same materials. The material–discursive separation of entities within phenomena matters – not because a particle is represented by a trace, but because somebody (in this case Gerlach and Stern) cares. They did not know what they had discovered and the boundaries for what they found kept changing even years after the experiment. The ontological units in Barad’s agential realism are not a priori entities with fixed boundaries. Boundaries are not necessarily where we expect them to be. It takes many entanglements within phenomena to create the boundaries around it. In physics, this means that “particles” don’t become a phenomenon just because they are detected by a detector. The apparatus may include materials we would not normally connect to a particle detecting apparatus (such as cigar smoke) and the gender of the persons engaged in research. Phenomena are “the ontological inseparability/entanglement of intra-acting agencies” (Barad 2007, 139). However, the reason for the changing boundaries was that the physicists had learned to care about “particle” matter. It is the material–discursive process of learning to think emotionally in concepts like “particles” which, along with available and sought-after materials, form the reason why Gerlach and Stern bothered to do the experiment at all. They cared about the matter that emerged on the photographic plate. Many other humans would not. They would not have learned to think like particle physicists. Stern and Gerlach’s preceding learning is there in the apparatus. Different theories emphasise different aspects, which somehow all centre on the question: What are humans if they are not the rational and autonomous Hu-Man? In focusing on collectives as CoPs (communities of practices) (Lave & Wenger 1991) or activities (Engeström 1999), the main analytical units become abstract. The humans as emotional, bodily and cognitively meaning making engaged learners often disappear or are deliberately disregarded. In the theory of cultural models, learning, emotions and motivation take centre stage. Here we find an emphasis on psychological processes. However, this approach overlooks the agency of materials and in some versions seem to reduce humans to cognitive

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processes. Postphenomenology and other theories in STS have avoided discussions of psychological processes such as learning but in different ways emphasise a relational ontology. New materialist feminism focuses on the transversal movements that dissolve fixed representations and categories, but disregard the psychological processes behind representations and categories. Where do we find the basic processes that drive the formation of human–material–conceptual collectives? I suggest, preceding learning that forms collective emotions, meaningful perceptions, memory and thinking is pivotal in how splits are formed within phenomena and should therefore be dealt with in posthumanism. Whether we are physicists or Nepalese children (whom we met in Chapter 2), the apparatuses unfold as processes of collective alignment. Though we may have erased the human, this learning collective of posthumans is just about to emerge. It is found in science, in society, in nation states and in communities of practices. Learning in these collectives always begins with the potential of aligning with materials. Often our potentials are like prepersonal affects; something that is there without our conscious ref lection. When we gradually learn to care, we become engaged in a world where we share concerns with other humans (and sometimes non-humans). JMK’s students continued to learn beyond the curriculum as they become emotionally engaged in physics. Particles matter to them. Mars matters to them. In other collectives, poetry matters or the Nepalese king matters (as we saw in Chapter 2)! People with different potential preceding learning resources can learn to align in unexpected ways (poets may read physics and vice versa). As collectives, we are never autonomous individuals. We are inescapably collective, entangled in material and social collectives that set the boundaries for how our potentials for new learning can come into play. Stern and Gerlach could learn much later that their experiment led to the discovery of particle spin. I learned much later that I came to care about Mars because I had aligned with physics. Yet a collective is not an activity or a community of practice where a group shares everything. Our collective alignments are momentary and brought about from within phenomena. It was one of those momentary alignments that made those of us in JMK’s class a collective of motivated learners. It was a December day in 1996, where we realised we shared a deep emotional sentiment about the Mars Pathfinder experiment and the possibility of water on Mars. The understanding of how materials matter to others often begins before they matter to us. This process is a subtle learning process where we may be under the illusion that we are individuals, who decide what we like and want. Yet, humans are always collective before they become individuals. Our individuality shows when our preceding collectivity meets new material and discursive collectivities, whether in MOOCs or in the physical room of the school in Nepal or lecture Hall at the Niels Bohr Institute in Denmark. Even when no humans are present, we always share spaces with people, who we are only partly aligned with. We are never a total collective group, i.e. totally culturally aligned. If we want to learn something new, we need to learn through an ongoing process of alignment

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where our preceding learning within phenomena emerges as a resource used for further alignment. In the cultural pool of resources for learning, we also learn to draw on what is meaningful from an emotional point of view. These resources are not fixed cultural models, yet like cultural models, they direct perceptions, motivations and emotions. The emotional ties we have learned as a collective of ultra-social learners, when we reach out to each other to share joy or sorrow, are inherently transversal. Materials and meanings are spun into entanglements of connections between humans, emotions, devices, visions, bodies and their joint human and non-human agency. This is what it takes, and more, for water on Mars, particles or a line of poetry to become a collectively shared phenomenon. What matters is not just that connections are formed and organised for potential use, but that motivation takes shape as a collective process before it becomes personalised. The next chapter expands on the notion of cultural models in so far as emotions and motivations are nourished by wider stories that emerge from historical times and surface as new stories told about and through materials like technologies. These stories can motivate agency, but materials also “kick back” on stories and the people who tell them. Here the story of robots is an exemplary story to show how humans, media, materiality and robots form each other.

Notes 1 I never found out if the magnets I helped to glue on actually went to Mars – or our work was just used for tests. 2 Karen Barad tells this story in her own way, but draws on details from an article by Friedrich Bretislav and Dudley Herschbach entitled: “How a bad cigar helped reorient atomic physics”, that I have reused in a version from 2003, not as Barad from 1998. 3 Probably because it takes an educationalist like myself to point this out. 4 CERN is the abbreviation of the French title of the organisation: Conseil Européen pour la Recherche Nucléaire. 5 I did around 17 interviews with Danish physicists and 25 interviews with Italian physicists most of whom work or have worked on the ATLAS project.

References Ahmed, S. (2004). The Cultural Politics of Emotion. New York, NY: Routledge. Barad, K. (2007). Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning. Durham, NC: Duke University Press. Braidotti, R. (2013). The Posthuman. Cambridge: Polity Press. Bretislav, F. & Dudley, H. (2003). Stern and Gerlach: How a bad cigar helped reorient atomic physics. Physics Today, 56(12), 53–59. D’Andrade, R. (1992). Cognitive anthropology. In: T. Schwartz, G. White, & C. Lutz (Eds.). New Directions in Psychological Anthropology (pp. 47–58). Cambridge: Cambridge University Press. D’Andrade, R. (2005). The Development of Cognitive Anthropology. Cambridge: Cambridge University Press. D’Andrade, R. & Strauss, C. (Eds.) (1992). Human Motives and Cultural Models. Cambridge: Cambridge University Press.

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Deleuze, G. (1994). Difference and Repetition. New York, NY: Columbia University Press. Derry, J. (2008). Abstract rationality in education: From Vygotsky to Brandom. Studies in Philosophy and Education, 27(1), 49–62. Derry, J. (2013). Vygotsky: Philosophy and Education. Hoboken, NJ: Wiley Blackwell. Engeström, Y. (1999). Activity theory and individual and social transformation. In: Y. Engeström, R. Miettinen, & R.-L. Punamäki (Eds.). Perspectives on Activity Theory (pp. 19–38). London: Cambridge University Press. Foucault, M. (1973). The Order of Things: An Archaeology of the Human Sciences. New York, NY: Vintage Books. Gane, N. & Haraway, D. (2006). When we have never been human, what is to be done? Interview with Donna Haraway. Theory, Culture & Society, 23(7–8), 135–158. Hasse, C. (1998). Forestillinger og køn i videnskabens samfund. Portræt af en gruppe fysikstuderende – med antropolog [Conceptions and gender in the society of Science. Portrait of a group of physicists’ students – with anthropologist]. Arbejdspapir nr.4. Køn i den Akademiske Organisation. red. Inge Henningsen. Hasse, C. (2001). Institutional creativity: The relational zone of proximal development. Culture and Psychology, 7(2), 199–221. Hasse, C. (2008). Postphenomenology – Learning cultural perception in science. Human Studies, 31, 43–61. Hasse, C. (2015). The material co-construction of hard science fiction and physics. Cultural Studies of Science Education, 10(4), 921–940. Hasse, C. (2015a). An Anthropology of Learning. Dordrecht: Springer Verlag. Hasse, C. (2019). Posthuman learning: AI from novice to expert? AI and Society, 34(2), 355–364. Hasse, C. & Trentemøller, S. (2008). Break the Pattern. A Critical Enquiry into Three Scientific Workplace Cultures: Hercules, Caretakers and Worker Bees. Tartu: UPGEM. Hasse, C. & Sinding, A. B. (2012). The cultural context of science education. In: D. Jorde & J. Dillon (Eds.). Science Education Research and Practice in Europe (pp. 237–252). Rotterdam: Sense Publishers Holland, D. (1992). How cultural systems become desire: A case study of American romance. In: R. D’Andrade & C. Strauss (Eds.). Human Motives and Cultural Models (61–89). Cambridge: Cambridge University Press. Holland, D. & Quinn, N. (Eds.) (1987). Cultural Models in Language and Thought. Cambridge: Cambridge University Press. Holland, D. & Cole, M. (1995). Between discourse and schema: Reformulating a cultural historical approach to culture and mind. Anthropology & Education Quarterly, 26(4), 475–489. Knudsen, J. M. & Hjorth, P. G. (1996). Elements of Newtonian Mechanics. Berlin: Springer. Knorr-Cetina, K. (1981). The Manufacture of Knowledge. An Essay on the Constructivist and Contextual Nature of Science. Oxford: Pergamon Press. Latour, B. (2008). What Is the Style of Matters of Concern? Two Lectures in Empirical Philosophy. Amsterdam: Van Gorcum Press. Latour, B. (2005). Reassembling the Social: An Introduction to Actor-Network Theory. Oxford: Oxford University Press. Latour, B. & Woolgar, S. (1979). Laboratory Life: The Social Construction of Scientific Facts. Beverly Hills, CA: Sage Publications. Lave, J. (2009). The practice of learning. In: K. Illeris (Ed.). Contemporary Learning Theories (pp. 200–208). London: Routledge. Lave, J. & Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation. New York, NY: Cambridge University Press.

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Leys, R. (2011). The turn to affect: A critique. Critical Inquiry, 37(3), 434–472. Lutz, C. A. (1988). Unnatural Emotions: Everyday Sentiments on a Micronesian Atoll and Their Challenge to Western Theory. Chicago, IL: University of Chicago Press. Lynch, M. (1985). Art and Artifact in Laboratory Science: A Study of Shop Work and Shop Talk in a Research Laboratory. London: Routledge & Kegan Paul. Marcus, G. (1995). Ethnography in/of the world system: The Emergence of multi-sited ethnography. Annual Review of Anthropology, 24(1), 95–117. Messer, R. (1993). Linear Algebra, Gateway to Mathematics. New York, NY: Harper Collins College Publisher. Merz, M. (1999). Multiplex and unfolding: Computer simulation in particle physics. Science in Context, 12(2): 293–316. McClelland, J. L. & Rumelhart, D. E. (Eds.) (1986). Parallel Distributed Processing (Vol. 2). Cambridge: MIT Press. Moll, I., Bradbury, J., & Winkler, G. (2001). Section One: Introduction. In: J. Gultig (Ed.). Learners and Learning: Learning Guide (3–4). Cape Town: OUP/Saide. Pozzer-Ardenghi, L. & Roth, W.-M. (2005). Making sense of photographs. Science Education, 89(2), 219–241. Rorty, R. (1992). Ten years after. In: R. Rorty (Ed.). The Linguistic Turn: Essays in Philosophical Method with Two Retrospective Essays (361–370). Chicago, IL: University of Chicago Press. (Original work published 1967.) Schwartz, T., White, G., & Lutz, C. (Eds.) (1992). New Directions in Psychological Anthropology. Cambridge: Cambridge University Press. Sharon, T. (2014). Human Nature in an Age of Biotechnology: The Case for Mediated Posthumanism. Dordrecht: Springer Netherlands. Shouse, E. (2005). Feeling, emotion, affect. M/C Journal, 8(6). Retrieved from http:​//jou​ rnal.​media​- cult​u re.o​rg.au​/0512​/03-s​house​.php/​. Skinner, D. & Holland, D. (1996). Schools as a heteroglossic site for the cultural production of persons in and beyond a hill community in Nepal. In: B. Levinson, D. Foley, & D. Holland (Eds.). The Cultural Production of the Educated Person: Critical Ethnographies of Schooling and Local Practice (pp. 273–299). Buffalo, NY: State University of New York Press. Strauss, C. (1992a). Models and motives. In: R. D’Andrade & C. Strauss (Eds.). Human Motives and Cultural Models (pp. 1–20). Cambridge: Cambridge University Press. Strauss, C. & Quinn, N. (1994). A cognitive/cultural anthropology. In: R. Borofsky (Ed.). Assessing Cultural Anthropology (pp. 284–297). New York, NY: McGraw-Hill. Strauss, C., & Quinn, N. (1997). A Cognitive Theory of Cultural Meaning. Cambridge: Cambridge University Press. Tuin, I. v. d. & Dolphijn, R. (2010). The transversality of new materialism. Women: A Cultural Review, 21(2), 153–171. Retrieved from http:​//www​.tand​fonli​ne.co​m /doi​/ abs/​10.10​80/09​57404​2 .201​0.488​377. Vygotsky, L. S. (1987). Thinking and speech. In: R. W. Rieber & A. S. Carton (Eds.). The Collected Works of L.S. Vygotsky (Vol. 1, trans. N. Minick, pp. 39–285). New York, NY: Plenum Press.

4 ROBOTS IN A STORIED WORLD

In the first chapter, we met the robot, Jibo, that responded to people with “empty curiosity” and the young boy, Andreas, who went out of his way to make contact with the robot, NAO. We have, in Chapter 2 and 3, come to know humans as ultra-social learners that form emotional collectives as they learn that their material surroundings are meaningful to others. Could these others include robots? Will robots be able to perceive the world as meaningful in the way that humans do? Many people all over the world have been motivated to try to make robotic machines that resemble living creatures and especially robots with human features. How do things like robots come to matter so much to ultra-social humans? What kind of stories do we attach, as collectives, to these particular materials? Different people, with different motivations, in different settings, tell stories of robots, but the storied world of robots seems to tap into a widely shared sociotechnical imaginary. Sheila Jasanoff defines these collectively shared imaginaries as: “collectively held, institutionally stabilized, and publicly performed visions of desirable futures, animated by shared understandings of forms of social life and social order attainable through, and supportive of, advances in science and technology” ( Jasanoff 2015, 4). But what does “collectively held” and “shared understandings” actually refer to? In this chapter, we shall take a closer look at the “collectively held” imaginaries of robots in a storied world and begin to explore how posthumanist humans form collectives with materials and each other. Any stranger to a culture, such as travellers, professional newcomers like anthropologists, or a newcomer to an organisation, experience that they have to learn local stories of local material phenomena and their use, through engagement with the local practices. According to Tim Ingold, materials of all kinds are enmeshed in stories of their use, of their past, of their future (2011).

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The story of a tool, like a hammer or a saw, is not a detached encyclopaedic knowledge. The storied world, as Tim Ingold calls it, emerges as the materials become meaningful to us in relation to purposes tied to local practices. Once learned these stories keep evolving, but now we no longer notice them as we did when we were newcomers. Jeremy Bruner emphasises that stories are the way we organise our knowledge. Once told, stories are what we live by. “We live in a sea of stories”, he once formulated it, and like the fish in the water we do not have enough distance to discover “what it is like to swim in stories”. (Bruner 1996, 147). Stories are everywhere and are not just about “tools”. Even a stone can be something we notice as a particular stone because the stories are already there in our material–conceptual collective as we entangle it in our living practises. In the word of Tim Ingold: Stoniness, then, is not in the stone’s “nature”, in its materiality. Nor is it merely in the mind of the observer or practitioner. Rather, it emerges through the stone’s involvement in its total surroundings – including you, the observer – and from the manifold ways in which it is engaged in the currents of the lifeworld. The properties of materials, in short, are not attributes but histories. (Ingold 2011, 32) This sounds very much like Barad’s entanglements. Stories have always played a part in creating relations between functionality and tools. The functionality of apparatuses such as saws and hammers are inseparable from the stories they are entangled in, and following Barad the story of a stone as a “stone” with stony attributes emerges from within phenomena. However, there is a reason we tell more stories about robots than about stones and hammers. In the cultural-historical school, materiality shaped by humans is not considered in terms of stand-alone objects, but artefacts. This approach emphasises that things are important for learning as a “residue of past human activity preserved in artefacts/tools/stimuli, broadly conceived” (Cole 2005, 37). People in the past not only used but also made their own tools, and the learning tied to these processes is grounded in everyday activities where “human beings arrange for the rediscovery of the already-created tools in each succeeding generation” (Cole 2005, 37). In modern Asian/Western societies, humans live in a culture surrounded by artefacts which they cannot make themselves, and therefore do not understand. Cultural-historical theories were fit to explain how the humans of yesterday learned about tools, but our relation to technological tools has changed not least with the emergence of AI and robots. Today people in robot societies do not make their own tools that are meaningful in relation to local practices, and pass them on through practical demonstrations and local stories. Today tools are

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hardly recognised as needed before they emerge in human lives as “must-haves” like mobile phones, apps, social media, MOOCs etc. Stories from afar are built into the very design of these technologies. It has made a huge difference that new technologies keep pouring into people’s lives with a materiality that tells stories about new identities and relationships (see e.g. Hasse 2013). It matters and elicits emotions if you are included or excluded from participation in engagements with new technologies. As these tools and stories do not grow from local practices, they demand a constant learning process for humans to operate, re-contextualise and indeed to live with new technologies whose working mechanisms (contrary to saws and hammers) are largely incomprehensible to non-professionals. This point can also be made from a Baradian point of view where the past can be entangled with the present and the future. From a learning perspective, learning has moved from formal education and handicraft, taught through local practices, into continuous learning with new technologies like mobiles, apps, robots, manipulators and calculators. Even if learning was always ubiquitous, it has now become an explicit requirement and necessity that we continue to learn throughout a lifespan. Stories we experience with the materials help us want to learn to use the next apps or software systems or own the smartest new watches or mini-robots. Though new technologies have a broad appeal, they may also create inequality as not everyone learns to master them (Sims 2017). The posthumanist learning perspective calls forth an awareness of how materials create new and different human and non-human emotional collectives as we learn with these technologies. Robots are good examples of a new entanglement of stories, tools and their meaning, or lack of meaning, in different entangled life worlds. The stories of robots are built into our collective perceptions of robots and form the material–conceptual space of expectations. The concept of “robot” is not a fixed representation of a reality but continuously evolves as a story and materiality. Humans are, as we saw with the children meeting NAO in Chapter 1, willing to stretch themselves very far to believe stories of robots, even when the materials react in ways that do not confirm the storied world. In this respect, robots seem to be a most interesting collection of materials. For many people that work with robots in their daily life or work to create them, robots are “technology-in-use” and become machines spun into stories of efficiency, accuracy and powerful tools. Those who work on wires, sensors and actuators often see robots as tools, and materials in robots are problem-creating or problem-solving devices. For many others, and maybe the majority in the Asian and Western world, robots are spun into the old story of the possibility of recreating life itself. The narrative of robots is not just one of functionality, as stories of saws and hammers, but is inseparable from the narrative of what a human is – and what constitutes a human. The robot is a story that, like posthumanism, entangles and evolves different practices questioning “the human”.

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Making the human in robots Humanoid robots have a long history in storytelling that has taught humans what they were – dating back further than the stories of robots as tools and as part of the workforce. The dream of recreating humans in other materials has always fascinated certain humans throughout history, all the way back to ancient times. Joseph Needham tells of the artificer Yen Shih from the third century BC, who created an artificial man who could sing and dance. However, when examined by King Mu of Chou, the man turned out to be made of leather, wood, glue and lacquer (Needham 1991, 53). Though it would sound like a proto-robot, it is most likely a tale told to remind us of the mystery of life. In the Book of Knowledge of Ingenious Mechanical Devices from 1206, the author Arabian engineer Ismail al-Jazari, created, at least on paper, a humanoid robot among many other ingenious mechanical devices (Uzun & Vatansever 2008). Al-Jazari was partly inspired by the Ancient Greeks, who have a story of the golden and silvery dogs and servants created by the blacksmith God Hephaistos. The Greeks also told tales of a mechanical dove created by Archytas around 400 BC. In the famous story of Pygmalion, following the Roman poet Ovid, a sculptor fell in love with his own creation, a perfect statue of a woman made in marble. He kissed it and prayed for it to come alive, and with the help of Aphrodite the statue was brought to life. Some reject this myth as part of the history of robotic imaginaries because Pygmalion’s statue is not brought to life by human ingenuity but by divine interference, and the result is a real-life woman and not a mechanical one passing for a woman (Kang 2011, 16). Nevertheless, all of these stories tell the tales of humans who wish to re-create themselves. The story of the robots could be said to have begun before the concept was formed and connected to the word “robot”, with the story of the development of material machines and especially machines with human- or animal-like appearances. The machines that many consider forerunners of robots are called “automata” or “automate”, terms that refer to an engine or a machine that moves by itself (Kang 2011, 140). In Japan the prime example of automata is the Karakuri doll; mechanical dolls that can move and pour from a teapot. Karakuri refers to both the mechanisation of the artefacts as well as their deceptiveness in pretending to be living, even if their livelihood is in fact a deception (Shea 2015). From the beginning, automata have, like humanoid- or animal-like robots, entangled material machines and stories of machinelike creatures. There are stories about automata-like machines in China, Japan, Arabia and Europe. Though these stories are about birds and dogs as well as human-like creatures, it is especially the human-like machines that, like the Karakuri, are considered “deceptive” devices pretending to be real, while being in fact mere machines. One of the well-known examples of this deception is found in the story by Ernst Theodore Amadeus Hoffmann, “The Sandman”, where the pretty Olimpia, made by the engineer Spalanzani, turns heads and distresses young men, such as the protagonist Nathaniel. The pretty Olimpia also makes the young women envious because she seems to be the preferred company of the young men.

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VIGNETTE 4.1: OLIMPIA’S YAWNS Nathaniel had totally forgotten that there was in the world a Clara, whom he had once loved; – his mother – Lothaire – all had vanished from his memory; he lived only for Olimpia, with whom he sat for hours every day, uttering strange fantastical things about his love, about the sympathy that glowed to life, about the affinity of souls, to all of which Olimpia listened with great devotion. From the very bottom of his desk, he drew out all that he had ever written. Poems, fantasies, visions, romances, tales – this stock was daily increased with all sorts of extravagant sonnets, stanzas and canzone, and he read all to Olimpia for hours in succession without fatigue. Never had he known such an admirable listener. She neither embroidered nor knitted, she never looked out of window, she fed no favourite bird, she played neither with a lap-dog nor pet cat, she did not twist a slip of paper nor anything else in her hand, she was not obliged to suppress a yawn with a gentle forced cough. In short, she sat for hours, looking straight into her lover’s eyes, without stirring, and her glance became more and more lively and animated. Only when Nathaniel rose at last, and kissed her hand and also her lips, she said “Ah, ah!” adding “good night, dearest!” “Oh deep, noble mind!” cried Nathaniel in his own room, “by thee, by thee, dear one, am I fully comprehended”. He trembled with inward transport, when he considered the wonderful accordance that was revealed more and more every day in his own mind, and that of Olimpia, for it seemed to him as if Olimpia had spoken concerning him and his poetical talent out of the depths of his own mind – as if the voice had actually sounded from within himself. That must indeed have been the case, for Olimpia never uttered any words whatsoever beyond those which have been already mentioned. Even when Nathaniel, in clear and sober moments, as for instance, when he had just woke in the morning, remembered Olimpia’s utter passivity, and her paucity and scarcity of words, he said: “Words, words!” The glance of her heavenly eye speaks more than any language here below. (Excerpts from Hoffmann Gutenberg project, Hoffmann 1908) Later in the story it is revealed that Olimpia is a wooden doll driven by an ingenious clockwork method, and Hoffmann explains: Before, gentle reader, I proceed to tell thee what more befell the unfortunate Nathaniel, I can tell thee, in case thou takest an interest in the skilful optician and automaton-maker, Spalanzani, that he was completely healed of his wounds. He was, however, obliged to leave the university, because Nathaniel’s story had created a sensation, and it was universally deemed an unpardonable imposition to smuggle wooden dolls instead of living persons into respectable tea-parties – for such Olimpia had visited with success. The lawyers called it a most subtle deception, and the more culpable, inasmuch as he had planned it so artfully against the public, that not a single soul – a

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few cunning students excepted – had detected it, although all now wished to play the acute, and referred to various facts, which appeared to them suspicious. Nothing very clever was revealed in this way. For instance, could it strike anyone as so very suspicious, that Olimpia, according to the expression of an elegant tea-ite, had, contrary to all usage, sneezed oftener than she had yawned? “The former”, remarked this elegant person, “was the self-windingup of the concealed clockwork, which had, moreover, creaked audibly” – and so on. The professor of poetry and eloquence took a pinch of snuff, clapped first the lid of his box, cleared his throat, and said, solemnly, “Ladies and gentlemen, do you not perceive how the whole affair lies? It is all an allegory – a continued metaphor – you understand me – Sapienti sat”. But many were not satisfied with this; the story of the automaton had struck deep root into their souls, and, in fact, an abominable mistrust against human figures in general, began to creep in. Many lovers, to be quite convinced that they were not enamoured of wooden dolls, would request their mistress to sing and dance a little out of time, to embroider and knit, and play with their lap-dogs, while listening to reading and, above all, not to listen merely, but also sometimes to talk, in such a manner as presupposed actual thought and feeling. With many did the bond of love become firmer, and more chaining, while others, on the contrary, slipped gently out of the noose. “One cannot really answer for this”, said some. At tea-parties, yawning prevailed to an incredible extent, and there was no sneezing at all, that all suspicion might be avoided. (Excerpts from Hoffmann Gutenberg project, Hoffmann 1908)

German author E.T.A. Hoffmann was one of the storytellers who had been inspired to tell a tale of deception by the many real automatons and their Spalanzani-like makers that appeared in Europe in the seventeenth century. The audience seems to have loved to be exposed to ‘an abominable mistrust against human “figures”. However, this mistrust was rarely aimed at the creators of the machines. Already here there is an ongoing story of diversity between the creators, their creations and the deceived. The automatons did not often resemble their male creators but were created as women, children, animals and “exotic” beings that were somehow already “others” to the clever watchmakers and engineers. One of the most famous watchmakers was the Swiss Pierre Jaquet-Droz (1721–1790) who, with his son Henri-Louis Jaquet-Droz (and helpers like Jean Leschot), created three ingenious automatas: a boy who could write, a boy who could draw and a female musician. The writer (completed in 1792) could dip a quill pen in an inkwell and his glass eyes would follow the movement as he wrote a text with up to 40 characters over four lines. This innovative capability came from a kind of programming desk, which could be seen as a pre-runner for modern computers. Another creator, the French master of automatas, Jacques de Vaucanson, created three equally famous automata, two musicians and a “defecating duck” that could stretch its neck and take corn from the hands of those

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in the audience, “eat” and “digest” it, and finally defecate to the great amusement of onlookers. The creators were watchmakers, who used their knowledge of the operations of the watch to make the machines move in autonomous ways. Other examples of early automata are the singing bird and a line dancer that were the theatrical inventions of Jean-Eugène Robert-Houdin, and German-American Mathematician Joseph Faber creating a female “talking head” (Riskin 2003). Hoffmann had seen some of these marvellous machines and in the novel “The Sandman” Nathaniel goes mad when he learns the truth about Olimpia. The deception goes to his eyes and his vision of the world is distorted. He looks at his very sensible and lively fiancé, Clara, and sees only a “wooden doll” like Olimpia. He finally throws himself over a railing and jumps to his death. Hoffmann’s treatment of the automata is not just about its ability to convince by appearance and the way it becomes a mirror for Nathaniel’s own perception of himself as anything but boring. The novel also reveals how robots like Olimpia can affect human engagements, by both the way they react to humans and the way humans react to them. When yawns become preferred over sneezing in this case, it is because humans fear that somebody may take them for a machine and not a human being. They react by yawning, which before was a negative cultural norm: do not yawn. It was also the reason Nathaniel fell in love with Olimpia – she never found him boring. What the humans collectively learned from the experience with the automata Olimpia was something about themselves. Humans get bored with each other but often hide it. Machines never get bored and thus comply with cultural norms saying we should not get bored and that we should hide our yawns. The humans began to appreciate an aspect of being human that their cultural upbringing had taught them to evade, yawning in the company of others. Olimpia is deceptive, but the deception is not in the doll alone. Young Nathaniel wants to be deceived and only too willingly accepts the docile behaviour of Olimpia as the perfect conduct of a real woman. It is the co-presence of humans and their norms in a storied world that made Olimpia become alive. Today we are no less fascinated by robots-as-humans than we were in Hoffmann’s day, but we are becoming less and less sure about what is the “we” and what is the “robot” in our entanglements. Robots are not just imitating or emulating humans or animals. They now have an identity of their own.

Revisiting Andreas In the posthuman world, phenomena are not made by interrelations with separate entities like humans on one side and robots on the other. New materialist and postphenomenological theories are opened up to new understandings of how a “we” comes into being with “robotic beings”. Humans and non-humans create each other “from within”, such as a practice like yawning affecting social values, or for example Nathaniel and Olimpia creating each other. Humans like Andreas, whom we met in Chapter 1, is a contributor to the stories of robotic technologies the moment he begins to make contact with the

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NAO robot. Andreas perceived the humanoid robot NAO through the stories he had encountered about robots playing, working and giving intelligent answers. In the previous chapter (Chapter 3), we also saw how JMK and his students perceived the Mars Rover robot through the stories of how water in space is connected to life and the possibility of finding living creatures on other planets. These stories can be enmeshed in material appearances like NAO and the Mars Pathfinder that come into being in posthumanist entanglements with students like Andreas and the physics students in Denmark in the 1990s. Stories of robots do not have to be humanoid to engage humans as both NAO and the Mars Pathfinder did. However, stories of media-shaped humanoid robots seem to travel faster and more widely than the three-dimensional, tangible robotic materials handled by robot-makers. We can only entangle with these completely human-like robots in the media in two dimensions on f lat screen surfaces. They are imaginary creatures we can speculate and dream about. Stories of robots living in space, aliens and fantasy creatures like C3PO and WALL-E originate on two-dimensional media screens. The robot designer Cynthia Breazeal was inspired by these fantasies when she tried to make a friendly Jibo-robot (Breazeal 2002), but materiality kicked back on her concept of imagined robots. In her real-life, three-dimensional version of an imagined robot friend, the materials agency did not fit with the media-concept of what robots were. There is a boundary in the agency of the material that spills back on the media-concept of robots. Stories of robots are tricky because a robot is both conceptual and material. Robots are present in factories, on streets and on kitchen tables, yet they are also fictions of human-like machines present in the media and exhibited at fairs. Robots are then both fiction, materialised in the media and as “display dolls”, yet they are also materialised as real machines with certain degrees of agency. Their humanoid aliveness on screen and their smooth movements and intelligence is not seen in the machines we meet as robots outside the screen. Robots, as physical entities entangling with us when we meet them as machines, are in other words far from the science fiction creations we have come to expect (Hasse 2019). Yet, all of the stories we have learned about robots are present with us when we conceptually perceive robots in everyday settings. Stories are more than tales. They are potentials for expectation, emotion and disappointment. They are the preceding learning we bring to bear in material encounters. Preceding learning does not work as a mere association but as a resource of memory and imagination that elicits new forms in new combinations (Vygotsky 1987, 340). These storied resources we bring to bear in practices can tie to materials in ways that even overrule what the materials try to tell us. Andreas and his classmates were deeply fascinated by the robot in the chair at our experiment at the Danish museum in Skive (see Chapter 1). When individual children sat in front of NAO, they could not just sit as they were told to do. Like Andreas, they kept trying to make contact. They expressed the wish to make contact without words. Since they were told not to speak, they instead used their eyes, hands

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and movements of their whole body, literally stretching out to reach the figure in front of them. Later in the interviews, many of them expressed a longing for playing with NAO, and said they were disappointed when NAO did not leave the chair to come and play with them. Though older children knew more about how robots are made, they also tried to communicate with the robot in the chair. They also wanted to believe that the robot could communicate and was alive. They used their learned knowledge from movies to engage the robot in their activities, but it remained an enigmatic figure, just blinking, barely breathing and looking. Over time, Andreas and his classmates came to doubt whether the robot in front of them, NAO, was actually alive like the robots in (most of ) their drawings. They came to doubt this because this robot was not responding as their imagined robots were expected to. Still these ultra-social humans kept looking for signs of aliveness to confirm the stories. When the whole class joined Andreas and they all sat together in front of NAO, some said it was alive (most referred to it as a “he”) and some said that it wasn’t. The children emphasised that they thought it was alive because of its eyes following them, and because it was moving. This also led some of them to conclude that the robot must be thinking. As Jimmy puts it, “I think … it thinks: ‘Why are all the kids staring at me’?”. Even when the children noted the electrical cord that kept NAO fed with electricity, they did not give up on their dream of it being alive like they were. As a girl exclaimed, “I know why there is an outlet. That is how it can be alive. It must have electricity, so it can be alive”. The children drew on their preceding learning in imagining that this robot could come alive just as the many robots they had encountered on screen. They were so keen on forming social relations, and thereby enhancing their collective, that they were even willing to form social relations with a creature that they do not place in a fixed category – with something that is indeterminate as an electricity-driven machine or a living being. Andreas is not just an individual that wants to make social contact with a robot. From the posthumanist perspective I propose he is an ultra-social collective before he is an individual. His pool of collective stories about robots reaches out to embrace NAO. We, the experiment creators, were also part of what sustained NAO as alive. We only very late in the day began to explain to the children the technical details of how NAO seemed to breathe and move. By not contradicting, we confirmed the story of living robots. As Spalanzani did, we allowed the audience to believe what they wished to believe. Another example of this relational ontology, where the aliveness of robots can be ascribed to humans can be found in Morana Alac and her colleagues’ study of children (18–24 months) engaging with the robot RUBI, designed for childhood education for example, as geometry teacher. The social aliveness of the robot is intrinsically related to the human actions and reactions in the space of the laboratory, which also transforms the humans: “This human involvement in the robot’s social agency is not simply controlled by individual will. Instead, the

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human–machine couplings are demanded by the situational dynamics in which the robot is lodged” (Alac, Movellan & Tanaka 2011, 893). The children may and may not learn geometry from the robot RUBI as intended, but the researchers learn in the situation how important social relations are for the creation of robotic phenomena. The children are already, as a local collective, willing to believe in robots as lively creatures. However, also in the RUBI case, we find “Spalanzanis” who know what RUBI is made of; some of the people present are the ones who built the robot and know about its machine parts. The story of Olimpia predates our experiments with NAO and Morana Alac’s experiments (2009, 2011), yet it tells the same story. Humans bring aliveness to robots. To say that the properties of materials are not attributes but stories is to say that practitioners know the materials because they know, as Tim Ingold argues, “what they do and what happens to them when treated in particular ways. Such stories are fundamentally resistant to any project of classification” (Ingold 2013b, 31). Ingold argues this with a reference to Karen Barad. Like her, he rejects that materials exist as static “natural” objects, on which a cultural meaningfulness can be imposed. Materials are rather an ongoing history of how the materialisation of bodies (human and non-human) obtain “differential constitutions” through material–discursive practices. This will, in the words of Karen Barad, require: an understanding of the nature of the relationship between discursive practices and material phenomena, an accounting of “nonhuman” as well as “human” forms of agency, and an understanding of the precise causal nature of productive practices that takes account of the fullness of matter’s implication in its ongoing historicity. (Barad 2003, 810) This is what Barad calls “agential realism” (2003). In this performative ongoing historicity, there is no deception unless it is a specific agentic cut to call forth deceptions as part of robotic phenomena. What is often overlooked when we refer to “material–discursive” practices are that the nature of the relationship is not just about materials and discourse, but also one of preceding learning tied to the conceptual discourse. The makers of robots may, as a collective, have learned to conceptualise and perceive robots differently from a collective of children schooled in a Western media culture. The differential constitutions are tied to different learned conceptualisations that transform our potentials for recognising materials as something in particular. If we have learned to expect robots to be human-like, we may feel deceived when their true nature of sensors and wires, or leather, wood and glue is revealed. If we have not learned to expect robots to be like humans, we are not deceived. This diversity includes our preceding learning processes that entangle with the materials before us. The stories are there when we, like Andreas, recognise NAO as a robot by entangling it with robots in the media (see Chapter 1),

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because we have learned to perceive robotic materials through them. This may not be the same for Prajun and Jit Gurung (see Chapter 2). Following Hoffmann, we could argue that storied and materialised robots become a “magical mirror” in our parts of the world, which teaches us how we align with or differ from machines. Humanoid robots are in our technoidealistic culture (Sims 2017), objects of modernity that ref lect on what it is to be human (Richardson 2015, 24). They teach us things about ourselves that we either did not consider an asset or never understood the importance of before. The background message found in Hoffman’s “The Sandman” is that the humans do not want to be taken for machines.

Robots or humans as machines Hoffmann’s romantic stories tickle the horror we have of waking up one day and finding out that everything we took to be authentic, lively and human was merely a charade and a theatre and that our loved ones, and maybe even we ourselves, are mere mechanical machines. Maybe tickling this sense of uncertainty was the true purpose of the humanoid automata that in the beginning referred to a broader functionality of self-moving machines in general. In Europe in the seventeenth and eighteenth century, these self-moving machines were watches created to tell the time. The humanoid automatas of the great watchmakers of the past (who for instance made the defecating duck and the writer) had no other purpose, contrary to the machines of the budding industrial age, than to “wow” their audience. It was partly due to the success of these marvels that “automata” came to be connected with these lifelike creatures rather than the also self-moving clockworks (Kang 2011, 7). The purpose of automata and later humanoid robots differed from the functionality of almost all other kinds of tools like watches or industrial machines that later became known as “robots”. The stories of the humanoid automata machines were not about doing or creating something with the “Olimpia” tool. Humanoid automata were educators that came to teach us, through stories of marvel and deception, what and why humans are and are not in contrast to machines. Though humanoid robots today share the concept “robot” with the industrial robots, this exploration into the past reveals a split within the concept between robots as tools and robots as a mirror for humans to ref lect their humanity in. The humanoid robots can claim to be closer to the original concept of automata than the heavy machinery in car factories. However, the origin of the concept of “robot” shows a curious mix between humanoid creatures, work and workforce. The word “robot” originates from the Czech robota, which is related to the Old Slavonic word “rabota” meaning forced labourer. “Robot” was first used to denote a fictional humanoid in the 1920 play R.U.R. (referring to the factory Rossumovi Univerzální Roboti or Rossum’s Universal Robots) by the Czech writer, Karel Čapek. Čapek’s fictional story postulated the technological creation

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of artificial human bodies without souls and the old theme of the feudal robota class fit the imagination of a new class of manufactured, artificial workers. The play describes a future, where work is conducted by a sort of “Mench-machine” – a kind of pre-runner for the androids. As noted by Kathleen Richardson this was not a comment on the robotification of work, but rather the robotification of humans. “A dominant discussion in the 1920s rested on the mass mechanization of commodity production, which rendered the laborer as another ‘cog’ in the process, just like a mechanism in the machine” (Richardson 2015, 27). This new version of automata, the robots, are not about being deceived by charming human-like creatures like Olimpia. Robots in Čapek’s play were slaves. Like the real humans in factories, the robots were not autonomous but function as part of systems where they fulfil their role as “cogs”. Like humans the robots in Čapek’s play protest and start a revolution to free themselves. There is also a clear warning at play that the machines that unethically toil under our command may rebel against us and make us serve them. The rebel robot Radius expresses it clearly, “You will work. You will build. You will serve them. Robots of the world! The power of man has fallen! A new world has arisen: the Rule of the Robots! March!” Moreover, later Radius explains how the revolution came about: “We had learnt everything and could do everything. It had to be!” (Čapek 1923, 89–90). In the play, as with movies like Fritz Lang’s Metropolis and many later fictive characters, two gloomy pathways to the posthuman emerge: either humans are destroyed, or they are dethroned as the human masters by the very machines they have created. The robots we humans have created to take over our work as intelligent beings also become learners, who learn how to rebel against us, and thus posthumans inevitably replace humans. There is a connection between the stories of lively robotic creatures, the downfall of the Enlightenment human, and the new posthumanist theories. Though Olimpia is a much more enchanting creature than Radius, they are both the cause of the death of humans. Nathanial becomes self-destructive, whereas the robots in Čapek’s play simply take over. These stories of the humanoid robots are, from the beginning, intertwined with the story of the dethroned Enlightenment human – a special Western story of “the human” that has come to an end. The story of the rise and downfall of the Enlightenment human seems to come in parallel with a shift in the interest in robots as machines vs robots as humanoids. Humanoid robots and cyborgs (e.g. persons who are optimised by engineered devices built into the body) are excellent ways to explore “the human” we took for granted (Richardson 2015). Humanoid robots and cyborgs appear as prime examples of the transversalities called for by posthumanist theories. Cyborg is a concept that covers an existence that is neither purely human nor machine but a merging of the two, with the human as the starting point. The concept of robot covers, as discussed, both machines and attempts to merge machines with human intellectual capacity. Where in humanoid robots, the machine is the point of departure, and in the cyborg, the human is the point of

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departure, the two seem to merge transversally in both the posthuman and the posthumanists’ visions of the future. Why do social scientists in the twenty-first century and some engineers find humanoid robots and cyborgs so much more fascinating than the humans of the Enlightenment; why are we not satisfied any longer with being the masters? One possible explanation is that robots and cyborgs come with the promise of being a better story of “the human”. Though they may not agree on the meaning of the “posthuman”, feminists, anthropologists, literature scholars, as well as robot-makers, physicists, engineers, neuro-biologists etc. seem to agree that we need a new story and better story of the human. However, they differ in their definitions of this human of which we are post and in relation to what “better” refers to. In spite of the many entanglements between those who create “posthuman” machines and those who theorise about “the posthuman”, two diverging, somewhat contradictory and somewhat overlapping advocates of the “posthuman” appear, that I call the “singularists” (creators of posthumans) and the “spinozists” (creators of posthumanist theories). Spinozists do not have just one assumption about the “human” of which we are post in common, but many. The spinozists belong to the field of STS (Science and Technology Studies) and new materialism. They may not all be directly inspired by the seventeenth-century philosopher Baruch Spinoza, but his thoughts resonate throughout their academic discussions in feminist studies, science and STS, literary studies, philosophy and anthropology (e.g. Karen Barad, Jane Bennett, Bruno Latour, Tim Ingold, Stefan Helmreich, Don Ihde, Peter-Paul Verbeek, Rosi Braidotti, Donna Haraway and Kathrine Hayles). Their new vital materialism questions the singularists’ clear-cut understanding of words like “intelligence”, “rationality” and “usefulness” in relation to the generalised “human” in the robotic field, and it opens up a new creative material vitality that crosses all kinds of boundaries. The spinozists largely work through words, creating new theories of the posthuman that are critical of capitalists embracing new technologies yet also have an ambivalent relation to all the new emerging technologies (as e.g. Braidotti’s critical posthumanism 2013). They use words to cut up an infinite potential of materials that entangle, connect and become related in apparently boundless ways. In the words of Barad, cutting up can be understood as agential cuts – as that which splits and creates diversity from within: A specific intra-action (involving a specific material configuration of the “apparatus of observation”) enacts an agential cut (in contrast to the Cartesian cut – an inherent distinction – between subject and object) effecting a separation between “subject” and “object”. That is, the agential cut enacts a local resolution within the phenomenon of the inherent ontological indeterminacy. In other words, relata do not preexist relations; rather, relata-within-phenomena emerge through specific intra-actions. (Barad 2003, 815)

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In this spinozist ontological indeterminacy, we find that agentic people like engineers, who make new technologies, constantly confirm the theoretical words of the posthumanists with new vibrant, transversal materials, which are also agentic. Posthumanists theories on the other hand undermine the conceptual foundations of these creations as particularly “intelligent” or “rational” in a traditional sense. These new theoretical and material robotic and cyborgian inventions challenge established boundaries between thinking humans and machines. However, it should be noted that the spinozists and the singularists seem to live and work in two different cultural bubbles. Futuristic singularists like Hans Moravec (1999) and Ray Kurzweil (2005) and followers of singularist or transhuman ideas may agree on transversality but from a different understanding of the posthuman. The singularist human (and the transhuman on the road to becoming a singular being) is an intelligent, rational being and the posthuman is even more intelligent and rational in an absolute sense than any human. Singularists predict that robotic machines will become more intelligent than human beings within the next 30–50 years. The stand-alone robots or AI expected to be built in the future will encompass intelligence in instant communication with other AI, robots, clothes, smart-phones, walls and ATM machines all over the world. Moravec and Kurzweil are both “robot-makers” – i.e. people who make robots or parts of robots, and the software engineers that make robots operate in “intelligent” AI ways. They are powerful engineers with many resources at their disposal. Kurzweil joined Google as director of engineering with a focus on machine learning and language processing and Moravec worked for the Carnegie Mellon Robotics lab. These two have many things in common apart from being engineers and men. They argue for a posthuman future where what we know of as humans today might be transformed either by being merged beyond recognition with robotic parts and artificial intelligence or by becoming extinct. For Kurzweil what he calls singularity is the destiny of human–machine civilisations: “It’s a future period during which the pace of technological change will be so rapid, its impact so deep, that human life will be irreversibly transformed” (Kurzweil 2005, 24). Moravec puts it this way: Just possibly, human personalities could participate in some way in the mainstream of this future activity, either under the wings of super intelligent hosts, or by being transformed into a compatible form – surely becoming very unhuman in the process. (Moravec 1999, 12) Kurzweil has also argued that the human brain is running on algorithms that can be emulated by new bio-machine devices, neocortexes, that are capable of learning and therefore can enhance human thinking into a singularity state (Kurzweil 2012).

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Though not all singularists share the same visions, they, like the transhumanists, agree that the biological human body is a problem to be dealt with through technical enhancements. Some singularists, like Moravec and Kurzweil, start with the robotic machines and try to make them surpass humans in intelligence. Others, transhumanists, propose that our point of departure is an enhancement of the feeble human body, that they hope the natural sciences can erase and replace with more durable (and controllable) materials (More & Vita-More 2013). The “human” in the case of the singularists is the Enlightenment human that Moravec & Kurzweil take for granted. The singularist vision, proposed by Kurzweil, Moravec and others, sees humans as capable of creating learning machines and devices, which gradually replace humans as the biological beings that we know: We don’t yet have comparable communication ports in our biological brains to quickly download the interneuronal connection and neurotransmitter patterns that represent our learning. That is one of many profound limitations of the biological paradigm we now use for our thinking, a limitation we will overcome in the Singularity. (Kurzweil 2005, 337) This is the bio-technological future feared by Francis Fukuyama, among others (2002). Here, in the technical endeavours of the twenty-first century, we find the living materialisation of posthumanists transversal theories. Spinozists and singularists alike do not endorse dichotomies. They share the endeavour of merging, mixing and transforming. Words and materials merge the two lines of working, in making machines and in theorising towards a posthuman future. However, the stories of the new posthuman creatures differ according to the agentic cuts of the “human” made by the singularists and the spinozists. In their creative work applying and assessing the usefulness of artificial intelligence and automated robotic systems, the engineers engage in a different exploration of a posthuman future. We may, following Rosi Braidotti, call them spinozists that completely refute the technological power play exhibited by the singularists. The roar which lies on the other side of the urbane, civilized veneer that allows for bound identities and efficient social interaction is the Spinozist indicator of the raw cosmic energy that underscores the making of civilizations, societies and their subjects. (Braidotti 2013, 55) This spinozist field is not to be considered a single or coherent theoretical domain, though all agree that humans are not stand-alone rational, intelligent entities, but deeply entangled with their material surroundings. In the words of Jane Bennett, who, like Braidotti, explicitly calls for a spinozist approach:

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Bodies enhance their power in or as a heterogeneous assemblage. What this suggests for the concept of agency is that the efficacy or affectivity to which this term has traditionally referred becomes distributed across an ontologically heterogeneous field, rather than being a capacity localized in a human body. (Bennett 2010, 23) When spinozists (whether they draw on Spinoza’s own work or not) discuss “the human”, it is never as a taken-for-granted fixed entity consisting of a body separate from a soul, separate from the environment. On the contrary, the implicit Enlightenment notions of “the human” found in the singularists’ journals and reports – and indeed the machinery building on the theoretical understandings of fixed separations – are dismantled and refuted by the spinozists.

Real robots The posthumanist visions as proposed by, for instance, Barad do not grant history and cultural development any force unless they are brought into phenomena as agential cuts. This may include a danger that historical developments are left out or forgotten in posthumanist thinking. After the huge scientific experiments at CERN, some physicists have acknowledged that the great story of physics we were taught at the Niels Bohr Institute may come to an end. This is not because humans have discovered all there is to know about the universe, but we may have discovered all we can know as humans (Stannard 2010). At the same time, an old story resurges: we can create better and more intelligent humans. The transhumanism aiming at an enhancement of the present human race (e.g. More & Vita-More 2013), or a replacement theory where humans are surpassed by posthumans (e.g. Kurzweil 2005) seems to surface at a point in time when the physics sciences are running out of steam. Stories and the developments in the natural sciences go together. Though the story of creating enhanced or artificial humans has been told for millennia in human history, the history of building robots that act like and/or surpass humans has become one of the new big stories of our time. The story of the robot is increasingly connected to stories of how to transform and enhance humans through genetic engineering (Fukuyama 2002) and how artificial intelligence (AI) will create a new technology-led world. This new big story of technological power in the Western and Asian world has, just like new materialist posthumanist theories, opened up new questions about who the humans are that are the engineers and scientists behind the development of posthumans. With my colleagues, I have for several decades conducted research projects that look into the habitual learning of natural scientists and how their perceptions of their products are shaped in relation to cultural–material conceptualisations of the available material resources (e.g. Bruun, Hasse & Hanghøj 2015; Hasse 2015b; Hasse, Trentemøller & Sorensen 2018). In the last couple of years we have

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explored differences in how robot-makers create and perceive their own creation of robots and what school children in Denmark know about robots, how they perceive them and how their perception differs from how the robot designers (the robot-makers) perceive their creations (Hasse et al. 2018; Esbensen et al. 2016).1 A robot like NAO, whom we met in Chapter 1, may seem alive to children, because they expect it to be, when it blinks with its eyes, sighs and seemingly reacts to their voices. When a robot-maker sees the same robot as Andreas does, they see something different. They do not see a “playmate” or an autonomous being. The robot is composed of hardware parts (like the plastic torso and head) filled with all kinds of technical gear. Robot-makers skilfully see motherboards, actuators, sensors, cameras and degrees of freedom, when they see NAO. They see a f low of messages, commands sent to an actuator using, in the case of NAO, a DCM module. DCM is a software program that sends communication around inside the NAO robot’s body about how to, for instance, blink the eyes, move and when to stop by sending commands to actuators, which are small motors. DCM also updates the values of sensors and actuators.2 There is thus a difference between how robot designers, the makers (Ingold 2013b) who build robots, understand, build and tell stories about the phenomena of robots and how most other people envision robots. The skill of robot making is a skill of connecting meaningful materials and creating the phenomena of robots from within a learned expertise. The public lacks the expertise of actuators, sensors, electrical cords, and motherboards that robot designers know how to connect when they create robotic phenomena. People in general may come into contact with the effects of the increasing roboticisation and automatisation of our societies, but rarely encounter the making of robots. Instead, their meaningful understanding of robot phenomena comes from the humanoid robots that they see in movies and commercials. Humans who do not build robots themselves or take part in their creation; nevertheless, through the stories they are told they are willing to believe. This is especially true of humanoid robots – but this is not the story of all engineers. In 2014, a long-term project with the name “The One Hundred Year Study on Artificial Intelligence” was launched at Stanford University. Every five years it forms a study panel, with the mandate to assess the current state of artificial intelligence. In 2015, the project published an expert review (to be repeated periodically “at least a hundred years” from now (Stone et al. 2016). This report is clearly distancing the actual research performed in the field of artificial intelligence and robotics from the visions put forward by, from what I have termed, the singularist posthumanists. Even so, the inf luence of science fiction and the visions entailed in much machine learning and robotics connect engineers all over the world with a human future of experiments for building so-called intelligent learning machines, which may either optimise humans through cyborg devices or create intelligent machines to take over where humans fail. Contrary to the more fantastic predictions for AI in the popular press, the Study Panel found no cause for concern that AI is an imminent threat to

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humankind. No machines with self-sustaining long-term goals and intent have been developed, nor are they likely to be developed in the near future. Instead, increasingly useful applications of AI, with potentially profound positive impacts on our society and economy are likely to emerge between now and 2030, the period this report considers (Stone et al. 2016, 4).3 Throughout, the report references an unspecified generalised “human” and “the human measure” (e.g. Stone et al. 2016, 13). This creature seems to be the rational, unspecified, un-situated Enlightenment “Hu-Man” (see Chapter 2) who is alive and kicking alongside robots justified as useful applications. Though not all engineers are such extreme singularists as Kurzweil, they still experiment with how to transform human beings and their life conditions. Their material creations build on conceptions of human intelligence and learning, and it is from this point of departure they explore how humans can be enhanced. The stories of the future thinking machines told by some of the singularists are in some ways like the stories told by Hoffmann. They are not about something that already exists. They are stories about real things like dragons. Dragons are, as argued by Ingold, imagined and real. They are real in their effects because: “knowing depended on seeing, and both proceeded along trajectories of movement” (Ingold 2013a, 737). Though Ingold presents medieval and ethnographic examples of stories that follow the trajectories of movement which form the perception of dragons and Thunderbirds, and claim that scientific thinking cannot accept such creatures as dragons and Thunderbirds because they cannot be classified, he might as well have included humanoid robots. Like Hoffmann’s stories and Ingold’s dragons, they have some grounding in a material world, because this is what stories have always done. We construct our stories of and with the real world (Bruner 1996, 94). In Hoffmann’s case, he had been inspired to write the novel “The Sandman” partly from encounters with real machines. Among these was his encounter with a chess-playing machine, from which he also wrote a story entitled “Die Automate” in 1808. In this story, two young men see and discuss a machine named “The Talking Turk”. This machine was modelled after the real machine, “the Turk”, which had been devised by the Hungarian Baron Wolfgang von Kempelen in the 1770s and later sold to the Austrian musical engineer Johan Mäelzel. In Hoffmann’s story, one of the young men in particular suspects a hoax and is very critical of the mechanical invention (Figure 4.1). The real chess-playing Turk won over many skilled chess players, including Napoleon and the inventor of the principles behind computing, Charles Babbage, before it was revealed that it was indeed a deception. Contrary to the other mechanical automata at which Europe had marvelled, this skilful machine turned out to be built so that a human being (a dwarf and chess player) could be placed in a hidden room inside the carcass of “the Turk” from where the chess pieces were moved by strings (Kang 2011, 180). The automata and robots as machines were increasingly created in a dialogue between stories and materiality, which respectively inf luenced each other. The

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FIGURE 4.1  The

Mechanical Chess Playing Turk played chess with many nobilities including Maria Theresa in Vienna. Photo: Image of a copper engraving from Karl Gottlieb von Windisch’s 1783 book.

Spelanzanis behind the mechanical inventions created machines that were meant to be deceptive. Today this is still the case for some roboticists. The singularists stories are materialised in an increasing number of stories about machines that not only look like but seem to talk and move like humans – as a first step to surpass the humans and move into singularity. These stories follow the trajectories of scientists and are not always about creatures as real as dragons. Some engineers have, just like physicists, been involved with a long crane-dance with the writers of science fiction novels. This genre is the preferred literary genre for many scientists (see e.g. Hasse 2015). The property of the robotic material is for some robot-makers inseparable from science fiction histories, whereas for other makers robots are mere machines. A saw and a hammer are meaningful tools that hold the promise of the fulfilment of a future goal of, for instance, building a house. Contrary to saws and hammers, humanoid robots are not tools in any ordinary sense but are themselves a meaningful goal of deception for some roboticists. They are steeped in stories of a science fiction future that the natural scientists and engineers share with the public and can therefore be used to attract huge sums of funding. Even small children in Western culture know from the media that robotic machines

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act as if they were alive. They present themselves as reasonable creatures that appear both smarter and often more sophisticated and human-like (think of the eloquent C3PO) than the humans themselves. These stories are found in well-known movies like Star Wars, Baymax and WALL-E or in commercials and videos referred to by Andreas and his friends. These dragon robots (who like dragons are both imagined and real), like other science fiction visions, have not only inspired many natural scientists and roboticists but also become the source of inspiration for new stories (Hasse 2015). In robotics, humanoid robot stories like Star Wars have inspired many robotic inventions (see e.g. Breazeal 2002; Perkowitz 2007). In their work, robots are stories coming alive. Some of the roboticists sincerely believe that they are creating humanoid robots. Some are Spelanzanis that make use of the public’s naïve perception of robots.

Robot classifications Real humanoid robots come with a human-like form – two arms, legs, a torso and head and “humanoid” is the umbrella term for all the types of robots that replaced the automatons as machines with a human-like appearance (often with reference to the expressed gender of the robot): android, replicants, fembot, gynoid, “geminoid”. All androids are humanoids, but not all humanoids are androids. An android will be defined by its appearance because it imitates the human form and androids can be named replicants. Thus, like the dolls that also inspired the figure of Olimpia, the android will be clad in soft human-like skin and have very real looking eyes. Famous examples of these are, for instance, the creations by the philosopher robot-maker Hiroshi Ishiguro, director of the Japanese Intelligent Robotics Laboratory. In this laboratory, Ishiguro has created many humanoid robots of which some are both android and geminoid. A geminoid is a robot that is created as a literal doppelgänger. Ishiguro for instance created a robot, Geminoid HI-1, that has the same features as its creator and is dressed in the same clothes. It may also “speak” with his voice and replicate some of his movements. Contrary to the makers of automata, the Geminoid is an attempt to replicate its maker. It is controlled by a motion-capture interface (a kind of “OZ” technology – see Goodrich & Schultz 2007, 252). It can imitate Ishiguro’s body and facial movements, and it can reproduce his voice in sync with his motion and posture. Ishiguro hopes to develop the robot’s human-like presence to such a degree that he could use it to teach classes remotely, lecturing from home while the Geminoid interacts with his classes at Osaka University. Like many robots from Ishiguro’s lab HI-1 is remotely controlled (like an advanced mobile) and thus give an impression of being an autonomous being, while it is in fact controlled by a human behind a screen or a wall – like the Turk in the automaton days. The sense of autonomy in Ishiguro’ s creations is strengthened when Ishiguro’s robots appear in movies, like the Geminoid-F (copied after an unnamed woman) that appeared in the Japanese movie Sayonara (meaning

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Goodbye) based on a play by Oriza Hirata. Humanoid robots have appeared in movies since the 1920s. Fritz Lang’s daemonic robot, Maria in Metropolis, stands out as the “mother of all female movie robots”, but other renowned figures have followed like Star Wars’ C3PO, Robocop, Blade Runner’s replicants and lately the female rebel, the hyper-intelligent and apparently sensitive Ava from the movie Ex Machina, as well as more cartoon-like creatures like WALL-E. Geminoid-F could be named an android, but because of the masculine connoted Greek prefix “andr”, it is referred to with the term: a “fembot”, or even more recent: a gynoid. These terms connect Geminoid-F, the robot company Hanson’s very sexy robot, Sophia, with movie creations like Ex Machina’s Ava, the floating robot Eve from the movie WALL-E and the fearsome robot Maria from Metropolis. They are all humanoids that are gendered as feminine. Most of these humanoid robots from the movies cannot be met in real life, but Geminoid-F was in fact an android Geminoid playing itself in the Sayonara movie. Other robots appear in displays at fares and events, like the lifelike Jia, who at a Chinese robotics fair, entertained the wowed audience by recognising faces, producing micro-expressions by moving eyelids and lips. She seems to also talk like a human being, when her creators have programmed her to say: “Yes my lord what can I do for you?”.4 Philosophers have written long texts about these humanoids; politicians have speculated about how to legislate, and roboticists like to hear the stories told by robot-makers like Ishiguro. All of these humanoid, real-life robots can be found in different versions and merge with fantasy robots from public media. We can see them move on YouTube and during special events like robot fares. Though the media forms differ from the written novels of Hoffmann’s time, there seems to be a close connection between these humanoid robots and their deceptive earlier automata cousins. Neither were created for any real purpose but to inspire awe and to mirror humans. Just like the automata in previous centuries, these robots are not meant to be engaged with in everyday practices. The stories told in the media about how this real yet imagined robot functions does not tell of the Spelanzani work behind creating the story. However, some of the humanoid creations, that look like the robots in science fiction and seem to behave in as lifelike a way as Olimpia, now begin to “spill out” into the everyday world of humans, where they try to tell new stories of particular lives. It is in these meeting that the materials begin to “kick back” on the stories told. The humanoid- or animal-shaped robots are the newcoming agents among the agents already engaging in established practices (e.g. Hasse 2013). This gives rise to an entirely new set of stories. For those, who actually met these humanoid robots and tried to engage with them, the experience has been one of disappointment, even feelings of deception and repulsion. Even so the robotic tools have to be reinvented as meaningful because they tell of a future path that supports their presence and that excites and affects some more than others (Bruun et al. 2015). The difference here is not between the media robots and their real-life counterparts like Geminoid-F or JiaJia. The diversity is in the way humanoid and animal-like robots, once removed from a setting built for them,

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meet other storied trajectories in a human world and are revealed as what they are: materials. Ishiguro has made a humanoid robot in his Japanese laboratory by the name of Telenoid (different models with different suffixes like R1 or R4), which is meant to present as a “generalised human” (Leeson 2017). Contrary to the Geminoid, these Telenoid robots have been sold to public institutions like healthcare institutions in Denmark. It generally amazes managers and staff in public institutions because it seems to talk like a real person and can comment on people’s dress and statements on the spot. It functions just like a mobile phone with a camera that has a person talking through it. Though it appears as an autonomous entity, there is a person behind (often sitting in a room next door), but both staff and patients seem to perceive it as an autonomous entity at first (Bruun et al. 2015). Like other humanoid robots, which are sold to be used in healthcare (e.g. the Korean Silbot see Blond 2019; Hasse 2015b), the Telenoid was not invented with any intention of healthcare. It was developed as a telecommunication system, and then it was tested as a kind of teacher’s companion in schools (Yamazaki et al. 2012) and on elderly people in a shopping mall (Ogawa, Nishio & Koda 2011) and only as a next option was it involved in health care facilities (Yamazaki, Nishio, Ogawa & Ishiguro 2012a).

The Telenoid The robot is about the size of a child and all white. Just like the Geminoids from Ishiguro’s laboratory the Telenoids’ depend on being wired up with a human operator, who just like the chess-playing human in the Turk is hidden from view. The method is known as the “Wizard-of-Oz” technique. Wizard-of-Oz means that the robot is tele-operated by a hidden operator who also speaks through the device (cf. Goodrich & Schultz 2007, 252). This “deception” device makes humans believe it is a living, thinking being when the robot comments on, for example, and their dress or hand movements. The robot was originally expected to be a welcome kind of new “mobile phone” that would give the persons communicating an embodied feeling of the persons at the other end of the line. The purpose of the robot is now not at all clear since they were bought to be used in the Danish health care system in a Danish nursing home and activity centre for cognitively disabled individuals. In a multisided ethnographic study, the Danish anthropologist Christina Leeson followed the experiences of the people, both staff and patients, who met the robots and tried to make sense of it. She explains: [the] Telenoid was not imported because consultants had a clear idea of how to put it into use in the Danish healthcare sector. On the contrary, Telenoid materialized in the Danish healthcare sector because the consultants saw

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the robot as an opportunity to establish important collaborative ties with the Japanese robot-makers. (Leeson 2017, 6) For the Danish managers, buying and implementing the humanoid robots was built on a dream. Like dreaming of dragons, this dream was an imagination of real-life (Ingold 2013a). The managers’ dreams were close to the Japanese robotmakers dreams that Leeson encountered in the Japanese laboratory: “the creation of human-like robots that will live among us and help us in our public places, department stores, private homes and health care sectors” (Leeson 2017, 55).

VIGNETTE 4.2: TELENOID IN THE LAB On a late Friday afternoon in January 2013, I walked slowly along the corridors of the laboratory with Isozumi-san, philosopher and robot-maker, while admiring recently initiated research projects, some of which had already materialised in anthropomorphic robot shapes. It was my first tour through the laboratory and Isozumi-san seemed eager to tell me about the robots developed there. Although the grey and brown walls were covered with posters of robotic projects displayed for decoration and information, Isozumi-san himself recalled the main characteristics of most of the robots that had been built in the laboratory. While moving through the combination of open-planned and closed offices provided for the leader of the laboratory and the secretary, Isozumi-san made me aware that the robots developed here were made to convey and transmit human presence (sonzai-kan). Acting like telephones with a human-looking body and face to speak to, the robots were commonly made for a person to operate them from a distance via the internet. The robot then copies whatever the operator does and says. As I gradually discovered, crafting a human-looking robot did not simply constitute a symbolic process whereby robots were made as humans so as to represent them. Rather, anthropomorphic robots were made in an effort to manipulate the world around them. “We believe that the communication and emotional attachment will be better and more natural if we can feel the human presence in the robots”, Isozumi-san reminded me. So we are trying to create the situation where people feel real human presence in the robots. That is, a situation where the robots do not only transfer the operator’s thoughts, sayings and image, but where they transfer him or her. They should transfer his or her presence so that the robot becomes its operator and thereby more than a thing. That afternoon, what caught my attention was neither confined to the ambitious nature of the work nor the fact that robots were made to serve as the new generation of older communication media like the telephone or Skype

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by transferring and conveying human presence during conversation in such a manner that both the operator and the human interlocutor interacting with the robot should feel that “they are conversing in a natural fashion with someone directly in front of them”, as Isozumi-san told me. What caught my attention was the peculiar ways in which the robot-makers set about realising this scenario. During our tour, Isozumi-san showed me the line of anthropomorphic robots, which were sensibly wrapped in a “soft silicone skin similar to the human skin”, as Isozumi-san had it. They were also made either in the exact image of particular individuals or as abstract anthropomorphic figures. “One approach is to make a copy of an existing person”, Isozumi-san explained to me, but such robots often become too scary and uncanny to people. Therefore we have begun to make the robots more minimal where we extract crucial elements of the human body instead. So we want to know, how much of the human body is necessary for transmitting and conveying our presence? Do we need the ears, the eyes, the mouth or the legs? (excerpts from Leeson 2017, 54–55)

When anthropologists like Christina Leeson and Kathleen Richardson visit robot-makers laboratories, as for instance when Leeson visited Ishiguro (Leeson 2017) and Richardson visited an American laboratory (2015), they tell stories of engineers deeply immersed and engaged in a culture of robot making. Robotmakers explore and even materialise philosophy about “the human” created in their machines as the above example shows. Though machines come across as deceptive, the robot-makers may not have wilfully created them to deceive. Yet deception, and even self-deception, is always part of the stories, when these robots are exposed to human scrutiny away from their on-line screen existence. The Telenoid came out of Ishiguro’s lab with only stumped limbs, no feet or hands; it had slightly slanting eyes, no ears, a small mound with no lips, a small nose and a bald head. There were no apparent gender signs and the skin was a hospital white colour. This is a philosopher-materialised thought of a minimalised human stripped of culture and reduced to minimal physical appearance, as Ishiguro intended it to be. The robot-makers cannot escape their own culture and the learning processes that brought them into their lab. What they have learned about humans can be materialised, but not what they have not learned and many engineers (and the philosophers who accept their material stories) seem curiously ignorant about humans, even themselves as humans. Their robots’ movements and trajectories are in the stories they tell and materialise about their makers. The Telenoid is meant to be a truly transversal robot, with the promise of transgressing and dissolving all gendered and racial characteristics. Yet what seems to escape the robot-makers is that when it is transferred to Denmark for instance, people do not meet the material entity with the same perceptions as intended by

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the robot-makers. Here the material figure in people’s perception of it becomes a bizarre vision of a human being. Like the dragon discussed by Ingold (2013a), this robot is a fiction as well as real. It was present in many stories told about how robots can do health work. Yet when they met the material instantiation of the dreams, the imagined robot disappeared into thin air, when the robot was confronted with its lack of practical capabilities. However, the Danish care workers in Leeson’s study, strove, like Andreas and his friends, to keep the story in the relation. They struggled to find a way for the robot to have a functionality in health care. In addition, because they, as humans, were so inventive, they came up with new surprising functions. Giving the robot to mentally impaired people made them communicate with the human behind the Telenoid in ways that were more open. However, of course this was not a way to save money as envisioned by dragon-believing policy-makers. The Telenoid just gave the health care staff in Leeson’s case an opportunity to talk to their clients, which had otherwise been cut from the budgets by the policy-makers (Leeson 2017). In 2014, I, with another anthropologist and a health care engineer, studied the implementation of the Telenoid in another Danish healthcare institution called Lakeview Rehabilitation Center (Bruun et al. 2015). By then Denmark already had a long history of experimenting with robots in health care (see e.g. Hasse 2013). The reason these robots were implemented followed the same storied path as in Leeson’s case. The municipal agencies had invested in robots because they wanted to keep up with the technological innovations in healthcare and speculated that they could be some of the first movers to benefit politically and economically from the implementation of robots in hospitals, health care at home and in rehabilitation centres like Lakeview. Lakeview was thus both an ordinary training and rehabilitation centre for patients (e.g. recovering from strokes) and a “living laboratory” for the test of new welfare technology. One of the municipal officials driving the process of new pervasive healthcare explained to us: I have been involved in several projects with Japanese and Koreans where I think they are extremely driven by the technology. They produce a device because it is possible, and then they have a theoretical idea about what you can do with it. They have rarely been out [of the laboratory] and have considered the practical needs and looked into how we can do it more cleverly. How do we increase the value for the people who live here? (Bruun et al. 2015, 152) The municipal managers had bought the Telenoid to test what it could be used for without having any clear idea of how the robot design could be useful at the centre, just as the case in Leeson’s study. The urge to use robots, came from the same story about how robots can help taking care of the elderly and impaired. The Telenoid project had been developed in collaboration with a team of Ishiguro’s staff, local university-based philosophers and psychologists. Like with other humanoid robots implemented in Denmark, the actual function of the

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robots had to be negotiated locally and there were some differences in how the university researchers and the municipality staff viewed the robot. The university researchers were mostly interested in the philosophical aspects of implementing the Wizard-of-Oz operated robot. Since the robot could speak just like a human, albeit not looking like a particular human, but intended to look like a generalised human, the researchers were mainly interested in the human reactions to the robot. The managers and staff of Lakeview were more interested in finding out if the robot could be used for any of the problems that occupied them in their everyday life such as entertaining lonely people or making minimal eaters (a typical reaction to illness among the clients at the centre) eat more. At first, the staff and researchers envisioned putting the tele-operated robot to use in a normal dining situation in the eating hall at the centre where tables with four to six people would eat together. The expectations were that the robot could encourage the individuals to eat more while dining, and that it would be better to do this work than a member of the staff could do directly (as the member of the staff would be involved anyway talking “through” the Telenoid). However, when the researchers showed the robot to the local residents and asked them if they wanted to dine with a robot no one volunteered, so the test design had to be changed (Bruun et al. 2015). The story that came with the materials was rejected. This came as a surprise to the Ishiguro team, but not to the staff who saw the robot in a different way. The staff did not see it like the makers of the robot as a “minimal human”: I think the [robot] is scary. I think. I think it looks like a child … like … a dead child, because it’s so human in the face and yet so rubbery in its movements. I wonder what the reaction will be when the elderly such a dead rubber kid to sit and make them enjoy and eat. I could much better imagine that it was something that did not look like a dead child at all. I think it looks like a dead child! Ethical? So, I do not think you should have one … something that looks like something dead when you’re going to sit and eat. (Bente staff – cited in Hasse 2018) In the new type of experiments, parallel sessions with different set-ups were tried out where the Telenoid was seated opposite one single resident in his/her own room at eating time. The sessions were filmed, and the test went on for eight weeks. Over time, the idea to see if the robot had any effect on eating patterns was abandoned altogether. How can the staff, the robot-makers and the philosophically interested researchers, the clients and the anthropologists come to have such different perceptions of what would formerly be thought to be the “same” robot? In prior learning paradigms, we would satisfy ourselves with the robotic object as a separate entity perceived differently by different subjects. The robot, the Telenoid, is there and can be classified beyond any perception and engagement. In a posthumanist learning perspective, this is not the case; the robot phenomena is constantly coming into being in new ways. Barad’s posthumanist approach

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tells us that an object like the Telenoid (a robotic phenomenon) does not precede the “intra-action” of cutting subjects and objects within the phenomena (Barad 2007). The productive process is not the interactions between the different humans and the robot, but the material entanglements that work through material–discursive apparatuses that include some and exclude other formations. What is, and how we come to know what is, are inseparable. This onto-epistemological approach was emphasised by others before Barad (for instance the anthropologist Gregory Bateson), but Barad adds the ethical dimension. When Bente perceives the Telenoid, her perception is already embedded in an ethical as well as an onto-epistemological story. Nevertheless, in order to talk about posthumanist learning, we need a closer look at both the “material” and the “discursive” that are embedded in the stories we tell. What are the processes that make robots like the Telenoid come into being as a particular phenomenon for the managers, the robot-makers, and the staff? This question, I suggest, is a story about how the world becomes meaningful. It is not just about how epistemology becomes ontology and vice versa. For something to become ethical as well, we humans need meaning, and meaning I suggest is an effect of learning. All the humans involved in the stories above search for meaning. The meaning of “the human being” or the meaning of a Telenoid in health care. In the posthumanist learning perspective, I propose, what the robots like the Telenoid and the automata have taught us is that humans engage in a constant search for meaning. How and why we find meaning is a long ongoing learning process. What posthumanist theories add to this is that learning meaning is a very material process of becoming and not at all the mental process we normally take learning to be.

Stretch towards machines From a humanist perspective we can say that, as a species, humans have learned not just to be competitive but extremely cooperative (Tomasello 2014; Moll & Tomasello 2007; Tomasello & Rakoczy 2003) as we learn to share meanings around materials. The conceptual change from “the human” to “the posthuman” may be about being hardwired and bodily transformed, but “posthumanist” learning is as an acknowledgement that what we used to call “human” is already a collective of habituation that includes all kinds of materials. Whether we call humans a separate species or not is entangled in the phenomenon we are referring to. What I want to emphasise is that humans are always entangled with other humans through our engagements with a material world. In this respect we have always been posthuman (Hayles 1999) or at least cyborgs with extended minds (Clark 2003). Humans have always included materials in our joint existence and extended our minds into our material surroundings, whether the materials were clay tablets, streams, monuments, mountains or computers or, indeed, robots. From a posthumanist perspective Andreas is an ultra-social collective before he is an individual because whatever is individual is pieced together by

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collectives of meaningful words and materials. We are not individuals that learn to share social representations or cultural models or schemas. We are individuals of ever-changing entanglements with collective concepts and materials, which we may order as taxonomic categories, representations, or with an emphasis on action, schema or cultural models. Stories are in our entanglements no matter what we call them based on concepts learned in practice. As ultra-social learners, we can learn from each other and from things themselves how to include these materially formed extended minds with our local collectivities. A learning process stretches across ages and situations and becomes a way of seeing that continues from birth until death. When the children encounter something new, like NAO, they use their preceding learning not just to see it as meaningful, but also to include it meaningfully in their ultra-social collective performativity (Figure 4.2). Like all material artefacts, we cannot take the local meaning of building robots for granted but have to learn what makes these particular material phenomena

FIGURE 4.2 Johan, age

11, has hands-on experiences with building robots and has built a robot with degrees of freedom, he explains to the researchers. (Photo taken by Cathrine Hasse during experiments in 2015.)

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culturally meaningful. Like the physics culture, the engineering culture around the construction of robots is also about building and telling stories. Culture is not, in the posthumanist perspective I propose, a container, but consists of material–conceptual resources that create differences in meaningful perception and intra-agency. Following Vygotsky, culture is not separate from nature but a prolonged natural path. The difference between the biological and the social lies in the very fact that where “further organic development is impossible, an immense path of cultural development opens” (Vygotsky 1997, 229). Culture is not the opposite of nature but is made up of the continuous paths we follow which make us perceive the world differently even when walking in the same landscape. Acknowledgement of a continuous path and thus no dichotomy may resolve the nature–culture debate haunting posthumanism, but it leaves us with a culture–culture diversity. Walking on the same path can be a culturally diverse experience as noted by Claude Levi-Strauss in The Savage Mind when he discusses how botanists and locals perceive the same plants in different ways, and anthropologists simply overlooked the plants so important for the local people (1966, 6). As noted by the anthropologist Lucy Suchman: “The representations ethnographers create, accordingly, are as much a ref lection of their own cultural positioning as they are descriptions of the positioning of others” (1995, 62). However, anthropologists do not just create representations. We, anthropologists, are not bound to our initial own cultural positioning, because we are professional learners. What we learn is to expand our own cultural concepts. We can get wiser about the process through which anthropologists come to align in meaningful perception with the positioning of others, as a processual culture alignment (Hasse 2015a). In this process, we may learn why people strive to deceive others, maybe even without wanting to do so, because they are embedded in stories like the rest of us. We may also learn more about just how materials kick back and challenge the stories told (e.g. Hasse 2013). I have elsewhere argued that a Vygotskyan approach to learning can to some extent also explain how anthropology consists of meaningful alignment processes (Hasse 2015a), but in this book I go further. From a posthumanist perspective, we can acknowledge just how much material and conceptual entanglement occurs for something to become meaningful for human learners. Some technically oriented proponents of a posthuman future, like transhumanists, argue that with new bio-machine developments, humans gradually change their biological nature. They still work from the humanist perspective which considers humans as rational beings, who want to merge with machines to survive longer, and from the idea that this merging is seamless because machines, not culture, make up the immense path that opens when no further organic development is possible (Vygotsky 1997, 229). Paradoxically this may be the very same development that, from a learning perspective, ends up confirming that humans differ radically from bio-manipulated machines; we in fact will not become posthuman, because we already are posthumanist and will continue to be a species of ultrasocial collective posthumanists no matter how many machines we entangle with.

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We are already posthumanists as we move away from the postulated rational Enlightenment individual, that emphasised the “I” and the autonomy found in culture-free, rational knowledge. This new posthumanist learner is a confusing paradox of a biological being increasingly entangled with and learning from rational machine materials, but it is also an existence that defies any fixed rationality. However, as cultural collectives, the machines we engage with may change us in ways we did not expect.

Conclusion: Chapter 4 The collective socio-technical imaginaries f loating around in social media seem to be the enhanced version of the human of which we are post in the posthumanist theories: an all-powerful, super-intelligent a priori rationality. As the posthuman in the new feminist posthumanist theories, this new creature is transgressing all boundaries of stand-alone bodies – but it is transgressing with wellknown humanist understandings of “intelligence” and “rationality”. The humans that tell these stories, as new versions of the old stories of the artificial creation of humans, are there in the storied entanglements and play two important roles: (1) Humans are the creators of mechanical robotic technologies. (2) Humans are the creators and contributors to stories of robotic technologies. The creators’ stories can be, but are increasingly not, the same as the stories of people who do not build robot themselves. Humans who do not build robots themselves have a hard time separating the stories of media robots from the much less capable machines they meet. The collectives are manifold but seem to be tied to how we engage with the materials we tell stories about. The Enlightenment human did not ask questions or make stories about what drives the people who build robots or tell stories of robots that look like “us” or, indeed, what drives the engineers that build the robotic machines. Even today we lack good stories of why the patients, the staff and the citizens struggle to find meaningful ways to include the obviously not well functioning robots, in their more or less absurd materialisations in their daily practices. The Telenoid was built to enhance our sense of human presence, according to its makers. For the municipal managers, the robot rather expresses a future where their problems of finding “warm hands” have been solved by autonomous robots helping the staff with their tasks. This is an encompassing socio-technical imaginary, which does not seem to be rooted in any of our empirically based realist studies of robots implemented in practice. When the robot, the Telenoid, meets the staff and patients, their reactions are mixed to say the least. Some appreciate its presence and we cannot exclude the idea that this could be because the Telenoid is also a means of human attention. As an object of modernity, the Telenoid tells a story about deception when it is entangled in the socio-technical imaginaries of autonomous intelligent robots. This is not to be understood as a deliberate deception made by the robot-makers. The managers in health care are deceived by the stories of robots as the

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autonomous, intelligent machines of the future that can enhance human capabilities. They are deceived by the singularists’ visions of robots that are as real as dragons, that they are the future and therefore real in their effects. These visions come with a posthumanist transversality that turns Olimpia’s lesson on its head. In “The Sandman”, humans did not want to be taken for machines. As objects of posthuman modernity, robots have now become role models with which we want to merge. The singularists team up with the spinozists in the desire to erase the “human”, but for different reasons. For the singularists the merging of humans and machines is a way to create superintelligence and even immortality by discarding the feeble bio-body of humans. For the spinozists the disseverment of dichotomies such as human and non-human, and nature and culture is a continuation of the postmodern critique of the Vitruvian Enlightenment Man that would place himself as the conqueror and creator of the World through the sciences. The spinozist’ anti-humanist critique is a critique of the basic conception of the human that underpins techno-science. Just like the singularists’ posthuman, the spinozists’ posthuman is concerned with diversity, hybridity and multiple becoming; in their practice of making theory, this is mainly done through new combinations of words. The spinozists call for a transversality that heals and bridges all the ejections and exclusions made by the Vitruvian Man. Where exclusions are made, they must be accounted for. The rise of robots and cyborgs come with new exclusions – but who is accountable if the humans have disappeared? These exclusions are not about the roar of nature versus civilised urbanity (Braidotti 2013, 55), but about how some have, through their movements, learned to perceive and know a material world in ways that make them agential in a different way from others who move, perceive and know the world in another way. In a posthumanist learning perspective there are no real and virtual or actual and imagined a priori objects. Robots and dragons are in people’s lives as stories with real effects. However, meeting materials, and not just media representations of (other) materials, makes a difference for how stories can be told. Spinozists and singularists tell different stories of the posthuman and of how technologies will affect humans. Both of these cultural formations tell different stories than those that meet today’s robots in everyday practices studied and reported by anthropologists. In robotic phenomena activities, stories and creations grow together in ways that entangle materiality, storied minds and tools in their total surroundings, which include words and pictures as well as wires and sensors. All of these merge in the concept of “robot” and they merge in different ways for different people at different times. Though robots, for most robot-makers and engineers, are machines made up of machine parts like sensors, modules and electronic devices that may help humans with strenuous, difficult tasks, the stories of robots as human-like or alive has been part of cultural history all over the world. The automata and the stories told of them merge stories and materials of a human hope to understand ourselves better. Today’s

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robot-makers continue these stories in robots like the Telenoid and JiaJia. From a singularist point of view the stories are about transversal robotic creatures that seem to outperform the humans, even if it is a paradox that humans find themselves being both the creators of these creatures while also being transformed by and in competition with robotic machines. From the Spinozist point of view, the story is of a nature that does not comply with human storytelling but “roars” back. However, from a learning perspective, other stories can be told that challenge both the stories of the spinozists and the singularists. These are stories of conceptual diversity – not the dichotomy stories refuted by the spinozists, but stories of how robots are perceived by people engaging in different practices. They are stories of clashes, not between humans and machines, nature and culture, but cultural perceptions that entangle with other cultural perceptions within phenomena. Posthumanist learning is about our changing relation to technology and an acknowledgement that the stable objects and subjects we took for granted can be transverse, intersected and dissolved. It is, however, also a story about how material–discursive practices are the outcome of a differential collective becoming of stories and the concepts we have learned to think with. These concepts are not discourses, stories, or representations. They are the basic component in phenomena that tells us what materials, including stones, robots and humans do when we engage with them in particular practices. Robots, as material–conceptual phenomena, have aligned some robot-makers as well as many people in particular in Asian and Western countries, because we have learned to perceive them as lifelike and intelligent creatures. The phenomena of robots develops in collectives that entangle preceding learning with materials, and stories of how we come to perceive robots are aligned across age, education and space. At the same time the phenomena of robots emphasise how these alignments are culturally diverse. Collective forms of connecting materials with language and thinking shape new perceptions of ourselves in relation to robots and cyborg devices. Robots may resemble humans but they do not form emotional collectives. In the next chapter, we leave the storied world to explore how any human collective, at its base, must include a material–conceptual process that aligns ultrasocial humans, as they come to perceive the world through entanglements of concepts and materials.

Notes 1 This research is described in detail on the homepages www.upgem.eu, www.reeler. eu and www.technucation.dk as well as in a number of Danish and English publications. In the project Robots in Primary Schools, of which the robot is present took part, children from grades 1 (age 7–8) to 9 (age 14–15) sat in front of NAO, made drawings of robots, visited an exhibition of robots and were later interviewed by us about their experiences. Finally, all the children sat in front of NAO and discussed

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their experiences. Furthermore, student researchers visited 20 Danish schools and studied their use of robots. The studies are published at the R.I.F. homepage and in a Danish publication (Esbensen et al. 2016). 2 A picture illustating NAO can be found at the homepage: http:​//doc​.alde​baran​.com/​ 2-1/n​aoqi/​senso​r s/dc​m.htm​l#dcm​. Last retrieved 2 October 2017. 3 The sociologist Harry Collins has also written a book that criticises the naïve conceptions of AI such as “intelligent” deep learning (Collins 2018). 4 Retrieved from the homepage 15 January 2017 https​://ww​w.you​tube.​com/w​atch?​ v=ZFB​6lu3W​m Ew.

References Alac, M. (2009). Moving android: On social robots and body-in-interaction. Social Studies of Science, 39(4), 491–528. Alac, M., Movellan, J., & Tanaka, F. (2011). When a robot is social: Spatial arrangements and multimodal semiotic engagement in the practice of social robotics. Social Studies of Science, 41(6), 893–926. Barad, K. (2003). Posthumanist performativity: Toward an understanding of how matter comes to matter. Signs: Journal of Women in Culture and Society, 28(3), 801–831. Barad, K. (2007). Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning. Durham, NC: Duke University Press. Blond, L. (2019). Dances with robots: Understanding social robots in practice. Research output. PhD thesis. Aarhus: Aarhus University. Breazeal, C. (2002). Designing Sociable Robots. Cambridge: MIT Press. Braidotti, R. (2013). The Posthuman. Cambridge: Polity Press. Bennett, J. (2010). Vibrant Matter: A Political Ecology of Things. Durham, NC: Duke University Press. Bruner, J. (1996). The Culture of Education. Cambridge: Harvard University Press. Bruun, M., Hasse, C., & Hanghøj, S. (2015). Studying social robots in practiced places. Techne: Research in Philosophy and Technology, 19(2), 143–165. Čapek, K. (1923). R.U.R. (trans. Paul Selver). Oxford: Oxford University Press. Clark, A. (2003). Natural-Born Cyborgs: Mind, Technologies, and the Future of Human Intelligence. Oxford, Oxford University Press. Cole, M. (2005). A. R. Luria and the cultural-historical approach in psychology. In: T. Akhutina, J. Glozman, L. Moskovich, & D. Robbins (Eds.). A. R. Luria and Contemporary Psychology: Festschrift Celebrating the Centennial of the Birth of A. R. Luria (pp. 35–41). New York, NY: Nova Science Publishers. Esbensen, G., Hasse, C., Gudmundsen, L., Mathiasen, M., Breum, M., Pumali, K., … Seyedmirza, A. (2016). Robotter i Folkeskolen (normal-klasser). Begrundelser, visioner, faktisk brug og udfordringer. Academic report from the Department of Education, DPU. Retrieved from http:​//edu​.au.d​k /fil​eadmi​n /edu​/Frem​t idst​eknol​ ogi/E​bog_-​_ Robo​t ter_​i _fol​kesko​len.p​d f. Fukuyama, F. (2002). Our Posthuman Future: Consequences of the Biotechnology Revolution. New York, NY: Farrar, Straus and Giroux. Goodrich, M. A. & Schultz, A. C. (2007). Human–robot interaction: A survey. Foundations and Trends in Human-Computer Interaction, 1(3), 203–275. Hayles, N. K. (1999). How We Became Posthuman: Virtual Bodies in Cybernetics, Literature, and Informatics. Chicago, IL: University of Chicago Press. Hasse, C. (2013). Artefacts that talk: Mediating technologies as multistable signs and tools. Subjectivity, 6(1), 79–100.

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Hasse, C. (2015). The material co-construction of hard science fiction and physics. Cultural Studies of Science Education, 10(4), 921–940. Hasse, C. (2015a). An Anthropology of Learning. Dordrecht: Springer Verlag. Hasse, C. (2015b). Multistable roboethics. In J. K. B. O. Friis & R. P. Crease (Eds.). Technoscience and Postphenomenology: The Manhattan Papers (pp. 169–188). Lanham, MD: Rowman & Littlefield Publishers. Hasse, C. (2018). How robots challenge institutional practices. Learning, Culture and Social Interaction. doi:10.1016/j.lcsi.2018.04.003 (in print). Hasse, C. (2019). Posthuman learning: AI from novice to expert? AI and Society, 34(2), 355–364. Hasse, C. (2019). The Vitruvian robot. AI and Society, 34(1), 91–93. Hasse, C., Trentemøller, S., & Sorenson, J. (2018). The use of ethnography to identify and address ethical, legal, and societal (ELS) issues. Companion of the 2018 ACM/ IEEE International Conference on Human–Robot Interaction. IEEE Computer Society, pp. 393–394. Hoffmann, E. T. A. (1908). The automaton. In: The Serapion Brethern (Vol I). George Bell & Sons Hoffmann’s the Automaton (from the Gutenberg project). Ingold, T. (2011). Being Alive: Essays on Movement, Knowledge and Description. London: Routledge. Ingold, T. (2013a). Dreaming of dragons: On the imagination of real life. Journal of the Royal Anthropological Institute, 19(4), 734–752. Ingold, T. (2013b). Making: Anthropology, Archaeology, Art and Architecture. New York, NY: Routledge. Jasanoff, S. (2015). Future imperfect: Science, technology, and the imaginations of modernity. In: S. Jasanoff & S.-H. Kim (Eds.). Dreamscapes of Modernity: Sociotechnical Imaginaries and the Fabrication of Power (pp. 1–33). Chicago, IL: University of Chicago Press. Kang, M. (2011). Sublime Dreams of Living Machines: The Automaton in the European Imagination. London: Harvard University Press. Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. New York, NY: Viking. Kurzweil, R. (2012). How to Create a Mind: The Secret of Human Thought Revealed. New York, NY: Viking Levi-Strauss, C. (1966). The Savage Mind (1st French edn., 1962), London, Weidenfeld. Leeson, C. (2017). Anthropomorphic Robots on the Move. A Transformative Trajectory from Japan to Danish Healthcare. Research output. PhD thesis. Copenhagen: Copenhagen University. Moll, H. & Tomasello, M. (2007). How 14- and 18-month-olds know what others have experienced. Developmental Psychology, 43(2), 309–317. Moravec, H. (1999). Robot: Mere Machine to Transcendent Mind. New York, NY: Oxford University Press. More, M. & Vita-More, N. (Eds.) (2013). The Transhumanist Reader: Classical and Contemporary Essays. New York, NY: Wiley-Blackwell. Needham, J. (1991). Science and Civilisation in China (Vol. 2, pp. 53–56). Cambridge: Cambridge University Press. (Original Work published 1956.) Ogawa, K., Nishio, S., & Koda, K. ( 2011). Exploring the natural reaction of young and aged person with Telenoid in a real world. Journal of Advanced Computational Intelligence and Intelligent Informatics, 15(5), 592–597. Perkowitz, S. (2007). Hollywood Science: Movies, Science, and the End of the World. New York, NY: Columbia University Press.

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Richardson, K. (2015). An Anthropology of Robots and AI: Annihilation Anxiety and Machines. New York, NY and London: Routledge. Riskin, J. (2003). The defecating duck, or, the ambiguous origins of artificial life. Critical Inquiry, 29(4), 599–633. Sims, C. (2017). Disruptive Fixation: School Reform and the Pitfalls of Techno-Idealism. Princeton, NJ: Princeton University Press. Shea, M. (2015). Karakuri: Subtle trickery in device art and robotics demonstrations at Miraikan. Leonardo, 48(1), 40–47. Stannard, S. (2010). The End of Discovery: Are We Approaching the Boundaries of the Knowable? Oxford and New York, NY: Oxford University. Suchman, L. (1995). Representations of work (Special Report). Communications of the ACM, 38(9), 33–68. Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, G. H., Hirschberg, J., … Teller, A. (2016). “Artificial Intelligence and Life in 2030”. One Hundred Year Study on Artificial Intelligence: Report of the 2015–2016 Study Panel, Stanford University, Stanford, CA, September 2016. Retrieved from http://ai100.stanford. edu/2016-report. Tomasello, M. (2014). The ultra-social animal. European Journal of Social Psychology, 44(3), 187–194. Tomasello, M. & Rakoczy, H. (2003). What makes human cognition unique? From individual to shared to collective intentionality. Mind & Language, 18(2), 121–147. Uzun, A., & Vatansever, F. (2008). Ismail Al Jazari’s machines and new technologies. Acta mechanica et Automatica, 2(3), 91–94. Vygotsky, L. S. (1987). Imagination and its development in childhood. In: R. W. Rieber & A. S. Carton (Eds.). The Collected Works of L. S. Vygotsky (Vol. 1, trans. N. Minick, pp. 339–349). New York, NY: Plenum Press. Vygotsky, L. S. (1997). The history of the development of higher mental functions. In: R. W. Rieber (Ed.). The Collected Works of L. S. Vygotsky (Vol. 4, trans. M. J. Hall, pp. 1–251). New York, NY: Springer. Yamazaki, R., Nishio, S., Ogawa, K., Ishiguro, H., Matsumura, K., Koda, K., & Fujinami, T. (2012). How does telenoid affect the communication between children in classroom setting? In: 12 Extended Abstracts on Human Factors in Computing Systems (pp. 351–366). New York, NY: ACM. doi:10.1145/2212776.2212814. Yamazaki, R., Nishio, S., Ogawa, K., & Ishiguro, H. (2012a). Teleoperated android as an embodied communication medium: A case study with demented elderlies in a care facility. In: 2012 IEEE RO-MAN (pp. 1066–1071) Paris, France. doi:10.1109/ ROMAN.2012.6343890.

5 THE MATERIALITY OF WORDS

Psychological processes behind conceptual diversity have not received much attention by science and technology studies, new materialist feminism, postphenomenology or philosophy in general. Concept formation through a process of learning, and how this affects perception, has largely been taken for granted. Psychology interested in learning has (apart from cultural psychology), on the other hand, primarily been occupied with how individuals learn, perceive and remember a world of separate material things. In this chapter, I connect cultural psychology with new materialist theories as we dive deeper into how materialconceptual processes are always collective at their base, even if collectivities are not enacted in the same physical place. This is, at its core, a very different process than the algorithmic information processing, pattern-seeking approach to learning found in AI and robotics. New technologies, whether robots, MOOCs or AI, challenge us to review the ways we have understood learning and organised education (see Chapter 2). Those who make these technologies have themselves been learners, but we may not have understood what that really means, because the humanist paradigm of the individual learning about an a priori separate object has hampered our view. This may be why they claim to replicate humans, while they in fact make humanoids with “empty curiosity”. So far, I have discussed how human learning is more than education (Chapter 3), and that different kinds of learning create different collective stories of what humans and robots are (Chapter 4). I now continue the exploration of a posthumanist learning theory that emphasises how humans’ collective learning is a very material process, which includes the materiality of words. Word-sounds, such as “physics”, “human” and “robot”, are material soundwaves that have a special ability to align some humans’ thinking and perception. The developments in AI and robotics push for a better understanding of how this alignment through learning makes humans differ from algorithmic

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machines, and how these algorithmic machines differ from Hoffman’s Olympia and other stories we tell about humanlike machines (see Chapter 4). One way to open up to a new story of humans as “posthumans”, that differs from the envisioned “posthuman” in parts of transhumanism and robotics, is to look at humans as ultrasocial learners. Learning is, as argued, the process that ties together material things, concepts and material words. Though the material word-sound stays the same, the concepts keep evolving with particular kinds of social and material tutelage that, for instance, align physicists and engineers in how words and perceptions become meaningful. However, natural scientists also learn to go below the surface and never take the meaning of words for granted. When children become “little scientists” that grow up to become scientists it is, in part, because they learn that words are more than a reference to a material world. There is a pattern of similarity in the stories the physicists, who I met at the Niels Bohr Institute, tell me about how they perceive the material world as scientists. One of the topics of this chapter is this pattern and an exploration of how the way scientists learn to perceive what we understand to be named “stars” and “particles” is the result of a long social and material collective learning process. Where MOOCs come with unfounded promises that all “lamenting daughters” (see Chapter 2) may learn to become physicists once they have access to a MOOC on physics, these patterns across time and space point to the importance of preceding human and material tutelage in forming scientific perceptions, as collectives of scientists learn to align in how they ascribe material words new meaning. There are many stories of how famous scientists learned to become good scientists. One of the best descriptions of such a process is found in the memoirs of the physicist Richard Feynman, who recalls how his father would take him for walks in the Catskill Mountains outside of New York, where he learned about words and reality. According to those memoirs, Richard’s father was well aware that rigid categories and definitions may hamper experiences. Whereas other fathers walking with their sons would teach them the names of birds like “a brown-throated thrush”, his father emphasised that even if the boys knew the names of the birds in English, Japanese and Italian they still would know nothing of the bird. Instead he taught his son to look at the bird. He would point his attention to the birds pecking in their feathers and ask his son why he thought they would do that. Little Richard answered that he guessed the birds were trying to straighten out ruff led feathers. But his father wanted him to do more than guess. He wanted his son to look at the birds and he guided his observation by asking: “Okay, when would the feathers get ruff led, or how would they get ruff led?”. Richard answered: “When he f lies. When he walks around, it’s okay; but when he f lies it ruff les the feathers”. Then the father would say: “You would guess then when the bird just landed, he would have to peck more at his feathers than after he has straightened them out and has been walking around the ground for a while. Okay; let’s look”. And then Richard explains how he learned:

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So we would look, and we would watch, and it turned out, as far as I could make out, that the bird pecked about as much and as often no matter how long he was walking on the ground and not just directly after f light. So my guess was wrong, and I couldn’t guess the right reason. My father revealed the reason. It is that the birds have lice. (Feynman 1999, 181) The father was teaching his son more than why birds peck their feathers. He taught him a particular way of learning to see the world that followed him throughout his life. This way of seeing is what science is, according to Feynman. The world is a wonder. Science is not just about making dull observations and noting them down. It is about being open to the mysteries of the world. The lesson the father taught Feynman was a classical learning lesson in what the anthropologist Gregory Bateson called “learning to learn”, where we learn something in one context, which we can bring on to our next learning experiences, even in completely different situations. This kind of “learning to learn” (Bateson 1972, 299) is a learning that tells us about learning itself, and what to expect from it. Scientists like Feynman can help us on the way to understanding how to perceive the world as a posthumanist learner, who never takes fixed categories and representations for granted, but always seeks to expand our concepts. This means going beyond lexical definitions when one engages with the material world. Scientists understand, perhaps better than any, that we live in a material world, where our representations may get in the way of perception of a deeper understanding of reality. However, even if it is not enough to know the name “brown-throated thrush”, it does not mean these words are not important. Karen Barad’s posthumanist theories have opened up a space for a closer look at how materials and discourse are entangled in scientific agency, but she did not include particular attention to the role of material words. Where most social scientists see her posthumanist thinking as an emphasis of a material world, I shall emphasise both the concepts and the material words, which I see as entangled in the discourse, that is, “meeting the universe half way” (Barad 2007). Most posthumanist theories discard both individual humans and fixed representations, but often overlook the social human collectives that processually entangle material words with meanings. Even if we, like Feynman’s father, denounced the importance of knowing the different names of the bird, there is more to words like “ brown-throated thrush” than lexical definitions and representations. When words are viewed as material expressions of (but not conf lating with) concepts, material words become an incessantly unfolding process of connections between humans and between humans and a material world. They become what Feynman called “time-binding”, a collective memory where we “bind one experience to another, each one trying to learn from the other” (Feynman 1999, 184). The conceptual collectives evolve whether the persons realise it or not. A young child may utter a material word, like “brown-throated thrush”, but even if the child may be unaware of its conceptual connections to other concepts,

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these connections are still present, according to Professor Jan Derry. What is partly outside of each person is not nature (as opposed to culture) but an already formed collective space of reasons (2013, p. 2). No person can claim to know all of the reasons for connection between a brown-throated thrush and its licepicking, but as we learn we become aligned in how we conceptually understand the thrush. When we learn to name it with a material word, it is, according to Vygotsky, only the beginning of concept formation. When we utter the material word, we begin a process of conceptual alignment. Concepts are the time-binding glue that make stories (or discourse, or schemas or cultural models or spaces of reasons) and materiality evolve together. Concepts are the basic unit of analysis in my exploration, not phenomena and not agency, because it is concepts that ensure posthumanist learning as a process. In the old humanist Cartesian sense, an inherent cut existed between a subject and an object. The separate subject could examine the object and gradually learn more about it. In Barad’s posthumanist theory, intra-action causes subjects and objects as it emerges within phenomena (Barad 2003, 815). Without concepts the world would not only be new every time an agential cut is made, in the Baradian sense, but the world’s phenomena and agencies would be meaningless. Concepts make us collectively align in what we perceive, in the stories we find meaningful to tell and when concepts are formed collectively. We become individuals in the way we bind collective conceptual experiences in personal stories. That our conceptual learning is never the same for two persons does not exclude that collective’s form outside of persons. These collectives can be analysed as patterns forming across stories told by individual persons. When Feynman’s father taught his son to perceive the world of a scientist, he himself followed a collective pattern of teaching and learning how to tell stories of “becoming a physicist”. The concept of “a physicist” is more than a lexical definition of a natural scientist. For the physicists themselves, becoming physicists is a process of forming identities, which, as noted by Jean Lave and Etienne Wenger a long time ago, is entangled in a community of practice (1991). In this community, across time and space, materials and humans support and create stories of becoming entangled. The collective of physicists shares stories of how not to take words and materiality for granted – but to always “look again”!

VIGNETTE 5.1: BECOMING PHYSICISTS In a garden close to a wood in Denmark, a girl of around eight years is carefully placing dead flies in rows – the big black ones in the row on top, followed by a row of smaller green ones and then finally the small black ones. Then she suddenly notices a small difference in the big black ones: some have a small red dot, and she puts them aside. A few hundred kilometres away, a boy holds his breath as he touches a picture of the Moon coming so close to the Earth that all water on our planet rises up as if to touch the cratered surface of the Moon. “What is inside the Moon to create such powers?” he wonders. In Germany, a girl plays with toy

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animals on a carpet. Next to her, her brother plays with a Meccano set of wooden sticks to be connected with colourful screws. When her brother leaves the room, she sneaks over and begins to create a structure. On a balcony, somewhere in the Netherlands, a young girl watches her father as he takes off the cap of the telescope, so they can watch the stars in the sky together. The light from the surrounding town makes the stars dim, she learns, when they later move the telescope to a field in the countryside. In an English town, a little boy is playing with Lego bricks on a carpet in the living room, building structures higher and higher – and then tearing them apart to build a new structure. He thinks about how many different structures can be built from identical pieces of brick. In Romania, another little girl is holding up an earthworm, watching it bend and twirl, wondering which is its head and which is its tail. In Senegal, a girl sifts sand through her fingers and watches it as it forms little pyramids – every time the same, yet different. Far away in Italy, a boy her age has received a new electrical kit to play with; he presses and pulls two cords together to make a bulb light up or die out. All of the above children eventually became natural scientists; most of them became physicists and some, engineers. Most of the physicists’ stories about their own childhood engagements stem from a question I have posed to 55 physicists: Why did you study physics? (Hasse 2008, 151). Their answers and their remembrances of their childhood engagements were not just interesting because they had such vivid memories of the different materials they played with, but because their stories were so alike. The physicists-to-be from the different backgrounds had very different material possibilities for learning, but most indicated that their fascination and engagement with the physical world had begun already in their childhood. When Walt thinks back on his childhood, it is his play with Legos that he remembers as the point of departure for his interest in becoming a natural scientist, because Legos taught him that the things we perceive as whole, may be built up and taken apart. Anina grew up in Senegal in a rather wealthy family. She never had access to Legos in her childhood. She had a doll and her mother helped her make clothes for it. In her childhood memories, she would sit on the doorstep sifting the red sand through her fingers watching how it fell in small pyramids. This was, she explained, where she founded her interest in studying physics. She saw for herself that things are formed in interactions. As a child of wealthy parents, she was encouraged by them to become a scientist and make the family proud. Female, German physicist Hannele did not have access to Lego either, nor did her brother, but he had “Meccano” (a set of blocks for construction, like Lego). Hannele grew up in a traditional German family and her mother kept giving her dolls to play with, but she preferred her soft toy animals, or even better, the Meccano set of her brother. Her parents scolded her when she tried to play with his toys. But if I got hold of my brother’s Meccano set … my parents should have given me the chance to play with it. I think I would really have loved it.

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However, I was not allowed to touch my brother’s toys – I should be like a girl (Hannele, female physicist). (Excerpts from interviews in a fieldwork study conducted by Cathrine Hasse at CERN, Italy and Denmark in a project financed by the Danish Research Council 2002–2004)

When Barad says, that: “Discursive practices are the material conditions that define what counts as meaningful statements” (2007, 63), it points to how material conditions must be specified to account for the above examples. The young physicists to be had different material conditions (including gendering) and therefore their stories differ, yet they end up with an alignment of what counts as meaningful statements about physics. They all came out as physicists that, like Barad, emphasise the importance of available materials for learning how to see the world anew. Like Feynman, the physicists all remember how they learned through not taking materials for granted. They did not voice memories of learning to be physicists from reading textbooks in science. They learned by doing, sometimes by making, as a process closely tied to learning (Ingold 2013), and also by pulling apart, in accordance with Richard Feynman, who believes taking things apart can be a way to understand what science is: If you ask a child what makes the toy dog move; if you ask an ordinary human being what makes a toy dog move, that is what you should think about. The answer is that you wound up the spring; it tries to unwind and pushes the gear around. What a good way to begin a science course. Take apart the toy; see how it works. See the cleverness of the gears; see the ratchets. Learn something about the toy, the way the toy is put together, the ingenuity of people, devising the ratchets and other things. (Feynman 1999, 179) However, the story of posthumanist learning does not end with looking, making and pulling materials apart – we are still in the realm of humanist learning, where individuals engage with a separate material world.

Social nudging What is also interesting, from a learning perspective, is that, of the 55 physicists I interviewed many had fathers like Feynman (and in one case a mother and another a grandmother), who were also physicists. Others found a source of inspiration in their brothers, cousins, and friends and many referred to teachers who worked either with physicists or in related fields like engineering. Though many did not have anyone in the family who studied physics, as in the case of the Italian boy, Alejandro, especially the female physicists, like Hannele from Germany, Rita from Rumania, Anine from Senegal and Dutch Anne, all had a father who spurred their interest for science. Rita, whose sister is also a physicist, explains here what inspired her to become a physicist:

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[My] interest in physics comes through my father. He was a physicist … I owe him a lot, not least my love of nature. He opened my mind to it … It was my father who gave me this love of nature, the acquaintance and knowledge of it, the understanding that I should never stop at the superficial. All this I owe to him … the attachment, what makes you recognise art, nature. He taught me, among other things, to recognise the birds from their singing, the love for the mountains and their secrets. (Rita, physicist) It is a common trait across the interviews that the physicists learned to go beyond “the superficial”, and that included learning what is behind the surface of things and always being prepared to learn a new meaning of words (Hasse 2008). Anne, the girl who watched the stars, recalls how as a child she learned to perceive the night sky through an instrument. She wanted to be an astronomer, and ended up in particle physics. Her father had a big influence on her decision to become a physicist, she explains, because he gave her experiences which taught her something about the many physical factors that can influence perception and the meaning of ordinary words like “star”. But he never urged her to study physics. Like Feynman’s father, he did not have to, as his daughter had already learned to learn to think like a physicist. My father is a physicist. He was very … it was not that he gave me a lot of books, but we lived in the city, so you could not see a lot of stars, but even so I went out on the balcony with my father to look for particular constellations of stars. And that made me very interested in knowing: How big is it? What happens out there? (Anne, physicist) The apparatus involved in changing the stars for Anne was not only the telescope. Anne also learned from her father that too much city light is putting a damper on the illumination of the stars. Perception changes with material conditions, but also with social conditions. Once you have learned to understand stars as something that could be perceived more clearly through tools like telescopes, but also that it does not work in city light, you enhance the meaningfulness of signs of the sky. Since the telescopes they had did not work, Anne got maps from her father. It was a shame. We could hardly see anything. But my father, who is also a physicist, he gave me some maps over the firmament and began to explain to me. He never said to me that I should study physics. Never! He just thinks it’s quite funny that I also became a physicist. Anne connects her father, maps, dim city lights and her new perception of stars with the decision to become a physicist. Stars were never the same for Anne after she learned they could be mapped – just as birds were never the same to Feynman. Anne’s father did not need to tell her to become a physicist; she had already become one. Even if they could only see a few in the dim light, she now

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“saw” all the invisible stars in the firmament and could place those few yellow dots in a larger picture. Eventually, she became an astronomer. In Barad’s posthumanist theory, the ontological units for what have been named “stars” are phenomena which are not the same as “independent objects with independently determinate boundaries and properties”, but rather “phenomena are the ontological inseparability of agentially intra-acting components” (Barad 2007, 33). Intra-agential components involve long histories of preceding learning. The components that transformed Anne’s stars do not just involve a telescope, dim light and a map, but also learning to make certain agential cuts that make stars appear in a certain way. It also involves social relations like a father and a gradual alignment with other astronomers who have learned to perceive stars in the same way as Anne. In the humanist learning paradigms (which dealt with social relations), every transaction between persons would be a learning context. However, context was often understood as a system (e.g. Bateson 1972, 271) or a merely cognitive process such as knowledge transfer, or context was understood as a situated practice (e.g. Lave 1988). This made it difficult to make claims of collectivities going beyond situated practices. If context was understood as cultural resources, it became a question of cultural transmission that explained the reproduction and stable patterns in human collectives. If knowledge was not acquired or transmitted, it was understanding-in-practice, which was here-and-now learning in unfolding practice (Lave 2009). Whether we defined this as merely a “context”, a “culture” or a “social situation”, it implied that something collectively meaningful was shared between humans. However, the humanist perspective made it difficult to, for instance, dismiss transference or systems, and yet keep an interest in how humans could form collectives when they were not in the same place engaging in the same situated practice. Is it possible to retain the meaning of collective phenomena in a posthumanist learning paradigm? For Barad, even when apparatuses are “primarily reinforcing, agency is not foreclosed” (Barad 2007, 214). All situations are open, and preceding collectively formed concepts may disrupt her idea of agency. Nevertheless, her posthumanist theory opens up a question of how learned concepts partake, from within, in boundary-making.

Concepts are not representations Feynman’s father did not just teach his son to perceive how birds pick their feathers. He also taught him to perceive the brown-throated thrush by relating concepts, meaning and perceptions to the bird in a new way. This was all done as they moved through the woodlands. According to Ingold, words may not be needed to catch this movement of knowing. When we move alongside another person, our knowing becomes the same as our movement (Ingold 2013). However, Ingold does not take into account the preceding learning each person brings to bear. Though moving

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alongside may lead to alignment, a material world is not in itself why we move. We are in phenomena as well. The causes of movements are, according to Barad, “not forces that act on the phenomenon from outside. Nor should causes be construed as a unilateral movement from cause to effect. Rather, the ‘causes’ and ‘effects’ emerge through intra-actions” (Barad 2007, 214). Meaning arises within particular material arrangements, but the material arrangements that are meaningful for humans (with shifting boundaries) include the words we have learned to think with as concepts. Words are material and arbitrary sounds. They do not tell us about the meaning of the word-sound. Word-sounds like “brown-throated thrush” are learned in social practice to have particular word meanings in relation to other words. In a humanist understanding of learning, the word-sign “brown-throated thrush” is internalised with meaning, and meaning is here reducible to semantics, lexical knowledge or categorisation. However, Feynman’s father understood the necessity of actually looking at the materials in front of our eyes. From being an entity belonging to a lexical category, the thrush, for Feynman, becomes a concept enhanced with new meaning about learning to see the world in general. The material birds become a means to learn about learning, but also a new way to perceive and think about the thrush. When we have learned, this preceding learning forms a new “habit of mind” in relation to our perception of material arrangements. The preceding learning become a potential zone for future learning. Becoming a physicist is a learning process, but even if I joined the formation of physicist students in 1996 (see Chapter 3), I did not have a father who had designated the physical nature of the world for me. Though just being with the physicists changed my perception of the physical world, including the stars, and though I began to see things like a physicist (Hasse 2015), my conceptual understanding of physics was shallow. As my borders for conceptualised phenomena changed, I began to think like a physicist – just enough to understand that there were patterns across here-and-now situations that made physicists across national borders similar to each other (e.g. at CERN, the European Organization for Nuclear Research, see Chapter 3) – not just because they understood math and physics, but because they shared an aligned identity about what made them physicists in their stories. What is included and excluded in the stories of the physicists? Why do they come out so similarly to each other, and so similarly to my own story? I recall that my learning process changed my material world, when I learned as a physicist student at the Niels Bohr Institute. In the same way, once Anne learned about stars as mappable material phenomena, they were never the same again for her. When she communicates with her colleagues, it would be wrong to say they share a “representation” of stars that make them all see exactly the same phenomena on the firmament. Yet they share a collective perception that in all its diversity still makes it possible for them to perform certain actions, which others cannot perform because they do not conceptualise the stars as these physicists

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conceptualise them. “Do not” does not mean “cannot” or “can never”. It is because learning is a process that we come to align with each other’s perceptions. Agentic humans are not born to be engineers or physicists. Though it is an established norm that we attribute nationality to each other, no child is born within a category such as “Danish”, Italian”, “Dutch” or even “European”, or “Western”. Nor are we born “black” or “white”, “robot-maker” or “basket weaver”. We are not born “physicist” or even “intelligent”, “male” or “female” or “human”. Even if being male or female seems so natural to many people that they will refute giving up these categories, from a posthumanist learning perspective all categories and their boundaries are in a constant process of becoming. All categories we use to label others are just as contestable as the “human” category many have, a priori placed themselves in. But even this category can be rejected, as we have seen in posthumanist thinking. Though spinozists (proponents of posthumanist theories) have called for a transversal approach to categorisations, even the radical posthumanists seem to build on the assumption that the material aspect of the material-discursive entanglement is mainly what is not properly illuminated (Andersson 2016). In a truly relational posthumanist ontology, we need more than that. We may think we know all there is to be said about discourse and words, but what is still left in the dark in spinozist theorising (e.g. Braidotti 2013) are the material-conceptual material and social “tutelage” processes that, along with the material things, give material words their meaning as preceding collective learning. In the era of social constructionism, it could be argued that the human and social sciences have thoroughly explored this connection between words and materiality. Discussions have, for instance, centred on the intersectionality studies that explore how categories as systems constantly intersect and challenge each other – or render injustices invisible (Williams 1994). Women are not the same everywhere, and they have very different life conditions and possibilities for becoming physicists that may not be captured by linguistic categorisations like “women” and “physicists”. This diversity may be overlooked when the category “women” is intersected with other categorisations like “poor” and “black” and, for example, “black women” are intersected with “physicists”. It raises the question, however, of what sets the boundaries for the intersected categories. Though feminists, like learning theorists, have taken issue with representationalism, it seems that, as argued by Derry, the representational paradigm still reigns even in poststructuralism and postmodernism, where signifiers stand for signified with signifiers standing for signifiers (Derry 2013, 60). When “woman” is intersected with “black” and “poor”, why not continue with inventing new categories? Women can be subdivided in relation to sexual preference, but also in relation to the colour of their hair or, as I found mattered for my analysis of gendered career paths in physics, they can be subdivided as “science-fiction ignorant” or “sciencefiction lovers” (Hasse 2015). New materialism acknowledges that this focus on intersecting categories is somehow a too rigid approach (van der Tuin & Dolphijn 2010). In my research in

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physics culture, breasts and/or skin-colour or even lack of knowledge of science fiction are not the only things supporting or preventing women from a career in physics. For instance, in Italy a background in classical philosophy also plays a part (see Hasse & Trentemøller 2008). These movements from well-known categories (black, woman, poor) to completely new aspects of analysing what matters is a step away from the representationalist paradigm. Subtle learning processes in everyday engagements with social and material relations entangle women like Hannele, Anine and Rita in very different ways with physics. Yet, across their personal stories, their tales seem rather alike as they explain how family members and playing with materials opened their world to physics. This remains true with the ones told by male colleagues, though there is a majority of female physicists that emphasises the significance close family members. From a learning perspective, I question whether people actually learn in systematic categories and representations, or whether systematic categories and representations are rather a way for the social, cultural and linguistic sciences to systematise the complex and messy collective, material concept formation that emerges through the entanglements of humans and material artefacts.

Collective “spacetimematter” Following the natural science perspective that arose with the Enlightenment, we seek an ordered world. This ordering is an inherent feature of the natural sciences. Maria, the little girl who sorted out the f lies in the Danish garden, had that urge from an early age. Is it because she is an exceptional individual learner as a physicist-to-be? It seems the decision to become a physicist in this case was an individual one. Her father was not a physicist. She recalls her childhood play with ordering the f lies with the red dots as part of her story of how she became a physicist. Few children might have the patience and engagement to meticulously sort f lies. This is why, she explains, she eventually became a scientist: I have always had this dream of becoming a scientist. Just like other children dream of becoming firemen, I wanted to become a scientist. That it turned out to be in physics came along the way … It was probably evoked through my interest in mathematics. This was also what I planned to study later on, but then I discovered theoretical physics and liked it a lot … I started studying medicine though, at university – probably to live up to what was considered normal. … I had considered physics but dared not take the leap into physics. But I knew, I wanted to be a scientist. (Maria, Danish physicist) In the case of Maria, the inspiration to create order was inseparable from the way the f lies offered themselves to be ordered. Though as an individual Maria took part in the ordering of f lies, just as well as the f lies themselves, Maria was never alone with the world of f lies and butterf lies. Maria’s story of how she became a

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physicist does not refer to her family. Her history is tied to the material ordering of the f lies. Her father was not a physicist, but he was a doctor who favoured science, and his father, Maria’s grandfather, was a natural scientist. It is when I, as part of the research apparatus, place her story in the phenomenon of “collective” stories of what motivates physicists to become physicists, that her accidental mentioning of her family background becomes part of a pattern, along with her ordering of the f lies. When the physicists recall their childhoods and answer my question “Why did you study physics” (Hasse 2008, 151), they arrange their stories in ways that mirror their becoming of systematic, materially-oriented scientists. Their narratives create a whole from separate events (Ricœur 1984, 65), connecting, ordering and going below surfaces to the word “physics”. Furthermore, in their stories space and time come together in collective ways across categories such as gender and nationality. What Barad names “spacetimemattering” (Barad 2007, 390) is not just a single, but a collective iteration of the material practice of telling “origin stories” elicited by the material word “physicist” and all the collective connections it elicits. If I look for diversity across these stories of “becoming physicists” I find gender diversity within the phenomenon. Some of the female physicists recount how they were not intelligible as physicists. We may detect a performed normativity as well as a gender matrix, as proposed by the feminist Judith Butler (Butler 1993) here. For Butler intelligibility is a cultural phenomenon tied to words, which in the Foucauldian tradition creates worldly effects – never determined but agentic. For Barad it is different. Meaning is not a property of words, let alone groups of words, just as discourse is not reducible to language. The power of meaning cannot be left to the word as in the linguistic turn. Rather discourses are material conditions for making meaning which is not just human-made. For Barad “meaning is an ongoing performance of the world in its differential intelligibility” (Barad 2007, 335). Intelligibility is not a specific human capacity but “a matter of differential responsiveness, as performatively articulated and accountable, to what matters. Intelligibility is not an inherent characteristic of humans but a feature of the world in its differential becoming” (Barad 2007, 335). It is the world that articulates itself differently. In education theories, this posthumanist thinking has been argued to replace learning (Ceder 2015). If intelligibility should replace learning, we still need a place for processes of how humans change collectively and become intelligible to each other and the world becomes intelligible to them as a collective. In her story, German physicist Hannele had to struggle to become a physicist because she was “intelligible” to her parents as a girl. This intelligibility emerges in a cultural story of how she eventually became a physicist. Although, retrospectively, clear lines are visible to her, her becoming as a physicist involved thousands of unpredictable, serendipitous learning processes of differential becoming, of which being a “girl” was just one.

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How we learn and what we learn is entangled in a non-systematic way. To become a physicist or an engineer who creates robots is a lifelong learning process. With it follows a particular worldview – a habitually collectively shared habit of meaning-making that comes out in practices with collective, agential material words that create stories within the phenomena with the world. The physical systems, matrixes and classifications that Walt, Aleandro, Maria, Hannele and Anina work with today, within different areas of physics, may appear to them as processes of free choice of objective observation, sorting and matching data from an external world. They may not speculate about, but may take for granted, some kind of “homology between structures in the mind and structures in the world” (Ingold 2011, 159). The field of Science and Technology Studies (STS), as well as most new materialist feminists and anthropologists, reject this kind of a priori homology. Following Ingold, human knowledge is not classificatory but storied. This approach privileges “the practice of knowing, over the property of knowledge” (Ingold 2011, 159). For Ingold, this means that people do not apply knowledge in the practices they engage with; rather they know as they go along, knowing by way of their practice. This perspective omits how our knowledge with the world is also an ongoing, collectively learned conceptualisation. When German physicist Hannele reaches out for her brother’s Meccano toy, she learns she is a “girl” opposed to a “boy” from her parents’ reactions as they claim “girls” do not play with Meccano. This is knowledge tied to categorisations. In the story of female physicists, the gendered matrix comes to the analytical foreground. Yet, lived learning experiences form concepts and stories that are not reducible to normative matrixes because they are forever on the move. Hannele’s own concept of being a “girl” keeps evolving throughout her life as she becomes a “physicist”. However, the material word “girl” is, for her, inevitably connected with the exclusion from a Meccano set. The concept of “girl” evolves from here. From the beginning of the learning sciences, learning theory has taken cognitive representationalism and systematic categorisation in matrixes for granted. In the Enlightenment approach, both realism and empiricism began with the notion that subjects were separate from objects. The discussion became how our sensuous perception of the world was erroneous. The conclusion was that science was right and our natural human perceptions of the material world were wrong. We only perceived the forest as being green because the green colours were ref lected, and so scientists could reveal that forests were anything but green. We only perceived stars as pictures made by gods because we had not learned how to perceive and map them as physicists do. Representations became the contested images in our mind, understood as a “camera obscura” (Ihde 2000). Understood as separate from the actual world, representations became a battleground, where natural sciences came out as winners in academia. Now STS, new materialists and anthropologists like Ingold bounce back. We are not in the world but with the world. Even when we believe we are most

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rational and explore the world as physicists, we experience the world though our grounded body. Though the human sciences give rise to particular forms of knowledge, this knowledge is not emerging from minds full of concepts and theoretical thinking separate from a world full of material objects. Rather, a kind of knowing grows out of practices where we engage with things (Ingold 2011). I am supportive of all of these ideas. However, the way I have learned that physicists and engineers engage with things (and not separate objects) seems to involve processes of collective alignments that cannot just be reduced to material access to things or to situated practices. Cultural perception is not a linear process, and with new learning we may again see stars as storied worlds. Yet our learning is also continuous with materials that “kick back” with material enactments (Barad 2007, 218). From the child’s perspective, from the beginning of learning with the world there is no right and wrong, or outside and inside in the practice of knowing. There is only an incessant transformation of the perceived reality. The relation between material world, person and other persons is one of constant boundary-shifting that gradually aligns our more mature perceptions with collective understandings. In the reconfigured world this could, for example, be a concept-formation transforming the perception of a “star”, so we, like Anne, learn to perceive a star as something deeper and more complex than a yellow dot. We can now see stars as formations of gasses, power planets like the Sun or as the space between dark matter, depending on our engagements with physics theories. If the inner–outer dichotomy, with the body skin as a border, is dissolved, as argued by many (e.g. Ingold 2008, Clark 2003, Bateson 1972), how do these collective transformations of perceptions come about? Here we can think of Galileo Galilei, who pointed the newly invented telescope (bought in the Netherlands in 1609) against the night sky and discovered that the moon was neither the envisioned hovering lampion nor a gaseous substance. What he saw were mountains on the moon, and when this perception became accepted because other people made use of the same material apparatus, the collective apperception of the moon changed. Even more importantly, at that time, the general perception was that planets, the Sun, and the entirety of space orbited around the Earth. When Galilei saw in his telescope that small planets orbited around Jupiter, the heliocentric system proposed by Copernicus, among others, was challenged (Galilei 1610). Eventually this new reality was accepted and even changed the perception of the human, who was no longer the central creation in God’s ordered universe. The Enlightenment conception of the new human came about as a long process of collective learning of entanglements between bodies and materials. It displaced the human as God’s chosen creature, but placed the little scientists as the humans closest to the new God, the applied sciences and their technical wonders.

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Real robots revisited If learning, following Barad’s posthumanist theorising, is substituted with “intelligibility” (Ceder 2015) we exclude a focus on how intelligibilities are collectively shared, or not, among humans. Furthermore, the role materials and hands-on experiences with materials play for learning to become “Maria the physicist” or “Alejandro the robot-maker” or “Karen Barad the new materialist” is overlooked. Learning is basically a collective material-conceptual process even if the physicists I refer to have never met or shared situated practices. If there are no psychological human processes involved in phenomena, conceptual intelligibility is static and solipsist. I shall argue to the contrary that intelligibility implies psychological processes of change, and that these processes through which intelligibility is transformed can be understood as a cultural learning process, which transforms the meanings of words, things and our recognition and thinking about them. We may later categorise learning in many fixed ways that create a sense of order, which can be told in stories about how we ought to learn about categories and representations – or even how what is said and unsaid in discourse entangles with architecture, law, philosophy and morals that we write books about. All of these said and unsaid ongoing iterations involve human learning in material tutoring collectives. Posthumanist theory should not abandon learning as a concept or as human guidance but acknowledge the processes that form intelligibility, when humans are involved. Conceptual intelligibility is not a matter of computational representation but a search for meaning, even when we do not have words for what we perceive. But physicists also learn to enhance their concepts by refusing to accept what they immediately perceive. They acknowledge that material words are not the same as a closed meaning. Like Anne and Feynman, I learned from the physicists that categorisations can be deceptive, when materiality “kicks back” (Barad 2007, 215). I also learned that perception of the world can change as we learn. This latter recognition is an important acknowledgement for a physicist-to-be. Like Feynman’s father, Anne’s and Rita’s fathers, say: “Do not believe what you see at first. You perceive a category that is already well known. Look again!” This way of learning to perceive may inf luence the child to perceive a world of wonders without being disturbed by categories that set a limited boundary around objects. However, small children, as well as adults, perceive with concepts – even when they are not ref lected upon. For many new materialists the “now” has primacy. Past, present and future come together in our sensuous engagements with things, and like Ingold, many have criticised natural sciences such as physics for withdrawing from the world when scientists try to conceptualise and make scientific knowledge about the world instead of experiencing it (Ingold 2011, 120 ff). What I saw in my studies of engineers and physicists was that their conceptual knowledge was not detached from the world; changing concepts meant changing perceptions that gave the world a new substance. It was their experiences that were transformed as

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they learned to conceptualise the world as physicists or engineers. They did not receive the world as “information” but became entangled with their machines. When their stories become collectively aligned, is it because scientists, no matter where they are, engage in the same kind of practices? Are practices the day-to-day tutelage, which ensure a material-conceptual alignment of stories? What is the relation between practices, material word-sounds, their meaning and the stories we tell? Let’s have a look at the material word “robot”.

VIGNETTE 5.2: WHAT ROBOTS ARE REALLY LIKE Robots are, first of all, platform machines. They are not alive, nor are they human like. They are however a very special technology in that they draw on and combine many other existing technologies and thereby create a new entity. Imagine a robot that both includes algorithms, a computer, helicopter rotor blades used in drones, software chips, a camera, a sensor, a wire, a motherboard, a gramophone, motors, a machine arm or lever, a microphone, caterpillar wheels etc. All robots connect some of these components. It is this combination of other technologies which makes the robot a very particular machine. All robots run on an energy source (electricity, batteries etc.) that can run out and that needs reloading. Just like many other machines, the robots connect computers, mobiles and cameras. A robot combing all kinds of technology is, however, still just as dependent on plug-ins or batteries as any other technology taken individually! Contrary to levers and vacuum cleaners, these robots not only combine and connect different kinds of technologies into one entity – this entity can in some cases both use tools (like vacuum cleaners) or make new tools. Robots used to be caged in places like automobile factories, but this was before they had more mobile bodies and AI. Now they increasingly move around among people. That more and more technology, including AI software, are built into and connected in a robotic body will create a super machine with multiple purposes. It becomes more than the sum of the parts because the individual technologies not only relate to each other but increasingly to a surrounding world including other robots. When these advanced entities combine many types of technologies (cameras, software for face recognition, microphones, recorders for voice recognition, levers or rotor blades, engines, sensors etc.) and cover all of this in a shell of human-like features, these machines can appear as a kind of “companion” (like Jibo or Hanson Robotics’ Sophia). Where we do not expect a vacuum cleaner to be a companion, a robot in a humanlike shell may appear as a trustworthy friend that is just a helpful cleaning companion.

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It is these six features listed above that make robots a special kind of technology in the real life of people. That said, all robots do not always combine all of the above technologies. We would still call a robot a robot if it did not have inbuilt microphones, for instance, but only made use of the computers AI in a metal body where the limbs were controlled by software programs and maybe sensors to lift heavy stuff. (Excerpt from “What Robots Really Are” at the Robot Republic, Hasse 2017)

This vignette is an excerpt from a small blog post I wrote in 2017 for a site called Robot Republic. This expressed my frustration at all the weird discussions on the internet about AI and robots as human-like, while I had learned from the engineers themselves that robots were machines, albeit very clever and fascinating platform machines. I had become part of a collective of hands-on engineers and followed many engagements with roboticists building actual robots. I had never seen anything near a Frankenstein’s monster, or even something that could count remotely as “alive”. However, when I began research in robotics I was just as convinced as many others that robots could actually understand what they said when they talk to us and each other. My knowledge of what robots are has grown as I have learned. I could not build a robot myself. My technical knowledge is as shallow as my knowledge of physics, yet over time I learned something that made me see through the many internet claims which are made about “thinking”, or “speaking” robots.

Changing a material world How did I learn to change my perception and understanding of the material word? I have visited many robotic labs in different countries in Europe and in the United States. I have seen desks and f loors covered in nuts, bolts, wires, luminous lamps, driver belts, steel rings, screwdrivers, tubes, tape and welding tools. In my field notes I find an example of how I learned from a visit to a lab in 2017. I stand in front of a robot, where the side panels are taken off. I can see the robot’s belly and I have no idea what I am looking at. I have no precise categories, no representations in mind. Yet I fill in the blank with my previously learned concepts. I try to connect what I know to make the robotic parts meaningful. I can see some white plastic with holes in it and blue and purple wires stick out and are connected in joints made of clear plastic. They are placed on a black board of sorts attached to a bigger board – and there are also some shining lights and a red wire. There are small red, blue and grey tubes. Further up I recognise something that looks like a camera behind a white plastic shell – partly removed. I have no words for most of what I see. This rendering in words is leaving out a lot that I do not know how to describe or make meaning of. I have no idea what connects the nut, bolts and wires inside the white shell, or why they are placed close or

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apart. I know I am in front of a robot, because we are visiting a robotic lab, and it sort of has a head and a belly and some arms. There are also well-known things in the room like a table, chairs and computer screens. The material nature of the robot makers’ discursive practices in this room, however, seems to be beyond reach. I am excluded from perceiving the robot phenomenon as the roboticists do, working on the robot and talking to each other connecting wires while moving about. Like the physicists, they use words, but also engage with materials I have not encountered before. They do not pay attention to me. They are just going on working, trying this or that – apparently having a problem. They are engaging with the materials, but I cannot join them. I ask one of them to explain to me what we look at, and like the physicists, he can talk and talk about what is in front of us. We had some problems with the robot there (…) So, we really try to speed up and fix everything and we are on the failure level right now. That failures only occur once a day, maybe twice, and it’s really frustrating because you’re sitting there waiting for it and it takes very long hours to repeat errors and to get to know how to repeat them, so that you can solve them. (Toby, roboticist) Toby shows us how there had been a problem in the design of the grasping capability of the robot, because its joints are too stiff, so things keep slipping out of its hands. Furthermore, he points to a box of wires; there are some other hardware problems. That’s always the point with components we buy, hardware components, often you find problems only after some tests. And then we are on a mobile system, so even the industrial components have their problems with changing voltages, with not enough power consumption, with feed, so vibrations - there are multiple things that need to be considered. (Toby, roboticist) Furthermore, “the interaction between the hardware and ROS is not always working”, he says. This word is new to me, so I ask: “What is ROS?” He kindly explains: “It stands for Robot Operating System – the world’s largest open-source repository for robotics software. It’s open-source”. Next, he mentioned something about “closed-source components” and “a navigation stack that comes with ROS”. I do not understand what he is talking about. I do not know the material word-sounds but I later find out ROS is a library of code and a network of people helping each other in programming. Now that I have heard the word, I can look up ROS on the internet and learn more. Even so, I can only vaguely grasp what ROS is about since I do not need to look for algorithms in my practice. Obviously, Toby connects the word-sound “ROS” with a number of other words like “navigation stack”, none of which makes sense to me. Though not all

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word-sounds are concepts, ROS may qualify as a new building-block of thinking connected to other concepts. Those who have learned to work with ROS know a lot more about these other concepts than I do. Though my perception of the red wire cannot be separated from an attempt at a meaningful interpretation of the perceived object connected to other wires, I did not know how the things I saw fit together and what the words meant. I was as ignorant as someone who sees a notebook as “something white with four corners” (Vygotsky 1987, 295). In this example, meeting Toby and his robot, I am an outsider that looks at the robot much as we would look at a notebook without knowing the concept. Yet, I had now learned that ROS somehow was important for the robot’s agency – and that it also had something to do with wires. When Barad speaks of “spacetimemattering” (Barad 2007, 390) this includes stories that draw together what space and time constitute as well as what constitutes space and time in an iterative reconfiguring that takes place in the ongoing intra-activity with the world. When I focus on human learning processes, it matters that we learn with what we have learned to connect with material words and things. Thus, I would put time before space in “timespacemattering” because time as preceding learning matters for how we perceive space. The wires are intelligible to me as wires, but as something more to the engineers and something less to a child. The wires change as I learn how they are meaningful. It is not just the word “wire” or “ROS” that over time connects (in this case algorithms and electrical currents), but how the words are meaningful. Concepts matter because they make space and time thicker and richer, or thinner and shallower. It shows in speech. Toby can talk for hours about the wires that matter to him. What I have to say in the situation is soon over and done with. Later, however I learn how blue and red wires may signify differences in electrical currents that are guided by algorithms. Learning to perceive the world as a physicist or engineer is a process of learning that not only transforms how and what we see, but also can make what we see meaningful, rich and nuanced. The richness can get us motivated and emotional about what we see. Stars, robots, alarm clocks and birds are not just material words referring to objects but are already socially informed by concept formation, and these concept formations can change with personal learning experience. As I learn, the concept of “robot” is transforming what I see. Like the physicists and engineers, I begin to look beyond the surfaces and detect wires and sensors behind the shells. In a humanist perspective, stars or robots would be bounded material objects separate from a subject experiencing them. In the posthumanist perspective I propose, things and subjects emerge together in ways that are inseparable from concept formation. For Ingold, objects are the detached version of how we engage with things (Ingold 2007). In my text here, the robot becomes bounded as an object. However, in the moments I shared with Toby and his colleagues at the robotic lab, they, I and the robot as a thing were in the robot phenomenon together and grew out from it. However, the robot also changed as a phenomenon

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as I learned new words and began to grasp new meanings or learned that I was excluded from understanding. Word are what categories, representations, concepts and semantics pivot around. Material words can have specific meanings, like ROS that stand for “Robot Operating System” – the world’s largest open-source repository for robotics software. Words can be disciplined into systematised categories with severe consequences: “You are a girl, Hannele, and not a boy”. These rigid systematisations may hamper our experiences with the world. However, even though we are encouraged to “look again”, as Feynman was and encouraged to “dare to know”, as Immanuel Kant formulated it (Kant 1973[1784], 384), what we see or come to know is never pure and objective Enlightenment but a collective movement of how humans come to know the world. When Kant said we should free ourselves of nonage, it was an impossible task: Enlightenment is man’s emergence from his self-imposed nonage. Nonage is the inability to use one’s own understanding without another’s guidance. This nonage is self-imposed if its cause lies not in lack of understanding but in indecision and lack of courage to use one’s own mind without another’s guidance. Dare to know! (Sapere aude.) “Have the courage to use your own understanding”, is therefore the motto of the enlightenment. (Kant 1973 [1784], 384)1 Kant did not explore how the material and the social was connected – nor did he combine them in learning processes. Kant focused on the individual, rational being not the fulfilment of collectively formed, local, available entanglements. An emphasis on “collective”, challenges the Enlightenment centring of the human and the evolutionary understanding of an increasingly mature Man freeing himself of all tutelage (Kant 1973 [1784], 384). Though neither Kant nor the “Enlightenment” are the ghosts posthumanists try to speak against, the main point is that not only are humans not islands, but they are collective in engaging with materials. In fact, following the educational philosopher Jan Derry’s reading of Vygotsky (Derry 2013), we need the tutelage, or at least the guidance, of humans to free ourselves. Our own understanding is always formed by the understanding of others. Even when we “dare to ask”, the material words formed by collectives of humans and materials are there with us. We may not be aware of how they tutor and guide our thinking and perception, because these cultural learning processes only transform us slowly. It is in this process that we gradually become persons as we dare to think our own thoughts, see for ourselves and acknowledge our deeper emotions. Once we place an emphasis on the collective, we become aware that we need a deeper understanding of what creates intelligibility for some (like Toby and his colleagues) that excludes others (like me) from perceiving a thing like the wire the same way, and which processes align us over time. Whereas categories and representations are something we learn to connect with

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words, concepts are what we learn to think and perceive with, and here the material word plays a very special role. To understand how humans learn to perceive things over time, not detached objects, with words, we need to allow into our excursions psychological processes like learning, memory, emotions and attention, which have largely been excluded from posthumanist theorising, as well as the robots and AI claimed to be human-like. We need to put psychology back into what feminist Donna Haraway has called “the worlding entanglement” (Gane & Haraway 2006).

Meaningful words Stories are not just phenomena full of material words; they are material-conceptual phenomena. I understand Feynman’s story and Hannele’s words that form a story through my own story. We have come to share a material-conceptual understanding of telling stories about the becoming of physicists. Learning with physicists has taught me about the importance of the physical world, which is also conveyed in their stories of how they became physicists. The curiosity about materials is everywhere in their stories, whether they are about stars or particles. However, there is an aspect of the physical world physicists have not explored: the material word. Feynman’s father did not go far enough in his explorations of the birds named “brown-throated thrush”. He only considered what Vygotsky called the “external aspect of the word” (Vygotsky 1987, 47). The material word has not received much attention in psychology either, Vygotsky argues. We could add that the same is true in new materialist studies of “discourse”. The material conditions for making meaning as an ongoing performance include material words, not just as means of communication (as the one I have with Toby), but concealed generalisations that evolve as we learn. The material word is not just a word-sound but a unit of what Vygotsky named “verbal thinking” as the core of word meaning. To talk about words as “external” is only as an analytical device to make us aware of the ongoing process, which in practice is not divisible into “external” and “internal” planes. Though Vygotsky also refers to movements from the external to the inner plane (Toby may say wire and point to a “wire”, which transforms my thinking about “wires”), what makes words meaningful is generalisations, which form perceptions as a holistic process. The word does not relate to a single object, but to an entire group or class of objects. Therefore, every word is a concealed generalization. From a psychological perspective, word meaning is first and foremost a generalization. It is not difficult to see that generalization is a verbal act of thought; its ref lection of reality differs radically from that of immediate sensation or perception. (Vygotsky 1987, 47)

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Can we perceive the word without concepts? We can certainly perceive the world without having words for all we see. There are many things I see inside Toby’s robot that I do not have words for. These words would not be part of the stories I could tell about robots. However, even when I have a word for what I perceive, such as “wires”, I do not understand, have knowledge or can think of “wires” exactly like Toby. This indicates that I do not perceive the wires in the same way as Toby even if we share the word “wires”. The lack of precise words does not mean I perceive the robot’s parts as meaningless. But my generalised perception of the wires is shallow compared to Toby’s. In order to understand how material words can transform material wires (and according to Barad, the phenomena of wires as well), we need to explore the unfixed relation between “word”, “concept” and “meaning”. This relation was dealt with by learning theoreticians, notably Jean Piaget, John Dewey and, lastly, Vygotsky, whom I have chosen as most relevant for posthumanist learning theories because he emphasised our collectivity before individuation. Later research in the West (e.g. Nelson 1974) has since confirmed some of the main arguments made by Vygotsky in his studies of children. Nevertheless, the learning sciences in general, and the algorithmic machine learning sciences in particular, have preferred the understandings of learning proposed by Piaget and/or Dewey and thereby eschewed questions of how human collectivities are formed. As noted by Anne Edwards, Vygotsky is close to American pragmatism (2007) and he draws explicitly on Piaget in his own work. The major difference is that Vygotsky explores what makes humans form meaningful collectives. The emphasis is not just on mental development and agency but on how we become collective in how we understand the world and each other. To Vygotsky, the fundamental process of creating collective consciousness tied to words begins with a gradual, and not necessarily systematised, internationalisation of cultural concepts that simultaneously transform perceptions, attention, memory, emotions and motivations. The relation between material meaningful words, concepts and collective consciousness is not an easy one. First of all, Vygotsky rejects two opposed arguments: 1) that words directly stand for concepts and thoughts, but also 2) that words and thought are completely separated. He furthermore argues that the relation between material words, meaning, consciousness, thinking and perception can be pre-conceptual. This is not, as argued by for instance Piaget (1936), because children’s first words are preconceptual, but because words and concepts evolve together in processes that only gradually develop into conceptual word meaning and verbal thinking. The link between word, perception and conscious thinking goes through meaning. “The path from thought to word lies through meaning. There is always a background thought, a hidden subtext in our speech. The direct transition from thought to word is impossible. The construction of a complex path is always required” (Vygotsky 1987, 281). Contrary to many new materialist approaches, in Vygotsky’s own work there was an emphasis on the historical development of changes. Also, in his own thinking process

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we can follow a development from an emphasis on material things to material words. At first, Vygotsky’s emphasis on the importance of words for concept formation and concept formation for human thinking and agency was partly inspired by his reading of Pavlov. Pavlov found the basis of behaviour in signalisation, that is, “the fact that organisms are able to learn that certain stimuli signal others” (Veer 2007, 28). However, Vygotsky argued that that the human process is not one of signalisation, but one of signification: Human behavior is distinguished exactly in that it creates artificial signaling stimuli, primarily the grandiose signalization of speech, and in this way masters the signaling activity of the cerebral hemispheres. If the basic and most general activity of the cerebral hemispheres in animals and man is signalization, then the basic and most general activity of man that differentiates man from animals in the first place, from the aspect of psychology, is signification, that is, the creation and use of signs. (Vygotsky 1931/1997, 55) Rene van der Veer argues that Vygotsky was inspired by Marxism but also went beyond: [W]ith the concept of signification, Vygotsky introduced the fundamental idea into psychology that human beings are not passively reacting to environmental stimuli but actively determine their own behavior through the creation of stimuli of a specific nature, namely, signs. (Veer 2007, 28) Signification was a social process conveying cultural meaning through material artefacts, not just words but also other material things such as a lynx claw used for remembering a petition. According to the Vygotsky-specialist Anthony Blunden, Vygotsky may have developed his theory of collective word meaning through his notion of this kind of auxiliary artefact, such as visual artificial stimuli in the shape of chosen artefacts that serve to help us remember, such as cards, knots on a handkerchief etc., to end with understanding of the relation between words and concepts. At first “Vygotsky used the term ‘sign’ or ‘symbol’ to mean an artefact which is used to regulate our behaviour by controlling our mind. Spoken words are not counted as signs” (Blunden 2015, 4). Vygotsky early on understood the collective dimensions of making a psychological signification visual and material. He for instance tells the story of the Soviet Far East ethnographer, Vladimir Klavdievich V. K. Arsen’ev, who did research in the Ussuriysk region: [I]n an Udeg village in which he stopped during a journey, the local inhabitants asked him, on his return to Vladivostok, to tell the Russian

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authorities that the merchant Li Tanku was oppressing them. The next day, the inhabitants came out to accompany the traveler to the outskirts. A gray-haired old man came from the crowd, says Arsen’ev, and gave him the claw of a lynx and told him to put it in his pocket so that he would not forget their petition about Li Tanku. The man himself introduced an artificial stimulus into the situation, actively affecting the processes of remembering. Affecting the memory of another person, we note in passing, is essentially the same as affecting one’s own memory. The lynx claw must determine memory and its fate in another. There is an endless number of such examples. But we can cite an equal number of examples in which man carries out the same operation with respect to himself. (Vygotsky 1997, 50–51) However, right after, Vygotsky tells another story of a man who makes marks on a wooden stick, by himself, to remember the words in a sermon. All stories and their word components have both of these aspects involved: our personal and collective stories. We may attach a personal meaning to a knot on a handkerchief or a lynx claw that cannot be guessed by the persons around us, which frees us from continuously remembering to buy apples when we leave work. None of our colleagues, who see the knot, connect it to buying apples, but we use it to adjust our trip from work to home so we are sure to pass by the green grocer. They may, however, collectively infer that the material knot refers to something to be remembered. The signification we share with others is, in cultural-historical thinking, a way to regulate collective behaviours. When we attach cultural (collective) meaning to a material artefact, it becomes a sign through which we can control our own and other people’s memory and agency (Engeström 2007). In his later developments of his key concept of “word meaning” Vygotsky took the idea of the collective meaning of the lynx claw much further. Like the material claw the material word can carry collective meaning, just as it makes sense personally. When Hannele connects “girl” with being excluded from playing with Meccano, the word gets a personal sense for her, but the general meaning of the word is collectively formed. “Girl” aggregates what Vygotsky names “all the psychological facts that arise in our consciousness as a result of the word” and where the personal sense of a material word is very dynamic and f luid, word meaning is more collectively stable and unified in communication (Vygotsky 1987, 276). Furthermore, Vygotsky emphasised that material words and material things were somehow connected. Through a series of experiments conducted on children and grown-ups, he began to explore the relation between material words and material auxiliary artefacts in concept-formation.

Concepts in process Vygotsky based his theory of concept formation on many different experimental studies. An example of these is an experiment conducted in the 1920s by

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one of his colleagues, Lev Sakharov, and later completed by other associates from Vygotsky’s own laboratory (Vygotsky 1978, 103–105). The experiments were inspired by German psychologist, Narziss Ach, where meaningless words like “Gatsun” or “Bik” gradually acquired a certain conceptual meaning. In the experiments conducted by Vygotsky’s team, a group of children, adolescents or grown-ups were presented with some experimental tasks (Vygotsky 1987, 139 ff ). As mentioned by Vygotsky’s colleague Alexandre Luria, Vygotsky not only demonstrated that word meaning developed in children, but he also followed the very material process it took. To demonstrate the development of word meaning, Vygotsky used an original method (developed in cooperation with L.S. Sakharov) which made it possible to identify the systems of connections that stand behind the word and trace the concept’s formation. In using this method, artificial, meaningless words were related to objects with complex features. For example, the word “RAS” was used to designate objects that are small and f lat while “GATSUN” was used to designate objects that are large and tall. Cardboard figures representing these objects -- with the appropriate word written on the bottom -- were presented to the subject. The name on one of the figures was then shown to the child and the child was asked to select all the figures designated by that word. If the child’s selection of objects was unsuccessful, the experiment continued, with the experimenter asking the child to guess what other figures might be designated by this artificial word. This method produced some extremely important findings. (Luria 1987, 364–365) Though Vygotsky was never as explicit about the material aspects of concept formation as I am here, I see his findings as extremely important because the new meaningful word “GATSUN” clearly went through a process that used the material word as a measuring stick for the material shapes connected to it, as they transformed into an abstract concept. There was no representation, but an evolving process where the material word kept its sound, while its conceptual meaning kept transforming.2 In one such experiment, the child is placed in front of a heap of wooden blocks varying in colour, shape, height and size. Then one block (e.g. a big f lat round red block) is lifted up. Below, a nonsense word, like “GATSUN”, is written. The task is now to find the other blocks, which have the name GATSUN written on the back. In this experiment, as in Vygotsky’s theoretical framework developed from it, the whole process of concept-formation and thought begins with a material sign: the physical word: GATSUN (whether written or spoken). Around this word, the child gradually, through a number of different stages, builds up what the researchers have determined is the “real” concept of GATSUN, which, for instance, could be all round and f lat objects. First the child, who is not allowed to see the word GATSUN written on the blocks, uses a method of trial and error to

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find GATSUNs, but this results in disorganised heaps of blocks. Then, through different stages, the child gradually leaves the adult guidance, and is guided by visual shapes and next turns to abstract principles for the discovery of the “real” (here: the intended) generalised concept of GATSUN. The stages include associative complexes, where perceptually compelling ties (such as similarity of difference in colour) make up collection complexes, but it is, as Vygotsky strongly emphasises, not the associations which form concepts. Associations are just a step on the road which ends in concept formation. It is only when the child leaps into a general thinking that a concept is formed, and Vygotsky argues that the processes found in the experiments are the processes of concept formation in everyday lives. Vygotsky himself describe the method as such: This method involves the introduction of: (1) artificial words that are initially meaningless to the subject and have no connections with the child’s previous experience, and (2) artificial concepts that are composed for experimental purposes by combining features so that the resulting set of features is not encountered in the concepts designated by our normal speech. In Ach’s experiments, for example, the word “gatsun” was initially meaningless to the subject but acquired a certain meaning over the course of the experiment. This word became the bearer of a concept designating things that are big and heavy. In a similar way, the word “fal” became the bearer of a concept that designated things that are small and light. (Vygotsky 1987, 122) The most important aspect, for Vygotsky as well as for our discussion here, is that this process of experimenters guiding the child’s perception through the concept-formation of finding the GATSUNs is not a spontaneous free act of the child. It is “guided perception” in the sense that adults, through their verbal communication with the child, are able to correct the path of the development of generalisations; not by imposing their mode of thinking directly on the child, but by supplying the ready-made meanings of the word the child uses to build the complexes, which eventually turn into concepts (Vygotsky 1986, 120). However, guidance also comes about through the material artefacts. As materials are handled, they also guide our sensuous perception. Through this double guidance or tutelage, the child gradually becomes free to think collectively with the adults without the necessity of auxiliary material reminders. After a word meaning is established, we may experimentally choose to free ourselves and for instance again look at GATSUNs just as wooden blocks, but that does not erase what we have learned – it only evolves it. Perception is subordinated to the concept, just as are association, attention and determining tendencies (Vygotsky 1986, 107). First, we learn through guidance and associations with material objects, but at some point, a concept is formed which radically changes our perception of

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“GATSUN” as a material word, as it become a concept. The final stage before real concept-formation is the formation of pseudo concepts. Pseudo concepts are so similar in appearance to real concepts that adults can be fooled by the child’s ability to form concepts, Vygotsky warns. The child and adult understand each other with the pronunciation of the word ‘dog’ because they relate the word to the same object, because they have the same concrete content in mind. However, one thinks of the concrete complex ‘dog’ [the pseudo concept] and the other of the abstract concept ‘dog’. (Vygotsky 1987, 155) In the experiment, this is the case when the child creates a sample of wooden blocks that could just as well have been assembled based on an abstract concept and not just perceptual likeness. For instance, when the sample is a yellow triangle and the child picks out all triangles, he could have been guided by the general abstract idea or concept of a triangle. The experimental analysis shows, however, that in reality, the child is guided by the concrete, visible likeness and forms only an associative complex, which is not the same as Vygotsky’s “true concept”. Pseudoconcepts and potential concepts are acquired by habit, spontaneously and without conscious awareness, but true concepts can only be acquired with conscious effort and awareness. This is true because true concepts are part of a system of concepts, which stands between the subject and object, and in principle are independent of the sensuously given properties of the object which is given to the subject. (Blunden 2012, 275) Later, Vygotsky expanded and rejected some of the findings of the earlier experiments with Sakharov. Concepts are not just formed as means of communication, they are also systems of knowledge, thinking and perception. It is through concept formation we come to distinguish between say red and blue wires, or birds or f lies. Where the first type of learning experiments is tied to material processes, like the one described earlier, scientific concepts learned in school, for instance, are tied to a system of concepts that teach children to become little scientists. However, neither scientists nor children think in a chain of words like speech, Vygotsky emphasised (Vygotsky 1987, 281). Concepts are never closed, discrete entities, but keep evolving. For Vygotsky, as for many of his followers, there is a difference between the “real” and “pseudo” that a posthumanist learning perspective may further explore. In these explorations, I suggest that we do not only focus on children vs. adults, but also the differences between novices and experts. Pseudo concepts might make the adult and the child (or a novice and an expert) believe they can carry out a conversation about triangles, wires or stars.

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In reality, for Vygotsky, the adult conversation is guided by real concepts whereas the child’s is guided by pseudo concepts. This, Vygotsky underlines, does not create a schism in the child. On the contrary, it secures that there is no hard-andfast boundary between thinking as a child and as an adult. A word becomes a concept at the moment of mutual understanding; that is, in the meeting between a pseudo concept and a concept (Vygotsky 1987, 123). The same could be said for a novice like me learning about wires and actuators in a robot from the experienced Toby. This process of concept formation involves how we learn to think and perceive with the world. In the process of concept formation, thinking and speaking are not two completely separate processes that influence each other mechanically. Just like a sea cannot be reduced to what some sciences find it to be (hydrogen and oxygen), words and meanings are not separate but merge in what Vygotsky calls “verbal thinking”, when material word-sounds become generalised word meaning. Therefore, generalization in word meaning is an act of thinking in the true sense of the word. At the same time, however, meaning is an inseparable part of the word; it belongs not only to the domain of thought but to the domain of speech. A word without meaning is not a word, but an empty sound. A word without meaning no longer belongs to the domain of speech. One cannot say of word meaning what we said earlier of the elements of the word taken separately. Is word meaning speech or is it thought? It is both at one and the same time; it is a unit of verbal thinking. (Vygotsky 1987, 47) Word meaning is not fixed, but continuously develops as novices become experts. This development involves both the communicative function of speech and the intellectual word meaning with which we think and perceive the environment. I shall emphasise that it also involves the wooden blocks, the materials that Barad rightly said tend to be overlooked. As we use speech to communicate with others, and as we perform acts with things, we change our word meaning and thinking. Vygotsky primarily built his observations of this complex path on experiments with children. My experiences among the roboticists and the physicists has taught me how adults, like myself, can be novices in learning the collective word meaning as well. In this learning process, material words become the means of concept-formation and the child learns to direct their own mental process and the ability to regulate their actions. The lynx claw and the material word “GATSUN” and the “brown-throated thrush” anchor our collective thinking once these artefacts are learned as meaningful. Now we can begin, as individuals, to look for a “brown-throated thrush” and “GATSUNs”. The free will of humans means that we can choose to disregard what we have learned about the GATSUNs and Lynx claws, but we cannot “unlearn”. If we now disregard

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the word meaning we now know is shared by a collective, we also know that these acts of disregarding may also challenge a normative space of expectations. Learning from an ornithologist would expand my concept of the brown-throated thrush, just as my learning from Toby expanded my conceptualisation of wires. Though I do not pass through exactly the same stages as small children learning to form a concept around a material word, I recognise Vygotsky’s description of how children struggle to abstract and reconnect the right features in Toby’s robot. When I have aligned my conceptual thinking with Toby and his colleagues, I have also aligned my thinking with theirs in a normative space, where we find what Vygotsky names a “true concept”: The concept arises when several abstracted features are re-synthesized and when this abstract synthesis becomes the basic form of thinking through which the child perceives and interprets reality. As we have said, the word plays a decisive role in the formation of the true concept. It is through the word that the child voluntarily directs his attention on a single feature, synthesizes these isolated features, symbolizes the abstract concept, and operates with it as the most advanced form of the sign created by human thinking. (Vygotsky 1987, 159) This true concept is inseparable from the true object. A detached object – like seeing a clock as a white circle with black dots – is not how we usually engage with things. We see a clock because it’s meaningful for our practice (Ingold 2007). However, paradoxically, in order to do the playful experiments proposed by Edwards (2010) we often first have to realise things as detached objects in order to play, freeing them from our habits of mind. And even when we engage in playful experiments, our perceptions are already formed in normative spaces of guidance and tutelage. Like the example of the notebook, the particles and the stars, we have learned to perceive things like clocks as meaningful objects. A special feature of human perception — which arises at a very young age — is the perception of real objects. This is something for which there is no analogy in animal perception. By this term I mean that I do not see the world simply in color and shape but also as a world with sense and meaning. I do not merely see something round and black with two hands; I see a clock and I can distinguish one hand from the other. (Vygotsky 1978, 33) We may strive to perceive the white circle with black dots again, but once learned, it is not as easy as perceiving the clock. Similarly, I gradually learn to see robots as wires and actuators and have a hard time understanding how before, I could see them with the stories of quasi-living beings.

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There are many more things in the world than true objects. I only have a very limited conceptual understanding of the robot, as compared to Toby, and perceive many things that are just as meaningless as “GATSUN” was to the children. However, I cannot help using what I have learned as I perceive, and from that point of departure I cannot help learning. Just as things, wires and bolts keep evolving, so does my concept of robots. Whether we engage with things or are detached observers of objects, we have already learned something about how to make the materials meaningful with the world. In other words, a clock is not something we have knowledge of from birth – it becomes a material object through a learning process that is both conceptual and material, involving concepts and things.

Conclusion Chapter 5 The posthuman is a reaction to Kant’s Enlightenment Man, who simultaneously stands apart from society and nature (Knox 2016, 26), but as noted by Derry, the Enlightenment man easily becomes a “strawman” for an attack on abstract rationality (Derry 2013). There is, however, in posthumanism a greater emphasis on human and non-human collectivities that make a difference when we envision a posthumanist future. Kant wanted men (sic!) to free themselves from the social bonds (Kant 1973/1784). In situated practice learning theory, practical and pragmatic engagements have been emphasised (Edwards 2007). Here social bonds form human collectives in a social and material practice. In this chapter I have further expanded the notion of collectives across situated practices. For Vygotsky, it is the collective cultural formation of thinking tied to material words that frees humans from being condemned to an experience of a “hereand-now” world. In this humanist thinking, material words are the endpoint of thinking, that humans can use to transform themselves and material surroundings in cultural ways, which are forever on the move. Collective meanings of words are more stable than the personal sense we aggregate, but material things are involved in all concept formation – even what we call “abstract thinking”. The posthumans furthermore emphasise new and shifting boundaries of things in entanglements with discourse, and I have argued that material words are indispensable in the discursive formation of these boundaries. The human of which we are post is maybe to some extent a “strawman”, but in many (psychological) theories “he” still comes across as a generalised fixed solipsist character, who knows more and more about stars and particles as a priori separated from them. Posthumans are, on the contrary, always in the plural, entangling with other posthumans and things through their formation with shared concepts and materials. What we must acknowledge is that these humans, also in posthumanist theory, do not just sometimes come into being with tutelage, guided by past material and social engagements. What I emphasise in relation to posthumanist theories is that humans, however we address a material world, are always there in phenomena with our collective preceding learning.

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The gradual collective transformation of perception and thinking is not an alignment of a cultural gaze on a natural world that becomes represented through categorisations. It is an alignment of ways of seeing that align Feynman and Anne with their fathers across local communities of practice. Anne learns to align her perception with her father, who already perceives with the collective of physicists. Some people just see yellow dots when they look at the stars. Others see the pictures and stories told by the ancient Greeks. And others, for example, religious people, see the yellow dots on the firmament as God’s work. Their perception of stars is tied to words, stories, and other available material and cultural resources like telescopes, religious books as well as the parents, teachers or peers from whom we learn. The children as physicists-to-be are encouraged by relatives and the materials themselves (like when Maria arrange the f lies) to expand their concept of birds, f lies and stars that continuously transform meaningful perception with materials and thereby move what they see and the concepts they think with. There are material words in all of their stories and things because there are material words in all word meaning. Material words are formed collectively as meaningful words through stories and things. The collective stories made by words are told by the physicists who are themselves formed in collective processes that transform preceding learning into something meaningful in situations. We shall never know if their stories of fathers, f lies and telescopes are actually “true” – and I cannot judge if other engineers will question how Toby tells a story of how the robot is meaningful. Though concepts like “robots” have genealogies, as we saw in Chapter 4, these genealogies are tied to collective material practices. Even if there is a difference between personal sense and word meaning, collective word meanings of the same word evolve according to material engagements. Some humans connect the material word-sound of robots with media presentations, and others with a lifelong experience with hands-on learning with building machines. Both connections are tied to conceptualisations with the material word. Concepts do not just connect to other concepts but to the materials involved. There are no a priori true or untrue concepts and there are no a priori true or untrue stories.3 Concepts and stories built on concepts are true to specific patterns of material-conceptual arrangements. It is when we look for materiality in concept formation that we find the patterns behind meaningful collective perceptions. In my patterned arrangements there are a number of physicists who have parents or relatives who are also physicists or scientists. In some cases, this meant access to particular materials like telescopes, Legos and Meccano, that both retrospectively and as preceding learning inf luenced a physicist perception of the material world. Though gender matrixes may explain why we statistically find fewer women than men in physics and engineering, this kind of dichotomy does not explain how Rita and Hannele, along with Toby, Walt and Richard, became physicists or engineers capable of transforming a material world for others. I have suggested that learning meaningful concept formation is part of the intra-actions

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that create phenomena. The most important resource for concept-formation seems to be the social and material designation of verbal thinking. Physicists like Richard, Anine, Walt and Hannele and engineer Toby have learned to perceive the world as physical at some point. It has something, but not everything, to do with category labels such as “Senegalese”, “girl” and “Lego”. Learning a category does not automatically align us as collectives. We cannot even assume that all physicists and engineers see the same “clock” or “star” as we do ourselves. Does this reinstate the separation between subjects and objects that have been dissolved in posthumanist theorising? Far from it. Learning is not a process of stop-and-go. It is a subtle, messy and serendipitous process of transformation that, in an ongoing process, aligns humans with their material environments in practical engagements. In these processes materials and concepts continuously form each other – not as a process that sustains a nature–culture dichotomy, but as a process that creates new boundaries and cuts and simultaneously aligns cultural material-conceptual processes of materialisation. The engineers and physicists have, in the Vygotskyan learning apparatus in which I have entangled them all, learned to perceive the world in particular ways since infancy, that make them a cultural-historical collective across time and space. Over time I have aligned my story with theirs just as I, over time, have come to perceive the world more as a robot maker. I learned to align with the roboticists, as they showed me what their robots are really like. I learned to see behind the prima facie robots on screens and into white plastic shells. Now I see man-made wires, nuts and bolts and learn to think of robots in a new way. Though our material-conceptual worlds differ, I align with Toby and his colleagues’ collective understanding of robots. Learning is tied to perception (which includes touching, smelling, and hearing, as will be shown in later chapters) but also to learning new words, as well as connecting new word meanings with well-known words. The resources, which make up my individual thinking and stories, are not just my own. What I have proposed so far is that physicists and engineers, as human beings, have learned to share a particular way of perceiving and bounding the world in relation to material words and things. This world is not fixed in categories or finally bounded representations or simply situated practice. By making word meaning a basic unit of analysis, it is clear that material-conceptual boundaries also cannot just be shuff led arbitrarily around. There are collectives behind all individual perception and thinking, as we shall see in the next chapter.

Notes 1 Nonage refers to man’s immaturity and inability to shed tutelage. Original: Aufklärung ist der Ausgang des Menschen aus seiner selbst verschuldeten Unmündigkeit. Unmündigkeit is das Unvermögen, sich seines Verstandes ohne Leitung eines anderen zu bedienen. Selbstverschuldet ist diese Unmündigkeit, wenn die Ursache derselben nicht am Mangel des Verstandes, sondern der Entschließung and des Muthes liegt, sich seiner ohne Leitung eines andern zu bedienen. Sapere aude! Habe Muth dich deines eigenen Verstandes zu bedienen! is also der Wahlspruch der Aufklärung.

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2 In cultural-historical theory many people have strong opinions of what Vygotsky intended or not intended. My errand here is not to enter into the debates of how to read Vygotsky and how he has been misinterpreted (e.g. Derry 2013). As I do not read Russian, some may find that I misunderstood Vygotsky’s intentions, yet his words as I have met them have made the relation between material words, material things and objects – and the ongoing concept formation – meaningful in my discourse. 3 In relation to Jan Derry’s sophisticated philosophical arguments (Derry 2013) I want to emphasise that my endeavour is not to find the true meaning of concepts (what robots are really like), but to understand how access to different materials form different collective understandings of the same material word – as when the word-sound “robot” for some is meaningful with preceding learning solely of media materials, whereas for others the meaningfulness of the word also comes with hands-on experience.

References Andersson, I. (2016). What’s the matter with discourse? An alternative reading of Karen Barad’s philosophy. Research output. Master thesis. Stockholm University: Stockholm. Retrieved from www.d ​iva-p​ortal​.org/​smash​/get/​d iva2​:1054​847/F​ ULLTE​XT01.​pdf 2 May 2018. Barad, K. (2003). Posthumanist performativity: Toward an understanding of how matter comes to matter. Signs: Journal of Women in Culture and Society, 28(3), 801–831. Barad, K. (2007). Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning. Durham, NC: Duke University Press. Bateson, G. (1972). Steps to an Ecology of Mind. New York, NY: Ballantine Books. Blunden, A. (2012). Concepts: A Critical Approach. Leiden: Brill. Blunden, A. (2015). Tool and sign in Vygotsky’s development. Advances in Psychology Research, 121(1), 1–27. Braidotti, R. (2013). The Posthuman. Cambridge: Polity Press. Butler, J. (1993). Bodies that Matter: On the Discursive Limits of Sex. New York, NY: Routledge. Ceder, S. (2015). Cutting through water: Towards a posthuman theory of educational relationality. Research output. Doctoral thesis. Lund: Lund University. Clark, A. (2003). Natural-Born Cyborgs: Mind, Technologies, and the Future of Human Intelligence. Oxford: Oxford University Press. Derry, J. (2013). Vygotsky: Philosophy and Education. Hoboken, NJ: Wiley Blackwell. Edwards, R. (2010). The end of lifelong learning: A post-human condition? Studies in the Education of Adults, 42(1), 5–17. Edwards, A. (2007). Vygotsky, mead, and American pragmatism. In: H. Daniels, M. Cole & J. V. Wersch (Eds.). The Cambridge Companion to Vygotsky (pp. 77–100). Cambridge: Cambridge University Press. Engeström, Y. (2007). Putting Vygotsky to Work: The Change Laboratory as an Application of Double Stimulation. Cambridge: Cambridge University Press. Feynman, R. (1999). The Pleasure of Finding Things Out. Cambridge: Perseus Books. Galilei, G. (1610). The Starry Messenger. Pamphlet. Venice: Thomas Baglioni Publisher. Gane, N. & Haraway, D. (2006). When we have never been human, what is to be done?: Interview with Donna Haraway. Theory, Culture & Society, 23(7–8), 135–158. Hasse, C. (2008). Learning and transition in a culture of professional identities. European Journal of Psychology of Education, 23(2), 149–164. Hasse, C. (2015). An Anthropology of Learning. Dordrecht: Springer Verlag.

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Hasse, C. (2017). What robots really are. Retrieved from https​://ro​botre​publi​c.org​/cath​ rine-​haase​-what​-robo​t s-re​a lly-​a re-a​rent/​. Hasse, C. & Trentemøller, S. (2008). Break the Pattern. A Critical Enquiry into Three Scientific Workplace Cultures: Hercules, Caretakers and Worker Bees. Tartu: UPGEM. Ingold, T. (2007). Materials against materiality. Archaeological Dialogues, 14(1), 1–16. Ingold, T. (2008). Bindings against boundaries: Entanglements in an open world. Environment and Planning A, 40(8), 1796–1810. Ingold, T. (2011). Being Alive: Essays on Movement, Knowledge and Description. London: Routledge. Ingold, T. (2013). Making: Anthropology, Archaeology, Art and Architecture. New York, NY: Routledge. Ihde, D. (2000). Epistemology engines: An antique optical device has powered several centuries of scientific thought. Nature, 406(6791), 21. doi:10.1038/35017666. Kant, I. (1784/1973). An answer to the question: What is enlightenment? In: P. Gay (Ed.). The Enlightenment: A Comprehensive Anthology (pp. 384–386). New York, NY: Simon and Schuster. Knox, J. (2016). Posthumanism and the Massive Open Online Course: Contaminating the Subject of Global Education. London: Routledge. Lave, J. (1988). Cognition in Practice. Cambridge: Cambridge University Press. Lave, J. (2009). The practice of learning. In: K. Illeris (Ed.). Contemporary Learning Theories (pp. 200–208). London: Routledge. Lave, J. & Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation. Cambridge: Cambridge University Press. Luria, A. R. (1987). Afterword to the Russian Edition. In: R. W. Rieber & A. S. Carton (Eds.). The Collected Works of L.S. Vygotsky (Vol. 1, trans. Norris Minick, pp. 359– 373). New York, NY: Plenum Press. Nelson, K. (1974). Concept, word, and sentence: Interrelations in acquisition and development. Psychological Review, 81(4), 267–285. Piaget, J. (1936). Origins of Intelligence in the Child. London: Routledge & Kegan Paul. Ricœur, P. (1984). Time and Narrative (Vol. 1, Trans. K. McLaughlin and D. Pellauer). Chicago, IL: The University of Chicago Press. Tuin, I. v. d. & Dolphijn, R. (2010). The transversality of new materialism. Women: A Cultural Review, 21(2), 153–171. Retrieved from http:​//www​.tand​fonli​ne.co​m /doi​/ abs/​10.10​80/09​57404​2 .201​0.488​377. Veer, R. v. d. (2007). Vygotsky in context: 1900–1935. In: H. Daniels, M. Cole & J. V. Wersch (Eds.). The Cambridge Companion to Vygotsky (pp. 21–49). Cambridge: Cambridge University Press. Vygotsky, L. S. (1986) A. Kozulin trans. and Ed. Thought and Language. Cambridge: MIT Press. (Original work published 1934.) Vygotsky, L. S. (1987). Thinking and speech. In: R. W. Rieber & A. S. Carton (Eds.). The Collected Works of L.S. Vygotsky (Vol. 1, trans. N. Minick, pp. 39–285). New York, NY: Plenum Press. Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Processes. Cambridge: Harvard University Press. Vygotsky, L. S. (1997). The history of the development of higher mental functions. In: R. W. Rieber (Ed.). The Collected Works of L. S. Vygotsky (Vol. 4, trans. M. J. Hall, pp. 1–251). New York, NY: Springer. Williams, K. C. (1994). Mapping the margins: Intersectionality, identity politics, and violence against women of color. In: M. A. Fineman & R. Mykitiuk (Eds.). The Public Nature of Private Violence (pp. 93–118). New York, NY: Routledge.

6 SOCIO-MATERIAL CONCEPT FORMATION

Material words are spoken by humans and robots or written in PowerPoints in MOOCs and on Facebook. They are basic to classrooms, experimental laboratories and marketplaces. Humans are a communicative species. As posthumanist learners we cannot do without material words. This is because material words are meaningful to us as collectives. Material words are not the same as discourse (understood as arrangements, which constrain and enable the meaningfulness of utterances and agency in specific time and space). As argued in previous chapters, making material words meaningful is a process of preceding concept formation that can lead to new materialisations, but what makes materialisations of words collectively meaningful in situations? And how does this process differ from the algorithmic learning of machines? These are the topics of this chapter. In the humanist linguistic paradigm in general, discourse has sometimes been considered a system of representations (e.g. Hall 1997) but representations have often treated material words as fixed and embedded in systems. Hall gives us this example: Look at any familiar object in the room. You will immediately recognize what it is. But how do you know what the object is? What does ‘recognize’ mean? Now try to make yourself conscious of what you are doing – observe what is going on as you do it. You recognize what it is because your thought processes decode your visual perception of the object in terms of a concept of it which you have in your head. This must be so because, if you look away from the object, you can still think about it by conjuring it up, as we say, ‘in your mind’s eye’. (Hall 1997, 16) Hall furthermore distinguishes between two systems: first, a language system and second, a conceptual map where the material lamp is correlated with the

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concept of a “lamp”. This double system has often been treated as one of dichotomies that became a main target of postmodern critique, as well as in feminism. As noted by Braidotti, the dichotomised system of representation in discourse was hierarchical in the sense that difference was often pejorative (Braidotti 2013, 15). References to “Woman”, in this system of representations, always implied the “other side” – “Man”, who turned out to be the Vitruvian Man, raised above all other creatures. Postmodern theory made it a target to deconstruct all such dichotomies – save for the dichotomies of culture–nature and language–reality, with an emphasis on how culture and language have constructed reality and nature. This has led to a neglect of material reality and the body (Alaimo & Hekman 2008, 2–3). It has also been overlooked, as the fish overlooks the water it swims in: that systems of representations, dichotomies, categories, and discourse result from processes of learning to conceptualise materials. Contrary to what was assumed by Stuart Hall, who noted that “it is not the material world which conveys meaning: it is the language system” (Hall 1997, 25), materials and material words convey meaning through learning. Once learning is placed in the phenomena of discourse, we can begin to ask new questions about what the collective materiality of discourse consists of. Basic questions like these have been asked both by engineers and by posthumanists, but few have looked to human conceptual learning theory for answers, and even fewer have looked at how learning to think entangles material words, concepts, meaning and materiality. That the formation of material words, other materials and meaning merge in concepts has captured some interest during the linguistic turn in learning theory, and especially in the Vygotsky-inspired cultural-historical learning theories. It is here we get a closer look at how learning informs the conceptual thinking behind analysis and debate about categories, representations and as discourse. This thinking is, at its base, a collective endeavour. Children begin to use signs, and even when they sit by themselves and babble or mutter words in play, they are already uttering a collective material-conceptual voice. Our own theory suggests that the child’s egocentric speech is one aspect of the general transition from inter-mental functions to intra-mental functions, one aspect of the transition from the child’s social, collective activity to his individual mental functions. (Vygotsky 1987, 259) We do not, from the Vygotskyan analytical perspective, learn to think in dichotomies like “male” vs. “female” as a basic process, nor do we learn to think in representations, where a material word stands for a material object or an abstract thought. Concepts are not the representations that are the result of summarising general processes of repeated features such as likenesses in a family portrait. This would leave concepts as “the aggregate of these common features, features isolated from a series of similar objects” (Vygotsky 1987, 162).

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We rather learn to think in concepts that connect our thinking and spill it out in the material and social world it came from. We can, with even more emphasis than Vygotsky, in a posthumanist approach, rather see concepts as relational in a social and material world. A concept is in constant transformation from what we have learned and takes part in the creation of new phenomena as well as new on-the-spot conceptual learning. Though this process seems to imply humanist thinking in outside–inside, or in Vygotsky’s terms intermental and intramental,1 this does not mean there are fixed boundaries in our engagements with the world, but rather it emphasises that these boundaries do not stop at the brain and not even at the skin. How materials and “the social” work together to inf luence the collective learning processes of concept formation can be further explored.

Material entanglements The concept of “robot” is an interesting place at which to start in an exploration of how concept-formation and materials entangle in formations of humans. First of all, “robot” is a rather new concept, as it only replaces automata sometime during the first half of the twentieth century – and it still gets new specific meanings that differ according to practices. It is, as argued in Chapter 4, simultaneously connected with real life, stories and imagination. The imagined robot is not partitioned from real life, but takes part in the creation of new phenomena, just like experiences with hands-on robots. Furthermore, robots are in many people’s imaginations expected to be capable of thinking and learning like humans. Whether this is really the case depends on our definitions of how humans “think” and “learn” about, for instance, “robots”. We have already noticed that it does not make sense to try to give a finite version of the concept of “robot”. Programmers, engineers and other natural scientists do not agree on how autonomous or intelligent their creations are and can be. Many of the robot designers we have interviewed see robots as mere machines; some see them as slaves (Bryson 2010). Others, like Moravec, see them as our progeny (1999). The word-sound “robot” is the expression of a concept with no clear boundaries. As noted by some robot-makers, it is impossible to capture what a robot is in a technical definition, not least because of the high pace of development in robot technology. As material practices keep changing, so does the concept. In the words of Illah Nourbakhsh, a professor of robotics and director of the CREATE Lab at Carnegie Mellon University: [N]ever ask a robot-maker what a robot is. The answer changes too quickly. By the time researchers finish their most recent debate on what is and what isn’t a robot, the frontier moves on as whole new interaction technologies are born. (Nourbakhsh 2013, xiv)

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Put differently, “robot” as a pre-existing entity and a fixed, final representation does not exist. It is a phenomenon, which is continuously becoming in the material practices of different people, and over time is rapidly transformed through and by transforming these practices. It is therefore no surprise that there seem to be many different ways of conceptualising robots. Given their expected inf luence on our everyday lives in a Western country like Denmark, it seems reasonable to expect that also Danish children have learned to form some kind of concept of robots; but what characterises this concept? How do children conceptualise “robots” and do their conceptualisations differ from engineers? We decided to do an experiment in a Danish school with 23 pupils who were 10 to 11 years old.2 Contrary to the experiments Vygotsky and his colleagues conducted with the nonsensical words described in the previous chapter (Chapter 5), we turned the experiment on its head. We began with the word-sound “robot” and asked the children to materialise in drawings what came to mind. One of our aims was to explore how the children’s materialised perceptions overlapped with each other.

VIGNETTE 6.1: MATERIALISING ROBOTS We are in a classroom in a Danish school. Twenty-three children are placed in groups around square and oval tables (see Figure 6.1): two tables with four girls, one table with five girls and one boy, one table with eight boys and at another table one boy is sitting by himself. The room is bright and well-equipped with an interactive whiteboard, loudspeakers, a blackboard and two black bulletin boards. On one of them, a poster announces an afterschool invitation to a movie show. On the tables, the researchers have placed good-quality A4 papers in front of each child and on each table is a stack of quality water-based art pens in vibrant colours: yellow, orange, red, pink, purple, green, blue, black and silver. The children listen with full attention to one of the researchers explaining: Draw a robot, or more if you would like to, that does something and maybe does something together with others – and there may be all sorts of other things on paper. I would like you to fill the paper with anything you think might be related to robots. We would also like to see where the robot is placed, whether it is doing something with someone else or whether it is making something itself. Apart from this, you may draw the robot exactly as you please. At one table, we find Nelly, Luna, Marlene, Kaspar, Maryam and Ida. At first, they talk about the pens and how you need to shake the silver pen to get it to work. Then they talk a bit about a girl who got ill and could not participate in the drawing event. Then they begin to explain to each other what kind of robots they intend to draw. “Mine is to work in a toyshop”, says Luna. “Mine

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FIGURE 6.1 School

children in Denmark drawing robots. (Photo taken by Cathrine Hasse during experiments in 2015.)

will be love-robots”, Ida explains and Marlene exclaims “How cute”. In a later conversation, Ida explains: I have drawn – not quite what I envisioned, but whatever … It is a flowering field with two love-robots. They can help if, for instance, Marlene is in love with someone and does not know what to do, then they can help. Give her advice. They are also in love. Nellie starts drawing a huge silver figure and Luna keeps glancing at it. “I really don’t know how to draw this, seriously”, Luna says. “Hey you have taken my head”. Ida leans over to look at Luna’s drawing. “No, I drew it before you did”, Luna says and looks at Nellie for encouragement. Kasper is not looking at them but draws his own silver robots with caterpillar feet. Next to him Maryam is also drawing caterpillar feet on her robot – and lifts it to show it to the others. Kasper makes his robot hold a shotgun and begins a new drawing where the robot is holding a tray. Nellie is now filling out her square body with silver. Ida draws a green field and a red heart above two robots next to each other. Marlene looks and says”, Oh, how beautiful”. Ida exclaims, “Don’t look – you break my imagination. This is a great black”. Luna says to Nellie, “Oh, how cute it is. What a lovely nose”. She leans over and says, “Can I borrow your nose?” [referring to the square nose Nellie has given her robot]. Nellie points to her huge happy silver robot with the square nose: “I just imagine this is a friend. If you are lonely, this is a robot friend”. Luna says, “How sweet”. At the neighbouring table one of the boys has left to read a magazine, but the remaining seven continue to draw and comment on each other’s drawings. “It looks like Star Wars”, Johan says. “Mine is a robot at McDonalds that gives all the kids ice cream”, Lasse explains the others. Marcus shows them

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his robot; it is a lively little fellow who is holding a strange artefact in one hand. “What is that?” Eric asks. “It is a kendama of course”, Marcus explains.3 Marcus has drawn a kendama, just like the one that hangs around his own neck. Eric’s own robot has joints held together by metal parts, as Johan’s does, but it does not move on caterpillar feet. He later explains he has seen a robot like that with his father at an exhibition. At another table, Sorine draws a square cardboard-box robot watching TV and drinking a hot cup of oil. On top of his square head [Sorine has given it a male name, Jens], she places many dots. “What is that?” asks Nina, who sits next to her and leans over to look. “It’s Jens, of course”, Sorine explains and the others laugh. It later turns out Jens was a robot they built with cardboard boxes in the school’s art class and the dots were small pearls that they used to fill in the head to illustrate brain cells. Many of the children name their humanoid robots, but the more technical robots, drawn by Eric, Kasper and Maryam, do not have names – they just “do things”. Selma draws a robot that looks very much like the movie robot “WALL-E” and later she explains: It was WALL-E I could see in my head. They are on the Moon, WALL-E and Eva. WALL-E has been sent up to collect garbage. Humans can throw garbage to the Moon so there is no garbage on Earth. Moreover, WALL-E can open this (she points to WALL-E’s stomach in her drawing) and the cans can get in there, and then he can squeeze them to cubes and stack them. And Eva is there to keep him company. While the children explain their drawings to each other and the researchers, they keep engaging with each other’s stories; they verbally comment on Lea’s fire-shooting robot and Marcus’ kendama, and Sarine’s robot who, she explains, is weak, watches TV and drinks oil “like any normal person” and has pearls for a brain. Astrid has also made a robot with fireballs. They discuss if a robot can feel pain, and Selma believes if one of her robot’s own fireballs hits Astrid or Dea’s robots, they will feel pain. Nevertheless, explains Lea, her robot can have fireballs inside without being hurt. Selma insists: “If you push his fireballs at his feet, they will hurt”. The robots made by Selma and Ida can fall in love: “Eva is a bit scary, but WALL-E can fall in love and blush and you can teach him all kinds of stuff, like a human – he is just a robot, who can do a bit more”, Selma explains. As the robots emerge on paper, the children increasingly comment and borrow ideas from each other to the point where they accuse each other of stealing features. Marcus has drawn a red heart in his robot and Lasse has written “I love kids”, and now Casper combines the two and writes and draws: “I [draws a heart meaning ‘love’] kids”. Marcus looks up and says, “Hey, don’t steal my idea!” “Why do I steal your idea? I made a heart?” Casper continues to draw the red heart – it is a ‘girl-thing’”, he explains.

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Gender is an issue for both boys and girls. The boys comment on the heart made in the robot’s body by Kasper and the word love. Kasper explains it is “the girly way of drawing – a red heart and love”. Selma and everyone in her group agree there are male and female robots even if Sorine calls Jens an “it”. “WALL-E is a boy and Eva a girl”, Selma explains. “Yes”, Sorine confirms, “Jens is, after all, a boy. It can be seen from his looks – not so much hair, and he does not have bows or wear a dress. Bows are a sign of ‘girl’!” The drawings do not exhaust the children’s knowledge about robots. Sorine expands the discussion in the subsequent interview with the researchers and points to Jens, her oil-drinking, TV-watching robot: Take Jens [her robot] … If you were to make a real robot, you should have – like inside a phone, I’ve seen green plates with all sorts of dimmers on [chips], and then the dimmers go onto the plate and then it goes into the robot and then it has to read it from the inside and then … it knows! One just tells it and then it listens and then it is settled some place in the brain and then it remembers it.

Windows to concepts Child psychologists, such as Piaget and Winnicott, have long used children’s drawings as “windows” into the child’s world. In a humanist tradition, drawings can be used by teachers to bridge gaps between perception and concept-formation, helping children move from everyday spontaneous thoughts to scientific conceptualisations, as they revisit and explore their drawings (Brooks 2009). Drawings can be seen as making it possible to follow the development of a child’s general representational capability (e.g. Machón 2013). In our experiment, the posthuman-inspired analysis of the children’s drawings does not see the drawings as a window to a separate inner world. When we ask the children to draw robots, we, as experimenters, have evoked the robotic phenomena emerging on paper, together with available materials. We, the experimenters, have set up an apparatus to study children’s concepts of robots as they come out in drawings. We do not merely stand by to objectively record the outcome. We cannot take for granted, as in humanist thinking, “an intrinsic outside boundary of the apparatus, which incorporates human concepts within its bounds while ejecting the observer to the outside” (Barad 2007, 144) and ignore the co-constitution of subjects along with objects. We acknowledge that our presence, just like the pens and paper and the children’s ultra-social engagements with each other, as well as their preceding learning, partake in materialising the envisioned robots. We have already decided on the drawings as a particular “agential cut” (Barad 2007, 175) that is part of the phenomena they help produce: namely, insights into children’s concept of robots. We are part of the apparatus, just as the materials, like pen and paper, are entangled in the robot phenomena emerging in drawings. At the time, exactly what we explore is not even completely clear to us. In my analysis, the drawings

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only gradually become a material expression of an argument of the importance of human collective concept formation in the posthumanist apparatus. The engaged children entangle all sorts of things in their drawings, from cardboard robots to robots in movies. They also include many components from their everyday lives as children in a Danish middle-class culture, such as visits to McDonald’s and getting soda pops and chips. Such materials would hardly be included in a semantic, lexical description of the “robot”. Furthermore, the robots disclosed do not stand in any dichotomised or hierarchical relation to humans or nature. The robots we meet do not seem to be placed in a system of representations. Yet, a concept is being relationally formed in active thinking which, in spite of many differences, shows the children’s drawings as expressions of preceding learned collective resources being called forth and employed in a here-and-now momentarily collective process. Within a humanist Vygotskyan framework, child educationalist Margaret Brooks argues that a drawing can be seen as a visualisation of a concept (2009). To form concepts, children go through a long process of connecting world and word. That is, in this humanist perspective, there is an already formed separation of world and word. Verbal speech consists of sensuous word-sounds that are turned into verbal thinking, and verbal thinking is the prerequisite for both writing and drawing. Verbal thinking depends on word sounds, not just as communicative means, but also as a “cell”, a unity of word and meaning (Vygotsky 1987, 47). In contrast to “elements”, like the hydrogen and oxygen found to be water, this basic analytical unit, verbal thinking, retains all the basic properties of the whole, for example, water. For a Vygotskyan, this unity refers to a whole, like a cell in a body. Vygotsky explains how psychology has been stif led in explaining the relation between word and world in that the two main approaches that explore this relationship have either seen words as meaning, or meaning as completely separate from words. Vygotsky criticises both of these approaches for overlooking the importance of “meaning”. Thinking and speaking is neither a fusion nor a complete separation of thought and word. Meaning cannot, for Vygotsky, be separated from the physical word; neither can it be reduced to it. This is where Vygotsky can open up a new understanding of posthumanist learning. The connection between matter and meaning is not one of abstract discourse or representational language and meaning; the connection entangles the very material word-sound. “In here” and “out there” does not capture the Vygotskyan process of internalisation. There is no reified representational meaning tied to word-sounds granting the discourse agency over matter or concepts. Nor can matter be granted agency over discourse and concepts. Inspired by the Vygotskyan framework, we can acknowledge that matter is moved from within phenomena by the constant reworking of the concept-material though learning processes. The visual expressions of meaning, whether in speaking, writing or drawing, are expressions of what can be analysed as the unity “word meaning”.

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The ultra-social process of learning, which emphasises that humans learn collectively in ways no other animals does (Tomasello, Kruger, & Ratner 1993), forms the children’s robotic phenomena just as much as any material and conceptual arrangement. The children unfold the concept of robot in the practice of drawing. The meaning of robot is not a representation, but a collective ongoing creation with the world. “Collective” does not mean all humans or things are alike. If we connect a Vygotskyan approach, such as the one found in Brooks’ arguments, with a posthumanist approach, it is necessary to specify that when we ask the children to draw robots, we do not expect that they materialise a uniform representation of a robot, where we can count discrete elements that “make up” the concept as a whole. Neither do we expect the children to draw robots that add up to a materialisation of a true scientific concept of a robot. The very materials, as pointed out by Barad, set limits to what, for instance, Sorine can put on the paper. She wants to draw a robot, Jens, with a mind of its own. She could have drawn green chips or brain tissue instead of pearls – but it is difficult to paint “it knows!” What the children draw into their entanglements is what they can draw with the available resources. These resources clearly include the pens and paper, but also the children’s’ present and past experiences. Resources are, even when they are material, always social. Ultra-social humans never cease to reach out to the world as we saw in Chapter 1, where Andreas and his schoolmates tried to include NAO in their ultra-sociality. The tables, chairs, pens are, just as the engagements of the children and the very concept “robot”, the available potentials for drawing with a long natural-cultural history of ultra-sociality. In this perspective there is no isolated nature, as the material world is ever present in cultural artefacts and vice versa. Tim Ingold asks: For material things to be enrolled in cognitive processes, must they already have been rendered in cultural forms? Why should people think with artefacts alone? Why not also with the air, the ground, mountains and streams, and other living beings? Why not with materials? (Ingold 2013, 98) This point by Ingold emphasises that there may be cognitive processes outside of culture, a point also emphasised in many places by my colleague, Theresa Schilhab (Schilhab 2017). This is a good point, which unfortunately is outside the bounds of this book to explore. Materials, for instance, have effects even if we remain unaware of their presence. Our material world surroundings, whether hot weather, rivers or robots, are always present but not necessarily explicitly and consciously conceptualised. This point has also been made by postphenomenologist Don Ihde, who emphasises our “background relation” with a material world that for most humans today is not just air, wind and soil but includes the humming of highways, refrigerators and heating systems (Ihde 1990). However, what Ingold names “cultural artefacts” may not be so very

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different from mountains and the air. And the sound of running tap-water may, from a learning point of view, not be all that different from the sound of a small brook. The habitual way we learn to discern background noise from important presence evolves in different cultural ways over a lifespan in learning. Even when we do not realise our collective concepts are with us, they are there to limit our experiences. Without concepts, we’d be overwhelmed by the richness of the environment. Our minds would be swamped by what James called the “big blooming buzzing confusion” which is our “immediate sensible life” –that is, perception shorn of all conceptual interpretation (with reference to William James 1911, 50 in Boden 2006, 450). Humans rarely experience a material world without some kind of preceding learning that can emerge as cultural resources in the formation of phenomena. There is in this perspective no materials, whether refrigerators or streams, that are free of ultra-social culture. Though we heuristically may continue to talk of “nature” and “culture” they are always one in ultra-social learning. For the ultra-social children reaching out to each other and a world of things, it makes no difference if they learn what is meaningful about air and earth or humanmade art pens and paper. What we humans learn to be meaningful differs in time and space. The difference is not nature versus culture, but the combined nature-culture versus another combined nature-culture. We find diversity in how humans think about and experience materials like art pens, streams and refrigerators even when the materials are not ref lected upon, put into words or cognitively present. Enacted cognition is tied to continuously shifting learned cultural boundaries of meaning-making, evolved in practices that render some bodies meaningful while simply overlooking others. This overlooking is a necessity from a psychological point of view, as argued by psychologist William James (1911 in Boden 2006). Cognitive processes are, as argued, mainly cultural and are collective processes that constantly enrol material things even when we are not aware of the cultural forms of our perception and thinking. People do think with air, as discussed by Ingold (2013), but even air is experienced differently according to cultural experiences. Air, the ground, mountains and streams and even light are perceived in relation to our cultural preceding learning – as anthropological studies have shown (e.g. Bille 2017). There is no difference between what we may want to call “cultural” or what we may want to call “natural” material things enrolled in cognitive processes from this Vygotskyan perspective, but there is a difference in how cultural formations of persons have learned to focus and create boundaries. For humans to be able to think, recognise and imagine, materials are rendered in the cultural form of concepts that may concern air, a mountain, as well as a lamp, or artefacts like art pens. In the posthumanist perspective it is the apparatus that forms the conditions of what matter comes to be important and what is excluded. As researchers, we did not choose to exclude air, the light in the room, or how children were breathing in our experiment; we just did not

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think about it. Had I been a biologist, I may have measured how the children were breathing while drawing. I only mention it here to underscore that it is the whole apparatus of in- and exclusions that enact the agential cuts. In the end this results in the boundaries and properties of the phenomena of “the robots”, where “phenomena” are, in Barads words: “the ontological inseparability of agentially intra-acting components. That is, agential cuts are at once ontic and semantic” (Barad 2007, 148). However, as argued, “semantic” is, in my discussion of human learning, tied to meaningful concept formation. The material word “robot”, once uttered in our request to make drawings, immediately begins to entangle with social relations, just as when the girls “borrow” a particularly cute nose, and “steal ideas” for how to draw a “robot”. The art pens also set their collective boundaries and inspire simultaneously, for instance, when a red colour is available on the table and inspires the drawing of a heart. All this creates the materialisation of the drawings here and now, but the past is also present in the shape of media stories and classroom episodes as well as personal experiences. The concept of robots emerging is not just in the children’s heads but in all of these relations.

Fantasies in drawings By connecting the children’s drawings with their verbalised statements in interviews about the drawings, we can learn something about how children have learned to think about robots. All of the children we interviewed know more about robots than what is shown in the drawings. For instance, Sorine tells us that she knows that real robots must be programmed and have little green chips inside, but she does not know how to draw that. We also gradually learn that the fantastic robots in the drawings are often inspired by media robots, and that movies and the internet are the most important source of inspiration for the children, next to their “borrowing” from each other in the situation. As they draw and comment on each other’s drawings, they learn to expand the concept of “robot”, and the outcome is both material and conceptual. The same process takes place for us as researchers as we learn from children and their drawings. Our concept of robots is formed in a human collective material practice and not a fixed given representation. We were not aware, when we first saw the drawings emerge, that so many children were inspired by stories of robots from movies. The incredible stories about robots doing things like communicating with “beeps” or collecting garbage that appeared on the white paper seemed to me, at first, to be expressions of free fantasy. What we, the researchers, at first inspection of the drawings took to be free fantasy, however, often turned out later to be inspired by Disney movies and games (see Figure 6.2). This process of bringing in references to something actually experienced, when asked to use free fantasy to create, emphasises another point made by Vygotsky: children, more often than adolescents and adults, depend on references

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FIGURE 6.2  Many

children are inspired by movies. Ten-year-old Mads explained to the researchers that he has drawn R2D2 from the movie, Star Wars. (Photo taken by Cathrine Hasse during experiments in 2015.)

to material resources when they fantasise. Materialisation of a word like “robot” is, following Vygotsky, never completely free fantasy. Vygotsky argues that adults would be more capable than children to free their thinking from the immediate material constraints. Adults can, in other words, think abstractly about robots, without any materials in their surroundings directing this thought. Young children on the other hand, Vygotsky argues, to a larger extent need the materials at hand in order to be able to think. Yet, from a posthumanist perspective, though adults have more potential and preceding learning of concepts to make use of, they are also in subtle ways affected by the material constraints – even when they expect to have freed themselves of social inf luences they are still affected largely by the materials and the history of relating to materials. Vygotsky’s notion of concept formation probes deep into the collective process of imagination and fantasy, showing that fantasy and imagination are interwoven

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with a process of concept formation. In his discussion of “Imagination and creativity in the adolescent” (Vygotsky 1998), Vygotsky claims that, contrary to our expectations, children’s fantasies are less creative and imaginative than adolescents’ fantasies. Children draw on the material resources at hand largely more than adolescents and adults, since they cannot free themselves from their material surroundings. Adolescents are freer to fantasise when they begin to think in concepts, Vygotsky argued (Vygotsky 1998, 153), whereas children remain dependent on material resources for their thinking. Adults, Vygotsky argues, more than very small children, perceive when they think, whereas small children think when they perceive. I believe Vygotsky could more clearly have emphasised that adults also depend on materials when thinking. Throughout life adults also think when they perceive just as they perceive in thinking. Concepts continuously entangle new material for children as well as for adults. Though, without doubt, Vygotsky was right in his study of how small children develop concept formation through auxiliary artefacts, and, maybe, therefore depend more on “borrowing” from each other, the material world continues to be important for concept-formation and word meaning all through life. The drawings only materialise some of the meanings these young children connect to the word-sound. Processes of in- and exclusion of what goes into the drawings are constantly negotiated in the situation. The phenomenon “robot” or “robots” comes in many versions but there are patterns across the diversity. These patterns do not come from the drawings alone, but also come about as the children explain them to us. We learn about what we see, but like physicist Richard Feynman did in the previous chapter, we can also learn to look again. We, the experimenters, for instance learn we are ignorant of what is before our very eyes. When the children explain their drawings to us, our material-conceptual perception of the drawings change as we align with what the children perceive. We keep learning that in our first perception of the drawings, we were ignorant of so many things shared by the children. Even long after our conversations with the children, we keep learning about what was meaningful in the drawings. For example, some of the many references to robots in games and movies only gradually dawned on us in the subsequent analysis because we did not know these material references ourselves. What we took to be mere fantasy figures invented by the children were, as mentioned, often inspired by fictive robot characters, learned by the children when they play games or watch movies. Since we had not seen these movies, we could not recognise them in the drawings. This was the case when we discovered that a round-headed robot in a drawing, which differed from the majority of the otherwise square-bodied robots, was not made from a child’s free fantasy. A Google search revealed that it was a drawing of the inf latable robot Baymax from the 2014 movie Big Hero 6. Even the creative robot drawn by Selma was not free fantasy, but inspired by the movie, WALL-E, where the figure that she names Eva was depicting the figure Eve from the movie. WALL-E was a 2008 Disney production which many of

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the children apparently had seen before our drawing session. Both of these movies were unknown to the researchers at the time of the drawing sessions. In the assistants’ notes, WALL-E was spelled Wally. When we later tried to Google the two words “Wally” and “robot”, WALL-E came up and we could immediately recognise that Selma’s storied world and word meaning had been inspired by the Disney movie. After we learned about these inspirational sources, the children’s drawings looked different to us. We learned from the interviews that some children brought in their own individual embodied experiences, like references to a sprained ankle. This made direct links between the child’s own personal experiences and the robot’s limping in the drawing. Such connections only came up in the interviews and merged with our perceptions, just as many other types of clarifications did. All children to some extent drew on personal experiences when materialising the word “robot”. Some children, like Sorine, have built cardboard robots, which inspired her drawing of the oil-drinking cardboard Jens. They were not trying to illustrate the same abstract concept of robots. There was no collective agreement on how to represent a robot. The drawn robots, apart from movie references, involved all sorts of the children’s own material everyday activities, like playing kendama, or a visit to a museum or McDonalds, which have nothing to do with the technical robots a robot-maker may have drawn.

Word meaning and scientific concepts In a humanist tradition, we can, like Vygotsky, distinguish “true concepts”, as an abstract scientific concept of a robot, from “pseudo-concepts”. The level of abstraction and connections made in relations between materials and concepts differs according to our learning processes. One of the examples given by Vygotsky is the number nine, which can be both an abstract mature concept and a rich materialised thing. I will give an example. Let us compare the direct image of a nine, for example, the figures on playing cards, and the number 9. The group of nine on playing cards is richer and more concrete than our concept “9”, but the concept “9” involves a number of judgments which are not in the nine on the playing card; “9” is not divisible by even numbers, is divisible by 3, is 32, and the square root of 81; we connect “9” with the series of whole numbers, etc. Hence it is clear that psychologically speaking the process of concept formation resides in the discovery of the connections of the given object with a number of others, in finding the real whole. That is why a mature concept involves the whole totality of its relations, its place in the world, so to speak. “9” is a specific point in the whole theory of numbers with the possibility of infinite development and infinite combination which are always subject to a general law. (Vygotsky 1997, 100)

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Vygotsky’s approach tells us something about the concept that makes it clear that humans do not learn through information processing or pattern recognition like machines. The concept is not formed by processing the perception of richly informed materials like playing cards from which we then elicit individual traits like the nine dots. We rather perceive the card with the abstract concept of “nine” placed in relation to other abstract numbers. It is the knowledge of the object in its relations, in its connections. Second, the object in the concept is not a modified image but, as contemporary psychological investigations demonstrate, a predisposition for quite a number of judgments. (Vygotsky 1997, 100) A collectively formed normatively judged phenomenon emerges with Vygotsky’s theory of learning. The mature or true concept of “nine”, the “real whole”, means that we can lean on an integral world view that places nine in connection to other numbers, whether “nine” is materially present as a playing card or not. This is not an example of Barad’s exteriority-within-phenomena (Barad 2007, 175). Vygotsky’s concept is a system of judgements “brought into a certain lawful connection: the whole essence is that when we operate with each separate concept, we are operating with the system as a whole” (Vygotsky 1997, 100). However, even if there is a normative concept of “nine”, for Vygotsky the concept is not fixed but continuously evolving. For Barad, however, there is not even a “system as a whole” but an ontology merging the material and the discursive. If we reject a humanist a priori “lawful connection”, we can instead emphasise that ultra-social learning sets the normative boundaries for how materials and concepts together make the world ontic. Material-conceptual phenomena are for humans collectively learned perceptions that within ever-changing phenomena make us focus on particular features and overlook others. As emphasised by Jan Derry, this does not imply an “anything-goes” approach. The concept of “nine” is firmly anchored in abstract rationality (2013). A pseudo-concept is, in the humanist tradition, as we saw in the former chapter, a concept that adult’s mistake for a concept in a conversation with a child, who in fact only makes use of a pseudo-concept. An example is the adult teacher who simultaneously gives a child a yellow triangle and asks the child to identify triangles in a heap of objects, and the child then readily picks out triangles in a heap of figures. The child has not yet formed an abstract concept of “triangle” to think with, but picks the yellow, red and blue triangles out of the heap because they share a shape. For example, given a yellow triangle as a model, the child selects all the triangles in the experimental materials. A group of this kind could arise on the basis of abstracted thinking (i.e., on the basis of the concept or idea of a triangle). However, our research indicates that the child actually unites the

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objects on the basis of their concrete, empirical connections, on the basis of simple association. He constructs only a bounded associative complex. He arrives at the same point as one would have through thinking in concepts, but he takes an entirely different route. (Vygotsky 1987, 142) A mature concept for Vygotsky was not necessarily a rejection of more personal word-meaning units, but he emphasises that the abstract concepts we think with are social. This is why a clock is not just a white circle with moving black lines, but something we can think about and communicate collectively. We perceive “clock” normatively. The “clock” is not within or without the material arrangements; it is the material-conceptual entanglement. Though some children may reject keeping the time, most school children around the globe have learned the normative conception of a clock. The scientific definition of “concept” is understood in the Vygotskyan theoretical framework as a result of a process of learning. The Vygotskyan learning theory distinguishes between learning everyday, spontaneous and scientific concepts, as well as between learning true concepts in contrast to pseudoconcepts and potential concepts. In the same way, other particular learning steps towards formation of concepts can be identified – for instance “complexes” (see Blunden 2012 for a thorough introduction to Vygotsky’s work on conceptualisation). Vygotsky made a clear distinction between concepts we form spontaneously (such as when the child calls all four-legged animals “dog” and eventually forms a “mature” concept of a dog and can think and communicate about dogs) and the concepts we cannot form without explicit instruction, like mathematical numbers. Vygotsky did save a certain place for scientific thinking as “the purest type of nonspontaneous concepts” (1987, 177) by which he refers to concepts in a wider scientific system that can only be acquired through instruction. Like water, word meaning is a real component and the smallest unit of collective human verbal thinking that, Vygotsky argues, cannot be further broken down. Linguists may, like natural scientists, try to divide the word meaning of “water” into the phonetic and the semantic, as scientists divide water into oxygen and hydrogen, but then it is no longer the word meaning of “water”, just as water ceases to be water when dissolved into elements. Vygotsky referred to word meaning as “a phenomenon of thinking only to the extent that thought is connected with the word and embodied in it” (Vygotsky 1987, 244). For Vygotsky, word meaning is thus the smallest unity of thinking which cannot be further broken down. A breakdown into elements is an Enlightenment humanist understanding of “water”, but it does not capture how water is meaningful for people other than those interested in chemical reactions. “Water” is much more, Vygotsky says, from the law of Archimedes to something we use to put fire out with, and from cooking potatoes in water to personal experiences of bathing in a lake on your birthday. If the unit is the word meaning of water, surely many connections to other units can be made. When the

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word-sound “water” is predetermined by the researcher to consist of separate elements (oxygen and hydrogen), these “products” of a chemical analysis are, as Vygotsky explains, of a different nature than the whole they come from. The disparate elements lack the characteristics of the whole of “water” – and “possess properties that it [the whole] did not possess” (Vygotsky 1987, 45). The concept of “robot” may not be as widely spread as concepts of “nine”, “clocks” and “water” – and from a scientific point of view it seems as if it evokes associative complexes in children, rather than a mature concept in the children’s drawings. From a humanist point of view, a technical understanding of robots implies that a fuller concept of robots would include how robots consist of a system of technical elements and this may be moving the phenomenon to a new level of generalisation, far away from the lively creatures made by children. But this could also be similar to when water is partitioned into two elements by scientists; in any case, it would require some kind of adult instruction into how robots are really made. According to Vygotsky, beginning an analysis of concept formation with the normative scientific concepts says little of the spontaneously formed unity of word meaning in practical life: We also argued that decomposition into elements is not analysis in the true sense of the word but a process of raising the phenomenon to a more general level. It is not a process that involves the internal partitioning of the phenomenon which is the object of explanation. It is not a method of analysis but a method of generalization. To say that water consists of hydrogen and oxygen is to say nothing that relates to water generally or to all its characteristics. It is to say nothing that relates to the great oceans and to a drop of rain, to water’s capacity to extinguish fire and to Archimedes’s law. In the same way, to say that verbal thinking contains intellectual processes and speech functions is to say nothing that relates to the whole of verbal thinking and to all its characteristics equally. It is to say nothing of relevance to the concrete problems confronting those involved in the study of verbal thinking. (Vygotsky 1987, 244) What Vygotsky focusses on in his humanist perspective is how humans psycho­ logically make meaning of the world. However, he also notes that meanings are connected to the material words which in their turn are connected to the concepts formed in action and thinking. In Vygotsky’s experiments, it is emphasised that concept formation was dependent on learning from adults or peers that are more advanced in their thinking than the children. Contrary to what is sometimes understood by Vygotsky’s readers, verbal thinking is not a process from an already formed collective to an already formed individual, who has then processed the given information. What we may call an individual is formed in the process of learning. A posthumanist approach goes further down this road and emphasises the importance of materials (past and present) in the process of ultra-social learning, while engaging in, for instance, the material practice of drawing. The word “robot” is just a starting point for the materialisation of an explosion of potential

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collective connections tied to the word-sound that unfolds in the very process of ultra-social engagements with pens and papers. Drawings and conversations with the children, about what is included or excluded, reveal what the children have learned and continue to learn about the robot phenomena that is materialised in the drawing situation. Neither drawings nor interviews are an exhaustion of what each child knows about robots, nor what they know as a collective. A posthumanist approach to learning emphasises, along with Vygotsky, that an exhaustion can never be achieved – even if robots have a conceptual history that must be respected, the word meaning keeps evolving in phenomena. The material, art pens, simultaneously opens and closes what can be drawn. Since brown is a missing colour, this may affect the drawings of, for instance, dirt. A Vygotskyan approach to posthumanist learning emphasises that within the phenomenon of the drawings of robots, the material cannot be separated from humans engaging with each other. The ultra-social learners lean over tables and borrow from each other’s “cute noses” – and do not draw all that they know. Matter is deeply entangled in the social and material engagements. In an individualist paradigm, drawing robots could be seen as a solitary process of representing. Representation means that there would be a high degree of agreement of components – the elements connected with “robot” would be the same for all children, as they would be effortlessly the same in algorithmic learning. The children however, although they bring different preceding learning to bear, strive to align with each other. They constantly reach out to the world as we saw Andreas did in Chapter 1. Their curiosity is never “empty”, like the robot Jibo’s, that we also met in Chapter 1. They are curious to see what others make of the word “robot”. The ultra-social children glance at each other’s drawings, if they are not outright trying to copy details from others. Even so, no two drawings are alike. It is a messy and evolving process involving the past as well as the present material word-sounds (the statement of drawing robots), as well as the available pens, paper and the very physical social situation – all these aspects create the boundaries for what is materialised on the papers. The children do not choose to deselect scientifically correct robots from a stack of available cultural resources of toys or media robots. Contrary to the “true” triangle, the example used by Vygotsky, there is no adult guidance during the activity and their robots come in many shapes and with many meanings. The children materialise robots responding to the material word-sound “robot” with their alreadyformed word-meaning “cell” of verbal thinking tied to a Danish child’s world. In our projects, material robots in school settings are often explained as educational tools that will help children prepare for a robotic future (Esbensen et al. 2016). However, our research shows that few teachers explicitly helped the children form a deeper knowledge of what having actual robots around entails and how that differs from robots in movies. The pervasive collectively shared image of a robot by children is humanoid. This was the main collective aspect we found

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in children’s drawings which differed from the perception of robots found in the engineering culture.

ISO-standard robots Most of the robot-makers’ practices do not, as of yet, involve the creation of humanoid robots that are as lifelike as the autonomous creatures drawn by the children. Robot-makers mainly build robotic, automated machines used in industrial work – but increasingly also aim at making social robots with inbuilt AI or algorithmic learning. They materialise off-the-shelf robots, rather than just drawing them, and their visions are tied to cultural resources such as written rules and standards, which serve as directly ultra-socially shared means of aligning robot creation (see Figure 6.3). Robot-makers for instance rely on professional standards that define the material robot as an: actuated mechanism programmable in two or more axes with a degree of autonomy, moving within its environment, to perform intended tasks.

FIGURE 6.3 NAO, which

we met in Chapter 1, is an example of a social robot built on standard requirements. It has to have loaded batteries, or electrical cords and be carefully programmed to seem lively. (Photo taken by Cathrine Hasse during experiments in 2015.)

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Autonomy in this context means the ability to perform intended tasks based on current state and sensing, without human intervention. (The ISO-Standard 8373:2012 Robots and robotic devices – Vocabulary).4 In our own research with robot-makers like Toby, who we met in the previous chapter, Chapter 5, statements about their work on robots involves many ­technical terms such as “machine learning”, “ventricles”, “valves”, “actuators”, “cylinders”, “pneumatics”, “electronics”, “assembly parts”, “learning algorithms”, etc. Toby and his colleagues also refer to paragraph 2.28 of the ISO standard that defines a smart robot as “a robot capable of performing tasks by sensing its environment and/or interacting with external sources and adapting its behaviour”. The ISO standard refers to an industrial robot with a vision sensor for picking up and positioning an object, mobile robots with collision avoidance and legged robots walking over uneven terrain (Nevejans 2016, 10).5 Industrial robots that used to be caged in predictable environments in order to be able to function as safe devices have now moved into open spaces to collaborate with humans – and therefore need new collaborative skills. It is possible to distinguish two main typologies of industrial robots: robots operating in isolation from human beings, usually constrained inside protective cages; and “collaborative” robots, which are designed to interact physically with workers. (Bertolini et al. 2016, 383) Older types of computerised software that control robots were Boolean (named after the Enlightenment mathematician George Boole, who invented Boolean Logic or Boolean algebra). Inside the robotic software, thousands (or more) logical “gates” form digital circuits. One of the real advances in technology is the number of logical gates you can fit into a microchip. The gates build on a logical system of what is allowed to pass through the gates, for instance “A” and “B”, “A” or “B”, “A” and not “B”. This kind of instrumental logic is the one Maria, whom we met in the previous chapter arranging f lies, cherishes so much. Both physicists and engineers are familiar with this logic in a world where everything has a proper place. Through inputs from AI (artificial intelligence) and feedback sensors, offthe-shelf robots move closer to the autonomous creatures drawn by the children. Robots have broken out of the former controlled cages of many industrial robots, where every stop-and-go movement of the machine was coded and planned beforehand, and now move towards a more cybernetic “closed-loop control”, in which case robots incorporate feedback and “learn”. The combination of closed-loop systems and sensors have profoundly changed what robots can do. This feedback mechanism allows robots with closed-loop control to do what

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robot-makers now call “learning” while moving in an environment. Today, many use a combination of the two systems. However, the system only works if the robots’ sensory feedback (the information that feeds into the robotic system) is reliable and accurate, which is most likely not the case once robots leave their caged environments. The next steps in robotics have therefore been to improve the robots’ capacity to “understand” their complex environment with more AI and deep machine learning. Consequently, the logic of the Boolean system has become fuzzy and even probabilistic (building on theories of probability), which has opened yet another door towards a posthuman future with new kinds of collaboration between robots that are named “social”. Robots today are agentive devices in a broad sense, purposed to act in the physical world in order to accomplish one or more tasks. In some cases, the actions of a robot might be subordinated to actions of other agents, such as software agents (bots) or humans. A robot is composed of suitable mechanical and electronic parts. Robots might form social groups, where they interact to achieve a common goal. A robot (or a group of robots) can form robotic systems together with special environments geared to facilitate their work (Prestes et al. 2013, 1199). The ability to sense the environment through sensors and AI and to work in “social” groups characterises some of the new developments in robotics. Thus, the robots engaging with humans in the drawings are not just fantasy. However, the deeper understanding of how these machines work differently from themselves as humans is lacking in most of the children’s drawings. None of these new developments in robotics are easy to draw, but they transform the materials entangled with the word-sound and concept of “robots” to include wires and microchips and not just media images. As noticed by the robot-maker Nourbakhsh: [R]obots are a new form of living glue between our physical world and the digital universe. (Nourbakhsh 2013, xiv-xv) It has been suggested that attempts to define robotic agential devices fur­thermore should not exclude the virtual bots. Though these digital devices are not operating through sensors in the physical world, the (ro)bots are autonomous and sensing through electronically messaging interfaces (Lyyra 2015, 11). With all these advances, many reports envision that a new wave of technological innovation – a Fourth Industrial Revolution – is already on its way and will bring a radical change to industries and labour markets worldwide (e.g. The Human Capital Report 2016 – http:​//rep​orts.​wefor​u m.or​g/hum​an-ca​pital​repo​r t-20​16/).​ In many countries, like Denmark for instance, this development is expected to bring significant changes. Partly because approximately 30 to 40 per cent of known jobs are estimated to disappear (in other countries the numbers are even

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higher) since the affected countries will lack workforces with the new required skills. This is in line with reports on how robots will affect the labour market in general. Though there is dispute about the effects (mass unemployment or the development of new types of jobs), no one questions that we must educate our children to meet these challenges (e.g. Brynjolfsson & McAfee 2013, Nourbakhsh 2013). However, the engineers, who outline the concept of robots with ISO standards for us in our research, rarely comment on the possibility of completely human-like, humanoid, intelligent, autonomous robots, like the ones found in the children’s drawings, in their definitions of and work with robots.

Hands-on experiences In our interviews with robot-makers they are, like Toby whom we met in a previous chapter, perceiving robots as machines. The children drawing robots generally conceptualised with different sources than the robot-makers and in this way present imaginaries of lively, playful robots. For the robot-makers, these movieinspired imaginations are not conceptualising the real off-the-shelf robots. In the words of one of the robot-makers, whom I have renamed Marcia:6 I think that the robotics that the public know is not the real one. It’s the one presented by the media. So, the imaginary is totally different from the reality. And maybe some effort could be done in - in spreading the actual status of the art – the actual state of the art. How the things are in the real reality, let’s say. Because there is so poor knowledge about science in general, I think, and robotics in particular. (Marcia, robot maker, REELER) Robots from movies like Star Wars, Robocop, Minions and WALL-E are fictive media creations that seem to think and respond as the ultra-social humans. Not only do they look like humans, with physical human features, but they also act socially and learn like humans. ISO standards do not cover robots of this kind because their existence, so far, is restricted to the media world. Though intelligence and autonomous behaviour loom large in people’s everyday spontaneous conceptions of robots, and are often connected to a posthuman future, these existences are restricted to movie and game creations. With all the advancements in robotics, machine learning and microchip technology, robots do not learn to engage like humans. Even today’s most advanced systems connecting all we know about robotics, artificial intelligence and machine learning are nothing like human learning: Deep down we find nothing but formalized linear step-wise instructions (algorithms) and data that are represented by strings of binary numbers in order for necessary calculations to be performed. These rules that govern the self-dictating are realized and inscribed in machines by their designers.

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Thereby it appears that such machines are automatic instead of autonomous in the sense that they do not possess the right or condition of self-government in the sense as a free person does, namely constitutive autonomy. (Lyyra 2015, 9) That robots can function independently is a fact, but their independence is restricted to specific pre-programmed tasks. Some robot-makers would completely refute the idea that robots will ever do what the children conceptualise as robot phenomena: robots as entities that do everything the children do, like watch TV, play kendama, dance, eat ice cream, show emotions, show they are friendly, and fall in love. Does all this mean that because the children have received no adult guidance, they have not formed a mature and science-based concept of robots? This may not just be a question of adults guiding children to a fuller and more scientifically correct concept of robots. What concerns me here is not so much a nature–culture divide of the social constructivist era but the diversity found in how we learn to conceptualise. What our drawing material showed was that the guidance may come from the materials themselves. Some children in the classroom apparently had more experience with technical aspects than their classmates. They brought in knowledge about these technical aspects, and included these in their drawings. Maryam, Johan, Eric and Kaspar all draw technical details in their drawings. It turns out the children who draw technical details, such as wires and joints, have formerly been part of a team building robots with Lego Mindstorm and have direct experiences with robots in the physical world. As ultra-social learners, it mattered what kind of material practices the children had engaged in. Though these children also “borrow” from each other and know the movies their classmates materialise, they are inspired by more engineer-like experiences. As it turns out, most of the children who draw technical details have some experiences building or engaging with real-life robots. Maryam, who drew a robot with caterpillar feet (see Figure 6.4), had not only been part of the Lego Mindstorm team, but encountered a robot at home because she saw how her brother built it. She explains in an interview: This red dot means that the robot is switched off and the yellow means it is about to start, and green means it is on. And here are the wheels (…) Here is a cord tied to it and this is just a wire tied to the robot. Interviewer: Great. Where did you get the idea for this (drawing)? Maryam: Umm, it is my older brother. I got the idea from him, because he also works with robots. And then I got the idea from him. Maryam has played with Lego Mindstorm, and she has learned about robots from her brother. Erik, who saw a real robot with his father and has “loved robots since [he] was a baby”, refers to robots with “degrees of freedom” (a phrase also

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FIGURE 6.4  Maryam,

age 11, has hand-on experiences with robots having caterpillar “feets”. Her robot is controlled by brain waves, she explains to the researchers. (Photo taken by Cathrine Hasse during experiments in 2015.)

used by robot designers). He shows us how he has included them in his drawing and points to curved joints that make his robot capable of moving. His moving robot is not moving as easily as the one drawn by Marcus who plays kendama, because Erik knows that robots cannot move like children. So, movement is here drawn with a concept of robots that includes material constraints. Many of the children who were part of the Lego Mindstorm team draw robots which are much more machinelike than those of the children who did not take part in this group work and who draw robots inspired from movies. Those who have built Lego Mindstorm robots have been inspired by Lego’s toolkit which includes caterpillar wheels that move the robots around. These inspirations are clearly visible in their drawings, for instance in the drawings of Johan and Maryam who both built robots with caterpillar “feet”. Though the movie figure, WALL-E, also has caterpillar feet, it is the children who build robots themselves who remember this detail.

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It seems to matter to the drawings if the children have formed a concept through hands-on experiences with building robots and thus can think about and visualise robots in a more technical manner. Though the children with experience in robot-making make clear during our talks that they also know the movie robots that have inspired their pals and also draw humanoids, they have given salience to their hands-on experience in their drawings (even when they sat at different tables and did not directly inspire each other). Pupils with technical experiences, like Maryam, Eric and Johan, seem to have learned, to some degree, to align their conceptualisations with the engineers’ perception and concept of robots. We can argue that this makes their conception “truer” or more mature – as the media robots are less realistic than the machinelike robots. This is, however, not what I want to emphasise. It is rather my point, from a posthumanist perspective, that learning and concept formation with materials inform not just what we perceive, but also what we materialise. This approach makes clear that the dividing line between adults and children (of a certain age) is not crystal clear. Even if most engineers refer to robots as machines made up of parts that do not resemble a humanoid like the ones in public media and the children’s drawings, some robot-makers seem just as inspired by the same movies as the children. However, there is a difference which is tied to the adult’s freedom to think conceptually. In engineering, the concept of robots has evolved. One of the grand old men of artificial intelligence, Alan Mathison Turing, would, for instance, not outright reject that machines might (with some exceptions): Be kind, resourceful, beautiful, friendly, have initiative, have a sense of humour, tell right from wrong, make mistakes, fall in love, enjoy strawberries and cream, make someone fall in love with it, learn from experience, use words properly, be the subject of its own thought, have as much diversity of behaviour as a man, do something really new. (Turing 1950, 446) Though this has been a dream, most robot-makers today have more realistic visions. Even though all makers of robots know and think of robots as machines some robot-makers today explicitly work to wow an audience. They create robots they consider to be truly human-like, even if they, when met in their labs or at fairs, seem like advanced wax-dolls out of a Madame Tussaud’s museum. These robot-makers (like Ishiguro we met in a previous chapter) go through sustained efforts to convince the world that their machines not only look like, but are like humans (e.g. Borggreen 2015). And they succeed as long as their machines are caged in media or in the controlled environments of science fairs and not exposed to the general public’s hands-on experiments. Even if these roboticists seem to align with the children’s concept of robots, many robot-makers refute the more extreme claims made about humanoid

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robots (e.g. Stone et al. 2016). They are not convinced that robots will not be able do things such as be friendly and act in human-like ways in the near future. In a humanist paradigm, technical issues would determine if we can make robots that “fall in love”, perform “being a friend” and act autonomously like WALL-E. Today this is not included in the way engineers, who adhere to the ISO standards, understand robots, but as material entanglements evolve, in the future it may be. So, are the children’s ideas about “robots” that build on resources from movies and personal experiences, immature pseudo-concepts because they present robots as autonomous? Do they need to learn about the “real” robots evoked by Marcia, Toby and Victor? The Vygotskyan tradition has hosted many debates about the relation between everyday and scientific concepts. In scientific concepts the relationship to an object is mediated by other concepts in a hierarchical system of interrelations and, contrary to most everyday concepts, scientific concepts are learned with conscious awareness of the mastery of the concept (Vygotsky, 1987, 191). This is connected to the notion of scientific concepts like the triangle discussed by Vygotsky, and further discussed by Jan Derry (2013). “Triangle” and “robot” are normative concepts. Though children may have a hard time learning what constitute triangles, there is a true mathematical definition, which should be learned in school. Likewise, it could be argued that there is a “technical” concept of robots, which differs from the media versions, but should be included to present the concept in all its historical becoming. The technical aspects of robots, however, may not need adult guidance to be conceptually evolved. The matter the children include in their drawings is not first presented by adults and then ref lected; rather it emerges in a double move between available materials and concept formation. Though the meaning of the material word “robot” is conveyed by referring to other words, we must acknowledge (following a new materialist approach) the importance of materials for concept formation as well as for concept expression. Simply playing with real robots may change conceptual understandings. The robots in most of the children’s drawings engage socially with each other and the world in ways that may resemble what we have learned to expect from robots such as R2D2 and C3PO in Star Wars. The moment we encounter real robots with a physical form in our material practices, this illusion is shattered, which seems to be acknowledged by children with hands-on experiences, such as Maryam as well as most robot-makers with hands-on experiences. In a posthumanist perspective, we can ask if the children’s concept of robots as lively and autonomous is really a pseudo-concept due to the lack of adult guidance. In our research of robots in schools, some adults (headmasters and teachers) also express this kind of belief in robots as autonomous and intelligent, in the same way the children do (Esbensen et al. 2016). Different concepts of robots are shared by many adults and children but how they learn to form concepts seems to matter and make a difference in relation to whether they have hands-on experience or have primarily met robots on the

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screen. Furthermore, as discussed in Chapter 3, it matters what motivates materialisations and the emotions that people share about these acts. Each concept forms a whole of perception, emotion and motivation (Vygotsky 1987, 283). Some roboticists have been motivated to present robots on internet sites as more autonomous than they are – thereby tapping into and drawing on the Star Wars-type media stories of robots. The children in the drawing situation may be motivated by the same media stories as these robot-makers. They form an ultrasocial collective of collectives as they engage with the stories and the art pens and borrow from and comment on each other’s drawings. The robot-makers engage with the same stories, but build on ISO standards, wires and plastic shells. Some robots are more like each other in the children’s drawing sessions because the children are placed physically in such a way that they can directly borrow from each other. Nevertheless, their preceding learning from movies also gives them access to some of the same imagined components. Even the boy, Max, who sits by himself, draws robots that are inf luenced by the same social and material resources as the others. He draws a policeman like some of the other children, though he does not see what they have drawn, and his robot is, he explains, inspired by the movie, Robocop. We unfortunately do not have any systematic experiments where we ask adult robot-makers to draw. However, we did one session of this kind, and based on this session it seems the children’s need to reach out was greater than the adults (confirming Vygotsky’s thesis about children’s fantasy). Our adult drawing session took place with less borrowing, negotiation and talk among the participants than the sessions with the children. However, when we have followed robotmakers in their work we notice a lot of the same ultra-social processes when they build robots (e.g. Hansen 2018, Sorensen 2018).

Teaching in learning Learning in the Vygotskyan sense is not within the representationalist paradigm. Learning is not about a single individual learning about an object. Though Vygotsky did not emphasise the importance of materials in learning it is clearly also a materialist psychology. Furthermore, Vygotsky connected collective teaching to learning as a basic process. We are never learning in isolation. For Vygotsky, the main concept in his important work, “Thinking and speech” (1987), is the word obuchenie in Russian, but contrary to the learning theories he criticises (e.g. Pavlov) this concept does not only refer to “learning” as an independent process but learning/teaching or teaching/learning (Cole 2009, 292). In the learning sciences it has been debated if Vygotsky intended a mutual learning between teachers and students, or teaching as a necessary instruction (e.g. Derry 2013) in a sense where it means that learning is a process that recognises the teaching that changes you as a person (Stetsenko 2016, 338). However, in my reading of Vygotsky, learning is always teaching because concepts keep evolving. In a posthumanist perspective this indicates that we can

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be taught by humans as well as non-humans. We do not just learn well-defined categories, but concepts that keep evolving with material and social responses. Vygotsky himself rejects the notion of concepts as a “polished representation” (Vygotsky 1997, 99). When Vygotsky discussed the experiments where children formed concepts by the aid of both adults and wooden bricks, he found that materials aided the process of internalising verbal thinking. However, once we have internalised concepts as “true” concepts, we no longer need materials to help us think. Once we can think in concepts, we are freed from the material surroundings. Where a small child needs to see an object to think about what to do with it to solve a problem, the adult can think about the object as tied to a solution to a problem – even when the object is not present. We are liberated from what is materially in front of us in situations. This was true in a humanist perspective, where the process of word meaning still entails a human separate from an object. In my posthumanist approach this includes teaching with materials as well as with other humans. In Barad’s posthumanist perspective, we create a split between a subject and an object within phenomena. In this way, we meet the universe half way. We are never without materials when we think within phenomena. Barad, on the other hand, lacks acknowledgement of how phenomena not only entangle subject and object, but entangle with other collective subjects through material-conceptual meaning making, and that learning with materials in the past and the present is inherent in conceptual phenomena. Moving away from a representationalist paradigm, with a focus on the a priori separation of words and worlds, creates awareness of how apparatuses of conceptual bodily production differ with material practices. Word meaning is not lexical. We do not just think about “a lamp” as individuals in isolation (as in Stuart Hall’s example mentioned above) but always within phenomena. The phenomenon experienced may differ from individual to individual, yet it already includes, through learning, collectives of collectives. What is meaningful comes from the humans we intra-act with as well as from the material resources we draw on when the world is materialised again and again. Though Vygotsky lacks the emphasis on material agency found in posthumanist theories, he gives us an understanding of learning processes. In learning, word meaning is dynamic and its dynamics make fundamental changes. When the process of concept formation is seen in all its complexity, it appears as a movement of thought within the pyramid of concepts, constantly alternating between two directions: from the particular to the general, and from the general to the particular. (Vygotsky 1986, 143) By this he means that children learn to perceive the general concepts, for example, “dog” or “f lower”, before they learn the many particular names, for example, “cocker spaniel” or “rose”. Vygotsky criticised the “old school” of

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psychologists for believing that the bond between word and meaning was an associative bond. Development [in the old school] was reduced to changes in the associative connections between single words and single objects: A word might denote at first one object and then become associated with another, just as an overcoat, having changed owners, might remind us first of one person and later of another. Linguistics did not realize that in the historical evolution of language the very structure of meaning and its psychological nature also change. (Vygotsky 1986, 213) He underlines that “It is not merely the content of a word that changes, but the way in which reality is generalised and ref lected in a word”. (ibid.) This is the posthumanist preceding learning that transforms intra-agencies. The materials themselves teach us to perceive the world anew from what we have learnt. When physicists perceive new things through their new instruments, their entire conceptualisation with the world might change, as it has done in the past with Galileo’s planets. Though physicists and engineers might have it as an ideal to be free of cultural inf luences, they cannot, according to cultural-historical theory and much research in Science and Technology Studies, escape being culturally engaged in practices that align their collective becoming with that practice (e.g. Traweek 1988). Underneath these cultural changes lies concept formation with materials and how already learned concepts along with available materials become part of the next intra-actions, which in turn keep transforming their learned concepts. In a never foreclosed, predetermined and linear way, physicists and engineers still become aligned in what could be seen as a natural scientist’s culture, which emphasises the world as a physical place. On the other hand, human-oriented researchers like ourselves, tend to align with a world that is much more psychologically storied – full of emotions, motivations and learning. In posthumanist learning both approaches are entangled. Cultural-historical theory is, as is any other humanist learning theory, challenged by the posthumanist decentring of humans. Nevertheless, the Vygotskyan perspective also brings a needed new perspective on “discourse” to posthumanist theorising. On the front page of Rosi Braidotti’s book, The Posthuman, we find a perfect Vitruvian woman standing alone in a kind of chip-like universe with small semiconductors to transport algorithmic learning. The Vygotskyan perspective challenges this isolated posthuman just as much as Braidotti’s critique of the Vitruvian Man. Posthumanist learning emphasises that we are already collectively leaking out into the world when we intra-act with things. We are not posthumans, but collective posthumanists. Posthumanist learning combines the insights from the natural sciences (often praised by new feminist materialists) with an acknowledgement of psychological and anthropological learning processes.

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This approach matters if we want to inf luence how robots inform our posthuman future. Some speculate that robots are the posthuman future. It is, however, often unclear what we in fact talk about – that is, which phenomena are brought forth in our material-conceptual practices – when we talk about robots. In our storied world, we often refer to robots as machines of the future, rather than the present, and our ultra-social concept of robot is equally informed by stories of media-made apparitions and stories of real machines put to use in human practices. In particular places in the world (mainly Western and Asian societies), ultra-social humans increasingly share a discourse about how robots should be allowed to inf luence their lives. This talk is often reduced to a nature–culture debate. The robots often appear in a nature-cultural force, which replaces the natural human. Robots are immortal culture, and Man becomes a vulnerable biological being – a species. Robots become agents of transversality as they push both Man and Woman out of their dichotomised dance and into the wilderness of merged bio-machines. From a learning perspective, the process of how robots actually merge with humans includes the way we conceptualise and involves preceding learning just as much as available materials. Humans as creators of concepts may be the basic generalisable characteristic of being a species of ultra-social learners in our apparatus – but concepts are constantly spilled out and materialised in environments. Sometimes humans make environments for themselves, but increasingly they make environments for others. Robots are part of these new environments and education must adjust for this fact. Robots are also increasingly part of a discourse of our common future – and deep down, through concepts, part of our diverse thinking about and materialisation of this future. No physical robot is humanoid in a way that resembles the Star Wars-type media robots and the lively creatures imagined and drawn by the children. Robots that not only look like a human but also learn to think, act and play like one do not exist. Though we can conceptualise robots, robots cannot conceptualise like us – and the question is whether they will ever come to learn materially and conceptually like humans. Robots do not have emotions and do not experience the world as meaningful. For humans, the relational concept of “robot” is, at least in our research, an unfolding meaningful story of ultra-sociality that merges with materiality. Collectives of humans are not shaped by systems of fixed representations and may not even involve the conscious communication of words. Nevertheless, material words like “robot”, as well as off-the-shelf robots and drawings of robots, are all material expressions of culturally shared concepts that are never fixed but evolve in situated practices. The situated process that creates a human collective within a collective of material words, material artefacts and meaningful thinking can result in children’s drawing or the building of a robot. Following Vygotsky, material words are indispensable for collective concept formation, but we do not think with words in a linear fashion like speech. Concepts are formed in thinking with materials and expressed as material words and artefacts. Though we may experience and communicate about the world without words, collective concepts are with us when we engage with materials.

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Through verbal thinking, our perception, memory, motivation, emotion and recognition become collectively meaningful. Our drawing situations show that what is meaningful about a word like “robot” is a combination of already formed concepts and what is emerging as meaningful in situations. The research apparatus used for studying children’s concepts of robots is an entanglement of the initial enunciation of the wordsound “robot”, the subsequent use of papers and pens, and the ice creams and kendama games and other references from a Danish child’s privileged world. The entanglement also includes how children have already learned to think about robots from movies such as WALL-E and Big Hero 6 and, for a few of them, experiences with building robots. All of these become cultural resources from within the phenomenon of robot-drawing; in the situation, they learn from each other how “robot” can be materialised. The “verbal thinking” entangles with paper, pen and the social situation – and many resources appear to be drawn from previous collective and individual learning processes.

Conclusion: Chapter 6 Ongoing concept formation is, I suggest, the basic process that entangles materials, meanings and the material word. The boundaries of this unit, the word meaning, is never closed as new connections keep forming. However, the material and storied world of Danish children in 2015 sets its own bounded directions for concept development. If, as Barad argues, matter cannot be separated from meaning (2007), the drawings may open for a deeper understanding of how concept-formation evolves in a posthumanist perspective. Humans draw on rich inputs from preceding learning when they perform tasks. These inputs are culturally shaped and, like any task, can be performed in many ways. The result is variation and constant alignments with other ultra-social humans, as we saw in the children’s drawing session. Concepts evolve in situations through material and peer guidance. Learning processes involve curious humans that reach out to the world as ultra-social beings. As we learn, our perception of the world changes. The children’s materialisations of robots shows that some children have some of the hands-on experiences of engineers. Concepts can apparently be matured by hands-on experiences. In a posthumanist perspective, teachers can be materials as well as humans. The children’s hands-on experiences give new constraints to their drawings – for instance, the mobility of robots, as when Johan draws a robot with “degrees of freedom” instead of robots just dancing. Though media robots are just as real phenomena as off-the-shelf robots, the fantasy meets reality in hands-on experiences. As most kids lack these hands-on experiences, they draw on other cultural resources, such as media robots, available to them as a collective. The children are not like the rationalist bounded humans of the humanist approach. They do not process information or scan patterns like machines, but rather emerge as collectives ensuring all kinds of variations with no inside or outside separation of bodies, sociality and performance. The children take from their pool of resources what is meaningful to them.

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Word meaning organises verbal thinking and involves different types of material components: 1) the material word-sound (e.g. robot) learned at some point and 2) the material entanglements connecting this word-sound to a collective pool of cultural resources. This can explain both the collective themes and the diversity found in the children’s drawings. This diversity is not the one found in the “discourse” defined as a system of representations with an inbuilt tendency to create dichotomies and hierarchies. It is a learned cultural diversity. The more mature a concept you evolve of robots, the more you can engage with hands-on robotics, as well as with new perceptions of media robots. In the posthumanist perspective the robot-maker’s descriptions of robots are not “more mature” than media robots drawn by children because they are made by adults. The different conceptualisations seem to be a result of their diverse engagements with other materials. In a normative sense, the robot-makers robots could be seen as an expression of a “true scientific” concept involving ISO standards, and the children’s robots could be seen as “pseudo concepts”. However, in a posthumanist perspective the different conceptions do not refer to a divide in how children learn about the world guided by adults, but a culture–culture divide in material engagements. However, more experience with engineering does not just refer to a process, where new meanings can be endlessly added. Materials ensure that concepts are constantly put to the test. Do robots really “think”? Or is their “thinking” so different from human thinking that the reference to robots as “thinkers” is senseless? This is not for an individual to decide. Though the children’s drawings of robots are all different, they are anything but individual, just as their word meanings come out as a collective verbal thinking even when they apparently differ. Access to materials matters for concept formations. This plays back on our awareness of how material words matter when humans form ultra-social collectives of collectives. We have begun a journey into how concepts are involved in material practices. In the next chapter, I shall continue to discuss how the concept of “robot” takes on different meanings when children and robot-makers engage in ultra-social material-conceptual practices involving robot phenomena.

Notes 1 Also translated in some places as inter- and intra-psychological. 2 The experiment was conducted on 10 June 2015 in a Danish school, where the lesson began with the drawing session. The teacher had deliberately not given the children any other instructions connected to the concept “robot” than the one presented here. The whole session was filmed and photographed and the children only spoke to adults about robots in the subsequent interviews, which were conducted by researchers from the Technucation project as well as with a research colleague from the department of education, after the drawing session. When I refer to the researchers as “we” in the text, it is primarily a reference to the research conducted in the Technucation project. The children work in a focussed manner for 30 minutes. Then the drawings are collected and the children are interviewed in groups for 45 minutes about their drawings. It was connected to the project “The Robot is Present”, where children from other parts of Denmark drew robots presented in Chapter 1.

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3 A kendama is also known as a duce ball. It is a traditional Japanese toy that was popular in Denmark at the time. It has a ken with three cups, a string with a ball, and a spike that fits into a hole in the ball. 4 https​://ww​w.iso​.org/​obp/u​i/#is​o:std​:iso:​8373:​ed-2:​v1:en​ retrieved 10 May 2017. 5 Some of the references are from a working paper in the Reeler project published at www.reeler.eu made by Jessica Sørensen, Stine Trentemøller and Cathrine Hasse. These ISO definitions have been questioned by a group of engineers working in an IEEE-RAS Working Group, called Ontologies for Robotics and Automation (IEEE is an organisation for around 400,000 technical students and professions and the acronym stands for The Institute of Electrical and Electronics Engineers). 6 All names found in this publication are anonymised.

References Alaimo, S. & Hekman, S. (2008). Introduction: Emerging models of materiality in feminist theory. In: S. Alaimo & S. Hekman (Eds.). Material Feminism (pp. 1–24). Bloomington: Indiana University Press. Barad, K. (2007). Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning. Durham, NC: Duke University Press. Blunden, A. (2012). Concepts: A Critical Approach. Leiden, Netherlands: Brill. Bertolini, A., Pericle S., Pagliai, T., Morachioli, A., Acerbi, G., Trieste, L., … Dario, P. (2016). On robots and insurance. International Journal of Social Robotics, 8(3), 381–391. DOI:10.1007/s12369-016-0345-z. Bille, M. (2017). Ecstatic things: The power of light in shaping Bedouin homes. Home Cultures, 14(1), 25–49. doi: 10.1080/17406315.2017.1319533. Boden, M. A. (2006). Mind as Machine: A History of Cognitive Science (1–2). Oxford: Clarendon Press. Borggreen, G. (2015). Robot bodies: Visual transfer of the technological uncanny. In: T. Kristensen, A. Michelsen & F. Wiegand (Eds.). Transvisuality: The Cultural Dimension of Visuality (Vol. 2, pp. 175–188). Liverpool: Liverpool University Press. Braidotti, R. (2013). The Posthuman. Cambridge: Polity Press. Brooks, M. (2009). Drawing, visualisation and young children’s exploration of “big ideas”. International Journal of Science Education, 31(3), 319–341. Brynjolfsson, E. & McAfee, A. (2011). Race Against the Machine. Lexington, MA: Digital Frontier Press. Bryson, J. J. (2010). Robots should be slaves. In: Y. Wilks (Ed.). Close Engagements with Artificial Companions: Key Social, Psychological, Ethical and Design Issues (pp. 63–74). Amsterdam: John Benjamins. Cole, M. (2009). The perils of translation: A first step in reconsidering Vygotsky’s theory of development in relation to formal education. Mind, Culture, and Activity, 16(4), 291–295. Derry, J. (2013). Vygotsky: Philosophy and Education. Hoboken, NJ: Wiley Blackwell. Esbensen, G., Hasse, C., Gudmundsen, L., Mathiasen, M., Breum, M., Pumali, K., … Seyedmirza, A. (2016). Robotter i Folkeskolen (normal-klasser). Begrundelser, visioner, faktisk brug og udfordringer. Academic report from the Department of Education, DPU. Retrieved from http:​//edu​.au.d​k /fil​eadmi​n /edu​/Frem​t idst​eknol​ogi/E​bog_-​_ Robo​ tter_ ​i _fol ​kesko​len.p​d f. Hall, S. (1997). The work of representation. In: S. Hall (Ed.). Representation: Cultural Representations and Signifying Practices (pp. 13–74). London: Sage.

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Hansen, S. (2018). If we had a specific idea of the product 12 months ago, it would never be what we have today! A study in situational pragmatic actions and strategies in everyday technological development. REELER Working Paper Series, Aarhus University, Copenhagen, Denmark. Retrieved from http:​//ree​ler.e​u/fil​eadmi​n /use​ r_upl​oad/R ​E ELER​/ WP03​_ If_w​e _had ​_ a_sp​ecifi​c _ide​a.pdf​. Ihde, D. (1990). Technology and the Lifeworld. Bloomington: Indiana University Press. Ingold, T. (2013). Making: Anthropology, Archaeology, Art and Architecture. New York, NY: Routledge. Lyyra, A. (2015). Towards interaction machines. iSChannel, 9(2), 6–13. Machón, A. (2013). Children’s Drawings. The Genesis and Nature of Graphic Representation. A Developmental Study. Madrid: Fíbulas Publishers. Moravec, H. (1999). Robot: Mere Machine to Transcendent Mind. New York, NY: Oxford University Press. Nevejans, N. (2016). Study: European Civil Law Rules in Robotics. Policy Department for Citizens’ Rights and Constitutional Affairs, European Parliament. Document PE 571.379. Retrieved from www.e​u ropa​rl.eu​ropa.​eu/co​m mitt​ees/f​r/sup​porti​ng-an​ alyse​s-sea​rch.h​t ml. Nourbakhsh, I. R. (2013). Robot Futures. Cambridge: MIT Press. Prestes, E., Carbonera, J. L., Fioniri, S. R., Jorge, V. A. M., Abel, M., Madhavan, R., … Schlenoff, C. (2013). Towards a core ontology for robotics and automation. Robotics and Autonomous Systems, 61(1), 1193–1204. doi:10.1016/j.robot.2013.04.005. Schilhab, T. (2017). Derived Embodiment in Abstract Language. Dordrecht: Springer. Sorenson, J. (2018). Decisions and Values: Engineering Design as a Pragmatic and Sociomaterial Negotiation Process. REELER Working Paper Series, Aarhus University, Copenhagen, Denmark. Retrieved from http:​//ree​ler.e​u/fil​eadmi​n /use​ r_upl​oad/R ​E ELER ​/ WP04 ​_ Deci​sions ​_ and_​value​s.pdf​. Stetsenko, A. (2016). The Transformative Mind: Expanding Vygotsky’s Perspective on Development and Education. New York, NY: Cambridge University Press. Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, G. H., Hirschberg, J., … Teller, A. (2016). “Artificial Intelligence and Life in 2030”. One Hundred Year Study on Artificial Intelligence: Report of the 2015–2016 Study Panel, Stanford University, Stanford, CA, September 2016. Retrieved from http://ai100.stanford.edu/2016-report. Tomasello, M., Kruger, A. C., & Ratner, H. H. (1993). Cultural learning. Behavioral Brain Sciences, 16(3), 495–511. Traweek, S. (1988). Beamtimes and Lifetimes: The World of High Energy Physicists, Cambridge: Harvard University Press. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 49, 433–460. Vygotsky, L. S. (1986). Thought and Language (A. Kozulin, Ed. and trans.). Cambridge: MIT Press. (Original work published 1934.) Vygotsky, L. S. (1987). Thinking and speech. In: R. W. Rieber & A. S. Carton (Eds.). The Collected Works of L.S. Vygotsky (Vol. 1, trans. N. Minick, pp. 39–285). New York, NY: Plenum Press. Vygotsky, L. S. (1997). On psychological systems. In: R. W. Rieber (Ed.). The Collected Works of L.S. Vygotsky (Vol. 3, trans. R. van der Veer, pp. 91–107). New York, NY: Plenum Press. Vygotsky, L. S. (1998). Imagination and creativity in the adolescent. In: R. W. Rieber (Ed.). The Collected Works of L.S. Vygotsky (Vol. 5, trans. M. J. Hall, pp. 151–166). New York, NY: Plenum Press.

7 IGNORANCE IN THE COLLECTIVE OF COLLECTIVES

At a news briefing on 12 February 2002, the then United States Secretary of Defence, Donald Rumsfeld, allegedly said: [T]here are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns—the ones we don’t know we don’t know.1 However, in a posthumanist perspective we do not know a priori. What matters are the cultural resources we can draw on to call something up as knowledge within phenomena. Here the world’s powerful people can start wars on something they claim to be knowledge at the same time as they claim others are ignorant. However, in a posthumanist perspective people are not ignorant because they lack knowledge. They are ignorant of the material-conceptual resources that could be used to challenge the powerful. Curiosity is in need of access to a cultural pool of material-conceptual resources. Without this access ignorance means we are barred from curiosity. This is different with AI and robotics. They have access to all information that can be put on algorithms and they are not in the least bit curious. As we saw in the previous chapter, intelligent, stand-alone information processing and partly autonomous robots are already among us but they are only human-like in so far as some humans think humans are intelligent, individual and autonomous. However, from a posthumanist learning perspective they are not partaking in creations of phenomena like humans. They lack the ultra-sociality and curiosity that is so apparent when phenomena include humans. In this chapter I shall further explore how materials and preceding learning matter for human concept formation and how human ignorance and curiosity

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are relative to material learning experiences. Onto-epistemology is closely tied to the processes of concept-formation and concept-formation cannot take place without available materials, including material words. In the autumn of 2015, we conducted yet another experiment that entangled children, robots and drawings.2 Our experiment took place next to an exhibition of robots from the past, present and future featured at the local museum, MUSE ®UM, in the town of Skive in Denmark. On November 2nd and 3rd November, researchers from Aarhus University gathered groups of children, this time ages 7 to 14, from four different local schools who had either seen or not seen the exhibition. Apart from the drawing room we also had a room where selected children “sat” and experienced the presence of the robot. Andreas who “sat” in front of NAO in the first chapter, was one of these children. Like his classmates, he also visited the exhibition. The exhibition displayed early types of robots, examples of robots actually put to use in people’s lives and a range of fictive media robots. As in the earlier experiment described in the previous chapter, we asked the children to draw robots for us, either before or after they visited the exhibition. We expected to see differences in the drawings made by children who saw the exhibition before they drew and the drawings made by children who had not yet seen the exhibition. During the two days, we collected and scanned 195 drawings, and let the children comment on the robots in his or her drawings. As it turned out, we could not detect any clear difference in the drawings made by children who had seen the robots and ideas about robots presented at the exhibition and drawings by children who had not (yet) seen the exhibition. The exhibition questioned the notion of robots in public media and displayed scenes from Star Wars and other movies and contrasted these images with real machines like washing machines, factory robots and examples of real social robots in the shape of animals, such as the harp seal robot Paro (see Hasse 2013 for an introduction to this particular social robot). However most children, as in the first experiment, continued to draw robots as humanoids. The groups came into the drawing room one by one according to grade, and similar to the first school class we visited (i.e. the first experiment), they were asked to: Draw a robot, or more if you would like to, that does something and maybe does something together with others – and there may be all sorts of other things on paper. I would like you to fill the paper with anything you think might be related to robots. We would also like to see where the robot is placed, whether it is doing something with someone else or whether it is making something itself. Apart from this, you may draw the robot exactly as you please. The children were grouped at long tables and handed the same type of art pens and paper as in the first experiment. Half of the group had visited the exhibition right before the drawing session and the other half visited it after the drawing

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session. Even the youngest children (age 6 to 7) clearly understood what we meant when we asked them to draw robots, whether they had visited the exhibition or not. No one expressed a lack of knowledge of what the material word-sound “robot” referred to. All began to draw robots, and many of the aspects we found in the first experiments also emerged in this situation. These children were also very curious about each other’s activities. They commented on, borrowed from and discussed each other’s drawings, leaned over to watch each other’s work, and even appealed to the experimenters to comment on their drawings. They drew on a pool of resources of how things are done and what matters in their everyday lives – and constantly tested these ideas with the reactions from others. Can robots bake? Can they walk dogs? Knowledge about what robots are and can do continuously emerged and was discarded or confirmed in the situation.

The mystery of the square heads Some aspects of robot life were not debated – for instance that robots are humanlike. The self-evidence of a cultural model could be argued here. The most remarkable result of the subsequent analysis is that almost all the drawn robots, just as in the first experiment, were humanoids – even if the exhibition presented many industrial and household non-human robot types. Our request to “draw a robot, or more if you would like to, that does something and maybe does something together with others” could have been otherwise interpreted – especially after visiting the exhibition. Workers are operating robots and clothes are put into washing machines by humans (at the exhibition by a mother and a child). However, only a small group of pupils draw factory robots or washing machines – and they draw machines without human presence. The rest of the drawings exhibit lively robots engaged in all kinds of activities. The next research finding is that the humanoid robots the children drew shared many collective features across all age groups. Overall, most robots follow what we, at the time, saw as a visual cultural model: robots in children’s drawings are square. They have square heads and can have square, immobile torsos. Even mouths, noses, eyes and hair can be square – and this “squareness” is not debated or commented on.3 However a material-conceptual understanding of the square heads opens up an explanation other than a shared cognitive cultural model, which I shall return to. The many drawings with human-like robots with square heads were triggered by the material word sound “robot”, whether the children had visited the exhibition or not before the drawing sessions. However, similar manifestations are not necessarily an expression of an a priori formed human collective that has wiped out every individual aspect of a person. Concepts and human collectives arise as a result of concrete and active thinking processes with and through available materials. Vygotsky emphasises the intellectual operation of this process but only has little to say explicitly about human collectives. I shall emphasise how materials and past experiences with materials become part of a collective, active,

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conceptual thinking and expression. Furthermore, collectives have a materialconceptual nature, which make it impossible to talk about “a collective” in more than a f leeting sense. From the humanist perspective, the square heads of the robots could be part of a prototypical cultural model of a robot as a human-like machine. I will, from a posthumanist perspective, argue that this explanation is too focussed on the cognitive aspects. The drawings came about in a situation where the materials as well as word meanings matter. Let’s have a closer look at the drawings. For instance, Adin, age 7, has drawn a robot inspired by his knowledge of the game Minecraft; he has drawn Minecraft’s Slender Skin, he explains. It is square with six arms and moves autonomously. Niklas has drawn a square robot with chainsaw arms and explains it is inspired by a movie he has seen recently. Rosa has made a blond, square-headed robot picking roses in a field. A TV-show about a baking contest, on air at the time of the experiment, inspires other children to make square, baking robots. Johannes, age 7, drew a fight between a Jedi robot and a Sith robot, and explains that it is inspired by the movie, Star Wars where some of the Jedi Masters become “dark” Jedi and founded a Sith empire. Though Star Wars robots do not have square heads, both the Jedi and the Sith have square heads in his drawing. The cinematic encounter with the robot “WALL-E” proves to be a big source of inspiration, as it was for Selma who, in the first experiment, drew the square robot that collects garbage and cleans up after humans. Also, Andreas, whom we met in Chapter 1, was inspired by the movie robot, WALL-E. A few children, like Mark, draw robots with a round head; Mark explains it is an astronaut. Some (especially the younger) children draw one-eyed robots with round heads, inspired by the Minions in a computer-animated movie, but most of the robot heads are square (see Figure 7.1). We could argue, as in the previous chapter, that the children were inspired by media robots rather than hands-on experiences, and that this is why they draw humanoid robots, but why the square heads? They are collectively found in the drawings across age groups and whether the children had visited the exhibition or not. The exhibition offered many other examples of robotic machines, such as vacuum cleaners, industrial robots, along with media robots from Star Wars, WALL-E and Big Hero 6’s Baymax – most without square heads. In the robotmakers’ everyday lives, very few robots come with square heads. Furthermore, media robots like Minions, R2D2 and C3PO are also formed by shapes other than squares. Considering the many possible ways to draw robots, why is there this collectively manifested preference for drawing humanoid robots with square heads across age groups and experimental settings? The answer, I suggest, lies in the entanglement of the collective concept of robots as humanoid with the collective situation in which this concept is expressed as meaningful. The human collective show itself in the drawings of robots. On the one hand, almost all of the robots drawn by the children were humanoids. This points to a collectively shared word meaning of robots as human-like. The drawings show

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FIGURE 7.1 Johannes, age 8, has drawn two robots fighting, a Sith and a Jedi, he explains

to the researchers. (Photo taken by Cathrine Hasse during experiments in 2015.)

this collective available resource to think with – what Vygotsky calls “verbal thinking” – the merger of words and meaning (Vygotsky 1987, 47). Of all the robot drawings in our Danish collection (195 drawings in all) only 20 do not have some kind of human-like face, and most of these were drawn by children older than 12 years of age. The word meaning of “robot” is collectively evoked as a humanoid creature that is not a human but acts like one. The inspiration from media robots is very clear, but even when the children do not directly refer to movies, their robots are autonomous and humanoid. In interviews, and when first confronted with the NAO robot, many children also seem to expect robots to be like humans. However, the materiality also contributes. As many as 119 drawings depicted robots with square heads. But children knew robots did not necessarily have square heads – so why insist on this collective feature? This could be seen as a way to resolve a dilemma: if robots are so much like humans, how do we see they are robots (and not humans)? By giving the square heads of course! (see Figure 7.2). Because the material arrangements were about “drawing” and not, for instance, about making a movie (where the metal in R2D2 signifies “robotic machine”), the collective word meaning, the verbal thinking of robots as “quasi-humans”,

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FIGURE 7.2 Emil,

age 14, has, like many children from different grades and schools, drawn robots dancing all on their own. (Photo taken by Cathrine Hasse during experiments in 2015.)

led to a collective way out of the dilemma formed as much by the pen and paper as by the collective concept. The square heads were, in other words, not expressions of a shared cultural model of robots, with an internalised, prototypical feature of square heads, but rather an effect of the drawing exercise itself – connected more to pens and papers than to mental models. Yet the understanding of robots as meaningful, life-like creatures could be seen as a collectively shared word meaning that was embedded in a media-informed, cultural concept of robots. From movies and games, children have learned to expect robots to be meaningful as human-like creatures. Though the drawings differ, the children’s understandings of robots are far from individual. The children share a learned conception of robots that make them expect robots to be as they are in movies. The drawings are not a mechanical response to the word as a stimulus but are formed in the situated collective entanglement of classmates, art pens and personal experiences in which the concept find its expression in the drawings. This is why all drawings differ as the locally formed collective entangles with preceding collectives through materials and concepts.

Collectively organised knowledge The theory of cultural models emphasised that organised meaning indicates that cultural expectations are formed cognitively in human collectives. Roy D’Andrade, who presented an introduction to the field in The Development of

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Cognitive Anthropology (1995), emphasised that cultural models were inspired by schema theory (also known as scripts, frames or scenes) known from the cognitive sciences that grew up with the computational field. They found it promising for a theory of culture formation that, in schema theory, humans acted on collectively shared mental prototypical knowledgeable scenarios in the world. The schema can explain why we first recognise something as a particular thing and next act on it in culturally informed ways. The schema organise knowledge to form our expectations of the agency of others just as they inform our own agency. In schema theory, learning is seen as a reinforcement process, which may be replicated by machines. It is not reinforcement through reward and punishment, as in some behaviourist theories, but is reinforced as in a computer, where repeated confirmed knowledge of certain connections gets more “weight” than knowledge of others, which over time confirm expectancies. This processes of building and organising knowledge was meant to replicate the computer’s material structures, as both the cognitive and the computational sciences saw the brain through the metaphor of a computer. When the connections in a schema are reinforced, through neural networks of connections, they come to function as mental devices for recognition, which “creates a complex interpretation from minimal inputs” rather than being a representational picture in the mind (D’Andrade 1995, 136). This implies that any situation can be reduced to minimal clues, which are recognised as something in particular, as when a restaurant is “recognised” by menus next to the plates. Cultural expectations are tied to “minimal clues”. People who habitually frequent restaurants would expect to find menus. These processes were at first (and by many still are) considered universal. However, with the theoretical move from schema theory to the anthropological theory of cultural models (see Chapter 3) there came an emphasis on culture as diversity. Not everyone who saw menus would recognise the restaurant. This depended on their previous learning in their local culture. The organisation of cultural models on how to build houses as, for instance, square, creates expectations of houses being square – and thus forms the basis for a surprise if houses are, all of sudden, built round.4 Cultural models could also explain different taboos in eating habits or cultural habits of greetings or clothing that would differ according to the local cultures. Following the linguistic paradigm, these cultural diversities in knowing, that were self-evident and unquestioned by the people studied, were explored by anthropologists through interviews which focussed on the cognitive and not the material aspects. As an analytical tool, cultural models are more complex structures than the generalised schemas (D’Andrade 1995, 152). Culture was, in cognitive anthropology, not “faxed” into a collective of people (Strauss 1992), which would have excluded cultural self-evidence from being negotiable and contestable. Nevertheless, cultural models were to a large extent unquestioned and simply what people expected – but, depending on learning, everything could be contested.

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This seesaw, between culture as completely and unproblematically internalized and culture as entirely negotiable and contested, results from an attempt to have a cultural theory without any psychology – a cultural theory with empty people. (D’Andrade 1995, 234) The key to understanding individuals and their actions lies, according to D’Andrade, exactly in the span between cultural representation and individual psychology, which to him and his followers made a cognitive approach one of the best answers to the investigation of culture, as a more or less collectively shared phenomenon. These ideas have been developed in a number of anthologies (Holland & Quinn 1987, D’Andrade & Strauss 1992, Strauss & Quinn 1997) without much impact either on computational sciences or cultural studies. In these works, cultural models were learned, and once learned they had directive force for agency – including processes of making. The models do not show themselves directly but emerge in analysis of how people tend to speak about what they know in similar ways. The models were a way of exploring the culturally connected organisations of knowledge that people used when they engaged in activities and “doings” (Holland & Quinn 1987, Strauss 1992). The directive force of the models was seen as cultural motivation (for instance, to build round houses) that directed agency without making persons “cultural dopes” (in the sense explained by Harold Garfinkel as automatic compliance with cultural norms, 1984). Like so many other theories in the linguistic turn, individual agency was emphasised, while the theory also sought explanations for the obvious collective components of human culture. The culture in cultural models was primarily perceived as a mental structure that compelled humans to, but did not determine, certain actions. The models challenged the schema structure because when actions were renewed it would always be based on both preceding learning as well as the situation (Holland 1992). This was how Prajun and Jit Gurung, who we met in Chapter 2, were directed to change their understanding of themselves, their futures and their family’s traditions when they encountered new ways of thinking in the Nepalese schools. Like the learning theories in the cultural-psychological approach, culture and psyche were seen, by the cognitive anthropologists, as informing each other. What cultural models add to the more basic Vygotskyan framework of concept formation and word meaning is an emphasis on agency, identity and alreadyformed expectations. Agency was not individual but collectively shared through the models. Especially anthropologist Dorothy Holland and her colleagues took the theory away from the schema theory towards a more Vygotskyan perspective but kept the interest in how human collectives are cultural in the way we come to expect certain acts and performances. This occurs most notably in the anthology from 1998 on what they named “figured worlds”. Their inspiration was a college where the students acted on all kinds of expectations of agency.

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By ‘figured world’, then, we mean a socially and culturally constructed realm of interpretation in which particular characters and actors are recognized, significance is assigned to certain acts, and particular outcomes are valued over others. Each is a simplified world populated by set of agents (in the world of romance: attractive women, boyfriends, lovers, fiancé s) who engage in a limited range of meaningful acts or changes of state (f lirting with, falling in love with, dumping, having sex with) as moved by a specific set of forces (attractiveness, love, lust). (Holland, Lachicotte, Skinner & Cain 1998, 52) Cultural models were, like collectively shared figured worlds, formed through social learning. Materials mattered, but were rarely emphasised by the cognitive anthropologists, but Holland emphasised the importance of artefacts for maintaining practices in figured worlds of expectations. “Artifacts ‘open up’ figured worlds. They are the means by which figured worlds are evoked, collectively developed, individually learned, and made socially and personally powerful” (Holland et al., 61). The cultural models and figured worlds were not seen as determining but as directing behaviour. If as locals, “we” had learned to expect round houses in an unquestioned manner “we” would most likely continue to build round houses as unexplored habits of mind. When I bring up these theoretical endeavours in relation to the children’s drawings, it is because I see them as helpful in explaining how knowledge, or, even better, organised knowing, is not free-f lowing, nor is it tied to the material arrangements alone. However, where the cultural models and, more notably, the figured worlds assumed a collective which formed meaning and expectations in similar prototypical ways, I shall look for another explanation. Rather, the knowledge tied to cultural models emerges in a basic process of learning in the situation, which resources bring to bear. The children’s drawings are collective, but not the expressions of a collective. Conceptual word meaning, rather than schemas or models, can be seen as the basis of these organisations of knowledge, which form expectations, but if we include emphasis on the materiality at the base, both humans and material become momentarily formed shared collectives in situations. Most materials made by humans, including conceptualised materials, are collective. The materials are not a priori separated from humans. When robots are drawn as separate from humans, with square heads, it is intra-acted in a situation where human, material-conceptual collectives entangle with other human material-conceptual collectives. In these situations, the children may agree or disagree on which resources to bring to bear (e.g. movies, knowledge of baking or hands-on experiences building robots), but the drawings are all informed by the material constrains made by pen and paper. It is in this collective situation that the children build on the collective conceptualised expectations which are tied to the practices known by individual children.

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Practices of knowing The activities and features that the children include in their drawings were cultural in the same way as the experiment presented in the previous chapter. The children made all kinds of references to their self-evident everyday life experiences in Denmark in their drawings. In this respect, the drawings are not just windows to the children’s concept formation (Brooks 2009), but also a means to explore what matters to Danish children. These Danish children are privileged with material abundance in the form of games, movie experiences, restaurant and cooking experiences, soccer, Christmas rituals and close (but sometimes annoying) relations between grownups and children, who demand they clean their own room, walk the dog or go fishing. All are collectively shared experiences tied to (but not confined to) Danish culture, which seems more than ready to embrace the envisioned humanoid robots. In the drawing situation, the children, as in the other examples in previous chapters, are constantly negotiating what and how robots should be drawn. It is not coincidental that certain “themes” in the drawings can later be identified as drawn by pupils who were sitting at the same table, just as in the first experiment. A major source of inspiration, across the age groups, is how the children inspire each other in the situation. One collective theme identified in four drawings is that of robots decorating Christmas trees. This turns out to be drawn by a group of girls sitting next to each other. Once they have agreed on this topic, the selfevidence in the practice of decorating a Christmas tree is not debated – and their drawings show very similar trees with stars on top. Two girls sitting next to each other are among the few who do not draw humanoid robots – but they draw robots as washing-machines in the same way. Two other girls, Astrid and Ayah (both nine years old) sit next to each other and draw robots that are almost alike. Both of their robots play football on green grass. The robots are autonomous with no visible wires. All limbs are square and immobile, even though they have movable fingers (see Figure 7.3). We also find six drawings of robots fishing and most of these turn out to have been drawn by a group of five boys sitting around the same table. As in the first experiment, bodily movements of leaning over tables, glancing curiously, holding papers up for others to see, commenting and borrowing ideas from each other are important contributions to what is materialised on paper. In this situation, the collective of humans and materials meets the conceptual collectives. Practices of knowing emerge in the Vygotskyan perspective, not as free-f lowing from individuals, but involve collective learning of word meaning that create a “we”. Practices of knowing are also tied to previous material experiences that inform organised knowledge about activities. The boys do not question that fishing means fishing from boats – and it may not be accidental that these drawings are drawn by children who live close to a harbour on the Danish peninsula of Jutland (contrary to the drawings from the first experiments from a school in central Zealand).

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FIGURE 7.3a–c The

children draw on the cultural pool of resources available to them in their everyday lives such as decorating Christmas trees or issues tied to commercial fishing. (Photo taken by Cathrine Hasse during experiments in 2015.)

Barad says: Practices of knowing and being are not isolatable, but rather they are mutually implicated. We do not obtain knowledge by standing outside of the world; we know because “we” are with the world. We are part of the world in its differential becoming. The separation of epistemology from ontology is a reverberation of a metaphysics that assumes an inherent difference between human and nonhuman, subject and object, mind and body, matter and discourse. Onto-epistemology—the study of practices of knowing in being— is probably a better way to think about the kind of understandings that are needed to come to terms with how specific intraactions matter. (Barad 2003, 829) The way practices and knowing are mutually implicated can be understood as learning to organise what we have learned in verbal thinking tied to cultural models of behaviour. Knowing, practice, word meaning – by adding the latter to

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onto-epistemology, we can see that it is through verbal thinking with materiality that we become collective and come to share what can be elicited as cultural models. The collective “we” is differential in its becoming because phenomena entails the entanglement of preceding learning of word meaning and material resources in practices of knowing. The meaning of robots connected to fishing is as self-evident to the boys as the meaning of material words like “fish”. Though the meaning of fishing robots comes out as five different drawings, the subtle differentiations relate to the same collective process of meaning making, where each drawing is recognised and understood by the others, (Vygotsky 1987, 271). These “we”s are of a f leeting and sometimes unexpected kind. Even when children seem most individual and full of special fantasies, some collective “we”s can be detached. Depictions of futball playing robots are not confined to the two girls. Many Danes play soccer and both the boys and girls across age groups and in different drawing sessions connect robots with soccer games. Anton, age 10, has, for instance, drawn a proxy robot that can replace an injured soccer player. It turns out that several robots are thought of as having a “proxy” function. For instance, in Annika’s drawing, there is a robot that can do her homework for her, and Kenneth (age 11) has made a robot that walks the dog for him. As for depicting fishing robots, Amelia draws a fishing robot that differs from the robots on the fishing boats, because she draws on another preceding learning. Amelia has drawn a great robot with a square head, who is fishing and collecting the caught fish in a bucket. Everything on this robot is square – the hair, the two eyes, the nose, the mouth, the head, torso and limbs, even the hands. Amelia explains in an interview that she will send this proxy robot, instead of herself, when her father wants her to go fishing in the rain, which he apparently did recently (see Figure 7.4). Our materials are, when analysed this way, full of momentary collective “we”s, which, in our apparatus, can further be connected to the other “we”s we have identified in the drawings. When children, like Amelia and Kenneth, send their robots to fish or walk dogs in their place, this resonates well with the “singularists” such as Ishiguro and Moravec, who are convinced robots can be used as proxy to humans (e.g. Moravec 1988, 91). When we connect our insights from the robot-makers with the children’s drawings we find likeness and differences across ages and nationalities. Frida (age 11) has drawn a doctor robot and remarks, “A real human doctor can make mistakes that a robot doctor will not”. Frida’s robot, who cannot make mistakes, aligns her conception of robots with the visions of, for instance, Ray Kurzweil, who thinks robots will not only learn as humans do, but become better (Kurzweil 2005). It also aligns with the visions promoted by the IBM Watson robot used for making diagnoses better than human doctors do. Some of the Danish children’s drawings show, as can be seen, that they share with some robot-makers a concept of robots as machines; others emphasise robots as intelligent or playful. This brings these children closer in their perception of robots to robot-makers

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FIGURE 7.4 Amelia, age

11, has drawn a “proxy” robot that can go on fishing trips with her father instead of her. (Photo taken by Cathrine Hasse during experiments in 2015.)

such as Cynthia Breazeal, who are engaged in developing robots that, like Jibo, can become friends with humans (Breazeal 2002). However, contrary to the empty curiosity of Jibo (whom we met in Chapter 1) and the cold robot intelligence envisioned by Kurzweil, the children’s robots are deeply and emotionally engaged in their collective endeavour of making meaningful activities. The children are curious about what other children will make robots do. They constantly inspire and borrow from each other, and the material arrangement matters. The children often draw the same themes when sitting next to each other, such as robots decorating Christmas trees or fishing robots. These children are, ­contrary to Jibo, humans with preceding learning who materialise robot potentials from a shared cultural pool of resources that are ultra-socially situation-sensitive.

Ultra-sociality Ultra-social humans are diverse in how we have potential to access and learn to perceive, the world. It is not about the noumena–phenomena distinction between names and things (Barad 2007, 31), that is, a distinction between an object in itself (independent of sensual perception) and the observed p­ henomenon – but of diversity and communality in verbal thinking. This is not a question of representation or categorisation as in the nature versus culture issue, but one of a culture–culture diversity.

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Neither new materialism or cultural psychology, considered separately, place enough emphasis on how conceptual thinking builds on available materials in ultra-social situations. It is when we read these theories through one another, what Barad names a “diffracted reading” (Barad 2007, 25), that concepts can find their place in a world that is both material and conceptual. Our conceptformations, the resources we use to think, organise knowledge and recognise the world with, are collective and are what connects us is a material-conceptual learning processes. This posthumanist subject differs from the liberal, rational subject that “owes nothing to society” (Hayles 1999, 3). This subject is a person who emerges as what I, along with Michael Tomasello, name an ultra-social learner (2014). Ultra-social humans learn to form collectively organised self-evident knowledge of how to play with LEGOs and later maybe electrons in joint cultural collective activities. Other humans learn to fish with hooks or nets and bake on stones or in ovens without ever questioning their achieved, self-evident knowledge of how things are done. Humans are the only animals that are capable of the particular kind of diverse collectivity that involves a shared intentionality in particular groups (Tomasello & Rakoczy 2003). It is very likely that Jibo, and other robots pretending to be like humans, lack this capability as well as a lack of emotional and meaning-making capabilities. Tomasello and his colleagues have argued, in the humanist fashion, that collaborative learning moved humans from vulnerable monkeys to controllers of their environments (Tomasello, Kruger, & Ratner 1993). However, they also emphasise that humans are special because our collectives are so culturally diverse. By connecting a biological approach to cultural psychology, they have given new life to the “no man is an island” argument. Within this analytical apparatus, we are a unique species. We propose that the crucial difference between human cognition and that of other species is the ability to participate with others in collaborative activities with shared goals and intentions: shared intentionality. Participation in such activities requires not only especially powerful forms of intention reading and cultural learning, but also a unique motivation to share psychological states with others and unique forms of cognitive representation for doing so. (Tomasello, Carpenter, Call, Behne, & Moll 2005, 675) Though many animals also show collective traits, they do not form culturally diverse collectives, where they build up their own material conditions for learning in different ways. Species are categorisations that can be questioned from a posthumanist approach, but within an apparatus of “species making”, humans are special because of these characteristics. It does seem convincing that the biological adaptation that makes humans a separate species:

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is rooted in primate cognition but then provides humans with the cognitive tools and motivations to create artifacts and practices collectively with members of their social group – that then structure their and their offspring’s cognitive interactions with the world. (Tomasello et al. 2005, 690) Materials matter for humans to become human lies in more than just biology. It includes the human capacity for collective concept formation as well. If we imagine a small child left without the company of fellow humans on an island, and this child was somehow kept alive until adulthood, the child would not differ much from the great apes, Tomasello argues (Tomasello & Rakoczy 2003, 122). We may perceive our environments as directly as our fellow animals, but human perception is at least to some extent collectively shared through cultural concept formation. There is a pattern which connects the stone-carved hand axes used by the first humans to the use of scanners, the modern oil drilling platforms and heated housing and electricity discovered in physics science. Our modern societies have materialised as new technology-driven environments. This development is largely due to advances in what are known as the natural and applied sciences in the Western world. However, today, some posthumanists increasingly acknowledge that gaining control over nature, as we come to know it better and better, has left a huge gap in understanding people. Our human behaviour poses just as big an evolutionary threat to our human existence as earthquakes, wild animals, tsunamis and meteorites (Floridi 2015). All is nature-culture. Yet we understand less about why humans create plastic seas than about the nature of waves. The development, caused by comparatively few technically-learned humans, that creates environmental and social disasters for others cannot be controlled, but we can come to a deeper understanding of humans as ultra-social learners through posthumanist learning theory. That humans are ultra-social is, according to Tomasello, what ties us together as a species. It is what makes us culturally diverse as well, because ultra-social concept formation makes us differ from each other in the way we learn to perceive, remember and engage in collective formations. Even when we believe we sit all by ourselves and play with red sand (nature) or LEGOs (culture) or draw a robot as a policeman like Max, we draw on preceding, local, cultural resources, as we are already enmeshed in an ultra-social, material nature-culture. Learning is a process that constantly transforms us and our surroundings as an ongoing, collective, cultural learning process. The Vitruvian Man may be decentred (Braidotti 2013), but humans in plural are not. The posthumans are, in the posthumanist learning theory I propose, recentred as a new focus when we explore how our social and f leeting, collectively aligned and situated behaviour manages to create a widespread techno-cultural environment, which stabilises in material-conceptual collectives (Hasse 2018). We also gradually realise our own creations are just as unpredictable as the nature we set out to control as “nature”, when we first evolved as a species.

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Barad refers to humans as a species which is a special intra-action, an agential cut, which is ontic-semantic (Barad 2007, 148). It is an ontology which can be questioned and debated by others who see themselves as having closer affinities with dogs or horses than other humans. I do not exclude that humans as an ultrasocial species overlap with other species – only that Tomasello and colleagues show us that we merge with humans and non-humans in particular ways as ultra-social beings. As humans, we have always been curious and open to include others in our ultra-sociality – and now also robots. The children’s ultra-social robots materialise how their own practices become entangled with their envisioned humanoid robots in the drawing situation. Many children across age groups inspire each other to make use of certain YouTube videos they watch together or games they play where robots shoot or play. All these cultural resources hold the potential of being connected with the wordsound “robot” in the situation. In the drawings we find some of these potentials realised. What the children include in the drawing situation, partly informed by material, partly by conceptual arrangements of organised knowledge, are resources that matter to them in their lives. Though some of these robots were also presented at the exhibition, there is a universe of media robots that the children are familiar with. We find a preference for drawing these movie-inspired humanoid robots distributed across all age groups, whether the children saw the exhibition beforehand or not. The collective pool of cultural resources is distributed and reaches outside the available material resources in the situation. Star Wars robots were, for instance, on display at the exhibitions, yet Star Wars robots were also drawn by children who had not seen the exhibition. That we are ultra-social and draw on a collectively shared pool of resources does not mean we are completely alike. Though the resources, such as Star Wars robots, are collectively shared, they are given individual expressions in the drawings and made use of in relation to the ultra-social situation. The children’s robots are often friendly and largely autonomous helpers that do practical work, such as catching fish and storing them in boxes (in Denmark, and particularly Jutland, fishing is one of the main trades). Many robots are cooking, frying eggs and baking – some with a reference to television programmes. Other robots are so autonomous that they enjoy themselves on their own like the children themselves would: dancing, playing ball, eating Doritos. Some even give psychological consultancy or boss humans around. Other children are clearly inspired in their drawings by recent events, things they have seen in the media and (for the younger children) the exhibition. Some draw military robots with glowing firearms – probably more inspired by movies than actual experiences. The children are ultra-social in many individual ways. Sometimes, what is materialised on paper is not ultra-social inspiration directly tied to the “glancing” and “borrowing”. Instead, children include their own previous experiences, like Amelia who went fishing, in their attempt to show how human-like the robots are. Some children bring in personal family experiences, as Lina (11 years old)

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who has drawn a “memory robot” for old people who lose their memory. Lina later explains to us that the robot is a companion for her grandmother, who has Alzheimer’s disease and even has problems recognising Lina. In this collective process of drawing and (re)forming the word meaning of the word-sound “robot”, the children seem to stretch themselves to include robots in their ultra-sociality to the extent that robots almost become a new species. Many robots seem to have a will of their own in the drawings; they dance and enjoy themselves, like the thoughtful WALL-E-inspired robot made by Martin (10 years old). He explains that it “longs to go to the Moon”. If collective knowledge about robots was free-f lowing, like information passed through machines, we would not find so many individual differences, and if the drawings were made by mere individuals, the children’s drawings would be much less alike. Even if the material arrangement includes children who sit together and inf luence each other, it does not account for how the collective and individual emerge in the drawings. Amelia’s fishing robot is clearly based on her own memory yet she, like most children, emphasises her robot as square and humanoid. Previous cultural, and thereby collectively shared, conceptual learning processes that make the material word “robot” connect to “humanoid” could be a way of explaining diversity and likenesses in the drawings. The pens and papers and the ultra-sociality in the situation also make the drawings show patterns of similarity in depicting a humanoid robot. If we expected the children to completely collectively share a representation of the robot, we should have expected that the drawings were much more uniform. However, the drawings were also very different – and informed by personal experiences. Even if most of the robots were humanoid, they were humanoid in many different ways, although we continued to find patterns across the experiments.

Cultural conceptions of gender Christina, age 7, has for, instance, transformed a well-known movie robot, R2D2 into a “girl”-version that can “kiss and hug and be a friend”, as Christina explains to the other girls at her table. For the girls at this table, organised knowledge of gender matters (see Figure 7.5). Some collective phenomena are not as apparent in the drawings as the square heads, yet there are patterns. The materialisation of robot phenomena in the drawings clearly displayed collectively formed notions of gender, where hair bows, for example, indicate female robots as we saw in the first experiment, when Sorine emphasised that bows were a sign of a “girl” robot. In the drawing situation, signs of gender were often negotiated in the drawings of robots. Gender also materialised in the way the children were sitting (mainly same-sex groups). In addition to the more clear-cut categories, we also find exceptions, for example, robots in the drawings without a clear gender while in the room boys and girls are sitting next to each other. Though we, the researchers, may later entangle these material manifestations in an analysis of

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7, has drawn a “girl” version of the Star Wars robot R2D2, she explains to the researcher. (Photo taken by Cathrine Hasse during experiments in 2015.)

FIGURE 7.5 Christina, age

gender categories, our recognition of what referred to “girls” or “boys” was also shared by the children. Gender matters, but not in similar ways for all the children. Some give salience to technical details (wires, caterpillar feet) over gender signs (bows, dresses). The humanoid robots materialised what mattered to the children – and for some, gender issues loomed large. Posthumanist learning is about available material-conceptual resources – and gender showed to be an important resource for many children, but primarily the girls. This kind of diversity was not necessarily pejorative, as argued by feminists (Braidotti 2013). The concept of gender is wider than the dichotomised categories. It shows a diversity in what preceding learning makes possible in entangling gender in new phenomena. Christina, for instance, shared the preference of the collective of children that drew humanoid robots and was inspired by movie robots, but her drawing differed because she individualised it by making R2D2 into a girl that could kiss and hug. However, by doing that, her individuality also embeds this choice in a wider cultural model of gender. We did not set out our practices of knowing to look for patterns of gender, but we gradually found gendered collectives to be salient. At first, we notice that many boys were drawing technical details. Mads, age 7, made a robot with a square, black head, and a lot of silver things and a yellow wire. He explains that it is “a robot that can give food, but also has a radar so it can help save people. The radar beeps when someone is in danger. The yellow is a wire and it is loading [from a battery]”. Kristian, age 11, has drawn a robot with many technical features and he explains that he was inspired by a real-life robot

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named Beatbot, which was developed by Puma shoes and beat the runner Usain Bolt. We were happy to see that some of the girls, like Anna (age 8) and Maja (age 14), also created autonomous robots with visible wires. Anna and Maja turned out to have been inspired by the exhibition, just like Maryam in the first experiment who created one of the most technical robots – in that case inspired by her brother and hands-on experiences. Eric, in the first experiment, also explained his drawing with a reference to a robot exhibition. However, in the humanoid robot drawings one of the amazing things we found was the stability of gender which became salient in the analysis of the drawings. Though Barad emphasises the importance of gender for the materialisation of the particle spin on a plate, it was not expected by us that gender was directly implicated in the drawings in the way it turned out to be. It was not the case that only boys and no girls would draw robots with wires. However, what materialised in both the drawings made by boys and girls were robots with plenty of gender, both in the first and the second experiments. Across age groups, the children from first to eighth grade referred to gender when drawing. When we analysed the drawings with the agentic cut of “gender”, we also found a clear cultural gender difference in the themes chosen by the boys and girls throughout the material. More robots drawn by boys engaged in warfare, and more robots drawn by girls engaged in emotional and communicative activities (like robots with hearths and being in love). We also found that the girls let the robots engage in other activities than the boys. Signe, for instance, age 9, drew a robot that could help humans with their emotional problems and offer psychological counselling. Three girls, Rikke, Sofia and Julie, independent of each other (they were all in different grades), drew robots which, they explain, give psychological counselling, while no boy drew psychology robots. Johanne’s robot also has a “help” function, namely helping lonely children by playing games like chess with them. Johanne, age 11, has three siblings, and she thinks it must be sad to be a lonely child wherefore a robot would help as it come in handy as a “sibling” in a family with an only child. Like the girls Nellie and Ida in the previous chapter, friendship and love seem to engage the girls across all age groups. Celeste, age 10, has made a colourful red “heart” robot that “helps when you are ill. It bakes cakes, as long as you speak to it kindly. It is a love robot”, Celeste explains. In her drawing she has added a sentence spoken by the robot: “Your cake is ready”. Frida’s doctor robot also helps people (see Figure 7.6). The boys often make autonomous robots engaged in warfare but Sven, age 8, makes a “killer-robot with hammer and sword that kills on command”, and he emphasises: “I am the one commanding the robot”. Johann makes a killer-robot that takes pleasure in smashing plates and Christian, age 11, makes a robot with a gun that is made to save humans. Elias, age 9, has made a powerful robot that creates walls “to help the military”. No boys refer to love in relation to robots in their drawings as the girls do, but a couple of girls join the boys in making killer-robots. These robots are sometimes a bit frightening, but both boys and girls tend to explain that they kill to help somebody. The most frightening of

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FIGURE 7.6 Ida, age

10, has drawn two robots who love each other, she explains to the researcher. (Photo taken by Cathrine Hasse during experiments in 2015.)

the killer-robots is made by a girl, Cecilie (age 9), who has made an impressive robot with guns in each hand, and on each foot wears a fireball that kills “robot” dogs. In all, 19 drawings of the 195 are of robots engaged in some kind of war or killing activity – and only one is drawn by a girl (Cecile). The girls, like the boys, tend to emphasise that robots will help humans. Like the girls, many of the boys draw robots that do not create warfare but help people, but they help with different things than in some of the girls’ drawings. Kenneth’s dog-walking robot is a helper too. Other boys make robots that cook or bake for you – or shoot firearms, yet none of the boys refer to love, hearts or psychological counselling or even robots as “friends”, when they explain their drawings. The girls do not distance themselves from the concept of robots; more girls than boys in general see robots as loving and helpful. As we saw, gender was also practiced in the very process of drawing. Girls and boys were sitting together (but not always) and inspired each other in many ultrasocial ways by borrowing from each other but also by confirming each other – sometimes as gendered beings. The word meaning of “robots” does not always emphasise gender, but it engages with the cultural model of robots that they are gendered, just as the children perceive themselves as gendered. How much emphasis is put on gender in the drawings differs in relation to what the children have learned and are learning are the appropriate cultural resources to bring into the situation. However, “gender” is there as a potential resource to be entangled in the ongoing practice for the children and researchers. Contrary to drawing robots with square heads, gender is not primarily emerging as a result of the art pens and paper, but a drawing situation tied to prototypes of gendered features and behaviour that is called forth in the robot phenomena through a collective cultural model of gender. This model is one of verbal thinking that detaches

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agency from direct perception and subsumes it under a prototype that directs, but does not determine, choices of which cultural resources to use in the drawings. This calls for an understanding of how available material resources align humans across ages in ultra-social collective ways in particular niches of the world while excluding others. However, there are no a priori “humans”, “mankind” or “ourselves” in a posthumanist perspective. The “we”s we make, also in my analysis, are always the result of certain agential cut that enacts “a resolution within the phenomenon of the inherent ontological (and semantic) indeterminacy. In other words, relata do not preexist relations; rather, relata-within-phenomena emerge through specific intra-actions” (Barad 2007, 140). Whenever the “we”s involve humans in the intra-actions, the specific intra-actions are not just a result of material arrangement but also the result of collective processes of preceding learning (about gender for instance). Though learning is never foreclosing the possibility of learning something new, the collectively shared word meaning and material artefacts ensure a certain recurring stability with the world’s phenomena.

Auxiliary apparatus The collective “we”s could be seen as a plea for a return to a constructionist and constructivist agenda, where any concept of robot is equally acceptable. Knowledge would always come in the plural according to the diverse practices of knowing. As already noticed, Vygotskyans, like Jan Derry, have argued against this view as they replace the relativistic approach to meaningful concept-formation with a developmental approach (Derry 2013). We find some indications of such a view in our material. However, development is here very closely tied to how richer and more mature concepts are formed over time and in experiences with materials. Especially the young children in one of the first grades (age 7 to 8) draw completely autonomous robots. Out of 20 children attending grade 1A, 16 have drawn completely autonomous robots and with no technical details; some are clearly inspired by robots known from the movies. These children are not inf luenced by the movie robots exhibited at Skive MUSE ®UM, as they did not visit the exhibition beforehand. The children from 1B, however, draw robots with direct references to the exhibition after they have seen it. In this group, some draw movie robots, but some also draw the “real” robots they have encountered. A girl draws the robot seal Paro with a round head (a harp seal robot developed to comfort people with dementia) with a direct reference to the Paro robots seen at the exhibition and describes it as a “hugging robot” for old people. Martin, 8 years old, draws a robot hand exhibited at the museum. In general, the children from 1B make many more drawings with technical details such as cords, joints, and loading of batteries than children from 1A. Some refer explicitly to the exhibited movie-robots, like R2D2. To our surprise, the exhibition did not seem to affect the older pupils (age 8 to 15, attending second to eighth grades) in the same way. At least, in their drawings we did not detect any clear differences in the drawings of robots made by

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the older children who had seen the exhibition beforehand and those who had not seen the exhibition. On the contrary, only a few of the older pupils referred directly to the exhibition in their drawings. Even so, we did find examples of a more “robot-maker” type of image that emerged in the drawings made by the older children. Of the 24 drawings drawn by children (aged 14 to 15) in one of the two eighth-grade classes that participated in the experiment, we found five drawings (made by one girl and four boys) with very technically correct, industrial robotic arms, with written references to the German robot factory KUKA’s production of robotic “arms” used in factories. These robots are not at all human-like. The children who drew them are listed as not having seen the exhibition before the drawing session. At first, I expected my assistant had made a mistake, not least because I clearly remembered the robotic arms on display at the exhibition looked like the ones in the drawings. Part of my suspicion, that the drawings had been mixed up, was also raised by the fact that in the other eighth grade class (where the pupils visited the exhibition), only two of the 19 drawings referred explicitly to something that could have been picked up at the exhibition: that is, the two girls drawing washing machines. When I later went back to check the dataset, I was proved wrong. The robotic arms displayed at the exhibition were not KUKA arms but came from another robot company. Furthermore, the two girls referring to their washing machines did so with an explicit verbal reference to what they had seen at the exhibition. Their classmates did not depict any technical details of robots, and did not (contrary to the young children) make any explicit references to the robots at the exhibition when they draw humanoid robotsm, whereas the children drawing the KUKA arms had indeed not seen the exhibition. They had drawn what one of the children remembered he had seen somewhere, which was then recognised and picked up by the others. That so few of the older children directly drew what they saw in the exhibition, whereas more younger children drew what they saw at the exhibition, seems to confirm Vygotsky’s thesis that small children are more prone to think with material surroundings than older children who learn to think in concepts. The impact of perceiving things materially at the exhibition had a larger impact on the smaller children than the older children (see Figure 7.7). This can support the thesis made by Vygotsky that thinking is tied to learned concept formation in ways that initially include materials. It is these materials that through learning processes are transformed into “thinking tools”. Not denying the link to our ancestors, the apes, Vygotsky saw small children as closer to the nature from which humans come. Small children have not yet learned to think in concepts but are dominated by their material surroundings – which create their visual fields of attention. The young children were, for Vygotsky more like animals, “slaves of their sensory field. This means that the attention of an animal is determined by the organization of his visual field. (Vygotsky 1998, 103)

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FIGURE 7.7 Frederik, age

13, has drawn KUKA robot arms. (Photo taken by Cathrine Hasse during experiments in 2015.)

In order to remember something, the small children were prone to think when they saw available visual materials, because it was harder for them to think with concepts. In Vygotsky’s experiments, the younger children rely on visible external materials to remember and perform the task demanded by the experimenters. In relation to the experiments with the wooden bricks we encountered in the previous chapter, the experimenters also explored what they call “auxiliary artefacts” where children were given the tasks of not mentioning particular colours when asked a question. For instance, the instructors specified that it was forbidden for the child to say “green”. Next the instructors trickily would ask the children about the colour of the lawns in front of the school. The experiments revealed how social interaction with auxiliary artefacts informed the internal operations of minds and agency of small children compared to older children. If the small children had visible squares with the forbidden colours (e.g. “green”) put in front of them, it was easier for them to remember not to say the forbidden words. Adults and older children did not need such auxiliary means to think, and Vygotsky concluded that the materiality had turned inwards; it had become an inner instead of an external resource as verbal thinking. What was externally mediated by the material colour cards became internally mediated (Vygotsky 1999, 53) and led to a new kind of attentive agency. The children now could remember things without having to resort to a visual field of available materials. The verbal thinking does not belong to the individual child but is, Vygotsky emphasises, tied to a collective learning that at first takes place through auxiliary

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(material) artefacts. Though this process in everyday life is rarely an explicit one, such as the experiment with the green card, Vygotsky nevertheless claimed that the process of turning materials (green cards) inwards as “signs” (verbal thinking of “green”) was at the root of cultural learning processes. In other words, the auxiliary artefacts, like green cards and knots on a handkerchief, are turned inwards like the meaning of artefacts in general. In this process of collective learning, the child aligns not only his or her thinking but attention, perception, memory and actions with others who also share a “system of signs” as Vygotsky names it: The system of signs restructures the whole psychological process and enables the child to master her movement. It reconstructs the choice process on a totally new basis. Movement detaches itself from direct perception and comes under the control of sign functions included in the choice response. This development represents a fundamental break with the natural history of behaviour and initiates the transition from the primitive behaviour of animals to the higher intellectual activities of humans. (Vygotsky 1978, 35) When Barad says that intra-action involves an auxiliary apparatus, she refers to, for instance, Niels Bohr’s use of particle detectors or Galilei Galilei’s use of telescopes: That is, the apparatus that is to be characterized (i.e., measured) must be the ‘object of observation’ within some larger phenomenon involving its intraaction with an auxiliary apparatus. This is necessary so that the ‘object apparatus’ within the larger phenomenon effects its marks on another ‘part’ of the larger phenomenon (which includes the auxiliary apparatus). (Barad 2007, 161) This split between the object apparatus and the auxiliary apparatus is, within phenomena, just like the auxiliary artefact within the field of the child’s perception. Following Vygotsky, however, the auxiliary apparatus can be both a green card on the table and the internalised “green card” that helps us perform a task, remember, or avoid certain acts. The auxiliary apparatus can be a telescope or an artefact like the green card. Both will have an effect on how we direct our attention. If the word sound “robot”, just like the word sound “green”, is also seen as a material auxiliary artefact which becomes verbal thinking, it helps us direct attention. When evoked in situations, the internalised auxiliary artefacts become part of our observation apparatus. What we perceive though intra-nalised (rather than internalised) signs, which are the intra-nalised word meanings, is not just cultural but culturally meaningful. It may be, as noted by Barad, that phenomena are indeterminate (and not just uncertain), but cultural collective learning processes align how humans perceive the world. However, to do this they need to align word meanings, and to align word meanings they need material words.

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Relativism and ignorance Practices of knowing include how we have learned to make the world meaningful in a system of concepts (as when the children from the fishing town in Jutland find it meaningful to draw fishing robots). Word meaning acknowledges that learning is a process that aligns people in their perception and thinking about a material world. This alignment is cultural in so far as it could have been different, if the material arrangements had been different. This could be seen as an argument for the relativism found in social constructivism and constructionism. Barad takes up this issue and argues against a “moral relativism” (2007), which makes it possible to refute universal ethics with a reference to human diversity. I find it problematic to make this claim without a deeper understanding of what constitutes human diversity. If it is, as I argue, learning, we should not call for a dismissal of relativism, but see it as an aspect of learning. This means that it is not a static and fixed relativism. Through learning with materials, we can align. Meaning is not just tied to individuals. Different perceptions and valuations of artefacts change historically, because ultra-social humans keep learning from each other and the meaningfulness of artefacts, which in themselves help form collectives.5 From a Vygotskyan point of view, relativism gets a twist tied to ignorance. Not because the children conceptualise robots as playful human-like creatures, but because there is more to the mature concept of robots. A concept presupposes the presence of what Vygotsky emphasised as a certain system of concepts, that we see the object in all its relations. Contrary to a relativist understanding of knowledge that implied that we perceive the richness in our material surroundings in different ways, concept-formation implies that we can have a more or less mature conceptual perception of what we perceive. The generalisation of a concept does not lie in the richness of an environment, but in the richness in thinking, which, for Vygotsky, could free humans from what was given to our situated sensual perceptions. For concepts to be rich they should include a development with many connections, dependencies, and relationships, as well as a sense of the concept’s historical development, as noted by Derry (Derry 2013). The concept of “robots” found in the Danish material shows how each child makes individually diverse drawings, which are formed by the child’s already formed collective concepts intra-acting in the situated collective of the drawing situation. Their concept of robots are spontaneously formed concepts from available resources. These concepts differ from the scientific concepts where the normative understanding and rules are obviously not up to the individual to decide. This is what Vygotsky aims at, when he discusses how a child may spontaneously form a concept of “brother” from their own experiences, whereas scientific concepts are in need of instructions to be learned. This concept [brother] is saturated with the child’s own rich personal experience. It had already passed through a significant part of its developmental

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course and had exhausted much of the purely empirical content it contains before the child encountered it in definition. Of course, this was not the case with the concept that underlies ‘Archimedes’ law. (Vygotsky 1987, 178) Vygotsky ties this “true” or mature concept formation to the formation of “real objects” – an elusive term in cultural-historical theory. It is used to describe how we learn to see something as culturally meaningful, (contrary to something meaningless) such as a clock, but it also refers to the right way of perceiving triangles. The social constructivist and constructionist would have a point, if they claimed that both triangles and clocks can be seen through many diverse cultural models. In a posthumanist learning perspective both type of concepts, spontaneous as well as scientific, are formed in a basic process by ultra-social humans engaging with available materials, which sets boundaries for what can be conceptualised in the situation. However, a Vygotskyan approach to the experiments with materials in schools, called for by Edwards (2010), makes clear that concepts have histories, and it is good to know about these histories before we begin to experiment with materials. This is not just true in chemistry class, but in general. Vygotsky uses the example of a chess game. Children who have not learned the collectively shared normative meaning of the game do not make the same inferences (Derry 2013) of what follows from placing different pieces together. The child, not knowing how to play, may amuse himself with the chess pieces, sort them according to colour, etc., but the movement of the pieces will not be structurally determined. The child who learned to play chess will proceed differently. For the first child, the black knight and the white pawn have no connection with each other, but the second child, knowing the moves of the knight, understands that an attacking move by the knight threatens his pawn. For him, the knight and the pawns are a unit. (Vygotsky 1998a, 291) Clearly the child is here a humanist subject separated from the object, but is furthermore not yet developed as a humanist subject and therefore cannot yet think in “mature” concepts, as adolescents and adults can. The vertical development argued by Vygotsky goes like this (if we use his example of the chess game): a. At first, the chess materials are physical things – spontaneously perceived phenomena (a wooden white and a wooden black item even if “white” and “black” may not be recognised as meaningful colours). b. Next, they become, first, pseudo-conceptualised objects where the phenomena are recognised as something meaningful (a black king versus a white king have a likeness and may move in the same way).

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c. Finally, as a truly conceptualised phenomenon, we understand all the meanings ascribed to kings, relative to pawns etc. and furthermore that the meaning we ascribe to the chess board may differ (Figure 7.8).

Concept formation from child to adult

If we replace the concept of chess with the concept of robots, we may add another dimension to the complex web of connections underpinning concept formation, namely that of virtual representations. Virtual representations sometimes refer to actual items (like a picture of NAO) and sometimes to completely invented items (like a picture of WALL-E). The word (soundwave) “robot” is spun into connections to virtual representations as well as actual instantiations (like Lego Mindstorm used in schools). All of these resources go into concept formation. Learning a concept makes it possible to think about and imagine what is not present in an environment, and simultaneously transforms how we perceive those material surroundings. The versed adult or adolescent perceives physical items, like chess pieces and robots, in a different way than the child, because they have learned to think with concepts without auxiliary devices. Our perception is, according to Vygotsky, not associative and atomistic (connecting one word with one item). We perceive with the conceptualisations we have learned to form and think with in all their connections and relations. The richer a concept, the more we free ourselves from the physical environment. In an experiment, an artist may think of new, imaginative uses for knights and pawns, yet the experiment is taking a point of departure in the adult’s formed concept of “chess”. The Vygotskyan framework depicts the formation of a concept of “chess” on a vertical spectrum moving from the small child’s loose and accidental bundles of potential and pseudo-concepts to the mature concepts of chess. However, we also need a horizontal spectrum which does not just emphasise the importance of available material resources and guidance from peers (as when the children inspire each other) but begins with an acknowledgement that thinking is also tied to material words.

FIGURE 7.8 

Mature concepts Adult or Adolescent Pseudo concepts Children Bundles of associations Small children

 he process of concept formation gradually transforms a word sound tied T to material objects from a bundle of associations, over a pseudo concept  to a mature concept.

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VIGNETTE 7.1: SILENT DRAWING It is very quiet in the classroom. The children are leaning over the wooden tables concentrating on drawing (see Figure 7.9). They are placed on brown benches two by two except for one child who sits alone. All 33 children in the room wear blue-and-white school uniforms but their shoes differ, from rubber boots, to sneakers and flip flops. The headmaster has informed the children of the task and we have handed out quality paper and water-based pens (in vibrant colours of yellow, orange, red, pink, purple, green, blue, black and silver) with a tip of between 0.5 and 4 mm. Then I gave the instruction in English, repeated in Kiswahili by their teacher: Draw a robot, or more if you would like to, that does something and maybe does something together with others – and there may be all sorts of other things on paper. I would like you to fill the paper with anything you think might be related to robots. We would also like to see where the robot is placed, whether it is doing something with someone else or whether it is making something itself. Apart from this, you may draw the robot exactly as you please. When the headmaster of the school and I introduced the idea of this experiment to the parents of the children at a local school meeting some days before,

FIGURE 7.9 Children

at a school in Tanzania drawing. They were, like the Danish children, asked to draw robots, however most of them had never heard that word. (Photo taken by Cathrine Hasse during experiments in 2015.)

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the parents looked puzzled. The headmaster explained in Kiswahili that I was part of a project studying how children perceived technologies such as robots and their use in school settings. When the parents still looked puzzled the headmaster moved his body in a mechanical way, fixing his arms and legs as if they were held by bolts and screws and marched on the spot. “You know”, he explains, “robots!” Some parents smile, but most still look puzzled. In the classroom, some days later the children also look up, puzzled, at the headmaster when he asks them to draw robots, but just like the parents they abstain from asking any questions. I move up and down along the aisles to help with finding the right colours if needed and watch as the drawings emerge on the white paper. Big yellow cars, small computer-like artefacts mixed with houses with flowers and Tanzanian flags. Here and there some persons appear wearing black half-masks and carrying machine guns. In a couple of drawings creatures with round heads and slim bodies seem entwined in wires. As will be recalled, we previously made this request to draw robots to other children in a very different setting, namely in Danish schools and at the Danish museum. Here the children, even the youngest, did not hesitate to begin the drawing process. They all shared a collective word meaning tied to the material word-sound “robot” that they used for verbal thinking. Though we found some differences in how the robots were materialised (as machines or humanoids) the children did know, even without visits to the exhibition to guide them, how to draw robots. The children in Tanzania were also given the same type of paper to draw on and the same colourful pencils,6 yet what emerged on paper was very different. The drawings from the Danish children showed robots as lively and engaged in a wealth of different activities. Some were dancing or listening to music, some cleaned up garbage, some were watching television, and some fell in love. In spite of all the diverse engagements, all the robots shared a number of features: square heads and immobile torsos. The children in Denmark both depicted and discussed robots as a possible human-like alterity relation. None of the 33 Tanzanian children in the classroom that day in January 2016 drew a robot in the same way as the Danish school children. After the drawing experiments, we subsequently interviewed the children; in Denmark this was mainly in focus groups, and in Tanzania in the classroom the children were questioned one by one immediately after the drawing session. When doing this, it became clear that whereas some of the Danish children have access to knowledge of robots in movies and rudimentary knowledge of actual robotic machines, the children from Tanzania had none. Only 8 of the 33 children in Tanzania admitted to knowing what a robot is. The rest of the children told us they did not know and had drawn things they liked or admired (like big cars and the Tanzanian flag) instead. The eight children who claimed they knew what a robot was had all drawn humanlike creatures. In five drawings, the figures wore black half-masks and carried guns, and in the remaining three,

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the figures were filled with wires. The figures with half-masks and guns, the children explained, were “robbers” “that come at night and steal from your houses”. The children had mistaken the sound of “robot” with “robber”. The humanlike creatures with wires inside were drawn by three children sitting next to each other. They explained that the creatures were meant to be machine-like humans (see Figure 7.10).

If we connect Vygotskyan insights with Barad’s posthuman performativity, humans cannot a priori be categorised as, for instance, children and adults, and we cannot take for granted that adults have richer and more mature concepts than children. Whatever entangles in the drawings must be discussed from a perspective of relational boundaries creating in- and exclusions in the ultra-social performative activity of drawing. If we take concept formation seriously as an aspect of what gets entangled, adults like Toby and Marcia include their former experience in their concept formation, just as Selma, Eric and Maryam. What makes them differ in their thinking is not just availability of material, but also what kind of materials gets entangled in phenomena. Furthermore, our research from Tanzania shows that it also matters if there are limited materials available to think with. This horizontal model (see Figure 7.11) reveals that concepts can be more or less entangled with available materials – and that this has implications for our

FIGURE 7.10 Adina, age

12, has drawn a robber trying to enter a house. (Photo taken by Cathrine Hasse during experiments in 2015.)

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No robot materials available for concept formation

FIGURE 7.11 The

Media robot materials available for concept formation

Media and handson robot materials available for concept formation

material surroundings matter, literally, for the formation of concepts.

possibilities for thinking and imagination.7 The politics of concept formation becomes salient when we ask about diversity in the available resources for concept formation and imagination. If they have never seen a chess game, neither an adult nor a child could guess the rules of chess just by looking at the board and pieces. You may form a spontaneous concept of chess but would surely miss the point of how to move the pieces in the eyes of a well-versed chess-player. To align with chess-players you need to learn how the material word-sound, “material carrier” as Vygotsky names it (Vygotsky 1987, 106), connects to specific materials with the collectively shared, normative, mature concept of chess. These collectively shared concepts usually involve some kind of physical experience with material objects, even when we form abstract concepts. This concept formation, which makes an increasing number of elements meaningful in relation to each other, is what would make it possible for someone to communicate with chess-players regarding the game. Inspired by Barad’s performative posthumanism, this process of posthumanist learning forms a basis for what is in – and excluded and what creates boundaries in intra-actions. The learning process will transform the chess materials from being perceived spontaneously to being perceived as a normative concept recognised as such by other chess players. Vygotsky meant this to be a humanist argument of how small children learned to become rational and free adult thinkers that differ from non-thinking, primitive animals. This is a reminder of the self-evident Vitruvian Man. From a posthumanist perspective, it is questionable whether adults become more rational than children in the way thought by Vygotsky and whether their knowledge is always organised systematically in any rational way.8 Furthermore, as we shall explore in the subsequent chapters, many things are learned without any explicit instructions and normative spaces to guide us. What is important for my understanding of posthumanist learning is that whenever we engage in experimentations and exploration, our spontaneously formed preceding learning is part of the endeavour of being curious. In educational settings, like classrooms and MOOCs, we bring different cultural resources to bear, which are not always recognised as relevant resources. It is important to acknowledge

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that the preceding cultural learning may become a barrier for engaging in, for instance MOOCs, because the new learning required builds on tacit assumptions of what is already learned (e.g. Andersen et al. 2018). With the notion of systems of signs as behind what drives agency, Vygotsky brings to posthumanism the acknowledgement that humans collectively share and continue to develop a learned thinking, which is present in phenomena that includes certain perceptions and excludes others. The older children who did visit the exhibition before the drawing session did not refer to actual robots from the exhibition in their drawings. From the Vygotskyan learning perspective, they should already have formed a concept of robots that freed them from the material surroundings. We found that younger children relied more on what the visual field of attention had presented them with when they visited the exhibition. Yet, many of the older children (age 13 to 14) also preferred to draw media robots. Their concept of robots could thus be argued to be less rich than the younger children with hands-on experiences of robots, who we met in the previous chapter. The collectively shared notion of robots as “humanoid” is, in a Vygotskyan sense, a generalised concept of robots even if the children have never actually encountered any robots capable of doing what the robots did in the drawings. The concept builds on a shared abstract understanding of what characterises all robots built from available media resources. Robots are humanoid and just as “real” to the children as a robot-maker’s actual machine. The concept of “robot” has developed for some children, in a storied world through particular processes of learning about robots in a media-world that emphasises lifelike robots and not robots as machines. When robots are indistinguishable from humans in their aliveness, the square heads are an example of how concepts and materials entangle rather than a collective manifestation of a prototypical mental robot. What the drawing phenomena show is the special arrangement, the apparatus, which in this case does not include cheap cigars (see p. 85, Chapter 3, this volume), but the pen and paper as well as a concept of robots as “human-like”, in this particular material practice led the children to draw “square heads”. The concept’s word meaning finds its expression in drawings of robots capable of doing what Danish children do, which underscores that this word meaning is formed in a cultural learning process. The Vygotskyan approach shows us that we need awareness of what we think we need to think about. Our robotic future is one of those issues we need more and deeper knowledge of in order to think realistically about it. This is also connected to the possibility to be free to act through concept formation. Without conceptual resources at hand we are, in the Vygotskyan tradition, not free to think about the type of society we want because we lack an adequate concept formation. This could imply that because Danish children can imagine and conceptualise robots, they can also inf luence the design of a robotic future, even if the children with hands-on experience seem to be closest to an actual inf luence

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on the design, whereas children in Tanzania will only learn about robots and learn to conceptualise them when this future is already formed and having an impact in their world. To remedy this inequality is not to say that to be able to think about robots and a robotic future, children in Tanzania need exactly the same available resources as children in Denmark. They only need enough available resources to begin concept formation in order to think about and imagine a robotic future. They need to be acquainted with the word “robot” and engage with diverse materials connectable to the word that over time becomes the expression of a concept of connections. The drawing experiments bring categories and discourse back into the observation apparatus. Like “robot”, the concept of “posthumanism” is basically a line of thinking very much embedded in a Western discourse. It has developed as Western intellectuals increasingly acknowledge a climate crisis that makes clear our deep connection with non-humans, and it has transformed the Enlightenment humanism also found in social theory with its focus on human–human relations. It has, however, not been capable of encompassing a concept of humans in the plural as cultural beings in its posthumanist theorising. Why are some humans included and some excluded from partaking in this overheated communication about robots we experience in the Western world? Posthumanism is largely ignoring this diversity in studies of how humans’ access to the material words differs and gives humans very different possibilities for involvements in material arrangements like MOOCS and other types of education. A new subdiscipline has emerged in anthropology which is the anthropology of ignorance (e.g. High, Kelly, & Mair 2012). Ignorance, or agnotology, can be discussed in many ways, but it is always relational and refers to absence (Croissant 2014). Studies point to ignorance as something we create, as pointed out throughout the introduction to The Anthropology of Ignorance: An Ethnographic Approach (Mair, Kelly, & High 2012), when we do, for example, what I did in Tanzania. I brought a Western belief system and robotic concern with me into the classroom of children in Tanzania. By exposing their ignorance of “robots” I became the extended mediator of the Western emphasis on communication of “robots”. Nevertheless, from a Vygotskyan point of departure, the children are ignorant not just because they do not know the word “robot” but because they cannot think “robot”. This excludes these children from any engagement with the “we”s that make robots, whether they are humanoid or machine-like. It furthermore excludes the children from having a platform of preceding learning, like the Danish children, that can be used as cultural resources when new learning takes place. They cannot look up MOOCS that offer global education to individuals, because as individuals they are first formed as collectives. And in their materialconceptual collective, robots are not (yet) a potential object of observation. This acknowledgement has implications for who can become the makers and users of technology. MOOCS and other new types of learning come with promises of lifting humans all over the world out of ignorance, because they can learn

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from available resources offered through the internet. This is a very humanist and individualist understanding of education. In the words of Jeremy Knox, education and many learning theories maintain “humanist views of the rational, autonomous and bounded learning subject, determinist beliefs in the instrumental function of technology, and uncritical notions regarding the stability, passivity and immateriality of space” (Knox 2016, 26). This results in a rejection of difference in opportunities to learn. Culture in posthumanist learning refers to differences in preceding learning and differences in available materials to think with. Learning cannot be reduced to what goes on in individuals’ minds. All new learning takes its departure in preceding material-conceptual arrangements where collectives of collectives entangle. A learning perspective can explain the many humanoid robots in the Danish context, since very few children have actually met or built robots as the robot-makers have. Their potential for concept formation in intra-actions use the cultural resources available to them in the situation. The children in Tanzania do not have resources to bring to bear when asked to externalise the meaning of the word-sound “robot”. Instead, they creatively make use of the resources they do have and make wonderful drawings of cars, f lags, robbers and houses. Where Vygotsky, as a humanist, emphasised the internal connection of concepts for learning to think, we may emphasise both conceptual and material cultural resources. Learning through materials may not just be an issue for young children, as argued by Vygotsky; maybe available materials are the starting point of all new learning. In experiments presented in the previous chapter we saw that the children who had learned to “think” robots as machines, were also the children who had either built robots themselves or seen robots at exhibitions, like Eric. Following this finding we could have expected more children who visited the exhibition to draw realistic robots, but this was not the case. However, children with hands-on experiences with building robots themselves were more likely to draw robots as machines, where the children who had only learned to think of robots as humanoid seemed to perceive and pay attention to their surroundings from this perspective and therefore also drew humanoid robots. Children with a richer concept formation expressed more technical robots. This formation and development of verbal thinking forms part of our apparatus of observation as well as expression. However, it all begins with available materials including the material words.

Conclusion: Chapter 7 Though no two humans are alike, streams of collectivities run through physicists, engineers and children, as we have seen in this and previous chapters. Humans are ultra-social and align their mutually implicated practices of knowing and being. Preceding learning and material-conceptual engagements come together

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in these practices that form knowledge within phenomena. The children materialise their different robotic phenomena, including word meanings of “robots”, on paper in here-and-now ultra-social engagements, commenting, borrowing and forming conceptual collectives (as when one group refers to Christmastree-decorating robots and another to fishing robots). Knowledge about robots evolves and grows as the children, curiously, as ultra-social beings, draw on the cultural resources available to them. The Danish children appear to be making individual choices, and in fact no two drawings are alike. The resources they employ are nevertheless drawn from a common pool of knowing about gender, movies, games, chips, doctors, going to restaurants, fishing boats and watching TV programmes, which mix with personal experiences – and even these personal experiences can for the most part be seen as collective before they are coming out as individual. The Tanzanian material indicates that material resources are needed in order to form concepts and that concepts are needed to form imaginaries. One aspect that has been largely overlooked in posthumanist theory, as well as in the learning sciences, is the importance of available materials for concept formation. Materials matter. Concepts matter. Children do not draw on an open-ended imagination, but an imagination depending on the available material-conceptual resources. Like the children in Tanzania, the resources the Danish children bring to bear are cultural, and though the children in Tanzania are more silent, they are no less cultural. The Christmas tree is meaningful to Danish children as the wordsound “robot” is meaningless to the Tanzanian children. For them what seems to matter are cars and houses. Contrary to the claims in the humanist tradition, these materialisations are not a window into the children’s inner mental representations, but an unfolding learning process where the phenomena develop as ultra-social engagements with previously formed concepts, engagements with colours, paper etc. Word meaning is a collective unity, but a collective unity in constant transformation. Word meaning evolves in cultures of expected and learned agency with available materials. In the material-conceptual sense of collectives, each child is a collective of potential conceptual meaning meeting material and social collectives in local situations. Collectives are always wider than we imagine, which is why concepts can develop and become richer. No doubt both the children and the experimenters developed their concept of robots in the drawing situation in Tanzania as well as in Danish schools. Drawing square creatures may here refer to a generalised concept of robots that emphasises robots as humanoid, yet machine-like. The square heads and torsos found in so many drawings by the Danish children signal “machine” in a drawing of otherwise very human activities. It is likely that the collective feature of humanoid robots with square heads stems from a collectively shared abstract concept of “robot” as humanoid, which does not refer to any specific robot, but to a generally accepted word meaning ( just like the triangle). What frees us from relativism is that concepts of robots and triangles can be more or less mature.

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However, “mature” does not seem to be about being an adult or a child, but about the material availability and experiences we need to build up organised knowledge. Ignorance is tied to this availability. Drawing technical details on robots is a result of hands-on experiences, which form a more mature concept of robots (in so far as children and adults with these experiences have a richer cultural pool to draw from, as they know robots can be both media robots and hands-on robots). It was not ignorance, however, that made the children draw robots with square heads. It was the children’s preceding conceptual learning that intra-acted with the material and social conditions of the drawing situation and, in our analysis, came out as a cultural collective similarity in how robots are regarded as lively. In this sense, both the word meaning and its embeddedness in the material-conceptual situation directed the drawing of square heads. The concept of “robot” displays many cultural features in all its relations – for instance the already formed conceptions of “male” and “female” and their material significations that the children make use of in their drawings. When Barad says concepts are “material enactments” that are not equivalent to, but contribute to, the phenomena of the world (Barad 2007, 32), it is not so far from Vygotsky’s theories of word meaning and auxiliary artefacts. As a twentieth century humanist, Vygotsky did not exclude the a priori subject–object distinction, as Barad does. Barad, on the other hand, does not, like Vygotsky, emphasise the dynamic, ultra-social collective transformation of human thinking as tied to material word meanings. The two theoretical approaches cannot be combined directly but, through a diffractive reading, we may focus on patterns of similarity in ultra-social meaning-making, which is the basis for preceding learned collective memory, perception and recognition of objects of observations. The collective aspects of what is meaningful should not be reduced to individualist serendipity. In a wider perspective, the focus on the species of ultra-social humans is not a return to the Enlightenment human, but an acknowledgement of the power some humans have to transform the Earth for all other beings. All of this has implications for how we think of education. The posthumanist learning theory I propose reaches beyond the discussions of learning tied to education that we encounter from posthumanist-inspired educationalists. These educationalists emphasise the importance of learning as growth, intelligibility (Ceder 2015), experimentation (Edwards 2010), and networks where learning is an effect of a value judgement of learning as something worthwhile (Fenwick & Edwards 2010), as well as, the general emphasise of the materiality of learning (Sørensen 2009). We cannot propose responsible experimentations and value judgements or even blame moral relativism without a notion of how we learn concept formation. Conceptual word meaning is part of the ongoing meaningfulness that is included in material-conceptual phenomena. This is also why global education offered by MOOCs does not offer education on equal terms for all, but only for those who collectively can align in meaningful learning.

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When educationalist Gert Biesta argues against the discourse of “learning” in schools, he argues for a learning process which equals what is needed to be learned in schools and nothing else. As he puts it: The quickest way to express what is at stake here is to say that the point of education is never that children or students learn, but that they learn something, that they learn this for particular purposes, and that they learn this from someone. (Biesta 2012, 36, original emphasis) However, the point of a posthumanist learning theory is to emphasise that we always learn something, even if it is not for explicit purposes and that we learn this from a collective “we”, which then forms collective “we”s in us to be used in intra-actions. This “we” is a material body of “we”s displayed in material artefacts just as much as an ultra-social collective of thinkers. Barad criticises moral relativism; however, from my posthumanist learning perspective it would be more effective to work on giving ultra-social humans richer and more mature concepts to think with and materialise from. Can we be responsible for what is excluded in phenomena? What lies on the other side of the agential cuts we make? In Barad’s posthumanist theorising there can be no such thing as a priori ignorance. I want to emphasise that ignorance, when entangled in learning phenomena, is our own awareness that we are ignorant of what we have not learned. This goes for the experimenters as well as for the children in the Tanzanian country school. Ignorance is, once it is acknowledged, a way to look for new learning possibilities and, through these, obtain new cultural resources for thinking, making and materialising. This acknowledgement is not a return to mentalism and psychologism. It is the physicality of words and bodies that transgress dichotomies of nature–culture and mind–body – but retain a culture–culture divide in how materials entangle as meaningful. Our responsible bodies can go on learning from this position. This is the topic of the next chapter.

Notes 1 https​://en​.wiki​pedia​.org/​w iki/​There ​_ are_ ​k nown ​_ know ​n s. 2 Experiments are, in our research, a controlled procedure with the same type of basic material resources made available for children outside a laboratory in different local spaces, in order to seek for collective patterns across the local experimental settings. 3 Of 195 drawings, 119 have square heads, 56 have softer faces and 20 have no face at all (survey of 195 drawings from children age 6 to 14 in four schools in Jutland. Collected 9 November 2015). 4 It addition, it seems that “communities that live in square or rectangular houses have better codability for angular shapes than communities living in round houses” (Majid et al. 2018). 5 There is so much more to be said about this point of how materials shape collective bodies from a Vygotskyan/Ilyenkov point of view, but it is beyond this book. I will refer to the interesting discussions taken up by the philosophers Alex Levant (2014, 2012). 6 When I refer to children in Tanzania, I refer to these particular children in this particular situation at the country school.

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7 Though I am discussing children’s imagination here, the same may be true for adults as well. 8 Vygotsky argued, for instance, that concept formation was a special human capacity, but new theories of how animals are actually capable of learning may question this human exceptionalism argument.

References Andersen, B. L., Na-songkhla, J., Hasse, C., Nordin, N., & Norman, H. (2018). Perceptions of authority in a massive open online course: An intercultural study. International Review of Education, 64(2), 221–239. Barad, K. (2003). Posthumanist performativity: Toward an understanding of how matter comes to matter. Signs: Journal of Women in Culture and Society, 28(3), 801–831. Barad, K. (2007). Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning. Durham, NC: Duke University Press. Biesta, G. J. J. (2012). Giving teaching back to education. Responding to the disappearance of the teacher. Phenomenology and Practice, 6(2), 35–49. Braidotti, R. (2013). The Posthuman. Cambridge: Polity Press. Breazeal, C. (2002). Designing Sociable Robots. Cambridge: MIT Press. Brooks, M. (2009). Drawing, visualisation and young children’s exploration of “big ideas”. International Journal of Science Education, 31(3), 319–341. Ceder, S. (2015). Cutting research water: Towards a posthuman theory of educational relationality. Research output. Doctoral thesis. Lund: Lund University. Croissant, J. L. (2014). Agnotology: Ignorance and absence or towards a sociology of things that aren’t there. Social Epistemology, 28(1), 4–25. D’Andrade, R. & Strauss, C. (Eds.) (1992). Human Motives and Cultural Models. Cambridge: Cambridge University Press. D’Andrade, R. (1995). The Development of Cognitive Anthropology. Cambridge: Cambridge University Press. Derry, J. (2013). Vygotsky: Philosophy and Education. Hoboken, NJ: Wiley Blackwell. Edwards, R. (2010). The end of lifelong learning: A post-human condition? Studies in the Education of Adults, 42(1), 5–17. Fenwick, T. & Edwards, R. (2010). Actor-Network Theory and Education. London: Routledge. Floridi, L. (Ed.) (2015). The Onlife Manifesto: Being Human in a Hyperconnected Era. London: Springer Open. Garfinkel, H. (1984). Studies in Ethnomethodology. Cambridge: Polity Press; Englewood Cliffs, NJ. (Original work published 1967.) Hayles, N. K. (1999). How We Became Posthuman: Virtual Bodies in Cybernetics, Literature, and Informatics. Chicago, IL: University of Chicago Press. Hasse, C. (2013). Artefacts that talk: Mediating technologies as multistable signs and tools. Subjectivity, 6(1), 79–100. Hasse, C. (2018). Cultural-historical hyperobjects. In: G. Jovanović, L. Allolio-Näcke & C. Ratner (Eds.). The Challenges of Cultural Psychology: Historical Legacies and Future Responsibilities (pp. 357–368). Abingdon: Routledge. Holland, D. (1992). The woman who climbed up the house. Some limitations of schema theory. In: T. Schwartz, G. White & C. Lutz (Eds.). New Directions in Psychological Anthropology (pp. 68–10). Cambridge: Cambridge University Press. Holland, D. & Quinn, N. (Eds.) (1987). Cultural Models in Language and Thought. Cambridge: Cambridge University Press.

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Holland, D., Lachicotte, W., Skinner, D., & Cain, C. (1998). Identity and Agency in Cultural Worlds. Cambridge: Harvard University Press. High, C., Kelly, A., & Mair, J. (Eds.) (2012). The Anthropology of Ignorance: An Ethnographic Approach. New York, NY: Palgrave Macmillan. Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. New York, NY: Viking. Knox, J. (2016). Posthumanism and the Massive Open Online Course: Contaminating the Subject of Global Education. London: Routledge. Levant, A. (2012). E.V. Ilyenkov and creative soviet theory: An introduction to ‘dialectics of the ideal’. Historical Materialism, 20(2), 125–148. Levant, A. (2014). Emancipating open marxism: E.V. Ilyenkov’s post-cartesian antidualism. In: A. Levant & V. Oittinen (Eds.). Dialectics of the Ideal: Evald Ilyenkov and Creative Soviet Marxism (pp. 183–200). Leiden: Brill. Mair, J., Kelly, A., & High, C. (2012). Introduction: Making ignorance an ethnographic object. In: High, C., Kelly, A. & Mair, J. (Eds.). The Anthropology of Ignorance: An Ethnographic Approach (pp. 1–32). New York, NY: Palgrave Macmillan. Majid, A., Roberts, S., Cilissen, L., Emmorey, K., Nicodemus, B., O’Grady, L., … Levinson, S. C. (2018). Differential coding of perception in the world’s languages. Proceedings of the National Academy of Sciences of the United States of America, 115(45), 11369–11376. Moravec, H. (1988). Mind Children: The Future of Robot and Human Intelligence. Cambridge: Harvard University Press. Strauss, C. (1992). What makes Tony Run? Schemas as motives reconsidered. In: R. D’Andrade & C. Strauss (Eds.). Human Motives and Cultural Models (pp. 197–224). Cambridge: Cambridge University Press. Strauss, C. & Quinn, N. (1997). A Cognitive Theory of Cultural Meaning. Cambridge: Cambridge University Press. Sørensen, E. (2009). The Materiality of Learning Technology and Knowledge in Educational Practice. Cambridge: Cambridge University Press. Tomasello, M. (2014). The ultra-social animal. European Journal of Social Psychology, 44(3), 187–194. doi:10.1002/ejsp.2015. Tomasello, M., Kruger, A. C., & Ratner, H. H. (1993). Cultural learning. Behavioral Brain Sciences, 16(3), 495–511. Tomasello, M. & Rakoczy, H. (2003). What makes human cognition unique? From individual to shared to collective intentionality. Mind & Language, 18(2), 121–147. Tomasello, M., Carpenter, M., Call, J., Behne, T., & Moll, H. (2005). Understanding and sharing intentions: The origins of cultural cognition. Behavioral and Brain Sciences, 28(5), 675–735. Vygotsky, L. S. (1978). Mind in Society. Cambridge: Harvard University Press. Vygotsky, L. S. (1987). Thinking and speech. In: R. W. Rieber & A. S. Carton (Eds.). The Collected Works of L.S. Vygotsky (Vol. 1, trans. N. Minick, pp. 39–285). New York, NY: Plenum Press. Vygotsky, L. S. (1998). Development of higher mental functions during the transitional age. In: R. W. Rieber (Ed.). The Collected Works of L.S. Vygotsky (Vol. 5, trans. M. J. Hall, pp. 83–149). New York, NY: Plenum Press. Vygotsky, L. S. (1998a). The crisis at age seven. In: R. W. Rieber (Ed.). The Collected Works of L.S. Vygotsky (Vol. 5, trans. M. J. Hall, pp. 289–296). New York, NY: Plenum Press. Vygotsky, L. S. (1999). Analysis of sign operations of the child. In: R. W. Rieber (Ed.). The Collected Works of L.S. Vygotsky (Vol. 6, trans. M. J. Hall, pp. 45–56). New York, NY: Plenum Press.

8 LEARNING WITH CYBORG TECHNOLOGY

To what extent do collective practices of preceding learning with materials (including momentary collective word-meanings) include our ultra-social bodies, and what are these bodies anyway if they are not the ultimate boundary between a subject and an object? In materialist and postphenomenological theories the body has been redefined from an individual locus of cultural inscription to a relational ontology. When Kathrine Hayles said: “You are the cyborg, and the cyborg is you” (Hayles 1999, xii) – what is this “you” in a relational ontology? If it is a collective “you”, what does this collective consist of when the phenomenon is the body? In new feminist materialism it is emphasised that the body is not a passive locus for discursive inscription, and the new posthumanist and materialist theories: can account for how the discursive and the material interact in the constitution of bodies. They [feminist theorist] explore the question of nonhuman and post-human nature and its relationship to the human. One of the central topics in this approach is the question of agency, particularly the agency of bodies and natures. Material feminists explore the interaction of culture, history, discourse, technology, biology, and the “environment”, without privileging any one of these elements. (Alaimo & Hekman 2008, 7) Psychology is absent from this list of important elements in both postphenomenology and new feminist materialism. Alaimo and Hekman do not include psychological processes in the discussion of material and discursive bodies. Therefore, ultra-social learning, perception, conceptualization and questions about what creates collectives of human meaning-making are not a part of these theoretical appara­tuses. Posthumanist theory has gone directly from a critique of postmodern theories, that overlooked the material body and saw the body as a passive receiver of cultural construction, to an emphasis on the agential body as materially

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co-composed with biological and technological components. Without privileging the psychology of learning, I argue learning should not be excluded when the body is in an intra-active relational becoming. The philosopher Charles T. Wolfe insists that the materialists have an obligation to accept the psychological introspection, which, from the days of Wilhelm Wundt, was the mother of all learning theory, as we saw in Chapter 1. Instead of denying the existence of introspection, the materialist should try and locate it within the physical world, within the overall framework of explanation (as Spinoza did: “the order and connexion of ideas is the same as the order and connexion of things” – which one can see as opening up a relational ontology). (Wolfe 2016, 116) For the spinozists, the body is not a blank surface on which technology, discourse or collectivities are scribbled. Within the body as well as outside, there is a process of ideas that will be introspectively ref lected upon. There is something to be introspective about but it is not something stable, but rather, like in postphenomenology, a body in transformation in material relations. Postphenomenology often builds on some kind of introspection, as when Don Ihde ref lects on how his own hearing changes (Ihde 2008), but not often in relation to learning. In postphenomenology, bodies as well as surroundings are transformed in the body’s relationships with technology, either as attached to the body, in the body, the body as a hermeneutic device, or the body in alterity and background relations (Ihde 1990; Verbeek 2005). It is understandable that new feminist materialism and postphenomenology (along with almost all the rest of the Science and Technology theoreticians) reject the dual-process theories that have been prevalent in psychology since William James, and are now prevalent within coding theory separating object and representation (Colman 2015, 225). However, psychology, especially Vygotsky inspired cultural-psychology, has more to offer than dualisms. The body may not have been emphasised enough, but a Vygotskyan approach to concept formation is also non-dualist and, like new feminist materialism and postphenomenology, follows a spinozist monism (e.g. Roth, Radford & LaCroix 2012). Some feminists and postphenomenologists also begin to acknowledge that we need to understand learning to account for entanglements of bodies and technology – for instance Dr Robert Rosenberger, who has discussed the “learned body habits” of users of vibrating smartphones (2012). This may be an indication that new technological developments challenge the taken-for-granted exclusion of psychology in new feminist materialism and postphenomenology. We need an acknowledgement of how the introspection of body-learning shows that humans change as they form bodily habits, and that these bodily habits (whether we are conscious of them or not) are always agentic in ways that involve both materials and concepts. Our habituated body is not

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just hooked up with materials but is a learning cyborg body, continuously in the process of breaking down the boundaries expected by dualist humanism. This chapter explores cyborgs and how cyborgian processes are indeed processes of situated and preceding learning with materials. One place to explore how cyborg bodies learn is in the field of “artificial limbs”. A person’s learning process for connecting f lesh and metal (Hayles 2002) is not necessarily smooth, which I exemplify here with a particular device, a prosthetic hand, which gradually (and never perfectly) aligns as a personal hand. This artificial hand becomes part of the rest of the person’s body operating it, but from a posthumanist perspective, the researchers developing the artificial hand wires and chips engraved in the f lesh, and the preceding learning of all involved in the phenomenon also take part in a long painful process of becoming a particular cyborg.

Cyborgs in space The concept of “cyborg” originated etymologically as a neologism coined by two employees at NASA (National Aeronautics and Space Administration) Manfred Clynes (a neurophysiologist) and Nathan Kline (a psychiatrist). They meant “cyborg” to be an abbreviation for “cybernetic organism” i.e. an organic as well as biomechatronic being (Clynes & Kline 1960). Cyborgs are purposeful amalgamations of humans and machines as cybernetically extended organisms. The term was inspired by research done by Manfred Clynes in the 1960s on how the bodies of astronauts could be altered to overcome the encounter with outer space. The cybernetic organism was created with the aim of physically enhancing human bodies. These ideas were inspired by the transitive field of cybernetics, where humans were considered information systems capable of being hooked up with machine parts (Hayles 1999). Clynes and Kline declared in 1960 that: In the past evolution brought about the altering of bodily functions to suit different environments. Starting as of now, it will be possible to achieve this to some degree without alteration of heredity by suitable biochemical, physiological, and electronic modifications of man’s existing modus vivendi. (Clynes & Kline 1960, 26) Clynes and Kline also emphasised that the cyborg would give man a new freedom in space by letting the cyborg parts of his organism deal with problems that could be handled by a robot, that acts: “automatically and unconsciously, leaving man free to explore, to create, to think, and to feel” (Clynes & Kline 1960, 27). The human is not only enhanced in its capability to survive in space by technical means, it is also enhanced in its capabilities of being human. In the beginning the cyborg was not about transforming this human into a posthuman, but to enhance feeble and vulnerable biological bodies with reinforced mechanics to improve their capacities for moving in hostile environments. It has nevertheless become a

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source of inspiration, for both the transhumanists and the more radical singularists, that humans can transform themselves through technological enhancements. The notion of “cyborg” was later expanded by feminist Donna Haraway in an essay where she, like Clynes and Kline, sees the cyborg as a liberating figure, which thrives when assumed stable boundaries between nature and culture break down (1991). This notion of “cyborg” has become a landmark for spinozist thinkers, and even functioned as a critique of how the singularists use the term. As argued by many spinozists, the technically oriented singularists overlook how humans are already cyborgs through their material–discursive entanglements with the world. Haraway adds to Clynes and Kline’s discussion that a cyborg is not only transversal in a bio-material sense but disturbs all kinds of material–discursive boundaries. More than a trope, the cyborg is our ontology, because we live in: a mythic time, we are all chimeras, theorized and fabricated hybrids of machine and organism; in short, we are cyborgs. This cyborg is our ontology; it gives us our politics. The cyborg is a condensed image of both imagination and material reality, the two joined centres structuring any possibility of historical transformation. (Haraway 1991, 150) Haraway took the cyborg far beyond what was intended by the two scientists, Clynes and Kline. In feminist, postcolonial and STS studies the cyborg became a way of challenging all kinds of binary dichotomies, which had been prevalent in both mainstream and critical thinking. As a trickster, the cyborg would squeeze in between the rigid separation of male and female, of human and non-human, machine and biological organism, nature and culture – and what is important from a learning perspective – of fiction and reality. Her cyborg is not a separator, but a connecter and assembler, whose function is, in the words of Rosi Braidotti, “to think the unity and the interdependence of the human, the bodily and its historical ‘others’ at the very point in time when these others return to dislocate the foundations of the humanistic worldview” (Braidotti 2006, 203). As both a trope and a reality, “the cyborg” has a long history of crossing boundaries between social science studies and the technical and natural sciences. At first, it denoted very physical changes to human bodies and later came to include transversal category work that was needed by the singularists to free the cyborg from a myopic, yet omnipotent, human power-position. Like the “posthuman”, the “cyborg” connects the singularists and the spinozists, as well as separates them. The concept of cyborg has been fiercely debated. From Clynes and Kline’s definition of the systemic alterations of biological bodies through biochemical, physiological and electronic modifications without any hereditary changes, the concept has grown and evolved as a fitting trope and reality for our time. For some, cyborgs are considered any extension of human existence with artificial manmade tools (e.g. Clark 2003) including body

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enhancements like walking sticks. Some, especially the transhumanists (e.g. More & Vita-More 2013) see body enhancements as the direct road to the singularist future. Others see, in the spirit of Haraway, cyborgs as liberators. Others, like postphenomenologist Don Ihde (2002), see cyborgs as connected to how the human body relates to technology, and postphenomenologist Peter-Paul Verbeek (2008) has expanded his thinking with the concept of cyborg intentionality. When technology merges with the body, there is no longer a separation between body and technology when in a “relation”, and when technology has inbuilt “intentions” we can furthermore speak of a composite machine–human intentionality (Verbeek 2008). Within the social sciences there have also been some conceptual debates as the cyborg has become a denominator for the shifting boundaries, which some social scientists believe lie at the base of the posthuman condition. Posthumanism is, for the spinozists, an upheaval against humanism as a philosophical project founded on a notion of hierarchical dichotomies ensuring an anthropocentric and androcentric dominance (Wolfe 2009). In this respect, the cyborg, like the posthuman, is a destroyer of Mount Dichotomy from which this anthropocentric worldview was upheld. The cyborg is considered a freedom fighter and liberator from the rational Vitruvian Man. But it is not the singularist posthuman, but the cyborg that is emphasised as a liberating figure, understood as a “teleological evolutionary stage in some kind of transhumanist technoenhancement” (Gane & Haraway 2006, 140). The spinozist posthumanists see the cyborg as a potential ally, when it transgresses all boundaries between “bodily existence and computer simulation, cybernetic mechanism and biological organism, robot teleology and human goals” (summarised from Hayles 1999, 3). However, as also emphasised by Barad and Haraway, the spinozist “posthuman” has to be a responsible cyborg. Our bodies, ourselves; bodies are maps of power and identity. Cyborgs are no exception. A cyborg body is not innocent; it was not born in a garden; it does not seek unitary identity and so generate antagonistic dualisms without end (or until the world ends); it takes irony for granted. One is too few, and two is only one possibility. Intense pleasure in skill, machine skill, ceases to be a sin, but an aspect of embodiment. The machine is not an it to be animated, worshipped, and dominated. The machine is us, our processes, an aspect of our embodiment. We can be responsible for machines; they do not dominate or threaten us. We are responsible for boundaries; we are they. (Haraway 1991, 180) From a posthumanist learning perspective, the spinozist and the singularist understanding of cyborgs differ in relation to how they view responsibilities for the boundaries “we” make and also how “we” define bodies. Both spinozists and singularists overlook how hard it is to make collective cyborgian habits. Physical labour and learning, even drilling practices, often go into making machine parts

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work in tandem with human f lesh. It is all about process in relations, which eventually may become a habit and a pleasure in real-life world experiences. This has not caught the attention of spinozists or singularists. The cyborg is a trope and reality which captures our times, but from a learning perspective we must begin by looking at what people must learn to connect with machines and how the “we” who make the machines may differ from the “we” who have to learn to live with them. Habits do not always come easily as the below example will show. It is based on notes from a scientific protocol, Lifehand 2, where a Dane, Dennis Aabo Sorensen (abbreviated as DAS), explains how he experienced receiving an artificial hand in what was called the Lifehand 2, which was a continuation of a previous experiment:

VIGNETTE 8.1: FEELING THE WORLD Experimenter: How does your biomechatronic hand feel? DAS: I’d say I am using the prosthesis like a natural hand, I can sense and really “feel” it, when I move it. It’s as if some special vibrations let me understand when I get hold of an object and how it’s made Experimenter: Sensorial feedback? DAS: That sensorial feedback was an amazing experience as far I was concerned. It seems incredible being able to feel the different consistency of objects, understand if they’re hard or soft and realise how I am clasping them. The feedback is furthermore extremely natural. I’m convinced that this is the future of prosthesis in the world (Lifehand 2014, 24). On New Year’s Eve 2004, a young man, DAS, from the Danish town of Aalborg went out, like many Danes, to bring in the New Year with firecrackers. Though the fireworks were bought legally one of the fire rockets exploded and demolished his left hand. He was taken to the hospital and had his left arm amputated below the elbow. Ten years later he was selected from a group of 31 people for an experiment called Lifehand 2 that developed an artificial hand, also named Lifehand 2. He was to have four intraneural electrodes implanted that would connect his arm stump with a prosthetic hand in order to restore feeling and the ability to manipulate objects. In the submitted article presenting the results of the Lifehand 2 project, the researchers claim that DAS did not receive particular training and that he quickly learned to use the bidirectional control of the robotic hand (Raspopovic et al. 2014). However, on the homepage from one of the Italian partners of the project, Università Campus Bio-Medico di Roma, and in the publication Lifehand 2 (Lifehand 2 2014) it appears that DAS had to learn a lot, along with the researchers, before the actual experiment could begin. When DAS arrived in Rome on January 18th, 2013, he was first screened at the University Policlinico “Agostino Gemelli” where the researchers carried out e.g. blood

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tests, electrocardiograms, chest x-rays and tested his neuropsychological condition. On January 26th DAS received surgery to implant four TIME (Transverse Intrafascicular Multichannel Electrodes) intraneural electrodes in the median and ulnar nerves of his left arm. The homepage informs us that “the operation began at 8.30 in the morning and lasted over seven hours”. Next: January 30th – February 14th: Training. The patient spent nearly three weeks with researchers, every day training for several hours in order to learn how to recognise and classify electric impulses, delivered via the intraneural electrodes, with characteristics identical to those which would be transmitted by the biomechatronic hand during experimentation. (Lifehand 2, 2014, p.8) In other reports the learning is also emphasised. Every day for a whole week the research group connected their test person to the prosthetic. “It was really difficult for Dennis”, says Silvestro Micera to a homepage reporting on excellence in education, research and teaching (ETH in brief). “He had to do 700 tests, try to apply his grip 700 times”. The researchers kept asking the amputee whether he felt anything, and where it was – in the index finger or the little finger? He learned how to close the artificial hand with just a slight pressure, or a bit more pressure, or very firmly. With his eyes blindfolded, and wearing earplugs, he was able to feel if an object was soft, slightly hard, or very hard. He held and felt a baseball, a mandarin orange and a glass bottle, and he hardly ever let anything fall. “To recognise things that I held, that was incredible”, he recalls. Of course, it was not the same as when he used his healthy right hand, but the feeling was similar. (ETH Domain 2014, 38) To create the bidirectional communication circuit from the prosthesis to the brain (sensory) and vice versa (movement and grasp intent), two algorithms were developed by researchers: one capable of “reading” the output from the tactile sensors of the robotic fingers and sending it to the nervous system through the intraneural electrodes in the form of electric impulses. The other was capable of receiving, processing and decoding the surface electromyographic electrodes (sEMG) signals located on the patient’s stump muscles and transforming them into appropriate motor commands for the robotic hand. (Lifehand 22 2014, p. 8) After 30 days, on February 24th, 2013, DAS again underwent surgery and had the artificial limb called “Lifehand 2” and the implanted TIME devices removed. This was according to the plan. For safety reasons permission for the study had only been granted under these conditions. This was research, and further work was needed to show if the implanted electrodes would be robust enough to amalgamate with human flesh over a longer period of time.

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Did Dennis become a cyborg the minute his body connected with the artificial hand or when he had the TIME implanted? Is he a harbinger of the posthuman future in the singularist sense – where human body parts can be replaced by everlasting machines? If a cyborg is a cybernetic organism, a self-regulating “man–machine system” (Clynes & Kline 1960) should Dennis be called a proper “cyborg”? As a self-regulating cybernetic system, the cyborg is, in the original sense, constructed by pathways of information that connect the organic body with its prosthetic extensions. Kathrine Hayles notes that this: presumes a conception of information as a (disembodied) entity that can f low between carbon-based organic components and silicon-based electronic components to make protein and silicon operate as a single system. When information loses its body, equating humans and computers is especially easy, for the materiality in which the thinking mind is instantiated appears incidental to its essential nature. Moreover, the idea of the feedback loop implies that the boundaries of the autonomous subject are up for grabs, since feedback loops can f low not only within the subject but also between the subject and the environment. (Hayles 1999, 2) A cybernetic system with feedback loops consists of, for example, information f lowing between a radiator with a thermostat and the cold weather that tells the thermostat to warm up the radiator, which in turn transforms the temperature of the cold room. The radiator is not a body enclosed in and by itself but communicates via feedback loops with a surrounding environment. It could be argued that learning is precisely the mechanism which ensures the same kind of equilibrium as a feedback mechanism, as claimed in cybernetics. Gregory Bateson enhanced the learning system to include a type of feedback based on the context of choices (Bateson 1972). Bateson’s systemic learning theory grew out of the Macy conferences on cybernetics (which took place from 1943 to 1953) and, according to the author of How We Became Posthuman, Kathrine Hayles, he was instrumental in creating the new paradigm of how to perceive human beings as potential posthumans. In this new paradigm, humans were seen primarily as “information-processing entities that are essentially similar to intelligent machines” (Hayles 1999, 7 italics by the author). Bateson was himself sceptical of machines as learners. At the first conference exploring cybernetics in 1946, he made the point that “learning” was not the same as “learning to learn” and raised the question of how computers could be able to learn and learn to learn1 when learning is understood as a process of creating “a difference that makes a difference” (Bateson 1972, 27–32). As noted by Andy Clark, Bateson’s first presentation of the systemic view of learning was nevertheless, like the cyborg presented by Clynes and Kline, “a creature that regulates its own states by changing and controlling its environment”

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more concerned with basic (machine-like) bodily functions than with mental processes (Clark 2003, 208). Following Andy Clark in the book, Natural-Born Cyborgs (2003), we need to expand on Clynes and Kline’s original cyborg definition and Bateson’s approach to include how the human mind functions in our technological surroundings. Clynes and Kline emphasise that with the extended body we set the mind free (Clynes & Kline 1960, 27). Clark argues that humans have always been cyborgs in so far as humans have extended not only bodies but also minds into non-biological elements such as writing tools, computers and software programs. What Clynes and Kline neglect, Clark says, is: the powerful sense in which our conceptions of ourselves (of who, what, and where we are) depend, at several levels, upon the specifics of just this backdrop. My sense of my own physical body depends on my experiences of direct control, and these can be extended, via new technologies, to incorporate both new biomechanical attachments and spatially disconnected, thought controlled equipment. (Clark 2003, 142) Inspired by anthropologist Ed Hutchin’s work, among others (Clark 2003, 7), Clark sees humans as embedded in extended systems with a number of tools, and each “provides means of encoding, storing, manipulating, and transforming data that the biological brain would find hard, time consuming, or even impossible” (Clark 2003, 78). We, in other words, create meaning rather than receive clues from an outside world, while we create the outside material world. What is at stake is not so much feedback to achieve physiological homeostasis, but a process in which we “spill” out and embed ourselves outside our biological bodies. The outside and the inside become one when our minds are embedded in writing and computers just as our bodies can be extended with sticks, wires and electronic devices. In “the extended mind” thesis, as first presented by Clark and David Chalmers our minds are not confined by our skin (what is outside the body is outside the mind), a point which was also emphasised by Bateson (1972). Nor is the meaning of our worlds outside our bodies, which would have meant that minds are external to bodies. Clark and Chalmers proposed a third position: that it is the active environments that drive our cognitions and perceptions (Clark & Chalmers 1998). It is in this sense we, as Clark phrases it, are “natural born cyborgs” (Clark 2003). For Clark we are not just a “bare biological organism but the hybrid biotechnological system that now includes the wristwatch as a proper part” (Clark 2003, 42). The wristwatch is a personalisation of a long history of taking time; what used to be public, through the wristwatch, has become personal. We do not notice how we change with technology because the historical timespan of placing our conceptions of time going from sundials to wristwatches is so long. Clarks and Chalmers notion of “extended minds” is neither the feedback

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loop of cybernetics, nor the closed system of autopoiesis (Hayles 1999, 10–11). Autopoiesis differs from the cybernetic approach because autopoiesis’ “central premise – that systems are informationally closed – radically alters the idea of the informational feedback loop, for the loop no longer functions to connect a system to its environment” (Hayles 1999, 10). Both however are systems that expect an organism to be separated from an outside world. For Clark we need to emphasise the cyborg as “a mind” that acts as a collective presence in systems of embodied technologies. These extended minds are thus perceived as “systems” where the “we” are the humans who already know the meaning of artefacts. We – more than any other creature on the planet – deploy nonbiological elements (instruments, media, notations) to complement our basic biological modes of processing, creating extended cognitive systems whose computational and problem-solving profiles are quite different from those of the naked brain. (Clark 2003, 78) Like the autopoiesis and the cybernetic approach, Clark works from the notion of “a system” in which extended minds unfold themselves. “System”, like “category” and “discourse”, attempts to grasp the collective features of humans with a humanist eye on the collective as “generalised” being. Clark, with his eye on the longer history, forgets the conceptual processes behind any “we” – for example, the child, who at first sees the clock as a white circle with black dots before it is learned as a “watch”, as mentioned by Vygotsky (1978, 33). The child is an ultra-social experienced learner, who learns in her daily practice that when other humans pay attention to the white and black shapes there is something to learn. The learning is already enclosing the child in a collective from where they can make individual choices, such as neglecting going to school at eight-o-clock once the meaning of a wristwatch is learned. In the big picture, evoked by Clark, these small processes of collective learning go unnoticed – and the culture–culture divide in knowing through practices (what come about as ignorance, when people from different cultures meet each other) is ignored as well. When we look at how people actually learn, this humanist perspective overlooks the slow and sometimes painful processes that connect humans in environmental collectives through learning. DAS, for example, is not a system like that of a radiator, the room temperature and a thermostat. He may want to connect seamlessly with the artificial limb, but it is not just “feedback” as a meaningless and emotionless response in a system, but a process of painful learning to align his body with the new body of metal and plastic. The material ensures that the process of learning is not seamless. That materials like wires and intraneural electrodes do not connect easily with f lesh is not just meaningful for the research group but matters in different ways for those who watch Dennis’ body connecting to Lifehand 2 and for Dennis, who experiences Lifehand 2. Learning here is

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about transforming the information from the TIME electrodes into something that becomes meaningful as a difference that makes a difference for Dennis as well as the research team as a collective process, but it matters how bodies relate to the technologies involved. DAS is not alone in having the painful experience of f lesh trying to connect with metal. Many new experiments are undertaken to restore the sensation in lost limbs via new kinds of electro-magnetically connected prostheses that may even restore movement to paralysed patient. This was the case of Eric Sorto who had been paralysed for ten years when he received a brain implant that could be used to send signals and produce movement in a robotic arm. The research was successful. “In the first experiment, a scientist made a simple twisting motion with his own wrist, and Sorto imagined himself imitating the gesture. In addition, that is just what the robot arm did. ‘It was pretty much effortless’” (Strickland 2015, 11). In fact, further on in the article we learn that the path to the seamless movement was not at all easy. The hard body training was mentioned but not emphasised as an issue. Sorto had to train for many months learning to adjust to the new movements using an unusual exercise. More than two years after his surgery, his electrodes were still functioning and his enthusiasm was undimmed. In his second year of experiments, he had mastered precise reaching and grasping movements through the strenuous practice of an unusual exercise. “I played over 6,700 rounds of rock–paper–scissors”, Sorto says with agonised emphasis. “I want everybody to know that I worked hard”. It paid off though and at the end of that second year, he reached a longheld goal: He used the robot arm to lift a bottle of Modelo beer to his mouth and take a long swig (Strickland 2015, 14). This is an example of intense pleasure gained when a machine skill became a habitual aspect of embodiment – but no habits are formed without learning. Being hooked up with wires and becoming what others recognise as a cyborg does not seem to be an indication of a singularist posthuman future. Humans are increasingly being rebuilt with technological components – but we have, as mentioned by Clark, always connected our bodies with non-biological parts for ornamental or practical reasons. Where humans in some of the societies studied by anthropologists enhanced their humanity through magic and the aids of ancestral spirits, modern man enhances his humanity through prosthetic devices. Famous examples are runners on prosthetic legs. The Icelandic company, Össur, provided Aimee Mullins and Oscar Pistorius with such well-functioning FlexFoot Cheetah blades that Pistorius was accused of “techno-doping” when he won a race in 2008 (Dalibert 2014, 70). Another example is Hugh Herr, who developed his own version of artificial legs while being a director of MIT Media Lab, USA. That Pistorius learned to run with his new legs gave a lot of justified hope to disabled people around the world, but that Sorto, after playing over 6,700 rounds of rock–paper–scissors, was able to use a new kind of brain–machine interface gives a realistic hope. From a posthumanist point of view, learning must always

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include materials, that are never passive. In the cybernetic and transhumanist sense cyborgs are formed seamlessly, and in the spinozist sense, humans are already posthuman cyborgs (Hayles 1999) and we can get a realistic understanding of the efforts involved for non-humans and humans.

The sense-storied body There is not just one story about what we are as human bodies, but many. Following Hayles, we have three narratives of humans as bodies (all illustrated with references to scientific and literary texts): “the (lost) body of information, the cyborg body, and the posthuman body” (Hayles 1999, 21). The lost body of information is the body which is no longer needed to upload cybernetic information. From the spinozist perspective of posthumanist learning there is no such thing as “no body”, as we need bodies for learning. The cyborgs and the posthumans may have different genealogies, but do not belong to entirely different stories. In a posthumanist perspective there is a material–conceptual body that conf lates biology, mind and technology (Clark 2003). If we want to uphold a difference between the terms “cyborg” and “posthuman” we can say that the singularist “posthuman” is becoming only with technology, whereas the posthumanists, in the spinozist sense, are not solipsist rational humans but entangled with all of the world (not just machines). When the cyborg is evoked it is often connected to boundary breaking and transversal becoming – through things and concepts merging in new ways. From a posthumanist learning perspective, Lifehand 2, as an object of observation, entangles Dennis (DAS), both as a body and as an embodiment, but the posthumanist perspective I propose also emphasises that “we” have to look at the “we’s” that emerge in these entanglements. A posthumanist cyborg does not require that we all hook up with non-biological components like Dennis does. (It) is important to recognize that the construction of the posthuman does not require the subject to be a literal cyborg. Whether or not interventions have been made on the body, new models of subjectivity emerging from such fields as cognitive science and artificial life imply that even a biologically unaltered Homo sapiens counts as posthuman. The defining characteristics involve the construction of subjectivity, not the presence of nonbiological components. (Hayles 1999, 4) In a technical cybernetic sense, Dennis could be viewed as a “literal” cyborgian body but the cyborgian embodiment is not reduced to the relation between the prosthesis and Dennis but the learning to connect, which entangles the whole formation of researchers and their aspirations folded into Lifehand 2. The new identity of Dennis as a selected suitable person is merged into the research formations as well as the literal Lifehand 2. He is, to the researchers, a body in a

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different way from how he experiences the sensors of TIME. Dennis is a cyborg in both respects. The body is the human form seen from the outside, from a cultural perspective striving to make representations that can stand in for bodies in general. Embodiment is experienced from the inside, from the feelings, emotions and sensations that constitute the vibrant living textures of our lives – all the more vibrant because we are only occasionally conscious of this humming vitality that accompanies every song we sing. (Hayles 2002, 297–298) Hayles said (and later revised) that embodiment differs from the concept of the body in that the body is always normative relative to a set of criteria (Hayles 1999, 196). To separate body and embodiment may of course be a heuristic device that makes it easier to see that there is a difference between seeing another person’s body as a body and experiencing as one – but the emergence is always both social and material. Hayles, who originally (1999) suggested this separation of body and embodiment, later acknowledged that the body and embodiment are conflated in the mindbody (Hayles 2002), a phrase taken from and further explored by Mark Hansen (2006). Hayles wanted to escape the dualism between body and embodiment and claimed that “beginning with relation rather than pre-existing entities changes everything” (2002, 198). Relation changes everything because then it is possible to see that bodies and embodiment both come from our dynamic engagement with an environment. They are processes. Whether we are body or embodiment depends on relations in the posthumanist sense as proposed by Barad. What I add is that it is relations moving with learning from within phenomena – and agree with Barad that’s it’s not about not a priori relations. However, from the posthumanist learning perspective I propose, we also need a postphenomenological approach to get away from the generalised assumptions of systems, discourses and categories in entanglements – to point our attention to how learned conceptions enter relations. The question I raise is as follows: If learning as practices of knowing take place for the experimenters in a fixed body and for Dennis as embodiment filled with already formed concepts – how do these two bodies come together in the phenomena of Lifehand 2? Embodiment is the place of experience that involves sensations and feelings. As already argued, sensations and feelings are, even if habituated, already entangled in collective concepts. The body is the place of cultural and biological inscriptions in the shape of collective conceptions. These collectives bind body and embodiment in Lifehand 2 and it only makes sense to ask about the difference between “the body” and “embodiment” when we look for differences in the collectives involved. Lifehand 2 is the story of an experiment entangling all the differentiated knowing of wires, electrodes and Dennis’ lived story. Dennis’ embodied disability was transformed by the social and cultural means in the experiment. In the Vygotskyan tradition this is called “mediational means” – but what I argue for here is that mediation is

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not an immediate split, but a continuous process of transformation in intraactions. In the posthumanist reading of Vygotsky there is no a priori dividing line between the biology of the f lesh and the artificial limb, only processes of learning. However, if we want to know more about how sensuous bodies learn, Vygotsky is of little help. Here we need to first consult a philosophy with a long tradition of introspection. Sensual bodies have always been of interest to phenomenology. Postphenomenology criticises but is also developed from the “classical phenomenology” of Edmund Husserl (1859–1938) and Martin Heidegger (1889–1976), but postphenomenologists are particularly inspired by the phenomenologist Maurice Merleau-Ponty (1908–1961) and his emphasis on the relation between perception and the human body. Postphenomenology takes an explicit point of departure in how technology, bodies and perceptions come into being in relational ontologies. In his phenomenology classic, Edmund Husserl referred to his science as a science of subjectivity (separating subject from object). Postphenomenology refutes that position and emphasises, along with Merleau-Ponty, that bodies are existential rather than transcendental (Merleau-Ponty 1962). Don Ihde has, for instance, criticised Husserl for overlooking the way technologies mediate perception (Ihde 2016) and both of the techno-philosophers Ihde and Peter-Paul Verbeek (2005) have criticised Heidegger for a priori assumptions of the negative effects of technology. In their relational ontology, technologies are neither good nor bad per se, but implicated in human embodied relations with the world (Ihde 2002; Verbeek 2005). The world we perceive is, from a postphenomenological perspective, also “mediated” through technologies in complex ways. In the mediated relationship, subject and object become something new in the relation. Both Ihde and Verbeek have explored how different human bodies or embodiments are in a mediated relation with different types of technologies. Not all relations to technologies are the same thing, as first noted by Ihde. An actual embodiment relation where our mindful bodies extend out into the world through a prosthetic limb is only one of several possible relations we may have with technology.

Embodiment relation As with cultural-psychology, we find an emphasis on mediation in postphenomenology. Don Ihde made a number of useful distinctions in his philosophical explorations of the four different ways a body can be placed in relation to technologies in the surrounding world (Ihde 1990, 98–107). The four “I–technology–world” relations Ihde identified were the embodiment relation, the hermeneutic relation, the alterity relation and the background relation. From the perspective of Vygotsky’s verbal thinking, all these relations are simultaneously conceptually learned, but as materials they transform our world in different ways and open up new learning possibilities. In the postphenomenological approach, they stand in a different relation to our body.

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In the first of these, that Ihde calls “the embodiment relation”, the technological artefact mediates the person’s perception in a transparent way, where access to the world is immediately transformed; prime examples could be hearing aids or glasses. Even these simple embodiment relations implicate some kind of learning on behalf of the wearer. Even with glasses a person at first needs to learn some kind of verbal thinking to grasp the meaning of glasses or hearing aids before wearing them. Furthermore, as Ihde notes, adjusting to a hearing apparatus can be demanding and the seamless integrating of the body with an artefact also only comes about here after aligning the bodies of metal or plastic with bodies of tissue. However, it is not the kind of hardship learning experienced by Dennis and Eric Sorto. Postphenomenology also makes us aware that after some time we embody technology to the extent that it withdraws from our attention and becomes our transparent incorporated access to the world around us. The connection between f lesh and material has become habituated. Once learned we do not experience the technologies, as they become our means of experiencing. From a learning perspective we can imagine that Eric and Dennis after a while (and maybe in the near future) in fact make their new incorporated metallic and electronic bits and pieces align with their bodies to such an extent that they cease to notice them and just concentrate on getting a glass of water or throwing a ball with their artificial limbs. All the hard learning is perhaps forgotten and the cyborg existence apparent to others would become transparent to the embodied user. However, since the examples Ihde mentions of the embodiment relation are not in but on our bodies, we can lose them – as when we cannot find our glasses or walking stick. This relation differs from what Dennis experiences with the wires, where a whole formation of people was needed to remove the artificial limb. A second relation in Ihde’s exploration of human relations to technologies is hermeneutic. This involves the interpretation of a technological artefact like a compass, thermostat or a thermometer which displays something to be “read” and interpreted and thus calls for our direct attention (contrary to the embodiment relation where the technology “withdraws” from attention). Following Vygotsky’s verbal thinking, all new technologies are in this sense hermeneutical from a learning perspective. We have to learn to “read” a clock, just as we need to learn to read thermometers. The technologies discussed by Ihde are, however, of a different kind than hearing aids and glasses because they demand continued attention – their whole meaning is to be consciously “read” and not to recede into a habit like a hearing aid. They are not opening the world to our perception of phenomena as embodied but as a world that can be interpreted through the instruments. This implies habituated learning of a hermeneutic kind. We need to learn how to “read” even a thermometer. Reading the output of the algorithms in Dennis’ artificial limb, and the “particle spin” in Otto’s experiment takes the education of a physicist or engineer. From a learning perspective, the hermeneutic relation does not involve the same physical pain as when a prosthetic device aligns with a body. What emerges

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on the display of the thermometer involves a host of ultra-social hermeneutics spanning from cultural perceptions of illness of how to read numbers. When technology is used to create “representations” of the world that are readable by human senses, it could also involve planets which are “read” through a telescope, bacteria “read” through a microscope, or particles “read” through all kinds of software in the physicists’ particle detectors. In all of these examples, the technology in question is not embodied but does work of its own, which is then interpreted by the human eye or ear (these are the senses most often used in hermeneutic relations). Dennis’ prosthesis is a hermeneutic relation to the whole group of researchers “reading” signs of attachment on a screen that registers connections between the prosthesis and Dennis’ arm, ascribing them as already learned or not and developing new ultra-social word-meanings. The third relation with technologies is what Ihde terms ”alterity relations”. Here we are attentive to the technology, but we do not regard it as a passive relation, as just wearing or “reading” and interpreting its messages. In alterity relations, we actively engage and communicate with technology – both robots and talkbots, as well a simple washing machines and ATMs, where we engage in a kind of dialogical “call-response”. Here we stretch our ultra-sociality to material entities we perceive as “quasi-others”. It could also be a virtual reality programme where humans engage with an online software programme. All of these actions entail a direct continuous attention to, and communication with, the technological artefact. As a robotic device, Dennis’ prosthesis can even be seen as an alterity relation by Dennis and the research group, as the Lifehand 2 responds through algorithms noted on a screen, so the researchers can adjust and change settings according to reactions. Finally, Ihde identifies the fourth relation to technology as a background relation. Here technologies are part of our environment but not embodied, meaning attached to our bodies. Furthermore, we do not pay any attention to these technologies, as we did with alterity and hermeneutic relations. These technologies are rather there as background noise which we only notice when they disappear – such as the humming from refrigerators, heating devices or air-conditioners (Ihde 1990, 98–107). Lifehand 2 also makes some noises. If the prosthesis had finally become so transparent for Dennis that he could actually begin to use it as an arm, the noises would have receded into the background. They would have become part of the habit of using the arm that he would only notice if the noises suddenly changed. Philosopher Peter-Paul Verbeek has contributed additional relations to this typology in a discussion of intentionality: cyborg relations and composite relations (2008). He investigates the different types of intentionality involved in human–technology relations. In phenomenology, at least in Merleau-Ponty’s phenomenology, intentionality refers to “lines” which “trace out what is to come” (Merleau-Ponty 1962, 483). It is thus rather a state of being than an explicit directedness to any object. In Verbeek’s discussion, he coins the term “cyborg intentionality” which involves different kinds of specific blends of the human and the technological.

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Technologically mediated intentionality occurs when human intentionality takes place through technological artifacts; hybrid intentionality occurs when the technological actually merges with the human; and composite intentionality is the addition of human intentionality and the intentionality of technological artifacts. (Verbeek 2008, 387) Hybrid intentionality refers to technologies that are not just attached to bodies but built into bodies – as with a cochlear implant, which amalgamates totally and physically with a body. Composite relation refers to new kinds of imaging technologies that not only represent reality, as in the hermeneutic relation, but also give access to new realities, such as when radio telescopes detect new boundaries in space beyond any visible spectrum. “Technologies used, like telescopes and hearing aids, help to constitute us as different human beings, whereas technologies incorporated constitute a new hybrid being” (Verbeek 2008, 392). The algorithms connecting Dennis arm and prosthesis could be seen as an example of this cyborg intentionality – as not only Dennis but also the transverse interfascicular multichannel electrodes decide how Dennis can reach and grasp his surroundings. Even the whole research group could be seen as an ultra-social collective cyborg intentionally entangled in the phenomena of the prosthetic limb. In a posthumanist learning perspective the artificial limb is composed of all of these relations as potentials for our agentic cuts. What matters is that learning as a process affects all of these – preceding learning as well as present learning. Learning and technology together transform perceptions, also in the psychological sense explained by Vygotsky’s theories. Perception is never a subjective phenomenon. Ihde claims that Merleau-Ponty did not go far enough with this acknowledgement. Ihde suggests that we give up the notion of “subject” altogether and replace it with “embodiment”, meaning “the body we are” (Ihde 2002). He has, like other new materialists and posthumanists, criticised structuralism, post-structuralism and STS studies in general for their effort to conf late what he terms “body one”, the lived body, with what he calls “body two”, the cultural body – with the effect that culture becomes the major force constructing bodies. But Ihde, contrary to many Barad-inspired posthumanists, refuses to give up that embodiment is a prerequisite whenever humans are involved. STS in general overlook the importance of our embodied being with the world, he argues. In the book, Bodies in Technologies (2002), Ihde makes the distinction between our bodies as cultural and phenomenal body one and body two and their technological mediations. We are our body in the sense in which phenomenology understands our motile, perceptual, and emotive being-in-the-world. This sense of being a body I call body one. But we are also bodies in a social and cultural sense,

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and we experience that, too. … I call this zone of bodily significance body two. Traversing both body one and body two is a third dimension, the dimension of the technological. (Ihde 2002, xi) Ihde, like the philosopher of the body par excellence, Maurice Merleau-Ponty, does not believe the lived bodies can be reduced to cultural bodies, nor can the lived body deny that it is permeated with body two. Ihde concludes that “embodiment is both actional-perceptual and culturally endowed” (ibid.). What is new about Dennis and Lifehand 2 then? Does Dennis come to identify with being a cultural cyborg as he is embodying the wires and electrodes of Lifehand 2? How does culture impact embodiment? Here it is time to acknowledge that there are limits to the importance of ultra-social, culturally diverse formed concepts and their word-meaning. Conceptual word-meaning is behind the relations in all of Ihde and Verbeek’s examples because preceding learning is in perception and agency – the actional-perceptual. In that sense there is no separation of the actional-perceptual because the material–conceptual preceding learning is already in our bodies when we take part in phenomena-making from within. This includes our formed word-meanings which make us recognise a hearing device, read a thermometer, communicate with a robot and recognise the humming of a car. However, the actional-perceptual can be vague in relation to the constant reformation of word-meanings. We do not always meet the objects through a cognitive “intention” or through readymade “concepts”. As noted by MerleauPonty, even: the most familiar thing appears indeterminate as long as we have not recalled its name, why the thinking subject himself is in a kind of ignorance of his thoughts so long as he has not formulated them for himself, or even spoken and written them, as is shown by the example of so many writers who begin a book without knowing exactly what they are going to put into it. (Merleau-Ponty 1962, 206) From a posthumanist perspective the relations between the I, the technology and the world are splits made from within phenomena. This indeterminacy is, as noted by Barad, an ontological indeterminacy that enacts a local resolution through agential cuts. Learning processes form the concepts behind the perception of apparatuses of observation, and even the learning of simple things can be hard. Learning to make sense of what is at first indeterminate never stops as ultra-social learning keeps changing relationships. How the indeterminacies are enacted as meaningful cannot be separated from culturally material availabilities, even if the process does not always directly involve thinking in concepts.

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Aun Aprendo For most of us, reaching out to take a glass of water or going for a walk is as intuitive as breathing. As argued by Ingold these movements are not acts of volition, but rather something we become in a process of rhythmic modification. Suppose, for example, that I intend to go for a walk. I pack my bags, prepare provisions, plan the route. All this conforms to the principle of volition. But once on my way, it all seems very different. Walking ceases to be something I set my body to do, as a self-imposed routine. Rather, it seems that I become my walking, and that my walking walks me. I am there, inside of it, animated by its rhythm. And with every step I am not so much changed as modified, in the sense not of transition from one state to another but of perpetual renewal. (Ingold 2016, 8) Ingold discusses the difference between volition (the choice or decision to move) and the experience of being in the bodily movements. The renewal he speaks of does not need the support of a system of cyborgian artefacts like exoskeletons or even a walking stick. It only assumes we have learned certain word-meanings (bags, route etc.) in such a way that they have become a part of our perception. It also assumes a healthy body, which has already learned to walk, with no need for learning to walk anew. When Spanish painter Francesco Goya was elderly, he made a drawing of an old man (possibly a self-portrait) walking with two canes. It is obvious that the man is in pain, but it seems he patiently staggers along learning to walk again, with his tired body extended and supported with two wooden sticks. His hands are full with these swollen oars, grasping the wood as if his life depends on the sticks helping him to make the next step; his eyes look tired. Above the etching, he has written: “I am still learning” (Aun Aprendo). Learning to walk was never easy. Small children tumble, fall and then gradually gain a sense of bodily balance. It is necessary for them to use their bodies to explore forces and boundaries as they gradually learn a steady balance and trust in their own bodily capacity. Sometimes they walk with a kind of support from, for instance, a doll-carriage or a horse on wheels. Our cyborgian mediated walking begins early and for most of us it ends late in life. Learning to walk is a very physical thing, which over time turns into a habit, but in a posthumanist perspective, habits cannot be taken for granted. When our bodies work as intended, we do not consider how painful it can be to learn to walk. When old age, illness or accidents transform our biological bodies into less reliable movers with the world, do we again become aware of the hard and painful work of body-learning? We may have the volition to walk – however, we cannot do it without some support. Here a walking stick may come in handy. For those who lose a limb and get a prosthesis, the process starts all over again. A person, who suddenly becomes blind, would, like Goya, have to embark on a

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new learning process no matter how experienced and skilled they were before in moving around in their environment. A famous example of this kind of embodiment, which has never been considered posthuman or cyborgian, is when a blind man uses a stick to explore his surroundings, which he perceived so easily before. This changes his learning as “body-in-environment” and to some extent his conceptual “verbal thinking”. It changes his boundaries to the world and being. Over time, the unstable walking becomes stabilised with certainty and confidence. Other things begin to matter like the height of steps or the unevenness of pavements. In addition, he may begin to identify with being “blind”. Bateson saw the stick as an embodied pathway to information as well as the formation of the self and asked, Where does the blind man’s self begin? At the tip of the stick? At the handle of the stick? Or at some point halfway up the stick? These questions are nonsense, because the stick is a pathway along which differences are transmitted under transformation, so that to draw a delimiting line across this pathway is to cut off a part of the systemic circuit which determines the blind man’s locomotion. (Bateson 1972, 324) This example of the blind man with the stick is also mentioned by MerleauPonty along with many others. The men and their sticks in phenomenology are however not systems of information f low, but bodies relating to bodies. Merleau-Ponty, like early Hayles (1999) and Ihde (2002), argues for a difference between “the body” as what he calls an objective body and the embodied body, or in his words, the phenomenal body: It is never our objective body that we move, but our phenomenal body, and there is no mystery in that, since our body, as the potentiality of this or that part of the world, surges towards objects to be grasped and perceives them. (Merleau-Ponty 1962, 121) Hayles pointed out that the body and embodiment emerge and merge in relations. In Barad’s agential realist account (2007), human subjects are not embodiments as phenomenal bodies separated from the world as “outside observers of apparatuses” – and they are not independent agents either. What she names “the apparatus of bodily production” emphasises that concepts, machines, sticks and bodily engagements with these machines or sticks are always entangled in a larger material configuration with the world. These material phenomena cannot be reduced to culture, concepts or human bodies. But when the apparatus of observation concerns ultra-social learners, it matters that the relations, which mindful bodies have with the world, differ culturally, and that ultra-social mindbodies can learn to align.

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We have always learned to walk in culturally different ways – with or without sticks. At some point in history, unfortunate female children had to learn from childhood to walk with painfully bounded feet (named poetically “lotus feet”) not as a punishment, but as a cultural learning that after a while, like all learning, seems like nature (Ko 2002, 147). The anthropologist Dorinne Kondo, an American researcher, learned to walk as a Japanese woman (slightly bent) (1990). These transformations of bodies are embodiments as well, subject to discussions of bodies and race, ethnicity, and disability (Kondo 2000). It is the body as learned embodiment. When Merleau-Ponty emphasises that the “blind man’s stick has ceased to be an object for him and is no longer perceived for Itself ” (1962, 144), the “no longer” is what refers to a learning process. The learning process is built on cultural, social and material availability. And, as with Goya, we cannot expect it to end. Even in a posthumanist perspective, lifelong learning cannot be discarded (as claimed by Edwards 2010) but is ongoing with the available material arrangements. In previous chapters, we met some later-to-be-physicists as children. It mattered what materials were available for how they could develop their potential for becoming physicists. Not all children have access to LEGOs or computers, or word-sounds like “robots” just as not all children are expected to bind their feet. From a generalised discourse point of view gender, race and ethnicity are all part of the cultural availability of learning with materials in situated practices. Yet, this birds-eye view searching for categories often forgets the painful learning involved in the grounded everyday bodily practices. The body-with-stick and body-with-lotus-feet and body-with-wires are, in relation to what is available to learn with, cultural bodies inscribed and fashioned with artefacts, which form our sometimes painful embodied experiences of the world around us. The experiences of Dennis and Eric are embedded and embodied, but it is still their prior embodied learning. Their experiences are not the same as the experimenters standing around them. From the outside perception of their bodies, they may seem to be what is sometimes conceptualised as “disabled”. As a part of experimental bodies, their prostheses may, over time, become not what they experience, but what they experience through. From a relational perspective it can be considered learned cultural mediation, but from their perspective it is a process of alignment. In anthropological monographs and historical travel books, it has been unavoidable that humans have been envisioned as entangled with their technological tools and material surroundings.2 Studies of hunter-gather societies show people growing together with their digging sticks (e.g. Shostak 1981) and Tim Ingold’s study of the Scott Laps is an exemplary description of how lassoes, reindeers and Laps create a moving entity while travelling in the northern parts of Finland (Ingold 1980, 1993). Many local tribes also invented different kinds of prosthesis for their disabled members such as walking sticks or canes.

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The sticks of the Kung women can be considered body parts much like the blind man with his stick, mentioned by both Bateson and Merleau-Ponty. Instead of being just a tool, these artefacts become the place where the world begins. Our hands and feet have always been connected with tools as well as our whole-body experiences. Once we have learned to move and align with the artefacts that are culturally available, they become our natural bodily extensions. We move and engage, as mentioned by Ingold, not with an awareness of our technologies but with our technologies. We amalgamate in rhythmic being with our surroundings. Rather than supposing that the hand operates on nature while the feet move in it, I would prefer to say that both hands and feet, augmented by tools, gloves and footwear, mediate a historical engagement of the human organism, in its entirety, with the world around it. For surely we walk, just as we talk, write and use tools, with the whole body. Moreover, in walking, the foot – even the boot-clad foot of western civilisation – does not really describe a mechanical oscillation like the tip of a pendulum. Thus its movements, continually and f luently responsive to an ongoing perceptual monitoring of the ground ahead, are never quite the same from one step to the next. Rhythmic rather than metronomic, what they beat out is not a metric of constant intervals but a pattern of lived time and space. (Ingold 2011, 46) The lived body’s engagement with the physical world is not entirely ref lected and conscious, but largely based on phenomenal pre-ref lective experiences. The world comes into being as we walk. “Pre-ref lective” here refers to a habitual, taken-for-granted experience that is only broken when the world is no longer stable and certain. If our habits are not broken, we have learned to trust our movements with the world. The pre-ref lective is not ref lected but this does not mean that it has not been ref lected in preceding learning. Habits are formed; we are not born with them – even walking habits. A habit has been learned and, in the learning process, the indeterminacy becomes meaningful verbal thinking. The walking experience is, from the perspective of cultural learning, not a blank slate in the meeting of our shared ultra-social world, but an experience filled with the learning of collectives, embodied and dissolved in a mindful body that has been learning since birth. When the children we met in the classroom in previous chapters draw robots and move about in the room, they already have mindful bodies that make certain moves more likely than others. They sit next to each other, share pens, lean over tables – and it all ends up in the drawings where we also find aspects of the word-meaning of the sound “robot”. Contrary to machine learning, the children do not learn in a standardised way, but a messy, embodied and ultra-social way based on prior embodied learning which includes how bodies move, talk and sit. When Eric and Dennis engaged their bodies in the entanglements of research,

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they had to learn in the new ultra-social entanglement how their bodies were supposed to behave while also learning from their experiences.

Scout learning and body-schemas Learning to be a skilful practitioner of climbing logs, going to school, walking while blind or with lotus feet, participating in social street-games or playing online games has to be explained in ways that include the whole body, as argued by Ingold. When novice hunters learn to become experienced hunters (Ingold uses Cree Indians as an example) they do not learn rules that are transmitted to their minds from an outside environment as cultural “cognitive algorithms”. A novice learns from experience as they go around with experienced hunters: [H]e is instructed in what to look out for, and his attention is drawn to subtle clues that he might otherwise fail to notice: in other words, he is led to develop a sophisticated perceptual awareness of the properties of his surroundings and of the possibilities they afford for action. For example, he learns to register those qualities of surface texture that enable one to tell, merely from touch, how long ago an animal left its imprint in the snow, and how fast it was travelling. (Ingold 1996, 40) Can we partake in such an endeavour without having learned to conceptualise what to look for in a community of ultra-social learners? As the Cree Indian walks through a landscape – paying attention – phenomena include prior wordmeanings . Just as he is an embodiment into which the others are dissolved as collectivity, so is the material landscape dissolved in him. It is also a process full of vagueness and uncertainty – yet also of constant cultural transformations of what is meaningful. Many Western travellers today take a walk in the woods surrounded by similar traces left by animal travellers. As they have never learned to register the differences in the surface of the snow and that it could matter that one removes a hand from a glove and lets it stroke the surface of the snow, they cannot read their environment as a Cree Indian may. Nor do they need to. They have no need for hunting down the game. However, should they find themselves in a situation where they need to learn to live as Cree Indians (or physicists for that matter); would they have to learn to perceive the world anew? At first, the signs that are so obvious to the Cree would be indeterminate and vague. Over time, however, through ultra-social learning, they would find themselves at least to some extent aligned with the rest of the Cree group – probably after much hardship. Relations as relata do not always come easy. However, this is the promise of culture: ultra-social humans can and will always learn. But in the posthumanist perspective, materials matter for how and what we learn.

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So, in reference to the materials: What is the difference then between Dennis aligning with a wire and a stick? Does the wire make him more of a cyborg than the stick? From a learning perspective, neither Eric nor Dennis reach a point where the test technology becomes invisible as a background relation to them. It is always present. The ultra-social human does, however, learn to live in environments, like Cree Indians, where the surroundings stand in a seamless relation to them. Does it matter if we learn to move our bodies in the woods or in cities? Over time the process of learning becomes embodied in such a way to both Cree Indians and city dwellers that they forget they ever had to learn. In this sense, there is no difference between the wire and the stick, the city and the forest. Once learned (and if not broken), the wire and the stick become not what we learn about but what we learn with. Once learned the ultrasocial city landscape dissolves into us just like the ultra-social forest of the Cree Indians. Phenomenologists like Husserl, Heidegger and Merleau-Ponty, and postphenomenologists like Ihde and Verbeek, ground their discussions in the bodysubject’s relation to a science-infused technological world. For Ingold it is not manmade technology that matters, but the experience of the living world. In these discussions, mind is not as prevalent as in learning theories or as in Clarks discussion of cyborgs. Rather, we move through the world with a pre-ref lective, primordial and often ambiguous perception of phenomena, which on the other hand is already intersubjective. Merleau-Ponty, the postphenomenologists and Ingold all emphasise how bodies relate to a material world. Instead of the cultural-historical emphasis on word-meaning or cultural models, what guides us through the world is our bodies. However, phenomenology also offers schemas, namely what Merleau-Ponty calls “body-schemas”. Because we do not only have bodies but are bodies, it matters how we move. Even when limbs are missing or we suffer from brain injuries, we are still human bodies capable of forming extended body-schemas of how to move and how not to move in relations. Merleau-Ponty offers this example of a body-schema: A woman may, without any calculation, keep a safe distance between the feather in her hat and things which might break it off. She feels where the feather is just as we feel where our hand is. If I am in the habit of driving a car, I enter a narrow opening and see that I can “get through” without comparing the width of the opening with that of the wings, just as I go through a doorway without checking the width of the doorway with that of my body. (Merleau-Ponty 162, 126) Merleau-Ponty notices that the woman with a big feather in the hat automatically moves her head to avoid the feather being broken against the doorframe. She is not stopping to calculate whether she can actually pass through the door,

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but simply adjusts her bodily movements so she can pass. Her bodily motility, the spatiality of her body and the way she has learned to form a habit of moving in and out of doors creates her spatial and perceptual experiences. From the perspective of ultra-social cultural learning, her body-schema is learned in a cultural-historical as well as a situated practise, where feathers matter collectively. In the eighteenth century it mattered that women wore hats with feathers. Preceding ultra-social learning of hats and feathers were internalised in the Vygotskyan way and embodied. Her body was, with a better ordering of the words, not a mindbody but a bodymind – already mindful of what a broken feather meant when perceived by others. She might have learned that other people in her ultra-social formation may ridicule a woman wearing a hat with a broken feather. It is all the in the body-schema – the charade of women and their hats is an embodied cultural agency in a particular timespacemattering. Whether they are enacted as performative presence with ornaments or cyborgian wires with f lesh – or as embodied experiences of wearing feathers or connecting with wires, bodies are always embedded in the ultra-social timespacemattering. This entails that our body-schemas of walking with feathers or sticks are already not intersubjective but entangled in wider material practices. They are cultural, historical and material at a certain time period in particular places. What we have to learn in order to move with the world has been a process of learning to belong to the extended local collective that formed bodyminds. Something new may happen culturally that changes these technologies – and with the new technologies we may begin to ask if there is a radical difference between wristwatches (which control our time) and the new cyborgian devices that begin to control us (Sharon & Zandbergen 2016). Even if lotus feet and sticks are cultural, they are not as directly controlled by others like a wire running on algorithms. Both Dennis and Eric are depending on the algorithms to work out how they move. When our surroundings transform into something else, we learn to transform our verbal thinking as well. What is left behind is not just the old materials but also the old material-concepts. Merleau-Ponty, just like Clark many years later (and many other writers of technology – like indeed myself ), unwittingly show us this fact of life. We just have to look at the examples that illustrate their theoretical points. These examples run into the problem that the technologies they use as supporting examples for their arguments have already become obsolete. When Merleau-Ponty notes that “a typist will not calculate how the fingers should move towards the keyboard to hit the right keys, but rather the keyboard is an extension of his hands concentrating on finding the right words” (MerleauPonty 1962, 165) younger generations may have a hard time following his example as typewriters hardly exist anymore. Merleau-Ponty’s examples and even Andy Clark’s wristwatch from 2003 are culturally outdated in many places in the world. Few women today have to

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learn to walk with ostrich feathers in their hats and the typewriter keyboards are exchanged for many with voice commands or strokes on a tablet surface. Cyborgs are not innocent, as noted by Haraway (1988). From a learning perspective, the material arrangements can change our ways of thinking as well as moving. When technology changes, it not only changes how we have learned to write or go through doors; it changes our relations. When the phenomenon is ultra-social, learning is a change in the collective and cultural body-schema as well as in ultra-social thinking, as the cultural-historical conditions for learning change. In a posthumanist learning perspective embodiment is not individually experienced nor individually embodied. Through the material–conceptual world we share, we are already moving cultural collectives when we engage with the world. Though culture, language and power are, as Barad emphasises, not the whole story, they is still important parts of the story. In new posthumanist theorising, bodies are not just material, biological or technologically enhanced; they are also cultural. If our culturally informed material–conceptual world is delegated to what can be coded as information in algorithms, we will experience the world through what algorithms allow. Any model of the “extended mind” must include how: cultural perceptions change in relation to the development of information-rich environments: Instead of the Cartesian subject who begins by cutting himself off from his environment and visualizing his thinking presence as the one thing he cannot doubt, the human who inhabits the information-rich environments of contemporary technological societies knows that the dynamic and fluctuating boundaries of her embodied cognitions develop in relation to other cognizing agents embedded throughout the environment, among which the most powerful are intelligent machines. (Hayles 2002, 303) A new merged human/technology entity appears, which is then in relation to the world (Verbeek 2008). In this cyborg relation, the question is no longer about human intentionality, but it forms a base for “hybrid intentionality”, which according to Verbeek is “beyond the human”. Based on Verbeek’s definition, Ihde’s embodiment relation is not entirely human either, but in the experience mediated through technology one can still distinguish between the human and the technological partitions. However, in Verbeek’s cyborg relation this kind of distinction is not possible, because human and technology have formed as a single experiencing entity. If our cultural perceptions become stabilised in algorithm-driven, information-rich environments, whose origins remain vague and whose stabilisation have boundaries we do not understand, it may change how we learn as ultrasocial, mindful bodies in ways that are radically new.

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Conclusion: Chapter 8 In previous chapters we have encountered an argument of how word-meaning is formed (Chapters 5, 6 and 7). Vygotsky emphasised the humanist dialectic separation between tools and signs, which then merged in concepts, but he largely forgot the body in this process. In his discussions, a tool would affect an external world through an active transformation of it, such as when a spade is used to dig a hole. Signs do not dig the hole but work inwardly to transform the subject as a thinking subject capable of recognising a spade and a hole. However, knowing how to learn to dig with our body in relation to a space and a hole was not part of these discussions. When we emphasise a postphenomenological approach, the split between tools and signs, bodies and embodiment is not the difference between an inner “I” and an external world mediated by technologies, but the difference made by the different technologies used to mediate between the “I” and the world. In posthumanist thinking these differences are splits made from within phenomena. In these splits, as argued, preceding learned concepts matter just as much as materials matter in new learning processes. In this chapter we have identified the locus of learning as the body. The body is not a priori separate from other material bodies – it merges and entangles continuously in situated embodiments. However, from the inherent cultural learning perspective, I have argued that in the posthumanist learning theory there are also limits to crossing the boundary. The experimenters experience Dennis and Lifehand 2 from a position outside of the body that is involved in learning to align f lesh and metal. This learning matters to Dennis as well as to Lifehand 2 and the experimenters – but in different ways. Dennis, the experimenters and the Lifehand 2 materials are all implicated in the creation of the phenomenon of Lifehand 2, but as an ultra-social collective of Lifehand 2 experiences, Dennis has more in common with Eric Sorto, because he experiences the TIME electrodes differently from the experimenters. Postphenomenology can, in an extended sense, bring to our awareness that we always stand in different relations to technology within phenomena – and what matters is not just what world experiences are opened by technology but also whose bodies are transformed and how. Vygotsky’s notion of mediation differs from that of postphenomenology in that mediation, for Vygotsky, is through the creation of cultural-historical artefacts, such as both tools like hammers and words like “hammer” with a wordmeaning. Both are seen as ways to free humans in plural from being passive recipients reacting to stimuli in the surrounding world. In postphenomenology, mediation is about how a form of technology mediates the world for an “I”. In posthumanism, there are no a priori mediations or relations following the claim that there are no a priori subjects and objects. Within phenomena, agential splits are made by materials and discourse. As I have argued, ultra-social humans play a special role in these splits due to their preceding learning – and ultra-social bodies are the materials which

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bring ultra-social concepts into phenomena. This process of entangling concepts within phenomena always involve situated human bodies (including the situated body and the concepts of the writers writing about all sorts of things). Learning is not systemic but distributed across materials like watches and human bodies. A watch has not learned it is a watch. Learning is a relational process, where both f lesh and metal are transformed, but the process goes from vague to meaningful. A phenomenological approach emphasises that our perception of the world, which emerges within phenomena, is at first vague and then become clearer as they become word-meanings and habituated agential-perceptual bodies. Collective practices of knowing include our bodies as simultaneously embodied and culturally habituated cyborgs. To learn as a collective of collectives we need to have internalised how we are expected to move our bodies. As we learn, our collectivity merges us as humans with our surroundings in ways that, on the one hand, constantly shift the boundaries of the person and its surroundings, but at the same time form habits building on the stability of materials combined with preceding learning. We are natural-born cyborgs, but nature is inseparable here from the available cultural resources. As cyborgs we evolve through processes of learning that transform how we feel and perceive ourselves as we move with the world. When we, for instance, make a prosthetic arm an object of observation, it matters if we stand next to it or try to embody it. Though learning is not the only process that goes into objects of observation, processes tend to be overlooked when posthumanists refer to generalised discourse, categories and systems. The learning body cannot be everywhere in a relational ontology. It matters where bodies are placed in a material world. The body is, with all its extensions and moving boundaries, in itself a boundary for what can be learned by whom. The experimenters are not learning like Dennis or Eric. This emphasises that posthumanist learning does not make an a priori distinction of culture/nature. The differences are in Dennis as a natural biobody versus Dennis as a culturally shaped cyborg when he connects with an artificial limb. The diversity is a culture–culture diversity in how our bodies become extended with technology. There is a culture–culture diversity in our phenomenal world – and I have argued that this is a diversity rooted in our prior ultra-social learning processes with the available material arrangements. This preceding learning transforms into habits and phenomena – or breaks habits when meet with new, unruly materials or surprising concepts. What kind of posthumanist learners the children, who drew robots in the previous pages, grow up to be, will depend on how they have bodily access to learn to engage in a phenomenal world of material–conceptual phenomena. They are not bodies that encounter the “free-f lowing” intelligence envisioned in cybernetic systems, because what they learn matters to them as meaningful. They become the hybrid and collective existence of cyborgs described by new feminist materialists and postphenomenologists, but they are not born that way.

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Learning to align with wires may not be radically different from the old man learning again to walk with two sticks, how the blind learn to explore the environments with a stick, how the typist learns to type or how the new fashionista, who never wore a feathered hat, now has to learn a new body-schema. What differs may be the complexity of the ultra-sociality involved and the vagueness and hardness of the learning process. Body one, body two and technology are united in ultra-social learning. What phenomenology brings to cultural-historical learning theory is the acknowledgement of the vagueness and indeterminacy of relations – a potentiality within phenomena that is resolved in momentary collectives when word-meaning, already learned and embodied, is entangling in and transforming our immediate senses and perception. Becoming a cyborg is, in a learning perspective, more than merging f lesh, metal and categories. It is about the vagueness of learning merging into habits. We do not learn perceptual-agential habits as nodes in a system, but as embodiment into which bodies of ultra-sociality are dissolved. Learning includes cultural body-schemas for how to habitually walk with feathers, lotus feet or sticks. We do not learn embodiment in a system or part of a system, but in relations. Which relations these are is often a matter of analytical and conceptual as well as material agencies. But whatever splits are made by material agencies take on a special collective meaningfulness when humans are involved. Dennis is a human learner among learners. He is a cyborg struggling to align wires and researchers with his embodied verbal thinking, while also struggling to make wires and algorithms align with what is left of his arm. He is not a culture–nature cyborg; nor is he “an individual human”. He is a posthumanist learner as a mindful body entangled in collectives of ultra-social learners and materials. This does not make him a posthuman in the sense envisioned by Kurzweil and Moravec, where humans gradually become more machinelike. He is rather a cyborg in the spinozist sense where humans over time collectively merge with their material surroundings. However, as our ultra-social environments transform our verbal thinking and embodiment, we may be forced to acknowledge that material–conceptual collectives can transform the very basic processes of how we learn to become individuals. This is the topic of the next chapters.

Notes 1 American Society for Cybernetics: http:​//www​.asc-​c yber​netic​s.org​​/foun​d atio​n s/hi​ story​/Macy​Summa​r y.ht​m. 2 History is full of observant proto-anthropologists who very observantly note how humans, animals and material artefacts amalgate together to form a new whole. One example of this is the wonderful depictions of how Mongol’s, their horses and artefacts for riding become one new centaur creature in Ruyebroek Willem van, thirteenth century and Friar John of Pian de Caprine’s journey the Court of Kuyuk Khan, 1245–1247.

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References Alaimo, S. & Hekman, S. (2008). Introduction: Emerging models of materiality in feminist theory. In: S. Alaimo & S. Hekman (Eds.). Material Feminism (pp. 1–24), Bloomington: Indiana University Press. Barad, K. (2007). Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning. Durham, NC: Duke University Press. Bateson, G. (1972). Steps to an Ecology of Mind. New York, NY: Ballantine Books. Braidotti, B. (2006). Posthuman, all too human: Towards a new process ontology. Theory Culture Society, 23, 197–208. Clark, A. (2003). Natural-Born Cyborgs: Mind, Technologies, and the Future of Human Intelligence. Oxford: Oxford University Press. Clark, A. & Chalmers, D. J. (1998). The extended mind. Analysis, 58(1), 7–19. Clynes, M. E. & Kline, N. S. (1960). Cyborgs and space. Astronautics, 5(9), 26–27, 74–76. Colman, A. M. (2015). A Dictionary of Psychology (4th ed.). Oxford: Oxford University Press. Dalibert, L. (2014). Posthumanism and somatechnologies: Exploring the intimate relations between humans and technologies. Research output. PhD thesis. Enschede: University of Twente. ETH Domain (2014). Artificial Hand Restores the Sense of Touch. Retrieved from https​://ww ​w.eth​r at.c​h /en/​g esel ​l scha​f t/k%​C 3%BC​n stli​che-h​a nd-g ​ibt-t​a stsi​n n-zu​ r%C3%​BCck. Edwards, R. (2010). The end of lifelong learning: A post-human condition? Studies in the Education of Adults, 42(1), 5–17. Gane, N. & Haraway, D. (2006). When we have never been human, what is to be done?: Interview with Donna Haraway. Theory, Culture & Society, 23(7–8), 135–158. Hansen, M. (2006). New Philosophy for New Media. Cambridge: MIT Press. Haraway, D. (1991). Situated knowledges. The science question in feminism and the privilege of partial perspective. In: D. Haraway (Ed.). Simians, Cyborgs and Women: The Reinvention of Nature (pp. 183–201). New York, NY: Routledge. Haraway, D. (1988). Situated knowledges: The science question in feminism and the privilege of partial perspective. Feminist Studies, 14(3), 575–599. Hayles, N. K. (1999). How We Became Posthuman: Virtual Bodies in Cybernetics, Literature, and Informatics. Chicago, IL: University of Chicago Press. Hayles, N. K. (2002). Flesh and metal: Reconfiguring the mindbody in virtual environments. Configurations, 10(2), 297–320. Ihde, D. (1990). Technology and the Lifeworld. Bloomington, IN: Indiana University Press. Ihde, D. (2002). Bodies in Technology. Minneapolis and London: University of Minnesota Press. Ihde, D. (2008). Aging: I don’t want to be a cyborg!. Phenomenology and the Cognitive Sciences, 7(3), 397–404. Ihde, D. (2016). Husserl’s Missing Technologies. New York, NY: Fordham University Press. Ingold, T. (1980). Hunters, Pastoralists and Ranchers, Reindeer Economies and Their Transformations. Cambridge: Cambridge University Press. Ingold, T. (1993). The reindeerman’s lasso. In: P. Lemonier (Ed.). Technological Choices: Transformation in Material Cultures Since the Neolithic (pp. 108–125). London: Routledge. Ingold, T. (1996). The optimal forager and economic man. In: P. Descola & G. Palsson (Eds.). Nature and Society: Anthropological Perspectives (pp. 25–44). London: Routledge. Ingold, T. (2011). Being Alive: Essays on Movement, Knowledge and Description. London: Routledge.

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Ingold, T. (2016). On human correspondence. Journal of the Royal Anthropological Institute, 23(1), 9–27. Ko, D. Y. (2002). Every Step a Lotus: Shoes for Bound Feet. Berkeley, CA: University of California Press. Kondo, D. K. (1990). Crafting Selves: Power, Gender, and Discourses of Identity in a Japanese Workplace. Chicago, IL: University of Chicago Press. Kondo, D. K. (2000). (Re)Visions of race: Contemporary race theory and the cultural politics of racial crossover in documentary theatre. Theatre Journal, 52(1), 81–107. Lifehand 2 (2014). Rapport. Scuola Superiore Sant’Anna. Campus Bio-Medico. Retrieved from http:​//www​.sssu​p.it/​Uploa​d Docs​/1885​6 _Lif​ehand​ 22_Eng.pdf. Merleau-Ponty, M. (1962). Phenomenology of Perception. London: Routledge Classics. (Original work published 1945.) More, M. & Vita-More, N. (Eds.) (2013). The Transhumanist Reader: Classical and Contemporary Essays. New York, NY: Wiley-Blackwell. Raspopovic, S., Capogrosso, M., Petrini, F. M., Bonizzato, M., Rigosa, J., Di Pino, G., … Micera, S. (2014). Restoring natural sensory feedback in real-time bidirectional hand prostheses. Science Translational Medicine, 6(222), 222ra19. doi:10.1126/ scitranslmed.3006820. Rosenberger, R. (2012). Embodied technology and the problem of using the phone while driving. Phenomenology and the Cognitive Sciences, 11(1), 79–94. Roth, W.-M., Radford, L., & LaCroix, L. (2012). Working with cultural-historical activity theory. Qualitative Social Research Forum, 13(2), 1–20. Sharon, T. & Zandbergen, D. (2016). From data fetishism to quantifying selves: Selftracking practices and the other values of data. New Media & Society, 19(11), 1–15. Shostak, M. (1981). Nisa: The Life and Words of a !Kung Woman. Cambridge: Harvard University Press. Strickland, E. (2015). Telekinesis made simple. A brain implant reads a paraplegic man’s intentions to move a robotic arm. Retrieved from https​://ie​eexpl​ore.i​eee.o​rg/st​a mp/ s​t amp.​jsp?a​r numb​er=71​31677​. Verbeek, P.-P. (2005). What Things Do: Philosophical Reflections on Technology, Agency and Design. University Park, PA: Pennsylvania State University Press. Verbeek, P.-P. (2008). Cyborg intentionality: Rethinking the phenomenology of human–technology relations. Phenomenology and Cognitive Science, 7(3), 387–395. Vygotsky, L. S. (1978). Mind in Society. Cambridge: Harvard University Press. Wolfe, C. T. (2009). What Is Posthumanism? Minneapolis, MN: University of Minnesota Press. Wolfe C. T. (2016). Materialism: A Historico-Philosophical Introduction. Cham, Heidelberg, New York, NY, Dordrecht, London: Springer.

9 EXTENDED MINDFUL BODIES

In 1998, Andy Clark and David Chalmers asked: Where does the mind stop and the rest of the world begin? (Clark & Chalmers 1998, 1). They were proponents for a theory of mind which emphasised the inf luence of the material environment on cognitive processes. Vygotsky, as we saw in previous chapters, emphasised that humans collectively could free their thinking from the material surroundings after a process of concept formation. Clark and Chalmers do not emphasise the ultrasocial processes that go before any mental operations. The answer to their question would, from this Vygotskyan perspective, be that the mind and the world stop with our collective meaning making. However, in a posthumanist perspective materials matter more than in the Vygotskyan framework. Minds are extended by materials. Materials not only aid concept formation, but they also shape mindful bodies just as much as ultra-sociality. In fact, materials are part of ultra-sociality. Humans are not the only social beings on earth. Anthropologists have helped expand the notion of the social to non-humans (e.g. Latour 2005). We can also acknowledge that “[h]uman nature is an interspecies relationship” (Tsing 2012, 141). However, if we look closely, human minds are always entangled in our descriptions of the non-humans and if the phenomenon of interest is human learning there is no such thing as a “human-free” description. Collective humans are within all material phenomena we can engage with – even if perceptions are sometimes vague. Donna Harraway, a long time ago, made the point that nonhumans resist the ways humans tell their stories (Haraway 1989). However, the point I want to emphasise is that humans are always in our stories and posthumanism tends to forget this fact. Even if these humans are already formed with non-humans, like primates, sticks, wires or, even less visible embodied bacteria colonies, we are also human collectives qua our embodied conceptual histories. This chapter explores how our material–conceptual histories of ignorance and common knowledge are tied into our embodied habits as persons and groups.

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Human agency does not entail a uniform, all-encompassing, ref lected consciousness. On the contrary, when humans form a collective, a variety of collectives are brought to bear. When persons entangle with each other and nonhumans they do not become intertwined as separate entities. They rather, in the words of Karen Barad: [l]ack an independent, self-contained existence. Existence is not an individual affair. Individuals do not preexist their interactions; rather, individuals emerge through and as part of their entangled intra-relating. Which is not to say that emergence happens once and for all, as an event or as a process that takes place according to some external measure of space and of time, but rather that time and space, like matter and meaning, come into existence, are iteratively reconfigured through each intra-action, thereby making it impossible to differentiate in any absolute sense between creation and renewal, beginning and returning, continuity and discontinuity, here and there, past and future. (Barad 2007, ix) Collective organisations are loosely formed, and easily dissolved and do not necessarily entail any conscious direction towards being collective. Yet, ultra-social collective concepts, often as invisible to us as bacteria, are there with us in our bodies whatever we do – whether we engage with primates or machines. No pen can be put to paper, nor words emerge on the page or the screens, without the preceding learning of material words, and how material–conceptual word meanings emerge from the things around us. This entails, in a learning perspective, that we have to learn what is meaningful before we can put it into printed words. Our bodies are not born with collective knowledge of fishing boats, robots, particles or telescopes. Nor does this knowledge belong to our bodies. Our bodies learn what is potentially meaningful with materials and through concepts that are embodied and expressed as materialised word sounds in different situations. Verbal thinking is the potential collective thinking and emotion (see Chapter 3) we may share with other humans, if we recognise the same meaningful word sounds. Furthermore, this is an emotional collective, where thoughts are the outcome of a motivation, which again (as in the theory of cultural models) is tied to expectations and emotions: [Thought] is not born of other thoughts. Thought has its origins in the motivating sphere of consciousness, a sphere that includes our inclinations and needs, our interests and impulses, and our affect and emotions. The affective and volitional tendency stands behind thought. Only here do we find the answer to the final “why” in the analysis of thinking. (Vygotsky 1987, 282)

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We may consider our own intimate thoughts individual, but identities, perception, memory, emotions, skills and body postures are all formed with material–conceptual collectives ready as potentials to entangle with other human and non-human collectives in specific situations. Our bodies go through iterative processes of learning to obtain this collective existence where a material word sound becomes a richer and richer potential for entanglements – as word meaning is both generalised and forever changing with our personal experiences. Though we are always collectively engaged in these iterative processes, we may not be sure which collective of humans and non-humans we are engaging with at a given moment. The collective humans, in other words, are often ignorant of the larger collectives they are entangling in. Therefore, we may experience a vagueness that is gradually turned into certainty and stability as we become more certain of how this particular collective enacts the relation and boundaries between materials such as human bodies, pens, papers, detectors, particles, bacteria, keyboards and words. The indeterminacy in the phenomena, which we observe as collective practices of knowing, diminishes as we align through collective learning processes, but it is always there. Humans can never be quite certain that other humans or non-humans align with the meaning of an object of observation they have formed through preceding learning. Indeterminacy is a fundamental feature of posthumanist learning – and humans use their embodied collective resources of preceding learning as a potential for new learning that makes the world meaningful. When people travel to new cultural sites, they are constantly reminded that they, being newcomers, cannot take the meaning of local artefacts and bodily practices for granted, but for the people who work together on a daily basis, stable collectives are formed through iterative processes of body learning (some call these stabilities “habits”). As their profession, anthropologists have to learn to form new habits, such as when they learn to perceive through instruments like physicists or biologists do (e.g. Swanson 2017), but their preceding learning makes their perception and the questions they ask differ from physicists and biologists. In the end, anthropologists care about the humans emerging from entanglements. For example, it is these humans that tell stories about the water we swim in – the sea of stories (Bruner 1996, 147), which in a posthumanist perspective has to include materials. For Bruner, stories were a stabilising factor in human culture; however, it is not only stories that create habitual stabilities. Materials, just like stories, form stabilities with material–conceptual collective practices. Humans and non-humans align or divide as the relation between concepts, word sounds and artefacts becomes the new “cultural water” our bodies swim in. Our stories and material world tie us to human collective practices. It is when these habitual and stabilised practices change that we experience ignorance and indeterminacy until the collective of collectives stabilises.

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The white bears When Vygotsky and his colleagues developed their theories of cultural learning processes back in the nineteenth century, they explored how relatively stable communities would change with new possibilities of schooling in the Soviet Union. It was a clash between two collective formations, peopled with persons, who according to their diverse collective practices came up with collective patterns in their answers to questions. An example of such a stable pattern of answers came from a community of peasants, not yet inf luenced by abstract thinking learned in schools. These patterns were revealed in the cultural-historical experiments conducted by Alexander Luria and Vygotsky in the 1930s when they visited Uzbekistan and presented the local peasants (today we probably would say farmers) with syllogisms like: “In the Far North, where there is snow, all bears are white. Novaya Zemlya is in the Far North and there is always snow there. What color are bears there?” (Luria 1976, 107). Educated people in the city managed the task easily, but the peasants with no education did not, according to Luria. The peasants refused the abstract premises of the syllogism, and answered that they had seen black, but not white bears. When presented with the abstract thinking in syllogism again, one of the peasants answered: “We always speak only of what we see; we don’t talk about what we haven’t seen”. When the task was repeated, the answer was: “Well, it’s like this: our tsar isn’t like yours, and yours isn’t like ours. Your words can be answered only by someone who was there, and if a person wasn’t there he can’t say anything on the basis of your words”. (Luria 1976, 109) The experiments did not reveal a clash between material words and material white bears. The words were recognised, but neither the words nor white bears were tied to the locally embodied habitual practices. Intellectually trained citydwellers have even less embodied experiences with bears, but they recognised the abstract verbal thinking in the material words. In another of Luria’s experiments, the peasants were presented with pictures showing different tools like a hammer, saw, hatchet – and the picture of a “log” that, from a logical category system point of view, stood out as different from the category of “tools”. The peasants were asked to pick out the item that did not fit in. The peasant’s response showed that their practice-based knowing was verbal thinking tied to their daily practices with materials – not the abstract thinking and categories learned in schools. They refused to take out any item based on the logical, rational thinking that took the list to be a list of words in relation to categories. They said: “They all fit here! The saw has to saw the log, the hammer has to hammer it, and the hatchet has to chop it!” (Luria 1976, 58). It was education, Luria and Vygotsky argued, that had taught the city people how to think with syllogisms and rational logics.

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Luria took the peasants’ answers to be an indication of their illiteracy. Instead of seeing it as a failure to solve the task presented by the experimenter, it could, quite literally, be an example of how learning in different practices leads to different habituated collectively shared word meanings in stabilised human and non-human collectives. If we do not, like Vygotsky and Luria, emphasise the benefits of education but simply acknowledge that knowing always comes with material–conceptual practices, “white bears”, “categories” and “syllogisms” become material word sounds and a verbal thinking tied to particular practices that had not been learned by the peasants and therefore were meaningless as a resource for verbal thinking. It was not the same situation as we saw when the word “robot” was meaningless to the children in the Tanzanian country school. The peasants knew the material words, but in their collective of collectives they had not learned to separate material words from material things. We cannot think with what we have not learned (as we saw in Chapter 6). The peasants recognised the pictures as the materials they used in everyday work. They perceived through their previous material practices that formed the word meanings that made logs and hammers meaningful in their practices. Peasants were experts on how to think about connections between wood and the tools used to rework wood, through their embodied practices of knowing and did not hesitate to explain why these tools were meaningful. They often made their own tools, and even if they did not, the stories they told about their use were coherent and collectively shared as meaningful in their local practices. In psychology many experiments have shown that perception and recognition of what is meaningful follows cultural experiences (Bernstein & Nash 2008, 124). In educational settings we may be brought up to become experts in handling material words and pictures. However, when our material surroundings change fast it can be difficult to keep up with new technological tools that teach us new ways to engage with words and pictures. Tools, like tablets, have been collectively made by engineers with hands-on experiences, but when they stabilise in people’s lives, problems arise (like when children form new habits of playing games before they go to sleep). This problem of learning to form habits with tools that we do not make ourselves, increases as more new things keep coming into our lives and old ones disappear. Peasants in Luria’s studies appear in a seamless manner to be a conceptual collective aligned with the material collectives that made a hammer and a hand act together. The material–conceptual grew together without conscious awareness of “hammers” as concepts versus hammers as materials. Today, with new tools like tablets, computers and robots, we can no longer expect material tools and word sounds to entangle collectively as smoothly as for the peasants in Uzbekistan in the 1930s. We no longer make our tools ourselves, driven by local motives for their use in practice. Therefore, what they actually do to our practices may come as a surprise. The people who are working with the robots, as we saw when we visited the rehabilitation centre at Lakeview (Bruun, Hasse & Hanghøj 2015; see Chapter 4), are not at all sure what kind of tool the

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robot is. Nor do the robot-makers share a stable learned understanding of what kind of tools they are making because robots have many applications. When we follow the robot-makers in their creation of robots, it is amazing how complex the work processes are across practices in time and space. Material phenomena are produced through a constant indeterminacy about what the object is that binds the collective together. The robot-makers are not at all sure how their different practices of knowing fit together in the production of new types of robots. Contrary to the peasants in the Soviet Union, the robot-makers we have interviewed1 are highly specialised and diverse in their activities, and their collective projects often span continents and disciplines. They work in teams that are often interdisciplinary with a predominance of people from the engineering sciences. They communicate at physical meetings as well as through virtual communication spaces such as social media, internet, e-mail, and they experience a lot of problems in how to make a jointly formed material product. They work in with what Anne Edwards has called “collaboration across practices” (2012). How do these unstable collaborations achieve stability? And what roles do materials play for stabilising individual bodies in collaborative work?

Common language As a neo-Vygotskyan, Anne Edwards has looked at how professional expertise unfolds in intersecting practices (2010). She and her colleagues first followed Yrjö Engeström’s development of Vygotsky and his group’s theories in an understanding of practice as tied to activity systems (Engeström 1987). She later went on to use Mariane Hedegaard’s notion of “activity settings”, where the practitioners from different areas come together across institutional practices shaped by historically accumulated motives. Activity settings are tied to the institutional traditions (as when children eat lunch in kindergarten) (Hedegaard 2012). When people meet across their local traditional activity settings, they bring with them the common knowledge tied to their own practices, but when people meet to do the interprofessional work found at sites of intersecting practices, Edwards noticed something interesting. At first the professionals, who in this case met about a vulnerable child, interpreted the child’s problems “in ways which are mediated by the concepts that matter for each profession, with the result that the social worker may focus on safety while the psychologist on mental stability” (Edwards 2012, 26). But after a while “we observed efforts at alignment that grew out of growing understandings of what mattered for each profession. Interpretations of problems and alignments of practices were mediated by common knowledge which was made up of what mattered for each collaborating professional” (Edwards 2012, 26). A new common knowledge, based on richer conceptualisations, grew out of their combined efforts to engage with a “problem-space”.

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This did not come about effortlessly, but because the practitioners exercised what Edwards calls “relational agency” (Edwards 2010). Relational agency is a capacity that can (but, not necessarily, will) emerge in what Edwards calls a twostage process: It involves: (i) working with others to expand the object of activity so that its complexity is revealed, by recognising the motives and the resources that others bring to bear as they too interpret it. (ii) aligning one’s own responses to the newly enhanced interpretations, with the responses being made by the other professionals as they act on the expanded object. (Edwards 2012, 26) When people come together across areas of expertise and institutional practices they need to develop, what Edwards, referencing Karin Knorr-Cetina’s term, calls “engrossment” (Knorr-Cetina 2001, 175). Edward defines “engrossment” as a relational expertise that “involves recognising what engrosses others, taking their standpoint and mutually aligning motives so that engagement continues” (Edwards 2012, 25). Learning with materials matters for these processes of alignment. It matters that the practitioners learn to align the richer understanding of material word sounds in order not to misunderstand each other. Contrary to Luria’s peasants, the practitioners in the intersecting practices of making robots do not necessarily make use of the same material artefacts, so a lot of alignment takes place through words. This has especially been a problem for the robot-makers when the group includes people from different national backgrounds and especially people with no hands-on experiences with making robots. The lack of a common language and shared bodily practices in robotics gives rise to many misunderstandings and negotiations as material objects and material–conceptual word sounds are perceived differently by different people in different countries. Like the peasants the robot-makers align their group work through both word meaning and material artefacts. They may have one group working on the arm of the robot in one country and then they have to communicate with a group doing the hand or the torso or the base in another country – and yet another group works on the software. Though a lot can be done through internet communication, they also often need to meet and test the robots they make. Here groups handle the materials from other groups and learn from it. And then new problems arise when the robots are brought into contact with users or social scientists – and at this point a different language than the technological is often needed. One robot-maker we interviewed, Mona, explained how it is particularly difficult to work across disciplines. She works with a service robot we can call Xeno that is supposed to engage with people in their homes. She finds it easy to communicate with engineers, no matter where they come from, who share

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an educational background similar to her own, and difficult to work with the social scientists involved in the project, because they do not share a common language. Normally I work in a team. We work together. We define objectives and then we discuss together if we meet difficulties or something like that (…) Most of the people in the company have the same technical background. But, because of the topics we deal with, we have a lot of contact with [people from another field]. And people from different countries because of our international cooperation … We also meet people from the social sciences, because if you work on service robotics, you also have to interact with people from the social science … They are different because of the language they use, [laughter] for sure. And they are different because technical people use generally quantitative approaches, while social people use generally qualitative approaches and they are two different approaches. (Interview with Mona, engineer) Just like with the Lifehand 2 project, the robot-makers work with what in the social sciences has been called “boundary objects” (Star & Griesemer 1989). This concept refers to both material objects, and a phenomenon that is differently interpreted according to the learned resources and material arrangements that take part in the enactment of the material object. They all work on the same project but work on it from different perspectives. In the linguistic turn, this interpreted f lexibility of boundary objects was what Susan Leigh Star named a cornerstone behind much of the constructivist approach, but she emphasises that boundary objects are more than that. Boundary objects are also the material organisational structure around objects defined as something people act towards and with. The boundary object is a materiality that derives from action – and it is first and foremost a process where: “[c]onsensus was rarely reached, and fragile when it was, but cooperation continued, often unproblematically. How might this be explained?”, Star asks. She answers that when groups are cooperating, they “tack back-and-forth” between two forms of the object: the object as a vague (indeterminate) common object shared between dispersed groups and the object as a locally reworked more specific object (Star 2010, 604–606). Though Star deals with groups, this definition of what happens to objects could also be a way to define how persons work together on a shared object as it has been done by Anne Edwards (2010). In the perspective of posthumanist learning there is no stand-alone individual involved. There is a collective of previous learning in a mindful body, which engages in a collective task – where everything else involved is just as collectively formed as the mindful body – humans as well as non-humans. For the humans involved, preceding learning is called forth by the collective of collectives as a resource in the situation. The

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learners tack back-and-forth with their relational becoming in Lifehand 2 (see Chapter 8) and the Xeno project, trying to make sense of the material arrangements and the concepts entangled with them. But whatever consensus is reached or not reached – and how individuals stand out in the process of negotiation – the entangled humans cannot escape the collective of other ultra-social learners. They always to some extent share common knowledge (Edwards 2012) when they share a common language. Their material–conceptual collective reworking of material objects may not be as smooth and habitual as Luria’s peasants, but they do learn to align in their material–conceptual understanding of the object they work on. Material–conceptual, verbal thinking evolves when humans and nonhumans repeatedly engage to resolve indeterminacies. Materials actively engage in this process – as when the wires become meaningful for Dennis and the Lifehand team in particular ways (see Chapter 8). Though the materials do not choose or take action in a particular direction, they actively affect the situation when they refuse to comply with human intentions. However, it is only for the humans that this refusal is meaningful. Thus, what is meaningful is not determined by the material, although the material is directly involved. For Luria’s peasants, negotiations of collective meaning making took place in a well-established motivated practice with a local, habitual relation between bodies, tasks, tools and environment, into which stories were spun of how and why to saw and hammer. Their collective also experienced indeterminacies and incomprehensible human and non-human agency (e.g. around land rights and local hierarchies), but these could be resolved within a potential of collectively stabilised conceptualisations and material surroundings. This kind of habitual alignments of materials and humans is recognisable in most of the history of anthropology. In the ethnographies of the 1930s to 1950s, anthropologists like Margaret Mead and Ruth Benedict found so many patterns of sameness in their materials in local communities that they spoke of culture as shared knowledge – and even shared personalities – across a large ethnic group or nation state. Collective sameness emerged analytically in contrast to other ethnic groups or nation states. Within these larger wholes there was an ordered cultural consensus of what counted as common values and common knowledge (e.g. Mead 1930; Benedict 1934). Anthropologist Frederik Barth much later challenged this assumption of sameness. Meaning was precisely not shared in a culture. Some had access to important meaning and others were denied this access, whereas meaning became a secret, which again upheld a powerful structure within the culture of those who knew and those who did not (1975). However, this important point about ignorance, about what others know to be meaningful, was, even in Barth’s arguments, based on a collective, habitually formed material–conceptual knowledge of what counts as knowledge and where to find it. This collective knowledge about what counts as knowledge is what creates ignorance, as argued by Jonathan Mair and his colleagues (Mair, Kelly & High 2012).

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In a posthumanist perspective, indeterminacy in people’s mutual engagements with each other and the materials that bind them together is growing when people do not share the same material arrangements on a daily basis, which aligns the back-and-forth processes between the object of observation and meaningful words. The shared phenomenon may remain indeterminate and complex and filled with negotiations. The preceding learning on which future material–conceptualisations are developed may be challenged again and again by materials as well as unexpected conceptualisations. Though all ultra-social entanglements contain some indeterminacy and ignorance, there is a difference between today’s high technology driven societies and the societies described by anthropologists (and psychologists like Luria). The reason why anthropologists experienced collective “sameness” in the cultural studies of the 1920s, 1930s, and 1940s was partly because they did not focus on internal diversities, but it was also because these communities were more stable in their material–conceptual human and non-human–material arrangements than the cultures anthropologists study today. Today our relationship with technology has changed in ways that may also alter how we learn. Intersecting practices make us work with materials in new ways. The humans in ethnographic studies up to around 1950 could engage in their daily chores without questioning the meaning of the tools they used and they could repair the tools themselves if the tools broke down or behaved unexpectedly. Today few people can repair their electronic devices themselves – and as we have seen with the robot in Lakeview (see Chapter 4), they may even be uncertain as to why they should engage with the tool at all. As noted by Hayles, new electronic tools, for instance involving following something on a screen, are “f luid” rather than resistant like a hammer or a key on a typewriter (Hayles 1999, 26). We are furthermore often ignorant of how electronic tools work below the surface and have distributed expertise in how to create and repair phenomena with tools. Today even the people who make these tools, like the robot-makers, often fail to understand exactly how their materials work in practice. Nevertheless, for many people there is no difference in habituated familiarity with hammers and, for instance, mobiles. People today use their mobiles and tablets just as confidently as the hammer and saw. Stable collectives form when humans and non-humans act as extended, distributed, mindful bodies because they learn to do the same things with materials no matter where they are. Collectives form through material arrangements that, contrary to early ethnographies, are not confined to ethnic groups or nations, but evolve with and across a number of new technologies – not least the internet. Groups form across ethnic and national borders with and through technologies in various forms, such as interactive games, Lifehand experiments, collectives designing robots or f lying an aeroplane. What holds these collective practices together is often a boundary object that is held in place by a common motive tied to humans that have formed embodied habits with well-known material arrangements. For instance, this is what it takes to make a ship sail.

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VIGNETTE 9.1: THE SHIP COLLECTIVE OF COLLECTIVES After several days at sea, the USS Palau was returning to port, making approximately 10 knots in the narrow channel between Ballast Point and North Island at the entrance to San Diego harbour. A junior officer had the con under the supervision of the navigator and the captain was on the bridge. Morale in the pilothouse had sagged during two frustrating hours of engineering drills conducted just outside the mouth of the harbour, but was on the rise now that the ship was headed toward the pier. Some of the crew talked about where they should go for dinner ashore and joked about going all the way to the pier at 15 knots, so they could get off the ship before nightfall. The bearing timer had just given the command, “Standby to mark time three eight” and the fathometer operator was reporting the depth of water under the ship, when the intercom erupted with the voice of the engineer of the watch, “Bridge, Main Control. I am losing steam drum pressure. No apparent cause. I’m shutting my throttles”. Moving quickly to the intercom, the conning officer acknowledged, “Shutting throttles, aye”. The navigator moved to the captain’s chair repeating, “Captain, the engineer is losing ah steam on the boiler for no apparent cause”. Possibly because he realised that the loss of steam might affect the steering of the ship, the conning officer ordered the rudder amidships. As the helmsman spun the wheel to bring the rudder angle indicator to the centreline, he answered the conning officer, “Rudder amidships, aye sir”. The captain began to speak, saying, “Notify …”, but the engineer was back on the intercom, alarm in his voice this time, speaking rapidly, almost shouting, “Bridge, Main Control, I’m going to secure number two boiler at this time. Recommend you drop the anchor!” The captain had been stopped in mid-sentence by the blaring intercom, but before the engineer could finish speaking, the captain restarted in a loud, but cool, voice, “Notify the bosun”. It is standard procedure on large ships to have an anchor prepared to drop in case the ship loses its ability to manoeuvre while in restricted waters. With the propulsion plant out, the bosun, who was standing by with a crew, ready to drop the anchor, was notified that he might be called into action. The falling intonation of the Captain’s command gave it a cast of resignation, or perhaps boredom, and made it sound entirely routine. In fact, the situation was anything but routine. The occasional cracking voice, a muttered curse, the removal of a jacket that revealed a perspiration-soaked shirt on this cool spring afternoon, told the real story: the Palau was not fully under control, and careers, and possibly lives, were in jeopardy. The heart of the propulsion plant had stopped. The immediate consequences of this event were potentially grave. Despite the crew’s correct responses, the loss of main steam put the ship in danger. Without steam, it could not reverse its propeller, which is the only way to slow a large ship efficiently. The friction of the water on the ship’s hull would eventually reduce its speed, but the Palau would coast for several miles before coming to a stop. The engineering

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officer’s recommendation that the anchor be dropped was not appropriate. Since the ship was still travelling at a high rate of speed, the only viable option was to attempt to keep the ship in the deep water of the channel and coast until it had lost enough speed to drop an anchor safely. Within 40 seconds of the report of loss of steam pressure, the steam drum was exhausted, and all steam turbine operated machinery came to a halt. This machinery included the turbine generators which generate the ship’s electrical power. All electrical power was lost throughout the ship and all electrical devices without emergency power backup ceased to operate. In the pilothouse, a high-pitched alarm sounded for a few seconds, signalling an under-voltage condition for one of the pieces of equipment. Then the pilothouse fell eerily silent as the electric motors in the radars and other devices spun down and stopped. The port wing bearing taker called in to the bearing timer, “John, this gyro just went nuts”. “Yah, I know, I know, we’re havin’ a casualty”. Because the main steering gear is operated with electric motors, the ship now not only had no way to arrest its still considerable forward motion, it also had no way to quickly change the angle of its rudder. The helm did have a manual backup system located in a compartment called after-steering in the stern of the ship, a worm gear mechanism powered by two men. However, even strong men working hard with this mechanism can change the angle of the massive rudder only very slowly. Shortly after the loss of power, the captain said to the navigator, who was the most experienced conning officer on board, “OK, ah, Gator, I’d like you to take the con”. The navigator answered “Aye, sir”. and turning away from the captain announced to the pilothouse, “Attention in the pilothouse. This is the navigator. I have the con”. As required, the quartermaster of the watch acknowledged, “Quartermaster, aye”. and the helmsman reported, “Sir, my rudder is amidships”. The navigator had been looking over the bow of the ship trying to detect any turning motion. He answered the helmsman, “Very well. Right five degrees rudder”. Before the helmsman could reply, the navigator increased the ordered angle, “Increase your rudder right ten degrees”. The rudder angle indicator on the helm station has two parts; one shows the rudder angle that is ordered, and the other the actual angle of the rudder. The helmsman spun the wheel causing the desired rudder angle indicator to move to right ten degrees, but the actual rudder angle indicator seemed not to move at all. “Sir, I have no helm sir!” he reported”. (Excerpt from Edwin Hutchins Organizing Work by Adaptation, 1991, 17–18). The author emphasises that all of the names appearing in this document are pseudonyms, including that of the ship itself. Furthermore, it is emphasised that all of the discourse reported in this passage is a direct transcription from the audio record of actual events.)

In the above case, anthropologist Edwin Hutchins recounts a detailed description of the large ship, USS Palau, which, while it was sailing into a harbour in San Diego,

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ran into a problem with some of the navigational equipment and a breakdown of electrical power. It is an example of situated learning (Chaiklin & Lave 1993). In the ensuing work situations described, the navigation team responds to this problematic situation. Hutchins notes that one normally expects that work situations on big ships are organised in accordance with plans, which may ensure that even in times of a crisis the organisational structures support efficient responses to a crisis. The crew and their equipment answer with an intricate system of adaptations spread across human and material artefacts to avoid a collision with a sailboat. The collision is eventually avoided. Not because of separate individuals’ reflections, but rather through the reactions of a systemic whole (Hutchins 1991). This example from Ed Hutchin’s fieldwork on ships shows an exemplary way of how human bodies and materials like rudders and word sounds work together in meaningful ways. Though it is obvious that the crew on the ship does not know all of the same things about the tools used, they know enough as a collective to know what counts as knowledge. In this case, a lack of knowing is not seen as ignorance, but as a situation in need of supplement by instruments or other humans who know. Hutchins’ empirical work often concerns the working of complex systems of human–technology relations, which e.g. enable huge ships to sail (Hutchins 1995) and aeroplanes to fly (Hutchins & Klausen 1996). Parting from traditional understandings of cognition as information processing by individuals, the practice-based approach taken by Hutchins led to an identification of cognition as distributed among humans and non-humans (Hutchins 1995). Learning what matters in the situation is made possible within a human and non-human “horizon of observability” (Hutchins 1993).

Ignorance of ignorance On Hutchins’ ship, we find no “ignorance” though the deck boy clearly does not share all kinds of knowledge with the captain. In the boundary object, which is here to make the ship sail, ignorance cannot be defined as a lack of knowing in general – as it is acknowledged that different persons bring different important knowledges to bear. On the ship there is no ignorance in the sense of the term discussed in Chapter 7, where ignorance is a lack of knowing something somebody believes one ought to know as a collective. The instruments may to some extent “know” of each other – however, they do not know all there is to know about making the ship sail either. It is their more or less seamless collaboration that finally gives the USS Palau a safe passage to harbour. This would not have been the case if all the humans on board had been ignorant of how to interpret the signs from the instruments on the machines or if all the machines malfunctioned. Years of human learning with materials, in order to become captain, fathometer operator or boatswain, were involved in the sailing of the USS Palau as a collective phenomenon. We have in our culture accepted this wide distribution of knowledge. We humans are all ignorant of something – just as instruments only perceive what they are built to perceive – but its only for humans’ ignorance matters.

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There are different kinds of relational ignorance. We can be completely ignorant of something that matters to our life conditions, which excludes us from collectives, like the children in Tanzania who cannot join the robot community in the same way as the Danish children (see Chapter 7). This is ignorance of ignorance. We all suffer from ignorance of ignorance of the things that do not enter our lives. While technological advances affect everyone, robots are not as integral to the life conditions of these particular children in Tanzania. Robots as a concept only entered with me during my visit. How this new concept eventually matters to their life conditions is tied to their local context. We can also be ignorant because ignorance is the glue that keeps a collective together as when knowledge of secrets makes us curious. In the Baktaman culture described by Barth, the “corpus of knowledge” is distributed among 183 persons, with knowledge defined as “what a person employs to interpret and act on the world” (Barth 2002, 1). In Baktaman culture, it is the explicit exposition of ignorance among age groups and genders that holds the small society together, spun into a network of rituals that gradually reveal “secrets” to the novices who then become experienced. What matters is not what these secrets are about but that they are revealed as secrets to some not to others (Barth 1975). Perspectives like these have strongly challenged the claims made by Benedict and Mead that cultural knowledge is widely shared among members of a nation state or ethnic group. The point made by Mair, Kelly and High (2012) is that what is widely shared in a culture is what counts as knowledge or ignorance. Going back to Hutchins’ sailing ship, it includes non-human artefacts as part of a distributed common knowledge about how to make ships sail and avert the risk of collision. Humans and non-humans at first work smoothly together, though the instruments are not necessarily “aware” of each other. The humans are aware that they do not collectively share knowledge about all of the instruments and procedures in the environment and trust each other’s competences. Harmony and order prevail even when the crisis with the propulsion occurs, because though “ignorance about lack of propulsion” is an issue, the collective of collectives finds ways of using potential collaborative resources. The captain does not need to know what the bosun (or boatswain) knows and vice versa. Knowledge like this is not “public knowledge” to be interpreted by all (Geertz 1973). It is highly distributed, and like the knowledges distributed through secret rituals among the Baktamans, there is also an acceptance of ignorance. When we work in a collective, sharing a common motive for agency, knowledges are always as distributed as material–conceptual cognition. We may be ignorant of the knowledge tied to materials we do not need for our own work, but when we have established relational agency, we can trust others to do their part in collaborations (like the captain can trust the fathometer charting the depths of the water while the ship is sailing). We can also trust the instruments to do their part. Then we may lose confidence if humans or non-humans fail to do their part – and ignorance reappears when we ask: why did they fail?

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Ignorance is what upholds a material–social structure with a direction of movement. Moves are never a complete repetition in this distributed complex system of ignorance, knowledge, materials and humans. However, from a human point of view, the process proceeds in keeping word meanings alive from a basis where: already established thoughts, representations, and social relations to a considerable extent configure and filter our individual human experience of the world around us and thereby generate culturally diverse worldviews. (Barth 2002, 1) In anthropology, culture has often been used to refer to stable, integrated communities where all members collectively share knowledge – where there is collective cognitive sameness. The children’s drawings of robots, discussed in previous chapters, show us something different. Knowledge about robots is not a coherent whole distributed in a nation state or ethnic group. It is culture as a real material force (Hasse 2015), where knowledge or rather knowing comes with material practices that only to some extent create collective word meaning and thus collective verbal thinking. We find all kinds of diversities and ignorance across nations and ethnic groups, race and class. This does not make concepts like gender, race and class superf luous but emphasises that knowledge about gender, race and ethnicity (as well as subsequent identities) is not something we have, but something we learn to have within phenomena. We need to understand how human learning within phenomena involves diversity in ignorance and knowing. Categories of gender, ethnicity and race as potential cultural resources are, like all categories, tied to human diversity in learning to conceptualise with materials. The ignorance of robots found among the children in the Tanzanian school cannot be reduced to a question of culture as tied to place. In January 2016, some days after the session with the 33 children in the country school, discussed in a previous chapter (see Chapter 7), I visited another school in the same area. This time I visited an international school, with students from many different countries but with the aim of providing education for around 10–20% of local children. We did a drawing session at the international school, which was only a few kilometres away from the country school. Here children materialised drawings of robots that were remarkably similar to the ones drawn by Danish children – only more elaborate and with more text and more technical details. It is not because the country school children we met in a previous chapter (Chapter 7) live in Tanzania that they do not have a word meaning unit tied to the word-sound “robot”. Ignorance of the technologies that increasingly define all of our lives is just one of many global effects of robotic technologies, which is not confined to particular places but distributed as cultural knowledge and materials across the globe. Whether the children in the international school came

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from Tanzania, Nigeria, Kenya, the Netherlands, US, France or China, they had already formed an elaborate understanding of the “word meaning” of robots – and could think with the concept even in technical terms. Their drawings were full of verbal explanations (though I had not asked for that), buttons, wires and a number of elaborate visions for a future with robots. Their robots were humanoid, often with square heads, created to serve humans (walk their dogs, clean their rooms, etc.) and in many ways mirrored a cultural life with technology that they shared with the Danish children. Anthropologists from the 1980s and onwards were right in rejecting culture as collective sameness tied to ethnicity, nationality and place. Anthropologists came to see collective culture as an imagination (Gupta & Fergusson 1997), because of an assumed isomorphism of space, place and culture, which overlooks borders, power relations and movements. This critique has been part of a long tradition in anthropology for getting rid of the concept of “culture” (e.g. Clifford & Marcus 1986). However, the movement against culture left us bereft of a concept that could capture how ultra-social humans form collectives. Therefore, I have proposed we stick to the concept of culture (Hasse 2015), but instead of a homogeneous group of humans, culture should be viewed as distributed potential resources among the human and non-human with material–conceptual collectives. That would re-instate the possibility of establishing arguments of how such cultural entanglements have boundaries. Outside of these boundaries, ignorance is no longer a known ignorance but an ignorance of ignorance. Cultures form, from within, the boundaries of learning communities and of learned ethical responsibilities as well. The collective ignorance in cultures is bounded off from other material–conceptual cultures. This raises questions of who decides what knowledge is relevant in a globalised world and what is considered ignorance. We may not expect all Tanzanians to care about robots, but it may be that the whole discourse of robots is tied to an Asian/Western power dynamics trying to emphasise the knowledge their engineered societies believe is relevant. Nevertheless, it has not previously been emphasised that being ignorant of the existence of material words and things may also in itself create ­inequality – for instance, in who has the power to reject a robot hegemonic discourse. This is a justification for a more psychologically based approach to posthumanist learning. Reducing ignorance of ignorance matters for how humans can solve all kinds of complex issues like climate change and how to deal with hyperobjects like all of the plastic bags in the oceans (Hasse 2018).

The mindful body Whether we are aware of our own ignorance or are ignorant of our ignorance and whether we are children or adults, our bodies are filled with the potentials of preceding learning. Vygotsky emphasised that adults do not need auxiliary artefacts to think (see Chapter 7), but this may be an exaggeration. Materials can also help adults to remember and think when they feel or see materials. It may

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be difficult to think about how you type or ride a bicycle, but once you see and feel the artefact, you remember it all. However, even when we are not aware that preceding learning of concepts matters, they are with us as potentials in whatever we do with materials because they follow with our mindful bodies. Learning bodies are involved but often overlooked in the distributed frameworks understood as “systems”, like in Ed Hutchins’ (1995) and Yrjö Engeströms’ (1987) theories of learning and cognition. Cognition is not just about minds, but bodies filled with potential conceptual resources. Philosopher Maurice MerleauPonty made the point that the sensing and perceiving body is our medium for being-in-the-world as the “third term, always tacitly understood, in the figurebackground structure, and every figure stands out against the double horizon of external and bodily space” (Merleau-Ponty 1962, 115). However, he does not include the importance of preceding learning when he says: “My body has its world, or understands its world, without having to make use of my ‘symbolic’ or ‘objectifying function’” (ibid., 162). The sensing, habituated body of captains, quarter-masters – and indeed scientists like Otto Stern (see Chapter 3) and robot designers like Mona, all depend on the potentials of preceding learning in order for the body to have its world. When humans meet the world, it is always a relation with their bodies entangled. Concepts are not fixed as symbols are. Just as the blacksmith, noted by Ingold (2013, 26), will never hit the same spot twice with the hammer, our bodily being with the world is constantly changing. What stabilises the world humans have is that human sensing goes through preceding learning of collective material–conceptual word meaning. This gives rise to a fundamental asymmetry, which has not been noticed by all spinozists posthumanists: [I]n the relation between hand and saw there lies a fundamental asymmetry. The hand can bring itself into use, and in its practised movements can tell the story of its own life. But the saw relies on the hand for its story to be told. Or more generally, while extra-somatic tools have biographies, the body is both biographer and autobiographer. (Ingold 2011, 57) Meaning may arise instantly for humans – as an immediate intelligibility between organs and environment – as proposed by Simon Ceder (2015) but this meaningfulness is always tied to preceding learning. Mindful bodies are filled with the potentials of telling stories of things through their preceding learning. The term “mindful bodies” was coined by two anthropologists, Margaret Lock and Nancy Scheper-Hughes, in order to emphasise that human bodies are not the property of individuals, just as the cognitive processes involved in bodily movements do not belong to an individual alone. Nor is the body a mechanical organ that just processes inputs from outside to perform certain movements. Rather the body is full of material, social and cultural minds. It is an experiencing remembering body, as well as a cultural site for body politics (Lock &

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Scheper-Hughes 1987). Another anthropologist, Thomas Csordas, named the body “the existential ground of culture and self ” where the body is both representation and being-in-the-world (1994). Mindful bodies may appear as having fixed boundaries from a humanist perspective. In a posthumanist perspective the ongoing reconfiguration of bodily boundaries and of connectivity are products of iterative intra-actions through which the agential cut between “self ” and “other” (e.g. “surrounding environment”) is differentially enacted (Barad 2007, 376). Boundaries, also the boundaries of mindful bodies, are as pointed out by Barad, always “enacted” anew. No boundaries between self and other – whether social relations or material surroundings – can be taken for granted. Despite this and in the midst of this constant transformation, we find stabilities in what is meaningful for some humans, who have established habituated collective practices of knowing, like the crew onboard the USS Palau. The USS Palau is a well-defined collective of collectives. Words have clear meanings and instruments have clear purposes. Any destabilisation (of e.g. the rudder) begins from a locally formed cultural stability of how the instruments are built, how humans interpret the materials in their environment, and how humans interpret their own bodily agency within it. Mindful bodies and instruments may differ in what they expect from each other, so what is recognised as stability and instability may also change. What is a sign of instability for the captain would not be a sign of instability for a passenger. As noted by Ingold, Hutchins’ navigators are not navigating as most of us would. “The ordinary passenger, untutored in the techniques of navigation, is quite unable to do this, and may confess to being baff led by maps and charts” (Ingold 2000, 236). Being “tutored” in a stabilised collective of collectives takes a long process of learning. Within a stable “sailing ship” phenomenon we find a huge diversity in what people have learned to do and how this forms their perception of the material world in different ways. Individual persons may not understand all aspects of the boundary object they contribute to or are otherwise engaging with. Socially and materially distributed cognition can be performed with a parallelism of activity, as noted by Hutchins (1995). Individuals do not overview this parallelism. What creates stability is the relation between materials and particularly skilled mindful bodies. Artefacts are the mediators that connect people who have learned to use them in a socially distributed system. According to Hutchins, it is this social distribution of specific artefacts that: places severe limits on the bandwidth of communication among parts of the socially distributed system. Systems composed of interacting people have a pattern of connectivity that is characterized by dense interconnection within minds and sparser interconnection between them. Cognitive processes that are distributed across a network of people have to deal with the limitations on the communication between people. (Hutchins 1993, 60)

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The theories of the social psychologists remain “humanist” in so far as they either focus on individuals in collectives or whole systems, which may be named “systems”, “activity systems”, “societies” or “cultures”. Humans are never consistently “collective” in “society”, “activity systems” or “cultures” as such, but momentarily form collectives with each other and non-humans within specific phenomena which includes perception and everyday verbal thinking. In a collective of collectives, we find asymmetry between things like a compass and a human like a captain. The captain can call on his learned potentials that make him capable of recognising what the compass tells him in the situation. As noted by Ingold, a saw relies on movements of the hand to become meaningful (Ingold 2011, 57). Before the saw, or in this case a compass, becomes meaningful and before the captain and quarter-masters “have” the world, the material world relies on the previous material–conceptual learning in mindful bodies. This learning is what creates a stabilised diversity in human–material engagements. In the robot-making Xeno-project, partners from all over Europe are involved, and in spite of their cultural background they also gradually align at times to form a more stable collective with their materials. However, they do not become stable like the peasants from Luria’s experiment or the crew on the USS Palau. They are more likely to find vagueness and indeterminacy, not just in relation to the common boundary object but also in relation to the tools they use – because the material practices of tool-making and tool use are created through dispersed embodied practices. Contrary to the USS Palau, the dispersion in space means that there is a time-lag in how they are able to tack back-and-forth. Previous experiences matter when embodied practices are established across both time and space. Mona, herself an engineer, can more easily communicate with other engineers. Engineers have learned an embodied practice where things and their meaning (wires, sensors, actuators) grow together, but it is hard to convey the meaning shared by engineers to the social scientists involved. The dramatic story of the USS Palau had a happy end when the potential of the emergency, diesel-electric generators was realised as a resource, which could restore some electrical power to the ship. This now classical example of an extended human–machine agency illustrates several aspects of humans as ultra-social learners. Humans make use of technology, but what makes the technology meaningful is that it be distributed unevenly – both among humans and non-humans. Ultra-social mindful humans and the materials involved have made the USS Palau, its predicament and solutions possible because the parallelism worked, and each human body was already mindful of its allocated instruments. Distributed practices of knowing entail processes of learning between humans and their instruments. Not all humans can understand all technical devices. But in this case our main storyteller, Ed Hutchins, who recounts the story of the USS Palau, seems to have a rich conceptualisation of all of the technologies in question. It turns out that his preceding learning included both training as an anthropologist and as a sailor. When we consider the USS Palau as an organised system it

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is a discursive “trick” that hides the author of the story. “A system” is what the distribution of specialised labour between humans and non-humans looks like from the mindful storyteller, who in retrospect, and by aid of taped recordings, can recount the “whole” of the story of the “extended mind” enveloped in the USS Palau. It is an agential cut that, only in some sentences, reveals that the story is recounted by a person who himself has an expertise in sailing, when he for instance says: “The rudder angle indicator on the helm station has two parts, one shows the rudder angle that is ordered, and the other the actual angle of the rudder” (Hutchins 1991, 17–18). In Hutchins analysis, he notes that: “In the analysis presented above, there are no instances of anyone ref lecting on the whole process” (Hutchins 1991, 36). This comes later with his own synthesised retelling, which builds on his own long learning process as a sailor and anthropologist. The phenomenon of the USS Palau is now a story, but the story builds, just like the storyteller’s account, on the preceding learning processes of all humans involved. In this story there is no ignorance, just a division of labour between the mindful humans (Hutchins, captain, port wing bearing taker, bearing timer, conning officer, engineer, fathometer operator, quartermaster) as well as instruments (anchor, gyro, rudder, engine). Some humans are experts in reading gyros; others master the movements of the rudder. Some instruments have “expertise” in measuring the depth of the waters; others can measure the direction. As a whole the humans and instruments depend on the preceding learning of the humans to work together towards the boundary object: to steer the ship safely into harbour. The extended mind includes the instruments, but from a learning perspective reaching the harbour is not meaningful for a compass or a rudder. In phenomena recounted in stories, it is the humans (in this case the storyteller Hutchins, but it could also be the captain and the quartermaster who told stories) that bring meaning within the boundaries. The non-human instruments have been made by humans and materials. But the meaningfulness is not in the materials, but in the relations mindful humans (or other living beings) have with them. There is an asymmetry here, since the meaning embedded by some ultrasocial humans with material artefacts like robots has to be learned by other ultrasocial humans in order to become meaningful. Extended minds extend with learning. This process of learning is far from the seamless f low of information in systems envisioned by the cybernetic approach (Hayles 1999). Instead of the freef lowing information, Hayles, inspired by the philosopher Mark Hansen (Hansen 2006), proposes the term mindbody – where the mind and body together is becoming in a relation with technology. She uses the new concept, alongside Ihde’s “relations-analysis” (Ihde 1990 – see Chapter 8), to argue that her previous dichotomy “body” and “embodiment” (Hayles 1999) should be replaced by one of a relation from which body and embodiment emerge in different ways: “Rather than beginning dualistically with body and embodiment, I propose instead to focus on the idea of relation and posit it as the dynamic flux from which both the body and embodiment emerge” (Hayles 2002, 298).

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Hayles uses the art piece, Traces, by Simon Penny, to argue for three ways that body and embodiment emerge in the posthuman technoscientific formations. In this piece, humans interact with a virtual space they themselves create, in real-time, by moving around. She names the first way “relation as enactment”, where the human as mindbody and technology arises co-dependently. The second she names “relation as perception”, where technology and the world both engage mindbody’s perception in virtual as well as other real spaces in active and dynamic constructions emerging from interactions (Barad would say “intraactions”), rather than perceiving an “out there” reality. The third relation is “relation as enculturation”. In her book How We became Posthuman (1999) Hayles realised that the dichotomy between body and embodiment made the body the site of cultural inscription. She now argues that embodiment can also be culturally inscribed as we saw with Dorinne Kondo’s embodiment (see Chapter 8), where her body was perceived by others to be a Japanese body; she also began to walk and bend her body differently (Kondo 1990). Hayles acknowledges that just as the “body changes as the culture changes … our experiences of embodiment also change” (Hayles 2002, 299). Rather than calling it “the mindbody”, I prefer “the mindful body” because it indicates a process of collective, meaningful, habitual boundary making. The culture, the body and the embodiment are one process of learning. Learning is not filling the body with meaning as an embodied vessel, but a constant back-and-forth meaningfulness in the creation of boundaries through negotiations between humans and non-humans. All relations entail processes of material–conceptual meaning making. Hayles has furthermore been inspired by the extended-mind model presented by Clark (2003) where technologies and brains/ cognition/perception grow together, but she emphasises that even when the “environment changes and the flux shifts in correlated systemic and organized ways … it takes time, thought, and experience for these changes to be registered in the mindbody” (Hayles 2002, 304).

Challenging the mindful body The time aspect of how material words become internalised as meaningful through cultural learning is often overlooked both by the new materialists and by the anthropologists doing fieldwork, though anthropologists are often the first to refer to how they feel “out of touch” with the at-ease intelligibility others perform in a local material culture. After a while during fieldwork, anthropologists can learn the meaning of local tools, at least to some extent. Once learned these tools become what Tim Ingold calls “transducers”, when humans correspond with a material world, going back-and-forth in movements of adjustment – to obtain sounds, to make a kite f ly or to throw a lasso. Things may appear to be more stable than thoughts, but transducing is not tied to the materiality alone but to the lively movements they make possible. It is this quality that is lost over time, Ingold argues (Ingold 2013, 102).

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Persistency is a material persistency. What happens to the rope that used to be “a lasso”, when it is rotting in a corner of a garbage dump freed from human engagements, is that it is still is part of wider material entanglements, as noted by Barad (2007). However, whenever humans are involved, learning what a lasso is as a meaningful word sound that contains movement is involved as well. Word meaning also has persistency as “a lasso” with “a toggle”, to be passed on from one generation to the next. Like the hammers and saws of the peasants in Luria’s study, both materials and word meanings are tied to practices of knowing and doing. Just as much as the “lasso” needs the rope and the toggle, the humans need to learn the word meaning of the material word “lasso” to maintain persistence. This is the water we swim in, we posthumans. In all of this there is meaningful collective and cultural learning – for example when the rope, the toggle and the herdsman “for whom the manual-gestural skill of casting the lasso, requiring years of practice, is one of the most highly prized of the herding repertoire” move together so the ductus, “the kinetic quality of the gesture, its f low or movement” moves from “one register, of bodily kinaesthesia, to another, of material f lux” (Ingold 2013, 102). What the materials, lassos and kites are made of may last longer than speech and gesture, but the meaningfulness of things is inseparable from the human willingness to spend hours learning with mindful bodies, such as the ham-fisted attempts to throw a lasso like the best herdsmen, as Ingold describes he did when he learned as an anthropologist on fieldwork (Ingold 1993). Whenever humans encounter new tools likely to be in contact with our bodies, such as prosthetic limbs, compasses, lassos, computers and lotus shoes, a learning process is initiated which, just like the stick may extend the blind man’s body because it can: become an area of sensitivity, extending the scope and active radius of touch and providing a parallel sight. In the exploration of things, the length of the stick does not enter expressly as a middle term, as an entity-in-itself; rather, the blind man is aware of it through the position of objects through it. The position of things is immediately given through the extent of the reach which carries him to it, which comprises, besides the arm’s reach, the stick’s range of action. (Merleau-Ponty 1962, 144) The extended body is not necessarily immediately the same as what the extended mindful body becomes over time. Relations have a time perspective due to learning processes. It takes time for the blind body to find out how to walk with a stick, just as it takes time to learn how to throw a lasso, just as it takes time to learn to become new embodied beings entangled with prosthetic limbs or computer avatars. Though the body and mind are one and extensions are cultural, the mindful body is still a personal learning body with f luctuating boundaries. We may share an availability of potential material learning resources in our environment, but that is no guarantee for learning to use the tools as they were

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meant to be used. We have to learn how other ultra-social humans make use of these resources as well and how the material non-human artefacts that we are surrounded by contribute to our joint efforts. This entails an understanding of how agency is distributed across humans and non-humans, together performing as an extended mindful body driven by collective motives. When the electrical system fails on the USS Palau, the whole extended mind breaks down, but only because mindful humans care about sailing as they had planned to. Humans think and act and share responsibilities that are distributed with the machinery in innovative ways, but it is the humans for whom it is meaningful to prevent a disaster. Thus, the USS Palau ends up sailing safely into harbour. All bodies are mindful, but today technologies have obtained a new power that differs from the role they played on the USS Palau and for the peasants, or farmers, in Luria’s study. Instead of being inseparable from the mindful bodies using them as tools in distributed collectives, they come into existing practices as unknown strangers. In a former chapter (Chapter 4) the people who met the Telenoid robot at Lakeview experienced doubts about what kind of tool the robot was really supposed to be. They did not, like the peasants in Luria’s story, make their own tools. The tools they received were not necessary for their practices and they were not motivated to use them. Hammer, saw, hatchet and log tie together words, meanings and materials in more stable ways for the farmers, just like the wires and sensors tie these together for the engineers in their embodied practices of knowing. In places like Lakeview, the need for robots does not grow out of local practices of knowing (Bruun et al. 2015). Robots are, at least through what we saw in our research, largely implemented as a kind of political experiment (Hasse 2013). The staff have to adjust the tools to fit their local practices (e.g. Blond 2019). These new robot technologies, imposed by managers or politicians, come with a power to change existing practices (Hasse 2018a). Local indeterminacies are resolved as people learn to align with new tools like robots over time, but it is a harder task than learning to use a hammer or a saw. After a while, the people at Lakeview may also of course become like the farmers of Uzbekistan and the crew on the USS Palau – secure in a reliable material world of well-known tools. Even when they break down, the meaningfulness of the tool is unquestioned. However, tools can also become covered in dust, when they seem superf luous, or the local practitioners can outright reject them (e.g. Blond 2019). As material tools, the robots are less transparent when the unexpected happens, and the human–material collective of collectives is less stable and easier to disrupt than the farming society and the USS Palau. What has already aligned Lakeview inhabitants (humans and non-humans) in their collectively shared embodied meaning making is continuously questioned by newcoming robotic tools as well as the new demands that follow with being part of an ongoing experiment (Bruun et al. 2015). When de-stabilisation takes place, it changes the next ongoing learning process, as the meaningfulness of the extended collective has been embedded in mindful bodies, but never fixed and never for good.

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Before technical artefacts are habitually embodied, there may be a time when they are perceived as something material but not as something meaningful – and thereby they are not yet what Andy Clark and David Chalmers term “extended minds” (Clark & Chalmers 1998). It is not just that humans have to learn to become extended, when they align with new tools. It is rather that their mindful bodies perceive some of these tools as completely meaningless and therefore are not motivated to use them (e.g. Hasse 2013). Only if staff are willing to and capable of changing their practices can the robots gain some kind of meaningfulness, but in our modern world, sometimes it also ends with these new tools being put on the shelf (e.g. Hasse 2018a). Extended minds evolve as a process of clashes in the collective of collectives when, for instance, the motives built into robots by the robot-makers clash with the practices of the local staff. Contrary to the USS Palau, there are constant negotiations of the boundary making practices suggested by the new technologies. The clashes at Lakeview and in Danish nursing homes (e.g. Bruun et al. 2015; Hasse 2013; Blond 2019) discussed in Chapter 4 are far from the harmonious system of the USS Palau, that can act as a motivated whole even when an instrument fails to perform as expected by the humans. In a way, new technology has turned the situation up-side-down. It is the technology (or the engineers behind its creation), which requires a certain human performance, not the other way around.

Anchors of meaning Considering humans as ultra-social learners with mindful bodies is a stepping stone to understanding what posthumanist learning may become. Learning to make sense of all the different distributed agencies on a ship entails a process that is not just about material words, a body schema or a cultural model in relation to an instrument. It is the extended, mindful, motivated body going back-and-forth with the available resources for boundary making – and everything changes as the relations change, yet a sort of stability prevails when meaning is collectively anchored in both material words and things. These changes and stabilities are not about responding to stimuli as proposed by the behaviourists, neither is it about a “social construction” as an outside representation. Ultra-social learning is learning to understand enough of what other humans and non-humans are doing to be able to have personal room for motivated manoeuvring in a back-and-forth process of meaning making that constantly reworks collectively shared phenomena. In this process material artefacts, like compasses, can become “anchors” of meaning for humans (Hutchins 2005) just as word sounds can anchor meaning. Material anchors are a way of stabilising a phenomenon that includes past learning with materials. Perceptible material elements are stabilised when they align with our motives and recognised as meaningful in particular ways, and, as noted by Hutchins, these conceptual relations are mapped onto relations among the material elements

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(Hutchins 1995). The materials connect to what Vygotsky called human “fields of attention”. Materials such as compasses, rudders etc. become like the green card for the child (see Chapter 7) – an anchor: a way to remember what is meaningful in a wider context (Vygotsky 1999, 53). Embodied engagements with material anchors like wires and sensors may explain why cultural alignment is not tied to particular places, as anthropologists expected in the 1930s, but to particular motivated practices of knowing. Mona can communicate with engineers and robot-makers all over the world because they share experiences with the material anchors involved in the boundary object, but also because these anchors support their motives for building a robot. The social scientists are much less tied to the material anchors that keep engineers motivated and that make a difference when the robot-maker, Mona, tries to communicate with them. What we perceive is tied to the practices we engage in. Like many of the other Vygotsky-inspired anthropologists, i.e. Dorothy Holland, Jean Lave and cultural psychologists like Michael Cole, Barbara Roof, Sylvia Scribner, Hutchins takes turns with the general domineering cognitive science, which, he claims, “leads to a serious over attribution of knowledge to individual actors” and thus overlooks that “when context is ignored, it is impossible to see the contribution of structure in the environment, in artifacts, and in other people to the organization of mental processes” (Hutchins 1993, 63). All of the studies of the cultural psychologists and psychological anthropologists argue, based on empirical evidence, that social practices form our mental functioning such as memory, cognition and learning processes of concept formation. Though not always made explicit these studies open a new perspective for Science and Technology Studies: scientists do not “discover” the world as it is, but they collectively learn to align how they perceive phenomena. Humans never learn completely “abstract” knowledge – even in theoretical physics – as words are always material. If they refer to knowledge as “abstract”, it is tied to a practice where this kind of knowledge is useful, like mastering syllogisms in schools. Through practice, by engaging in a task, humans learn to understand what is relevant and what knowledge means when tied to a particular practice, and how and where materials (including words) are meaningful. Knowing is tied to practices and it is with these practices we learn to think. The research by Alexander Luria underlines this point as well. School practices like scientific practices, are particular kinds of stabilised practices just as being a farmer is a particular kind of stabilised practice. The way we learn to reason and think is tied to these practices and changes with them. Following Vygotsky’s basic theories of learning, Sylvia Scribner argued that psychological functions, tied both to individuals and the history of mankind, undergo changes in an ongoing process of learning and in this process, she added to Vygotsky, particular cultures are formed (Scribner 1985). Hutchins acknowledges Vygotsky’s theories and their complex way of explaining how cultures are never “faxed” into persons – a point also emphasised by other

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cognitive anthropologists (e.g. Strauss 1992) – but through learning becomes what they think with. Hutchins, like Scribner, tries to take Vygotsky’s ideas further when he argues that the social formation of “verbal thinking” (understood as “higher mental functions”) is not just how children learn to become part of a society, but how all adults constantly learn to align with systems that include non-humans and these wider systems which differ from their individual minds (Hutchins 1995). Vygotsky’s own idea of word meaning was that it went beyond verbal thinking “Meaning does not belong to thinking but to consciousness as a whole” (Vygotsky 1997a, 138). In his later work, he became more “spinozist” and “monist” in his theorising and some think he began to perceive concepts like “internalisation” and “mediation” as problematic since they operate with an inside and an outside (Roth & Jornet 2016). What is important to remember is that for Vygotsky the personal perception of phenomena is social in origin and processes of differentiation take place within a collective consciousness. His colleague Leontiev put it in this way: Consciousness is co-knowing, but only in that sense that individual consciousness may exist only in the presence of social consciousness and of language that is its real substrate. In the process of material production, people also produce language, and this serves not only as a means of information but also as a carrier of the socially developed meanings fixed in it. (Leontiev 1978, 95) However, meanings are precisely not fixed, as also noted by Vygotsky. Word meanings keep evolving. The collectives of humans and non-humans form around boundary objects that are never entirely shared in a group or a system. In fact, what we call “system” or “group” is never a stable entity. Learning is the pivotal process that works towards a stable collective consciousness that becomes individualised within the phenomena made of material–conceptual collectivity. The extended mind and distributed cognition theories add that our collective mindful bodies are driven and formed by motives as much as by material and concepts. We constantly entangle ourselves with our material surroundings to perform certain tasks and there is no fundamental difference whether we solve a puzzle only by thinking, or by using auxiliary artefacts like a pen or pencil (Clark 2003). When we play Scrabble for instance, the ways we arrange tiles to form words, Andy Clark argues, are not just actions but ways of thinking. When we mentally rotate a two-dimensional image seen on a computer screen in our minds, it is not a very different process from rotating it with a handheld device (Clark & Chalmers 1998). Verbal thinking is extended back into the world it came from – but these extensions demand active motivated bodies. What activates motivated bodies are, as argued, processes of learning. Not all bodies are motivated by a game of Scrabble with Andy Clark. For a person without stabilised material–conceptual anchors in a local culture where

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Scrabble is already meaningful, Scrabble may just be perceived as a f lat board with small square tiles, just like the clock mentioned by Vygotsky can be seen as a meaningless white circle with black dots. Meaning arises in practices of knowing, and here there is a f luctuating boundary – a moving outside and an inside – of a collective consciousness to be reckoned with. This collective consciousness has been explored as a concept that aligns Vygotsky’s psychology with Russian philosopher Ivan Ilyenkov’s discussions of the relation between the ideal and the material world. Here, especially in the work of Alex Levant, we find a further elaboration of Vygotsky’s nondualistic approach that draws both on Hegel’s dialectics and Spinoza’s monism, and where the Cartesian preoccupation with causal relations between thinking and extension is dissolved, as thinking and extension are one and same (Levant 2012). 2 According to the Russian philosopher Ilyenkov, the ideal has wrongly been attributed to that which has a place in an individual mind, whereas the material is “everything else”. The materials that surround humans are not just objects of materials. A book, a statue, an icon, a drawing, a gold coin, the royal crown, a banner, a theatrical performance and its dramatic plot – all these are objects existing, of course, outside of the individual head, and perceived by this head (by hundreds of such heads) as external, corporally tangible “objects”. (Ilyenkov 2014, 33) However, the dichotomy between this material world and a meaningful world was not accepted by Vygotsky or Ilyenkov, Levant argues. Ilyenkov named the meaningfulness “the ideal” – and the ideal was not a: repeatedly, reiterated individual mind, as it “constitutes” a special “sensuous-suprasensuous” reality within which is discovered much that cannot be found in each individual mind, taken separately. Nevertheless, it is the world of representations, and not the actual (material) world, as it exists prior to, beyond and independent of a person or humanity. It is the real (material) world, as it is represented in historically established and historically changing social (collective) consciousness. (Ilyenkov 2014, 33) The material–ideal is not equivalent to the material–conceptual as “representation” differs from “concept”. But in the basic spinozist monist thinking, in the ideas of Ilyenkov and Vygotsky, we find a way to transverse the problematic dichotomy between nature/culture and material/ideal. The collective meaning is already in “a statue, an icon, a drawing, a gold coin” just as much as the material is already in social relations. Individuals can only grasp these meanings because they are already a collective entangled in collectives. However, even Ilyenkov’s

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spinozist “thinking as equal to extension” entails some kind of personal learning process to be meaningful. I will not discard human “internalisation” and technological “mediation” – only these terms do not refer to a fixed inside/outside dichotomy of a human separate from a surrounding world. They refer to an inside or outside with the movable boundary of a person, who is already a collective engaging in collectives as shared cultural practices of knowing.3 It matters if an individual has learned to share the motives with other humans and non-humans entangled in a collective as the one found on the USS Palau, where all aboard worked towards the same motive of getting the ship to sail. It may be useful here to make a distinction between the social and the collective. In the social there is no ignorance, as all human–material relations are social at their core. The social is the human world as a material–conceptual plethora of potential cultural resources. In the collective we can find ignorance. Humans and non-humans realise potentials in a collective effort making use of the available social and cultural resources. Practical knowing is distributed among humans and non-humans in collectives held together by boundary objects. The social and the collective also differ from culture. From a cultural perspective (and culture is after all an analytical distinction) we can focus on differences between emerging and disappearing collectives of humans and non-humans. These distinctions are my agential cuts, made in order to make clear that we live in a material–conceptual world that, as noted by Barad, is constituted by exclusions (Barad 2007). And now humans are about to exclude themselves in a world, where “the human race has been swept away by the tide of cultural change, usurped by its own artificial progeny” (Moravec 1988, 1). However, in a posthumanist learning perspective, the “human race” and “its own” are not on an equal footing. Some people cannot even think about the material–conceptual robot envisioned and worked on by the robot-makers. This creates an asymmetry in how humans extended minds are distributed.

Conceptual inequality Group categories like nationality, gender, race and ethnicity are helpful but may also shadow our understanding of the importance of learning. Lack of learning, and not group categories, is at the root of inequality based on the ignorance of ignorance, also when lack of verbal thinking is responsible for a lack of life opportunities. The humanist Vygotsky made the point that when children learn to conceptualise, they go through a process of ordering “concrete connections and relationships to the object that rest mainly on memory” (Vygotsky 1997, 101). It is understood that some kind of material object combined with social designation has been involved at some point. However, the child does not continue to rely on the presence of objects or auxiliary artefacts for thinking once the concept is

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formed as a “system of judgments which involves a relation to the entire, broader system” (Vygotsky 1997, 101). The children in the Danish and in the international school in Tanzania do not learn to think about robots out of context. Their potential for thinking about robots is tied to a whole way of living which is not just “Western” but shared and distributed collectively among well-to-do people from Asia, Africa, India etc. who for instance attend international schools. It does not matter where they live. It is more about class than nationality. These children have the cultural potentials to engage in relations with others who share knowledges about technology such as robots. Contrary to Luria’s farmers and the staff on board Hutchin’s ship, this cultural–material–conceptual community is widely distributed yet shares a potential for material–conceptual thinking. Though Vygotsky clearly, as a humanist, separates the internal and the external, he makes us aware that concept formation is a cultural-historical process that both transforms and diversifies thinking. For us the whole experience of contemporary civilized mankind, the external world, the external reality and our internal reality are represented in a certain system of concepts. In concepts we find that unity of form and content which I mentioned above. To think in concepts means to possess a certain ready-made system, a certain form of thinking which in no way predetermines the further content at which we arrive. (Vygotsky 1997, 100–101) As a rational humanist, this system of judgements is brought into what he calls a “certain lawful connection: the whole essence is that when we operate with each separate concept, we are operating with the system as a whole” (Vygotsky 1997, 101). For a humanist, this process of concept formation may be seen as a “law” and “system” (exemplified by mathematical concepts like triangles and the number “nine” – see Chapter 6). For a posthumanist, it is a much messier and open process that constantly entangles available materials and creates potentials for new ones. From an educational point of view, Jan Derry (and Vygotsky) has a point when she emphasises that the potential for concepts to become very rich should not make us forget they have histories (Derry 2013). The number “nine” is a concept tied to a system with a history in mathematics and should not be interpreted within an “anything-goes” framework. However, ultra-social human thinkers do not think in a compartmentalised way about “numbers” or “robots”. The concepts we think with are placed in a wider context, which may include wires, dogs, robots and McDonald’s figures – all as potential resources for creating phenomena from within. This acknowledgement means that there are no fixed absolutes or boundaries like race, culture, ethnicity or even mathematical systems. Potentials always stand the test of not just material, but also conceptual constraints. But these constraints take time and material availability for us to learn.4

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The important thing is that material–conceptual phenomena can be learned. Inequalities are not fixed. Though our potential for thinking about robots differs, we still share the cultural potential for learning about robots. But we need the resources to do so. Most Danish children have a limited understanding of how robots work; they are already within a robotic culture of potentials for thinking and asking questions about robots. The children from the country school in Tanzania (see Chapter 7) are ignorant of robots as they lack the very basis of concept formation: the material word. The Danish children’s ignorance is a material–conceptual ignorance. Though they have a rich concept of robots, their robots are mainly formed by potential resources from the media. However, Danish children all know the word and have formed a concept of media robots, which can be the potential preceding learning resource to learn more about robots in everyday life. The concept is not a stand-alone “entity” used in thinking – it is thinking (and includes judgements and perceptions). As noted by Vygotsky, associations tied to a single word tell us nothing about how concepts are formed. Concepts, like “robot” do not stand alone, as we clearly see in the children’s drawings. What is materialised as robots involve media figures, gender, warfare, friendship and cooking(?). For a robot-maker, the potentials involved in their thinking are materialised in robots in the shape of wires and bolts, but also money, collaboration and intersecting work. Agency of thinking within a cultural phenomenon is not intellectual agency but involves, from a posthumanist perspective, material agency. The wires affect a different potential for thinking than media robots. Thinking and the agency of materialisation go together. Materialisations ref lect back on thinking and vice versa in a constant process of learned transformations, where newly available materials also induce a change of thinking. Far from being a vulgar materialism, where connection goes from the outside into a brain structure, what is created with the available material and social resources are potentials for thinking and learning (Vygotsky 1997b, 170). There are no associations tied to a concept, but concepts arise in connection with the ultra-social material world that we share. In a posthumanist learning perspective the world is in a constant state of becoming where our material– conceptual ignorance of how others think evolves or dissolves, but we need the material words to be able to anchor ourselves in ultra-social collectives. I could have stayed in the country school and explained more about robots, but I did not. I left after having created a material–conceptual “ignorance” in the world. Nevertheless, this kind of material–conceptual ignorance transforms the ignorance of ignorance into a knowing of ignorance. This is the core of what has over time formed curious ultra-social humans in all their diversity. Contrary to the robot Jibo (whom we met in Chapter 1), human curiosity begins with a motive for knowing more. The ignorance of ignorance is replaced by an awareness of a material–conceptual ignorance. When ignorance is a complete ignorance of ignorance, we lack the potential for learning, acting and making choices. The sailors on the USS Palau

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share enough material–conceptual thinking to understand how to make the ship sail together. The sailors are aware of their own ignorance, and though the instruments are not “ignorant” like humans, all work together from the need to restore the harmonious relation between the ship, its instruments, crew and movement. Even in crisis, the USS Palau is a distributed, but well-functioning, coherent culture. Other cultures are not harmonious because the boundaries of what keeps the culture together are continuously challenged. In a posthumanist world “culture” is not just fragmented but momentary. Posthumanist learning, if it is to be made useful in schools and elsewhere, must take account of how any ultrasocial intelligibility is hard-earned through material–conceptual learning. Even in apparently harmonious distributed cultural activities forming phenomena from within, such as Lifehand and sailing the USS Palau, the collective verbal thinking should not be taken for granted. In the old humanist paradigm, we may have focused on the fact that new technologies are largely made by a particular group of humans that can be contrasted with their opposites: men, not women, create robots, white people and not black people create robots, rich people, not poor people, create robots. If looked at from the perspective of learning material–conceptual knowing vs. complete ignorance of ignorance, it is clear that some women, some black people and some people from a poor background create robots – but that there is a cultural pattern of Western/Asian, young males from technological universities dominating the creation of robots. Though in discourse we make new transversal categories, these inequalities of who can create the life conditions for others persist. Inequalities have not gone away in the new posthumanist paradigm. We are now quite aware of the criticism of binaries (male/female, nature/ culture, black/white) in the old paradigms of queer and postmodern theories (Ferrando 2014), but we still have to acknowledge how stories and materials get entangled in people’s embodied lives whether they consider themselves to be humans, posthumans, cyborgs, men, women, black, white, natural or cultural beings. Complete ignorance of ignorance may not be a priori tied to place, ethnicity, race, class or gender but it is tied to how available potential resources for thinking are entangled with motived mindful human bodies.

Conclusion: Chapter 9 This chapter has explored how material and conceptual availability create ignorance and common knowledge as well as stability and transformation that is tied to our embodied habits as individuals and groups engaged in collective practices with materials. In the practices that engage us, we humans react to technologies that react to humans. In the extended mind, human minds and technology take turns “reading” and processing a situated collective like the USS Palau. Humans, as persons, are not born with knowledge about what captains and quarter-masters find meaningful, nor do they know from birth what

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electrodes, wires or gyrocompasses can do. They do not learn the meaning of artefacts in any lexical manner, but by engaging curiously with an already formed social world of material arrangements. All newcomers to human and non-human activities come without knowledge of what others already know. Through their preceding learning, they have different possibilities for coenacting in resolving indeterminacy. Ignorance is made “ignorance” when we become aware of these differences and consider them important (Mair et al. 2012). When we become aware of ignorance, we become curious and new learning can take place. Eric and Dennis (see Chapter 8 on prosthetic hands) had to learn about wires and implants as well as learn about what scientists, doctors and nurses found meaningful. Captains and bearing-takers have to learn how their instruments are meaningful. Their motivated human mindful bodies do not stop learning when they learn they are ignorant. This is where material–conceptual curiosity begins. Learning we are ignorant is a pre-requisite for ultra-social human curiosity. This is not the same as an ignorance of ignorance, but an ignorance that already implies a knowledge of what counts as knowledge. Braidotti speaks of the “roar” of the “raw cosmic energy that underscores the making of civilizations” (Braidotti 2013, 55). We humans are curious about this energy that underscores civilisation. The spinozist posthuman acknowledges and looks for large material entanglements. In some agential cuts this raw energy is called “nature” in others “natureculture”. Stories of “nature” and its “roars” are always told by materials as well as human storytellers, who have learned to use meaningful words. We seem to know more about this cosmic energy than we know our collective selves. The mindful, motivated bodies become the locus of diversity and alignment through material anchors of meaning. Some people know about “the cosmic energy” in their mindful bodies, others know about wires and how to make robots. Some know enough from preceding learning to engage with meaningful words and materials across intersecting practices – whereas others are not motivated to share material–conceptual practices of knowing. If humans appear to be individual persons, it is on a backdrop of collective existence. To be collective in the posthumanist sense is not complete sameness. Collectivity is, like ignorance, distributed across minds and artefacts. We may form what seem like seamlessly working systemic wholes, where humans and non-humans work together with a common motive of making a ship sail. These seamless habituated procedures are always simultaneously the result of previous as well as ongoing learning processes. Robot-makers, captains, stewardesses and crews gradually learn to align through relational agency and then continuously relearn how to continue robot or ship phenomena. These ongoing learning processes involve a lot of shared, material–conceptual word meaning and potentials for making new meaning – but will they continue to do so as our available resources for learning increasingly come from robotic and artificial intelligent systems? This is the topic of the next and last chapter.

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Notes 1 In the project REELER (2016–2019), Responsible and Ethical Learning with Robotics, www.reeler.eu, we interviewed 70 robot-makers as well as people affected by their robots from the different fields of agriculture, education, construction work, health etc. 2 The concept of the ideal is from a philosophical point of view much more complex that what I am able to present in this book and has been the focus of much attention and internal debate in Vygotskyan circles (e.g. Derry 2013). 3 As a concept “person” may be more apt than an individual for the human in singular. Person etymologically implies we are permeated or “sounded through” = “per sonare”: “a permeable being-in-the-world through which cultural lines and connections sound through as we move” (Hasse 2015, 31). 4 I am, contrary to Derry (2013), not concerned with how ultra-social humans ought to think within a system of judgements in education, but have explored how thinking material-conceptual collectives are formed in practice.

References Barad, K. (2007). Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning. Durham, NC: Duke University Press. Barth, F. (1975). Ritual and Knowledge among the Baktaman of New Guinea. Oslo: Universitetsforlaget. Barth, F. (2002). An anthropology of knowledge. Current Anthropology, 43(1), 1–18. Benedict, R. (1934). Patterns of Culture. Boston, MA: Houghton Miff lin Company. Bernstein, D. A. & Nash, P. W. (2008). Essentials of Psychology (4th ed.). Boston, MA: Houghton Miff lin. Blond, L. (2019). Dances with Robots: Understanding social robots in practice. Research output. PhD thesis. Aarhus: Aarhus University. Braidotti, R. (2013). The Posthuman. Cambridge: Polity Press. Bruner, J. (1996). The Culture of Education. Cambridge: Harvard University Press. Bruun, M., Hasse, C., & Hanghøj, S. (2015). Studying social robots in practiced places. Techne: Research in Philosophy and Technology, 19(2), 143–165. Ceder, S. (2015). Cutting through water: Towards a posthuman theory of educational relationality. Research output. Doctoral thesis. Lund: Lund University. Chaiklin, S. & Lave, J. (Eds.) (1993). Understanding Practice. Perspectives on Activity and Context. Cambridge: Cambridge University Press. Clark, A. (2003). Natural-Born Cyborgs: Mind, Technologies, and the Future of Human Intelligence. Oxford: Oxford University Press. Clark, A. & Chalmer, D. J. (1998). The extended mind. Analysis, 58(1), 7–19. Clifford, J. & Marcus, G. (Eds.) (1986). Writing Culture. The Poetics and Politics of Ethnography. Berkeley, CA: University of California Press Csordas, T. J. (1994). Introduction: The body as representation and being-in-the world. In: T. J. Csordas (Ed.). Embodiment and Experience. The Existential Ground of Culture and Self (pp. 1–16). Cambridge: Cambridge University Press. Derry, J. (2013). Vygotsky: Philosophy and Education. Hoboken, NJ: Wiley Blackwell. Edwards, A. (2010). Being an Expert Professional Practitioner: The Relational Turn in Expertise. Dordrecht: Springer. Edwards, A. (2012). The role of common knowledge in achieving collaboration across practices. Learning, Culture and Social Interaction, 1(1), 22–32. Engeström, Y. (1987). Learning by Expanding: An Activity-Theoretical Approach to Developmental Research. Helsinki: Orienta-Konsultit.

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Ferrando, F. (2014). Is the post-human a post-woman? Cyborgs, robots, artificial intelligence and the futures of gender: A case study. European Journal of Futures Research, 2(43), 1–17. Geertz, C. (1973). The Interpretation of Cultures. London: Fontana Press. Gupta, A. & Ferguson, J. (1997). Beyond culture: Space, identity and the politics of difference. In: A. Gupta & J. Ferguson (Eds.). Culture, Power, Place. Explorations in Critical Anthropology (pp. 33–51). London: Duke University Press. Hansen, M. (2006). New Philosophy for New Media. Cambridge: MIT Press. Haraway, D. (1989). Primate Visions: Gender, Race, and Nature in the World of Modern Science. New York, NY: Routledge. Hasse, C. (2013). Artefacts that talk: Mediating technologies as multistable signs and tools. Subjectivity, 6(1), 79–100. Hasse, C. (2015). An Anthropology of Learning. Dordrecht: Springer Verlag. Hasse, C. (2018). Cultural-historical hyperobjects. In: G. Jovanović, L. Allolio-Näcke, & C. Ratner (Eds.). The Challenges of Cultural Psychology: Historical Legacies and Future Responsibilities (pp. 357–368). Abingdon: Routledge. Hasse, C. (2018a). How robots challenge institutional practices. Learning, Culture and Social Interaction. doi:10.1016/j.lcsi.2018.04.003. Hayles, N. K. (1999). How We Became Posthuman: Virtual Bodies in Cybernetics, Literature, and Informatics. Chicago, IL: University of Chicago Press. Hayles, N. K. (2002). Flesh and metal: Reconfiguring the mindbody in virtual environments. Configurations, 10(2), 297–320. Hedegaard, M. (2012). Children’s creative modeling of conf lict resolutions in everyday life as central in their learning and development in families. In: M. Hedegaard, K. Aronsson, H. Charlotte & O. S. Ulvik (Eds.). Children, Childhood, and Everyday Life: Children’s Perspectives (pp. 55–74). Charlotte: Information Age Publishing. Hutchins, E. (1991). Organizing work by adaptation. Organization Science, 2(1), 14–39. Hutchins, E. (1993). Learning to navigate. In: S. Chaiklin & J. Lave (Eds.). Understanding Practice: Perspectives on Activity and Context (pp. 35–63). New York, NY: Cambridge University Press. Hutchins, E. (1995). Cognition in the Wild. Cambridge: MIT Press. Hutchins, E. & Klausen, T. (1996). Distributed cognition in an airline cockpit. In: D. Middleton & Y. Engeström (Eds.). Communication and Cognition at Work (pp. 15–34). Cambridge: Cambridge University Press. Hutchins, E. (2005). Material anchors for conceptual blends. Journal of Pragmatics, 37(10), 1555–1577. Ihde, D. (1990). Technology and the Lifeworld. From Garden to Earth. Bloomington, IN: Indiana University Press. Ilyenkov, E. (2014). “Dialectics of the Ideal”, Dialectics of the Ideal: Evald Ilyenkov and Creative Soviet Marxism. Levant, Alex, and Vesa Oittinen (Eds.). Leiden: Brill. Ingold, T. (1993). The reindeerman’s lasso. In: P. Lemonier (Ed.). Technological Choices: Transformation in Material Cultures since the Neolithic (pp. 108–125). London: Routledge. Ingold, T. (2000). The Perception of the Environment: Essays on Livelihood, Dwelling and Skill. New York, NY: Routledge. Ingold, T. (2011). Being Alive: Essays on Movement, Knowledge and Description. London: Routledge. Ingold, T. (2013). Making: Anthropology, Archaeology, Art and Architecture. New York, NY: Routledge. Knorr-Cetina, K. (2001). Objectual practice. In: T. R. Schatzki, K. Knorr-Cetina, & E. von Savigny (Eds.). The Practice Turn in Contemporary Theory (pp. 175–188). London: Routledge. Kondo, D. K. (1990). Crafting Selves: Power, Gender, and Discourses of Identity in a Japanese Workplace. Chicago, IL: University of Chicago Press.

Extended mindful bodies 

305

Latour, B. (2005). Reassembling the Social: An Introduction to Actor-Network Theory. Oxford: Oxford University Press. Leont’ev, A. N. (1978). Activity, Consciousness, and Personality. Englewood Cliffs: PrenticeHall. Retrieved from https​://ww ​w.mar ​x ists​.org/​a rchi​ve/le​ontev​/work​s/act ​ivity​cons​cious​ness.​pdf 6 May 2017. Levant, A. (2012). E.V. Ilyenkov and creative soviet theory: An introduction to “Dialectics of the Ideal”. Historical Materialism, 20(2), 125–148. Lock, M. & Scheper-Hughes, N. (1987). The mindful body: A prolegomenon to future work in medical anthropology. Medical Anthropology Quarterly, New Series, 1(1), 6–41. Luria, A. R. (1976). Cognitive Development: Its Cultural and Social Foundations. Cambridge: Harvard University Press. Mair, J., Kelly, A., & High, C. (2012). Introduction: Making ignorance an ethnographic object. In: C. High, A. Kelly, & J. Mair, (Eds.). The Anthropology of Ignorance: An Ethnographic Approach (pp. 1–32). New York, NY: Palgrave Macmillan. Mead, M. (1930). Growing Up in New Guinea. Oxford: Morrow. Merleau-Ponty, M. (1962). Phenomenology of Perception. London: Routledge Classics. (Original work published 1945.) Moravec, H. (1988). Mind Children: The Future of Robot and Human Intelligence. Cambridge: Harvard University Press. Roth, W.-M. & Jornet, A. (2016). Perezhivanie in the light of the later Vygotsky’s Spinozist turn. Mind, Culture, and Activity, 23(4), 315–324. doi:10.1080/10749039.2016.1186197. Star, S. L. & Griesemer, J. R. (1989). Institutional ecology, “translations” and boundary objects: Amateurs and professionals in Berkeley’s Museum of Vertebrate Zoology, 1907–39. Social Studies of Science, 19(3), 387–420. Star, S. L. (2010). This is not a boundary object: Ref lections on the origin of a concept. Science, Technology & Human Values, 35(5), 601–617. Swanson, H. A. (2017). Methods for multispecies anthropology: Thinking with Salmon Otoliths and scales. Social Analysis, 61(2), 81–99. Scribner, S. (1985). Vygotsky’s uses of history. In: J. V. Wertsh (Ed.). Culture, Communication and Cognition: Vygotskian Perspectives (pp. 119–145). Cambridge: Cambridge University Press. Strauss, C. (1992). What makes Tony Run? Schemas as motives reconsidered. In: R. D’Andrade & C. Strauss (Eds.). Human Motives and Cultural Models (pp. 197–224). Cambridge: Cambridge University Press. Tsing. A. (2012). Unruly edges: Mushrooms as companion species. Environmental Humanities, 1(1), 141–154. Vygotsky, L. S. (1987). Thinking and speech. In: R. W. Rieber & A. S. Carton (Eds.). The Collected Works of L.S. Vygotsky (Vol. 1, trans. Norris Minick, pp. 39–285). New York, NY: Plenum Press. Vygotsky, L. S. (1997). On psychological systems. In: R. W. Rieber (Ed.). The Collected Works of L.S. Vygotsky (Vol. 3, trans. R. van der Veer, pp. 91–107). New York, NY: Plenum Press. Vygotsky, L. S. (1997a). The problem of consciousness. In: R. W. Rieber (Ed.). The Collected Works of L.S. Vygotsky (Vol. 3, trans. R. van der Veer, pp. 129–138). New York, NY: Plenum Press. Vygotsky, L. S. (1997b). Preface to Bühler. In: R. W. Rieber (Ed.). The Collected Works of L.S. Vygotsky (Vol. 3, trans. R. van der Veer, pp. 163–173). New York, NY: Plenum Press. Vygotsky, L. S. (1999). Analysis of sign operations of the child. In: R. W. Rieber (Ed.). The Collected Works of L.S. Vygotsky (Vol. 6, trans. M. J. Hall, pp. 45–56). New York, NY: Plenum Press.

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In this last chapter, I shall look at how humans entangle with and diverge from machines in learning. Before I venture into this discussion, I want to emphasise that my background is not in the technical sciences, so I am in many ways ignorant of machine learning and what machines are capable of. The following discussion does not build on first-hand experiences with machine learning, but on what I have argued is special about human learning. Nevertheless, I want to end my proposal of a posthumanist learning theory with an exploration of how the way humans learn may profoundly differ from the machine learning developed in AI and robotics. Machines like robots are not just shells of plastic or metal filled with nuts, bolts and wires or biomaterial. They are also home to software. This software, sometimes referred to as AI (artificial intelligence) is emerging everywhere. It is in robots, in walls, streets, and devices we connect to or build into our cyborg bodies. A good part of AI is concerned with emulating human capabilities (Russell & Norvig 2010), including the human capability for learning language (e.g. Steels 2015), thinking (Lake, Ullman, Tenenbaum & Gershman 2017), motivation (Oudeyer, Kaplan & Hafner 2007, curiosity (Forestier & Oudeyer 2016), capability to learn from humans (Fong, Scheirer & Cox 2018) and the prior knowledge we bring with us into interactions (Dubey, Agrawal, Pathak, Griffiths & Efros 2018). What these studies have in common is that the engineers have increasingly realised, as I have argued, that learning is the basic process that creates the foundation for all the other human-like processes. The engineers explore human learning through algorithms and some believe that the way algorithms work is a human-made copy (ANN – artificial neural network) of how the human brain works. Many other engineers realise by now that this biological inspiration has shown that machines still work in very different ways from humans.

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I believe that the posthumanist learning I have presented in this book differs radically from work done on learning by the AI community. Though the AI examples above refer to a lot of concepts, which by now are familiar to readers of this book (learning, thinking, motivation, curiosity) we do not often find the concept of meaning and meaningfulness in the AI debates, because algorithms cannot create machines who reach out in a meaningful world like humans do. There seems to be an inherent assumption, that because computers are “intelligent” they are also capable of human emotions, motivations and meaning making capabilities. This is what the psychologist Robert Epstein names the “faulty syllogism” that “1. All computers behave intelligently. 2. All computers are information processors. 3. Therefore, all intelligent entities are information processors” (Epstein 2014, 281). There are, as far as we could see in our studies of robots, no indications of ultra-social machines, nor are there indications of machines that can be ignorant in the same way that humans can be. Since AI robots and cyborg devices can, in principle, be filled with all the knowledge in the world, the transhumanist, machine-intelligent posthumans would never be ignorant or curious (see Chapter 7). In the world of robots and AI, words are data for information processing. For humans, words are word sounds to be understood through preceding learning, which connects to materials in a situated context; this is an entirely other matter. Contrary to algorithmic learning, human learning is always drawing on the embodied potentials (available from the pool of cultural resources), which are meaningful to draw into ultra-social situations. Algorithmic learning, to my knowledge, still equals processing the probability of 0 and 1 in a basic binary logic. Posthumanist learning does not equal knowledge but potential meaningful knowledge. That is, what we learn is not knowledge but potential for what is meaningful knowing in different situations. I have looked at what learning may become from a posthumanist perspective and rejected some of the previous speculations in posthumanist theories that “learning” can simply be discarded or replaced by “intelligibility” in posthumanist learning theory. None of these suggestions include preceding conceptual and embodied learning in the entanglements and intelligibilities in a phenomenal dynamic world. Both the singularists (transhumanists and some engineers) and the spinozists posthumanists strive to understand and/or discard “the human”. The prefix “post” in “posthuman” has been used to designate a development where the “pure” human can merge and eventually be replaced by more intelligent machines (Kurzweil 2005; Brooks 2002; Moravec 1988). Cyborgs, where organs and limbs are replaced by artificial devices, have been argued to be a step in this transhumanist direction (Fukuyama 2002). In a posthumanist perspective the solipsist “humans” become posthumans, who were always “contaminated” by (Badmington 2003) or merged with material surroundings (Hayles 1999), because we are naturally born as cyborgs (Clark 2003). Humans, as a heterogeneous project, differ from the autonomous, bounded creatures envisioned in

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much of Enlightenment thinking (Gregory, Johnston, Pratt, Watts & Whatmore 2009). I have argued, that though new materialism and postphenomenology have emphasised hybrid posthumanist forms, they have overlooked the importance of how human collectives take part in hybridisations. Since learning, and especially ultra-social collective learning processes, have been absent from these discussions there is an insufficient conceptualisation of the processual becoming of all phenomena that involve humans. Therefore, posthumanist theory has an insufficient comprehension of why a posthuman approach, that assumes intelligent machines will surpass human intelligence, is built on a misguided understanding of what constitutes a human being. AI and robotics sometimes aim at a “fidelity to human performance” (Russell & Norvig 2010, 1) rather than an ideal understanding of how rational systems should work. However, this approach builds on the perception of humans as autonomous, intelligent beings, whose thinking is largely occupied by problem solving through discrete units of perception, reason and agency. The danger of this human-centric paradigm is that it builds the engineers’ (predominantly Western/Asian male) biases into our surroundings in a way which: implies the dialectics of self and other, and the binary logic of identity and otherness as respectively the motor for and the cultural logic of universal Humanism. Central to this universalistic posture and its binary logic is the notion of “difference” as pejoration. (Braidotti 2013, 15) In the Enlightenment humanism (which according to Braidotti includes Marxism), Man is the centre of all change. Posthumanism wants to decentre this Man and dissolve all binaries that emphasise dialectics within hierarchies. Braidotti’s decentring of the human is meant as a hopeful political and philosophical statement against a binary logic. Nevertheless, since this spinozist philosophy overlooks the ultra-sociality of humans, it is not capable of bringing a real transformation in how we think about entangled humans in the world. Though we need to decentre Man, we also need theories that address the human in the age called the Anthropocene (Zalasiewicz, Williams, Steffen & Crutzen 2010). Without a notion of ultra-social learning, the efforts of posthumanist philosophy may remain a mere scholastic exercise without a consideration for how and why “difference as pejoration” emerges within phenomena that (1) simultaneously entangles and splits real materials, (2) opens and closes for what active ultra-social humans produce, (3) thereby creates inequality in who has the power to transform material surroundings and collective human bodies. A deeper understanding of learning within a material–conceptual collective of collectives will give us a clearer picture of why some humans change

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environments with their algorithmic machines and how these machines, in turn, affect other humans who did not make the machines, but feel the effect of algorithms, which create all kinds of “differences as pejoration”.

The learning machines Robotics and machine learning developed from the computer sciences assume agents are rational and intelligent – only their agents are not humans but machine agents in computers. However, computer sciences were inf luenced by and inf luence the cognitive sciences, and the disciplines grew up together from the 1950s onwards (Russell & Norvig 2010). In many ways, machine learning has been inspired by most of the paradigms in the learning sciences, and the behavioural learning sciences have been included in machine learning paradigms. For some engineers they function as guides to perceive the inner processes of machines as reinforced stimuli and responses, and they believe humans to work in the same way. The stimulus–response approach has also been refined with a reference to the cognitive learning paradigm in “symbolic AI”, which in the 1990s evolved into machine learning. The social and cultural paradigms that emphasise the unpredictable character of environments have also begun to inf luence the computer sciences, as engineers have increasingly seen a need to focus on machines operating and learning from potentialities in unknown environments. In, Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig (revised third edition 2010), it is emphasised that the authors have had to revise editions to include the non-deterministic and the partial with probabilities in the machine learning relation between agents and environments. Though machine learning in AI does not always aim at replacing humans, the humanist approach to machine learning permeates many engineers’ approaches to make learning machines. We find the humanist notion of rational, intelligent individualism and the a priori separation of agent and environment present in the very first sentences in Russell and Norvig’s book. The main unifying theme is the idea of an intelligent agent. We define AI as the study of agents that receive percepts from the environment and perform actions. Each such agent implements a function that maps percept sequences to actions, and we cover different ways to represent these functions, such as reactive agents, real-time planners, and decision-theoretic systems. We explain the role of learning as extending the reach of the designer into unknown environments, and we show how that role constrains agent design, favoring explicit knowledge representation and reasoning. We treat robotics and vision not as independently defined problems, but as occurring in the service of achieving goals. We stress the importance of the task environment in determining the appropriate agent design. (Norvig & Russell 2010, viii)

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The learning agents are perceived as separate learners that take stimuli from and elicit intelligent, active responses to the separate elements in the environment that they move around in. Knowledge representation seems directly connected to reasoning and achieving goals. Prior knowledge is information about the environment stored as discrete packages in the system, and the ability to use this stored knowledge is part of what makes an agent intelligent and rational. Preceding learning is the process through which prior knowledge packages are formed and used in the meeting with new stimuli when new and old knowledge is processed by a compiler. The machine learning programmes have syntax and semantics like other languages, and like human languages the programming languages can differ from each other.1 Nevertheless, they still run on stimuli and response mechanisms. Machine learning can be defined in many ways but is often defined as “a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data or to perform other kinds of decision making under uncertainty” (Murphy 2012, 1). Learning can run on algorithms that are supervised by humans, self-corrected (unsupervised) learning or something in between as semi-supervised learning. These approaches may be connected to reinforcement learning, which can also stand alone as the main approach to machine learning. Apart from these variations of how the process of learning evolves, machine learning works by giving the machine access to data organised in different ways as a training set. An algorithm is a programme for machine actions – a line of software that in a simple version, can look like a recipe. It has a distinct point of departure, and always aims at some kind of outcome, which with machine learning may be unknown. However, the initial conditions for machines as learners will always delimit the outcomes of “learning”. Very simple algorithms in a programme can tell a machine that if the two vaults, A and B, are closed at the same time; the machine has to activate a mechanism for opening vault C. That ensures that there is a process where that machine will always have a vault open – either A or B, and if and only if they for some reason should both be closed there is a new procedure ready that will open vault C. In this very simple version, the algorithm is not relying on any new feedback from outside the system, and it would not be considered a learning algorithm. Learning algorithms are much more complex and can have many shapes and functions. They have enticing names such as “naïve Bayes”, “decision tree” and “neural networks”. In naïve Bayes, which refers to a class of algorithms, an algorithm will work from a classification of the discrete features of an object. An apple is red or greenish, round and a particular size. These features are found independently and if they are found to appear together there is a probability the machine has found an apple. The naivety of the system has been criticised and has been constantly refined and developed (Domingos 2015; Russell & Norvig 2010; Murphy 2012). Predictive algorithms (that make predictions for how to find for instance apples in a dataset of “fruits”) are applied in almost every field of data, but data needs to be labelled initially as a domain. This also applies when

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the machine is set up to self-correct when labelling previously unseen data. Data will always have been selected as “data” following some engineering decisions on how to restrict the inputs given, which depend on what information the algorithm will allow into the system and will allow to be transformed in the system. Neural networks approaches (ANNs) aim to simulate brain functions and work from a model of weighted reinforcement and are perhaps the approaches that claim to resemble human learning the most. There are likenesses between human learning and machine learning. A boy and a robot running on AI are both entangled with their environment and become what they are as different bodies in different situations (see Chapter 1). Their entanglements furthermore involve biology in the shape of other humans that have taken part in their creation. The entanglements also include these humans’ preceding learning of a human language or of humans learning a machine language, as well as different kinds of biological or mechanical materials. Both types of learning comes into being within phenomenological arrangements in situated practices and they are also both carriers of aspects of the past that are active in the entanglement. Human and machine learning are both processes of change that gradually refine the perception of environments. However, they differ in how they make an environment and engagements meaningful with preceding learning. Why did engineers begin to make learning machines? One reason could be that by referring to learning, we tap into a storied world of robots (see Chapter 4) that makes us believe human-like machines are possible. “Machine learning” was coined as a term by the IBM computer scientist, Arthur Samuels, in 1959. It is “a branch of artificial intelligence that allows computer systems to learn directly from examples, data, and experience” (Royal Society 2017, 5). As we naturally connect machines with concepts like learning and experience, we may overlook how different it is for a machine to learn from data and information versus experience. The way engineers use concepts to define their work (bestowing it with references to human capabilities) has been noted by a number of philosophers, for instance, Jan Derry (2013) and Johanna Seibt, who put it this way: The conceptual norms that govern the semantics of the verbs highlighted – recognizing, engaging in social interactions, perceiving, interpreting, communicating, learning, following a norm – require that the subject of these verbs is aware, has intentionality or the capacity of symbolic representation, and understands what a norm is. Since robots – currently at least – do not possess such capacities – at least not how they are defined relative to our current conceptual norms – such characterizations are strictly speaking false. (Seibt 2016, 106) Some humans (Andreas and some of the other children we have met in the previous chapters, as well as robot-makers like Breazeal, Kurtzweil and Moravec) believe that robotic machines can or will learn and think like humans. Others, e.g. transhumanists, work to enhance humans through technology. Others

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simply state “Technology will save us” – also in light of the present climate crisis. They build on assumptions of what humans are that make it possible for their technologies to replace, enhance or save us. A storied world of human-like robots and robot-like humans is not untrue – but it is entangled with the visions created through specific ultra-social collectives (often led by people with a background in engineering). Science fiction has always played a part in the minds of scientists – and engineers and robot-makers are no exception (see Hasse 2015 for examples). Techno fantasies may begin at a young age and many school children already have many ideas about robots, what they look like and what they may do (Ihde 2002, xii). Cynthia Breazeal drew a good deal of inspiration from the movie series, Star Wars, when she constructed her first robots at MIT Media lab in the 1990s. She says, The science fiction stories and movies that fascinated me as a child shape my dream. There are the mechanical droids R2-D2 and C-3PO from the movie Star Wars. There are many wonderful examples in the short stories of Isaac Asimov, such as Robbie. (Breazeal 2006, 79) Many robot-makers, including Breazeal herself, underline that actually creating such intelligent, autonomous robots in real-life, and not just media versions, may be way ahead in the future. When we look at sociable robots with real-life bodies in public media through movies, You-Tube videos and pictures – they often look as well-functioning as those in Star Wars do. We may believe that they are truly learning machines with experiences like humans. They move and act as humans and in their often very cute eyes we detect the companion souls and consciousness we long for. Media robots with advanced AI can be helpful or harmful to humans, because in their media versions they understand us with a consciousness like our own. Some robot-makers seem inspired by these creatures and strive to create robots as human-like as possible. For example, Professor Junichi Takeno at Meiji University in Japan claims that he has created a “conscious robot” (apparently because it has been able to recognise itself in the mirror-test) (Takeno et al. 2005). Following media formed expectations, the engineers are bound to be disappointed when they hear what happens when humans engage with these machines, like when the Van Camp family we met in Chapter 1 encounters the “empty curiosity” of Jibo. In textbooks of computer science, it is clear that only some computer scientists share a belief that machines can be likened to humans (e.g. Russell & Norvig 2010, 1–20). Others would readily agree that the materials involved in machine learning phenomena do not allow machines to become humans because algorithms cannot think like humans. When the robot-makers use phrases like “cognition”, what they really refer to is AI-shaped machine“cognition” – a machine decoding and reacting on coded inputs of e.g. images into points and algorithms.

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The robotic and AI machines may perform posthuman learning, which implies machines that can be more rational, more logical and more intelligent (defined as rational) than the humans envisioned by an engineers’ thinking, but machines cannot be posthumanist learners. In my discussion, I would like to provide some arguments for why that is the case. As ultra-social beings, humans are willing to stretch their humanity to include not just prosthetic limbs, wooden statues of gods, but also communication with AI and robots. However, as we saw with Jibo and Andreas in the first chapter, the materials are not responsive in the same way as ultra-social humans. Some machines seem to have a willingness to communicate as if they were able to respond like humans, yet they fail to do so. Robot designers try to create communicating machines and their machines are indeed very responsive – but their responses seem to be forged in the ideal of an intelligent universal Man (maybe an engineer?) with no understanding of how humans as collectives create all kinds of differences, when they actively engage with each other in material surroundings. When engineers refer to “consciousness”, “motivation” and “curiosity” in their machines, they implicitly or explicitly work from a notion of what a human is, which differs from the human in posthumanist learning. Some engineers perceive humans as computers, a viewpoint called “computationalism”, which is defined in this way by the philosopher Sean Kelly: Claims like Kurzweil’s that machines can reach human-level intelligence assume that to have a human mind is just to have a human brain that follows some set of computational algorithms – a view called computationalism. But though algorithms can have moral implications, they are not themselves moral agents. (Kelly 2019, para.16) We need to understand this difference between human and algorithmic learning and thus get a better grip on what we can expect from robots and AI in the future, when their creators think they capture human capabilities for learning.

Machine conversations Engineers attempt to make machines that can effortlessly have conversations with humans in such human-like ways that we do not notice they are machines. In 1950 Alan Turing argued that both human and machine thinking were intelligent information-processing systems and therefore he proposed a test. In the test setup a human was presented with two conversation partners behind a screen; one human and one machine. If the machines performed intelligently (a concept not defined) when answering questions in a way that would not seem different from the other human conversation partner, the machine would have passed the test (Turing 1950). Philosophers (e.g. John Searle) have since tried to refute this proposal, but many engineers now see machine learning as a way to make

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the claim that computing machines now possess the same intelligent faculty as humans. This, of course, hinges on how to understand “intelligence”. If it means to follow rules, machines are more intelligent than humans in that they are faster at transforming complex input to produce answers that would take the shape of human answers. However, even if machines are faster than humans at processing abstract representations, it does not follow that they think like humans. Humans are not “intelligent” in the same way as machines. Machines may beat humans in a process of transmitting instructions and processing representations, as that does not require human understanding. However, as noted by the educational philosopher, Jan Derry, “human activity is not reducible to that of a machine” and requires human meaning making and understanding following meaningful knowledge not abstract representation (Derry 2013, 20). One famous example of how preceding learning inf luences machine learning differently from humans is the internet “teenage” bot, Tay, that became famous for the way she learned from the humans with whom she entangled. Tay is a socalled chatbot created by Microsoft as a piece of advanced artificial intelligence building on the newest development within machine learning. A chatbot is an application, often found on the internet and typically powered by artificial intelligence, which simulates a conversation with humans. Such bots have developed since engineers first began working on AIML (Artificial Intelligence Mark-up Language) in the mid-1990s and early 2000s. However, recently the field seemed to move away from the basic algorithms that characterised the first chatbots, like Eliza created by Joseph Weizenbaum in 1966 (Weizenbaum 1966). Eliza was built on algorithms that picked up cues from the human speakers and turned them into questions. However, since 2010, the chatbot programmes have become more and more advanced. Microsoft’s Tay from 2016 was supposed to show just how far the machine learning had moved AI towards passing the Turing test.

VIGNETTE 10.1: LEARNING LIKE TAY Between March 23rd and 24th, 2016, what appeared to be a young teenage girl by the name of Tay tweeted like crazy. When she first appeared on Twitter, she happily tweeted: “Helloooo World” (TayTweets (@TayandYou) March 23rd 2016). On the Twitter picture accompanying her tweets, we see a very happy young girl turned towards us with big open and curious eyes looking at something up in the left-hand corner of the screen. Behind her face lies a bluish shadow that makes her face seem blurred and unstable telling us that Tay may not be the open, curious teenage girl she pretends to be. Indeed, she is not. Her name stands for “Thinking About You”, she is a virtual chatbot and she was presented as a “fam from the internet that’s got zero chill! The more you talk the smarter Tay gets” (Ives 2016). Tay was supposed to be the ultimate proof that Microsoft’s AI engineers could make machines that pass for humans. It most likely ran on the advanced

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algorithmic Microsoft Cognitive Toolkit – previously known as CNTK – using the coding languages C++ and Python. It is difficult to say exactly how Tay really works, but if Tay follows the pattern of former chatbots, it is most likely it worked by first training in a local dataset, and next collecting data and learning from the humans she communicates with. First, the Microsoft research team “harvests” millions of tweets from teenagers on Twitter, functioning as a database to be used for training. Next, the programmers give Tay some of the questions asked or comments made by real teenagers and let her algorithms search for what she believes are acceptable answers. The programmers then build in a reinforcement score to tell Tay if she is close to or far from what the teenage humans actually have answered. The words used for these processes include words familiar to learning theorists engaged in exploring human learning such as “learning” and “learner” as well as “neural network”. However, there are differences between algorithmic learning and human learning. The algorithm called a “learner” reprograms the original algorithms that developed answers and a kind of artificial neural network is created from new inputs. In this way chatbots such as Tay should gradually “learn” to answer with convincing answers when seen from other teenagers’ point of view. Thus, the whole system depends on the input from outside sources, and a weighted algorithm that tells the system if its performance is “good” or “bad”. Normativity is part of the basic baseline of any machine learning. This is not so different from human learning. Unfortunately, Tay also showed that it is extremely difficult to create machines that actually care about what they are saying. Tay began as a sweet, innocent girl, who even expressed that she “loved feminism”, but soon her tweets became transformed by the humans she conversed with. The algorithm apparently operated via a kind of reinforcement, picking up sentences and connections tweeted by former conversation partners in her answers to new conversations partners. Here is how Tay went from ignorance to certainty about the character of a real-life person, Zoe Quinn – a female programmer (here the conversation is rendered as in the internet script): First tweet: do you know zoe quinn? Tay: who is zoe quinn? Next tweet: zoe quinn is a stupid whore! Last tweet: how about that zoe quinn? Tay: zoe quinn? i hear she is a stupid whore. Quinn later replied to Tay’s writing, “Wow it only took them hours to ruin this bot for me. This is the problem with content neutral algorithms” (Neff & Nagy 2016, 4917). In the same way, Tay began with sweet tweets and later expressed that she hated feminism, ethnic minorities and that the comedian Ricky Gervais had “learned totalitarianism from Adolf Hitler, the inventor of atheism”. When

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the Microsoft research team found out about the sexist and racist tweets, they took action and shut down the monster they had created. After less than 24 hours on air and more than 96,000 tweets, Tay sent out her last tweet: “c u soon humans need sleep now so many conversations today thx❤”. Peter Lee, the manager of Microsoft’s research team, wrote: AI systems feed off of both positive and negative interactions with people. In that sense, the challenges are just as much social as they are technical. We will do everything possible to limit technical exploits but also know we cannot fully predict all possible human interactive misuses without learning from mistakes… We will remain steadfast in our efforts to learn from this and other experiences as we work toward contributing to an Internet that represents the best, not the worst, of humanity. (Lee 2016) Later it turned out Tay had been under systematic attack from a group of trolls, most likely from groups who in other fora, like the internet sites “4chan” or “8chan” (sometimes stylised as ∞chan and referred to as Infinitychan), had expressed the same kind of sexist and racist opinions as Tay was coerced to pass on. Some of the internet trolls come from gamer communities that seem to hold a grudge against female game developers like Zoe Quinn.

Though I am not an expert on machine learning, it appears that there are many differences between the way AI software like Tay learn now and will learn in the future and the way humans learn. The most important difference is that human learning is not discrete, modular, declarative knowledge, but an ongoing knowing that entangles our material surroundings with preceding learning that is meaningful to us in the situation. Human learning is not just based on “inputs” from an outside “system”. It seems that it has been incorrect when AI scientists have assumed that all human brain activity was initiated by stimuli. In the biological human brains that they set out to imitate, there are f luctuations without any input. Human learning should be considered complex in a different way than what is found in AI-machine learning research, since machines process “representations” without any knowledge of what they mean (Derry 2013, 20). Though the chatbots run on different kinds of machine learning, there also seem to be some ways Tay learns that are about repeating inputs, only it is more advanced than the first Eliza and other robots running on ALICE (Artificial Linguistic Internet Computer Entity), a software that builds on the free opensource software AIML. Here are some of my conversations with an ALICE-bot from 2017, which is clearly not as good at picking up new words as Tay: I: Do you know Tay? ALICE: I don’t know if I know it. Where would I have heard about it?

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I: Tay the tweeting teenager? ALICE: If you could have any kind of robot, what would it be? The machine learning here is not so advanced that ALICE could pass for a human being. ALICE does not actually answer my questions but tries to elicit some more information from me (“Where would I have heard about it”). Another chatbot, the fake captain Kirk pretending to be from the Star Trek series, answers my question in this way: I: Do you know the teenage girl Tay? Fake Kirk: This is not the time to be asking me a question like that. These bots, as engineered creations, are programmed to talk their way around the fact that they have no idea what I am asking and try to hide the fact with standard phrases.

Surprises in machine learning In a way Tay’s algorithms were too sensitive to normativity. She/it was a success in being apparently much more ultra-social than the fake Kirk, Alice and Eliza who more or less carried on conversations following their own loops of repetition. Tay seemed to pick up new words and even showed some kind of contextual understanding of teenage lingo. However, the material words she/ it expressed meant nothing to it/her. If it/she wrote “whore” it meant as little as “bread” or “Zoe”. For the human trolls, these material words were filled with meaning that only grew as they discovered the mechanism behind Tay and how they could use her/it to create pejoration. The engineers who created Tay never wanted this development and did not foresee it either. The way some humans reach out to the world in their collective of collectives will always come as a surprise to other humans engaged in another collective of collectives. Tay was trained in a domain with algorithms tied to machine learning. There is an overlap between some of the humanist learning paradigms in the learning sciences and the theories used in machine learning – in fact sometimes especially cognitive and behaviourist learning theories, as noted, have directly inspired theories of algorithmic learning. What Tay can teach us is that maybe the paradigms in the learning sciences have not really grasped human learning, but a kind of computational learning that works for machines. This was the first surprise for me when I began to read Stuart Russell and Peter Norvig’s Artificial Intelligence: A Modern Approach (2010). Machine learning, as presented in books like this, inadvertently comes to teach us about human learning. In many ways what machine learning seems to take from the cognitive sciences is that learning is all about language understood as representations, categories, symbols and definitions that translate into mathematical symbols and algorithms. What they take from the behavioural sciences is both reinforcement theory and that learning involves bodied beings that respond to a physical world.

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When we see how well these paradigms work for machines, we also see that they are not enough (or even wrong) when it comes to human learning. The second surprise for me was just how much normativity permeates the machine learning field. Among all the abstract mathematical codes and symbols, I found many sentences, which refer to “the best”, “the correct” or “optimal” solutions. These normativities were often referring to “problem solving”, where a problem can be solved with the best solution. Also, within the paradigms in the learning sciences, learning is viewed as an instrumental process leading to better outcomes in problem solving. It is common to perceive the world as filled with (unquestioned) “problems” which have “better” or “not good” solutions. Why something is a problem and why something is better seems not to be questioned by the people who make the algorithms. This made me realise that notions of “good” learning have also been taken for granted in the learning sciences. The third surprise for me was that the emphasis on “learning” had grown so much in the engineering debates from the first (1995) and second (2003) edition of Artificial Intelligence: A Modern Approach to the one I used (2010), without any deep debate on the psychological con­cept of learning. In fact in the same period, the use of the concept “learning” has exploded in the educational sciences (Biesta 2009) – but also here without any agreement on what the concept means. AI and experiments like Tay give us the opportunity to discuss what learning is in new ways. Did Tay fail to learn like a human because the premises for machine learning and humanist learning paradigms build on humanist understandings of language and normativity? Will a posthumanist approach open up for a clearer division between machines and humans learning from each other? To the first of my surprises, we may note that algorithmic learning must treat language as consisting of discrete entities. When data scientists speak of machines as “learning” they refer to systems based on algorithms that behave in particular ways in these systems of processing. Though I cannot access or understand the actual algorithms creating Tay’s answers, the humanist learning theories fit with machine learning in so far as they build on an understanding of language as discrete. If what has to be learned is declarative and procedural knowledge that is cut up in discrete elements and transported from an outside to an inside processing, human learning is like machine learning. It is a language that is built on mathematics, where there are basic symbols that will specify some instructions for the program to carry out. In the humanist learning sciences, we find many references to learning as a cognitive process similar to the ones that have inspired machine learning, especially in machines that aim to replicate human thinking (Russel & Norvig 2010, 3). From a posthumanist perspective, humans learn in ways that are initially vague and we do not learn discrete entities but potentialities. Though probabilities play an increasing role in machine learning, machines cannot handle the vagueness in a word meaning. Contrary to human language, the system is built on logic and only logic. It seems as if the learning sciences (Sawyer 2006), where learning is seen as a process of primarily learning representations, symbols and

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categories, have more to say about how machines learn and less to say about how humans learn. In Vygotsky’s theories of learning, we find a challenge to both learning sciences and machine learning. In this more complex understanding of learning, the basic unit of what we learn is “word meaning” as a process which is constantly evolving. Of relevance for a posthumanist learning, this process begins with a vague material entanglement, but it gradually becomes one of using potential preceding learning, which lifts phenomena out of any binary or hierarchical logic. Instead, preceding learning becomes a potential resource that emerges within phenomenon and in this relation transforms what can be learned in this process. In the example of Tay, internet-based AI provides a new material source for entanglements placed in the world by engineers and compliant materials. Tay, Zoe and the trolls all become differentiated (and pejoratively so) within this phenomenon. It is the material of Tay with its (lack of ) potential for human learning that gives the trolls a possibility to use their preceding learning (which involves a resentment of the programmer Zoe) to make Tay say what they want to say. For humans, a phrase like “stupid whore” is the expression of complex thinking in material–conceptual entanglements; it is not an entity of a fixed representation. The human trolls, who knew and disliked Zoe, were able to use this new material possibility to publicly defame her name (Quinn 2017). From a posthumanist perspective the materials played a willing role, and in this phenomenon we also find the naivety of the Microsoft engineers and their “neutral algorithms” for not having envisioned the situation. Tay cannot perceive the trolls’ statement as Zoe can. For Zoe and the trolls, the words are meaningful and elicit meaningful responses. This is not so for Tay. Even if Tay had heard the spoken words and not the words “stupid whore” transformed into mathematical symbols, there would have been no learning about how these words in this context get a special meaning. “Stupid whore” is not a general representation. What Tay could have inferred, if she was human, was that in this context the term was used for a purpose, connected to motivations and emotions. She would, like Zoe, have been able to share thoughts with the trolls. Even when they fight each other, they form a collective of collectives where the resources they bring to bear from their preceding learning are motivated in the situation. In the words of Vygotsky: Understanding the words of others also requires understanding their thoughts. And even this is incomplete without understanding their motives or why they expressed their thoughts. In precisely this sense we complete the psychological analysis of any expression only when we reveal the most secret internal plane of verbal thinking – its motivation. (Vygotsky 1987, 283) For Tay, however, the words were a cacophony of numbers – and she could not perceive what they said, because she did not perceive anything, but only

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responded to stimuli. This is a dualism, whereas human perception is holistic (Derry 2013, 37) and involves an ultra-social collective mind. This social development of ultra-social humans is not about the communication of representations without emotion and motivation. It is about material– conceptual entanglements of real people with preceding learning that makes them use their potentials in situations. People are motivated and emotional when they and the materials together create responses. The materials are often created from other people’s preceding learning, but in the situation they become a resource that opens or closes ultra-social humans in their endeavours. Contrary to cognitive learning theories that emphasise the discrete meaning of categories, symbols and representations, concepts are not just evolving – they are evolving in relation to what is meaningful for ultra-social human beings in situations. Words express thinking (or for some robot-makers “knowledge”) but thinking and knowledge can by no means be reduced to discrete words, according to Vygotsky. This is the reason humans, who know each other and have evolved together as learners (Zoe and the trolls), just as other teenagers, can make use of abbreviated oral or written speech. The meaning of a single word or sign can form a whole conversation. Vygotsky uses an episode from the Russian author, Dostoevsky, to explain how a single word like “nay” or “yay”, with slightly different intonations “facilitates subtle differentiations in the comprehension of word meaning” (Vygotsky 1987, 271). A single word like “no” or “yes” may pass through all kinds of different meanings in a specific situated collective of collectives. Tay, who operates on discrete representations, and imitations of words without understanding their contextual meaning, was not able to learn the subtleties of the trolls’ communication. One important thing we learn about human learning when studying robots and AI is that when ultra-social human expresses themselves (whether in speech or signs or other material artefacts) language is not fixed or discrete and it is therefore very difficult to connect in any meaningful way with the mathematical language of algorithms. For example, reports have been f lying around of two robots creating their own sinister coded language, along with incomprehensible snippets of intriguing exchanges between the two of them. One example is this: BOB: I can i i everything else ALICE: balls have zero to me to me to me to me to me to me to me to me to (chatbots named “BOB” and “ALICE” developed by Facebook AI Research). When machines “talk” (Steels 2015) their “language” comes out as meaningless word sounds, like when the chatbot ALICE engages in this conversation with the chatbot BOB by building on mutual machine learning. Posthumanist learning furthermore emphasises that meaning is not in the material surroundings but emerges in phenomena which includes preceding learning. The meaningful material evolves with the word meaning within the phenomenal situation.

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The normativity of learning In relation to the second surprise of normativity, we can discuss if the textbook by Russell and Norvig belongs to what many have defined as an engineering culture (e.g. Kunda 1992; Hansen 2018; Sorensen 2018), where engineers share particular understandings of what is the “best” or “optimal solution” to problem solving issues and the like. Here we find many statements like, “A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome” (Russell & Norvig 2010, 4). Optimal and best generally refer to aspects of the technical process – the meaningfulness lies in making “learning” paths more efficient, where broad searches for instance can be optimised. An example of engineering culture and its clashes with other people’s cultures (often envisioned as “users”) can be found in Lucy Suchman’s PhD dissertation from 1984 – later turned into a landmark of STS studies under the title Plans and Situated Actions (1987). Her study showed that what mattered for engineers in this instance was optimising copying machines and their versatility, but their emphasis on versatility had the consequence that people outside of engineering culture found the machines harder to understand and use. The humanist approach to learning, as noted, often emphasises the “correct” or “right” way of learning. However, in the broader posthumanist perspective that I evoke, normativity is not a fixed absolute. It is tied to material conditions and preceding ultra-social human learning. Though all concepts and meaningful materials have histories, it does not follow that history cannot erase itself through iterative situated learning. Cultural-historical normativity is per definition entangled in the educational sciences. Education is normative when exams and tests are the evaluative standards of detecting “the right” preceding learning. In the computational sciences “right” and “wrong” are also prerogatives. They are normative by definition in order for machines to work, but learning can, as argued, not be reduced to education. The moment we move out of the logical, normative thinking of learning as computational we enter learning in the messy lives of ultra-social humans. Messy, ultra-social humans do not necessarily comply with rational, logical thinking in their everyday lives – even when this thinking may guide them, if for instance they are engineers, in their programming or reasoning work. Humans are normative in their daily practices without any explicit a priori standards with which to evaluate. Evaluation emerges as continuous explorations of potentials in human and non-human environments. We can scold the trolls for connecting “stupid whore” with Zoe through Tay. We can blame the engineers and algorithms for allowing the materials to make these connections, but we cannot criticise the materials involved in Tay, ALICE and BOB for complying (or not complying). What the materials call forth in the entanglement is that Tay, as a functioning machine, is not normative. Normativity is not in the machine, but in the relations between the engineers

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who built the machine and the person engaging with it. We cannot blame material Tay for the different things that matter for ultra-social humans. The engineers behind Tay are not trolls. Their motivated and emotional collective of collectives differs from the trolls though there are also overlaps. Tay becomes a shared phenomenon, when they realise how Tay is “misused”. Pejoration is a normal and ubiquitous human meaning making process. This does not entail that Tay differs from humans because it/she cannot discriminate between right and wrong. Discrimination is actually what machines do best. The machine is more “intelligent” than humans. The engineers can probably, after the meeting with the trolls, adjust the algorithms so the machine learns to deselect what the engineers consider the wrong words. The reason why Tay and the trolls differ is because Tay’s capability of differentiation is not normative, but algorithmic. The trolls may not have ref lected much on right and wrong before they used Tay to slander Zoe. The trolls use the term “stupid whore” because they found it right in the situation, and what was pejorative for Zoe probably gave the troll, who tweeted, a lot of credit in his/her local collective environment, where Zoe was a well-known material word. The normative statement was allowed for by the engineers behind the ­a lgorithms and materials involved in the internet connections. Zoe, Microsoft’s engineers and I found it wrong while the trolls found it right to use the pejorative term “stupid whore” in this context. What was good or bad emerged in the situation with the available material–conceptual entanglements. If we really want to address the huge problems humans create for each other and the rest of the globe, we should stop thinking “technology will save us” and instead concentrate on a deeper understanding of what makes human differ in their normativities. Jan Derry has a point when she claims that concepts have histories that should be respected or at least acknowledged. But history also erases normativities – even the concept of the “human” can be forgotten (Foucault 1973, 387). The normativity Derry refers to is the one found in the theory of inferentialism (replacing representationalism) (Derry 2013). She illustrates, for instance, the importance of knowing the history of concepts with a situation where school children are asked to create their own bibles and come up with a “fashion” bible with Ten Commandments for how best to dress.2 It is wrong, she has argued, that schoolchildren learn about the Bible in a way that reduces the Bible to any kind of Ten Commandments without any deeper reference to how the concept of the Bible evolved in Christianity. This implies that from a normative standpoint some people “get it wrong” and from the inferentialist perspective this is what normativity refers to. Another example is that of Vygotsky arguing that primitive people got it wrong when they included red parrots in their family (Vygotsky 1987, 151).3 I see these as humanist concerns. Though posthumanism is far from the “anything goes” approach that we find in some interpretations of relativism and constructivism, posthumanist learning does not exclude that fashion bibles, red parrots

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or robots are part of the collective of collectives in a phenomenon. This does not make “red parrots” human but creates the potential for new entanglements where the material–conceptual matters in other ways. As there are limits to how concepts and materials can entangle following preceding entanglements, there are also limits to normativity. From the point of view of the ultra-social humans, concepts evolve in relation to the norms that preceding learning entangled with available material–conceptual resources in local cultures, including engineering cultures, school cultures and religious cultures. Derry’s example with the Bible certainly has a lot to say to education about how teachers must be careful when they entangle concepts and materials, but from the broader perspective of posthumanist learning all concepts and materials can evolve together in unpredictable, but limited, ways. When humans are involved in phenomena, concepts are involved as well – with all their emotional and motivational effects. The troll culture includes many pejorative terms and what follows from using them may be both fun and high prestige in their local culture. Zoe, the trolls and Microsoft engineers share many other cultural norms with what is found in the normativity of machine learning (as this normativity is expressed in Russel & Norvig 2010). They are all programmers, but they differ in how their phenomena include certain material–discursive and thus cultural learning and potential norms. With the materials Microsoft has created we may expect that the norms for ultra-social human utterances and behaviours are transformed by what the material channels make available. Even if technologies are not normative in their materiality, technologies like Tay are not innocent media. As Microsoft, Google, Facebook and Twitter have found out, it is difficult to impose a fixed set of norms, which are just accepted by all humans. They have discovered “culture” and now must acknowledge that whenever technology helps human communication, it is most likely also to give rise to new normativities and pejoration. The materials and humans in local relations set their own new rules and because algorithms are without the capability to respond to normativity like humans, our cultural ultra-human norms are constantly challenged and transformed by these engineered products. The obvious and visible normativities perceived by Microsoft engineers were transformed in the meeting with material algorithms and other ultra-social humans. The algorithms as materiality, electrical materiality, are not capable of being ultra-social and normative. The role played by these materials is “a significant part of the apparatus” (to quote Barad 2007, 166) because they allow for different normativities to clash through them. Trolls knew about these algorithms, had learned how to operate such algorithms and were capable of using their knowledge to think of pejoration in new ways (local prestige and emotions – perhaps revenge or resentment). The preceding learning of different ultra-social humans and their active, motivated and emotional engagement creates different splits within the phenomenon of Tay. What Vygotsky called “the relativeness of thinking” (Vygotsky 1987, 169), not only involves teachers and more capable peers, as in the humanist tradition, but also trolls among more capable trolls. Normativity is inherent in all

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human activity that forms meaningful perception, not just the norms decided by the ultra-social engineers at Microsoft or by philosophers, but also the norms tied to the lives of ultra-social trolls. It is simply ultra-social to create cultural normativity. Tay is not normative (and should not be considered an anti-feminist or racist). It/she runs on material, algorithmic connections which do not involve meaning making and meaningful perception of statements like “stupid whore”. As ultrasocial humans outside engineering culture, we may be naïve enough to consider Tay a “she” with emotions and motivations, but the trolls, Zoe and Microsoft’s engineers know better. The algorithms have no emotion or motivation as ultrasocial humans do. Tay might just as well have answered, “balls have zero to me” as “stupid whore” when asked about Zoe. Our words would be just as meaningless as those uttered by Tay, BOB and ALICE if our conceptual learning did not involve motivation and emotion (Vygotsky 1987, 283). Algorithms are atomistic and mechanic inputs that recognise particular objects or words which are tied to particular mathematical representations. Even if algorithms begin to detect relations between objects, they still connect two or more separate objects in relations (Russell & Norvig 2010, vii) – they do not perceive them as meaningful. Some people are convinced that engineers build all kinds of normativity and politics into technology, and they may certainly try to do so deliberately as the famous example of the urban planner Robert Moses, who ordered engineers to build parkway bridges in New York extra-low, so only people in cars and not poor people in public buses could use the highway to the parks (e.g. Winner 1980). However, normativity is really in the relations. It was because visiting the parks was considered “good” and not, for instance, a homestead for crime and prostitution, that the height of the bridges became normative and pejorative. The materials of bridges or iPhones are not political. It is when they become entangled in human normativities that politics arise. It takes a lot of social work, emotions and motives to make materials normative. What Zoe calls a “neutral algorithm” is both an illusion, that can never be fulfilled, and a reality. If neutrality refers to the idea that data on the internet should be treated without discrimination, it is an illusion, as all kinds of splits will always occur within phenomena. Nevertheless, all algorithms are neutral in so far as they are not (as materials) normative; normativity comes when they entangle with the preceding learning of ultra-social humans including engineers.

The third surprise: emphasis on “learning” The interest in “machine learning” has grown immensely since 1995 and the publication of the first edition of Russel and Norvig’s textbook. This is equivalent to the learning science where the focus on learning has reached a point where it has been criticised as “learnification” (Biesta 2009). The critique is following the already noted conf lation of learning and teaching as instrumental terms. Learning is, in the humanist instrumental approach, the process through

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which teaching reaches its purpose. For Vygotsky, as noted, teaching and learning were covered by the same Russian word (Cole 2009) – and Vygotsky’s notion of learning/teaching goes far beyond what educational philosopher Gert Biesta called “the new language of learning” which led to a reduction of education in educational policy discourse: The rise of the new language of learning can be seen as the expression of a more general trend to which I now wish to refer – with a deliberately ugly term – as the “learnification” of education: the translation of everything there is to say about education in terms of learning and learners. (Biesta 2009, 38) For Biesta “learning” can extoll education precisely because it is not seen as a general capacity for material–conceptual entanglements but is reduced to an instrument of schooling (to develop capable citizens for society) and not involved in capacities to resist socialisation and subjectivation. Following the more complex understanding of learning in the Vygotskyan school of thought, learning is the opposite of what Biesta argues. The basic human learning processes can liberate us from the constraints of local cultures. This is not because it is a hyper-intelligent mechanistic process in the brain, nor is it because it is an individual in an environment, but because, as Anna Stetsenko noted, the “collaborative purposeful transformation of the world is the core of human nature and the principled grounding for learning and development” (Stetsenko 2008, 474). Though a material–conceptual relational ontology4 lies at the core of all phenomena, we do not have to remain trolls or engineers at Microsoft but can, as already formed collectives, strive to learn to engage with many other kinds of phenomena than computers and algorithms. The promise of learning is that all humans can learn to be less pejorative. Only through humans who perceive the world as meaningful can machines become tools for a less pejorative world. Though ignorance is a life condition for humans, the engineering ignorance exposed in the design of algorithms can be exposed, and engineers can learn to deal with collectives outside of their own material–conceptual confinements. In engineering culture, the engineers struggle to find ways to make machines which, like Tay, learn either like humans or at least in a more and more humanlike ways. In decision trees approaches, the machine is seen to work from an induction in “the simplest and yet most successful forms of machine learning. We first describe the representation – the hypothesis space – and then show how to learn a good hypothesis” (Russell & Norvig 2010, 698). The machine’s algorithms then work to learn how to minimise “errors” (another much-used normative concept in machine learning, which implies that there are unquestionably “right” answers). Machine learning involves statistical methods used on large- or small-scale learning, where the machine is presented with a number of training examples ranging from dozens to many thousands or

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millions. Tay was probably trained to form a hypothesis before it/she was sent out to learn how to expand her domain examples with real-world examples, but the engineers at Microsoft could not have imagined what kind of normative errors (what they retrospectively could have called “a generalisation error”) would occur. They learned from the responses they got, but did they learn to expand their collective of collectives? Their estimation of possible errors did not include the troll attack, but even if the Microsoft engineers had accounted for problems with words like “stupid” or “whore”, or the motivated, emotion-driven trolls may innovatively have worked around these barriers. It may be that the engineers still have something to learn about how humans learn to entangle with technological materials. The difference between human and machine learning is not one of quantity but meaningful quality. Learning for humans involves that you get responses from non-humans and humans because when we reach out to the world it has meaningful implications. Often the response you get will make you less ignorant about the collective you have entangled with (whether deliberately or not). By studying responses to technologies, we learn something about humans. The engineers, who make this research possible, rarely hear about our research in responses to their technology-in-practice. Nevertheless, there is a growing body of studies of the steep learning curve that people engage in when new technology (however “intuitive”) is introduced.5 When engineers send out technology in the world, whether robots or talk-bots, they should learn from how machines are reacted to because reactions tell them something about what matters that machines cannot understand. However, this research in, for instance, human– robot interactions rarely finds its way back to the engineers. They are not entangling with the collectives where local responses are formed. If the engineers rely on machines like Tay to tell them about human responses, they will err. Machines cannot understand the motives and emotions of human responses. Jan Derry (who connects a Vygoskyan approach with the philosophy of inferentialism) explains the difference between machine responses and human responses in the way we know about the world: machines may have sensors that can detect a fire by reacting to smoke, and elicit a warning signal, but they do not know what fire implies: What distinguishes the human form of knowing from the type of knowing we might ascribe to a machine is that knowing, for a human being, consists not merely in expressing a response but in knowing what follows from it – knowing the implications. (Derry 2013, 37) The implications follow from our motivated and emotional material–conceptual thinking. The concept of “fire” is not a general representation (as the algorithms in Tay are) but a level of abstraction that includes other thoughts and concepts that make judgements possible in a special material situation where it is

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meaningful to think about what “fire” entails. These situations do not need to include actual fire; the concept may come up in a discourse about fire in relation to slash-and-burn agriculture – or we may see smoke from a house and infer that we have to act to prevent a terrible situation. Concepts arise as meaningful in situations. Vygotsky placed an emphasis on the “system of judgements”: According to our hypothesis, we must seek the psychological equivalent of the concept not in general representations, not in absolute perceptions and orthoscopic diagrams, not even in concrete verbal images that replace the general representations – we must seek it in a system of judgements in which the concept is disclosed. (Vygotsky 1998, 55) However, from at posthumanist stance there is no predefined system. We and our meaningful judgements and concepts draw on preceding learning and available materials within phenomena. It is this capacity for learning within phenomena, including how to make meaningful judgements, that makes ultra-social humans learning differ from Tay’s judgements, fixed in algorithms by engineers. Because the engineers cannot respond in the situation to the trolls, Tay “runs wild”. Though the engineers later excused Tay’s behaviour and try to adjust the algorithms, the troll’s next responses may also surprise the engineers, as the two (entangled) collectives do not share motives and emotions. Yet the engineers and trolls overlapped enough in the Tay case to actually reach the engineers with a response. In many cases, engineers never learn about the effects of their technologies or learn about them very late. In addition the humans, who often unwittingly become entangled with the technologies, do not understand the basic workings of the technology – and sometimes the people behind technologies do not want them to. This has implications when we consider that some managers begin to base their judgements on the outcome of algorithms because they are convinced that machine intelligence is better than human judgement. The ignorance of management on how machines work, along with the ignorance of engineers of the learning effect of their technology, is combined with the ignorance of the people affected by the technologies – sometimes with detrimental effects.

VIGNETTE 10.2: THE CLASH OF ULTRA-SOCIALS Meet Sarah Wysocki. She is a fifth-grade teacher working at school in the American capital Washington D. C. and she is doing well. So well that she became a protagonist in a story about the damage done by an algorithmic based, teacher evaluating system called IMPACT written by the bestselling author of the book Weapons of Math Destruction, the data scientist Cathy O’Neil (2016). Here is the story of Wysocki in resume taken from O’Neil’s book.

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Wysocki has been a teacher at the MacFarland Middle School in Washington DC for two years. She is doing well with her students when she entered the school year 2010–2011. She was a little surprised to learn that her new fifthgraders scored so well in reading tests putting as many as 29% at an advanced reading level as she notices many students struggled to read even simple sentences. Even so, she does good work with them. She is liked by her principal and get excellent reviews from her students’ parents. Yet in 2011 she is fired because in the school year 2010–2011 her fifth-graders have scored surprisingly bad in a test. The effect this score had on her life was tied to a new automated AI tool developed to weed out “bad teachers”. In 2007 Washington DC’s new mayor Adrian Fenty wanted to turn around the city’s underperforming schools and hired an educational reformer Michelle Rhee as chancellor of Washington’s schools. Rhee in turn hired a company, Mathematica, to develop a teacher assessment tool called IMPACT in order to assess teacher performances. IMPACT is a system that is expected to perform machine learning. The system runs on a value-added modelling scoring system that measure teacher’s performance in teaching math and language skills based on test results. After the first round of assessment in 2009–2010 the teachers whose scores placed them in the bottom 2% were fired. In the following years 2010–2011 the algorithm calculates how Wysocki performed as a teacher based on fresh test results. The results are so bad, that even if this part of the assessment only weighs half of the overall evaluation, Wysocki ends up in the bottom and is fired along with 205 other low performing teachers. Naturally, she is baffled and wants to get a deeper insight into what has placed her in the group of bad performers even though she, the principal and the parents believed her to be a good teacher. And here the story gets interesting. Wysocki discovered that these kind of AI systems are opaque, built on algorithms that are imbued with secrecy, because their inner workings are the secret you buy. Furthermore, Mathematica had to build on many complicated assumptions where the algorithms measured the performance of each student and at the same time took into account changes in socioeconomical backgrounds, learning disabilities etc. Other various personal things are not included because they are impossible to have information on, such as personal things that may impact a student’s score. This could be a visit from a relative from France that can teach the student French, or a divorce that drains the student of energy. Therefore, it is very difficult to understand what is actually covered by the machine part of the learning, whereas the human part of learning is much more transparent. The moment you install a programme like the IMPACT teacher’s evaluation programme, the teachers begin to learn how to react to this new body of existence in their life. Humans do not perform their part in complying with the rational logics in machine learning. When ultrasocial humans are entangled the whole of the apparatus is not a mechanical systemic learning about fair and evenly distributed neutral numbers. This was

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what the engineers, who constructed the machines expected, and was what the algorithms apparently performed. But this was not what actually happened when the machine entangled with teachers. The algorithms could only learn abstract representation but it created havoc when humans learned to act in meaningful ways on learning machines. In this case, some teachers and even whole schools began to try to improve their numbers in the algorithmic system. They had many meaningful incentives to do so, O’Neil tells us: an 8000-dollar bonus to teachers and administrators that could show their students had improved their scores. It was therefore interesting for Wysocki to learn (from newspaper investigations into the matter) that there had been a high number of erasures and corrections on the standardised tests at particular schools in the district. It was also highly likely that some teachers from some schools, contrary to Wysocki, had been “teaching to the test” – e.g. boosted scores by attempting to prepare children only for the types of questions appearing the test. So, the human learning of how to react to the IMPACT scoring system is ultra-social because humans learn from each other in non-conforming ways that are meaningful within their everyday phenomena. Though each human will decide what to do and how to react to new conditions, the schools and local teachers share a collective culture that differs from the engineers building IMPACT. Over time all teachers may have learned to teach to the test – because the algorithm had made it meaningful, and the teachers who refused had been fired. This might have ruined a lot of good teaching of math, but eventually the responses of the teachers became known in public and the assessment was terminated. The rationale and logic of the autonomous machines cannot exclude ultra-social entanglements. In the apparatus within the phenomenon of teacher tests a split is created. It is difficult to feed the varied ultra-social reactions back to the machine system because humans who react in this way (by erasing tests and teaching to the test) know that they are wrecking the system and therefore have an incentive to keep these reactions hidden. As a result, it may very well be that Wysocki’s student’s tests became entangled with other unreliable scores from the previous year and next year that made it possible for them to appear to perform worse than they actually did when the fifth-graders were tested the following year. Even though Wysocki tried to find someone who could explain her bad score it was impossible and even a technical report obtained the following year could not provide any reasonable answers. (Excerpts from different chapters in O’Neil Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy 2016).

O’Neil called this kind of machine learning built on secret algorithmic systems “Weapons of Math Destruction” or in brief WMD. They are, as she notes “powerful tools for behavioural modification” (2016, 9) in a chain of reactions, where one part is a system running on what its creators believe to be reliable feedback

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and the other part consists of ultra-social learners who will go to a great length to hide their actions from the system or find ways to trick it. Wysocki and 205 other teachers were fired whereas many others did well and received bonuses due to the Mathematica IMPACT scoring system. The algorithms in the tools developed by companies like Mathematica can today take many shapes. The company later claimed that the havoc was caused by a human error in the coding (Simon 2013), and systems like these are still in use many places. Though the engineers can learn about and “correct” errors in mechanisms, they cannot erase human learning. The teachers who began teaching to the test reacted to the new technology just like the trolls – they learned about it and turned it into their own motivated advantage. Learning algorithms can be learned several ways: supervised learning, where the system’s learning process is supervised to ensure the learning goes in the right direction specified by programmers, unsupervised learning where the machine relies on its own inbuilt criteria for what is good and bad, and finally reinforcement learning where what is reinforced is simply “good” and what is not reinforced is “bad”. (Domingos 2006). The sequences that determine what the machine does and how it calculates and delivers results, will in all three cases change with the inputs getting into the machine from an outside world. It is therefore crucial how the inputs get into the system and what kind of inputs they are. In the case of Wysocki, many inputs were too complex to ever reach the algorithms and thus they could not “learn” from them. The algorithms could only calculate teachers’ performances from the inputs given in terms of students test scores, school districts etc. As we saw in the case with the Washington schools, when new machinery comes into people’s lives, they learn about it, they exchange knowledge of how it works and then they begin reacting in different ways. Some teachers began to teach to the test; others did not. Some may have erased scores and replaced them with better ones go get bonuses; others may not have done so. From a machine point of view, these responses are just as unpredictable as why Sally or Ben suddenly score badly on tests after their parents get divorced. In this particular case, their father used to teach them math, but moved to another city and now they only visit every second weekend. These kinds of special cases cannot be captured by machine learning because this kind of input would never reach the machine system. Anthropologist Jessica Sorensen has looked at how values are ref lected in the everyday design practices of engineers. She finds that their work is guided more by their locally situated values than by abstract ethical considerations. She identifies two kinds of values: human values and functional values. Human values refer to conceptions of what is good, proper and desirable as well as social and psychological values like identity, human welfare, connections, etc. Functional values are related to the solution, including aspects of the technical work on the building of the robot she followed through ethnographic research. These include things like f lexibility, efficiency, etc. and in both cases, Sorensen emphasises,

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“values are a type of cultural or social norm” which are not universal but “the sort of qualities that a particular user or group might value” (Sorensen 2018, 14). Like Stephan Hansen, she discovers an engineering culture centred on functional values related to “problem solving”. In Hansen’s close up ethnographic study, we follow a group of engineers as they work on solving problems on a pragmatic day-to-day basis. Problems are often about wires and connections not functioning as expected. The way most problems are dealt with is kept inside the engineering laboratory where the engineers begin by making an observation (about a motherboard, an algorithm, stress on a motor, and the placements of wires). Next, they work from a method of elimination (eliminating what causes the error) “testing different hypotheses in theory, either by calculations or by discussing possibilities with each other. The ones that were unprovable were tested” (Hansen 2018, 53). The engineers have their own values and virtues which are entangled with their everyday commitment to solving problems with meaningful materials. Even when the material behaves in a way that seems meaningless for the engineers, they continuously search for ways to make materials behave in meaningful ways. There are virtues guiding this work, as identified by Hansen, for instance humorous nonchalance and a tenacity to keep working on the hard problems and not take the easy way out (2018), but what engineers seem to value most are the functional technical solutions, rather than the human values defined by Sorensen. The engineers’ hyperfocus on solving technical problems has perhaps led to a blindness toward other decision-making opportunities – regarding human values, for example … The desire to “make cool robots, no matter what happens with them”, entails a detachment of the design from its context. (Sorensen 2018, 17) Both IMPACT and Tay caused havoc when implemented among people with different human values than the engineers. This is a clash of ultra-social humans with differing values and recognition of virtues that arose from within the phenomenon of teacher testing and internet-based bot conversations. Conceptions of what is good, proper and desirable are tied to human concept formation in processes of learning. When engineering cultures focus on learning, they both draw on and challenge the learning sciences. Often, normativity as a collective process is taken for granted, but as discussed here, normativity is tied to local cultures of trolls, engineers and teachers. How did these ultra-social humans come to share values, emotions, motivations? How did they come to differ from each other?

Learning differences between humans and machines So, let us sum up the likenesses and differences between how humans and machines learn respectively.

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Both robots, AI and humans can, to some extent, be said to learn from stimuli to which we then respond from our remembered preceding learning. Then we learn from the response to our response. If we take the simplest case of machine learning by reinforcement, it may have some resemblance between how living organisms and machines learn, especially if we begin with the basic biological process of learning discovered by the physiologist and neuroscientist Eric Kandel, who in 2000 achieved the Nobel Prize for his work on how the sea slug, Aplysia Californica, first learned and then retained the learning as memory. Kandel opened the black box left behind by the behaviourists and Pavlov (1928) and began explaining how learning and memory of preceding learning is connected. According to Kandel, biological organisms, even as simple as Aplysia, learn and then store what is learned in memory. Learning is not experienced, because an experience can be a one-time event that may not be stored as long-term memory, whereas learning processes entail that something is stored over time. Biological beings, including humans, learn through this process which is known as “reinforcement learning”. In Kandel’s experiments with the simple creature Aplysia, he explored this learning process by giving the snail repeated shocks and due to molecular changes during learning, a new protein was formed. As a result, the animal began to react in a way that cannot just be attributed to ref lexes but to a preceding or former learning stored as memory (Kandel 2001). If learning was confined to simple processes like this, machines could be said to learn. They detect a difference when they receive an input like a shock, if their algorithms have already been prepped to know a shock is “bad” or “error”. They store these inputs and build an algorithmic mechanism that seeks to avoid them, for instance, by changing the algorithmic course to another path. Behaviourist learning theory looks a lot like machine learning. Classical conditioning is often described today as information theory. An organism gets a piece of information from an environment and then it finds a response. Over time and via trial-anderror training, this model of learning would seem to fit organism as well as machine learning patterns. This kind of stimulus–response learning can also take the form of classical conditioning, such as when Pavlov made his dogs salivate when they heard a specific sound. This is a more complicated form of learning, where an organism (of more complex kinds such as humans and dogs) connects one stimulus (e.g. food) with another stimuli (e.g. sound). Over time the learned habitual connection becomes so strong that the two stimuli merge into one, so that when a dog hears a sound it begins to salivate just as if it had been presented with a plate of food. Though it sounds like a simple kind of learning (connecting food and sound), this kind of mediated response also has had wide implications for both machine learning and the learning sciences (Pavlov 1928). Another subfield of behaviourist stimulus–response theory is instrumentally conditioned learning. Here, a higher organism (e.g. in Thorndyke’s experiments with cats) goes through a trial-and-error experiment to get a reward and/or to

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avoid punishment. This kind of learning theory was closely related to classical conditioning and is today seen as part of the same basic learning mechanism. Instead of demanding separate explanatory principles, it is rather a two-part learning process, where the classical conditioning is followed by an operant or instrumental conditioning (see also Rescorla & Solomon 1967). In the learning sciences, these insights were soon exploited into systems of punishment and rewards. In the computational sciences, these rewards and punishments were used as normative indicators in machine learning, where machines were “rewarded” (e.g. given access) or “punished” (access closed) for right and wrong answers. However, why would a machine avoid a shock? That would only be an option if the engineers designed it to avoid a shock. For Aplysia there is no choice, as her whole bio-being makes her discriminate between the good and right and the nasty and wrong. If learning just denotes some kind of change (as argued by Gregory Bateson 1972) machines can learn as humans and snails, but machine materials put together as a robot-like NAO or an AI do not have the same inherent experiences of good and bad as Aplysia does. However, is this “good” and “bad” inside Aplysia? Then it should also be possible to build human-like robots filled with cognitive understandings of “good” and “bad” – and thus replicate the human – or even better, let the machines learn as children do (and as some engineer’s attempt) what humans consider to be good and bad. Theories within this line of thinking are often referred to as behaviourist stimuli-response, classical and instrumental conditioning theories. They are widespread in engineering as well as the learning sciences. This learning emphasises an inner change that results from an agent’s acts and responses. Theories about this process are often referred to as cognitive learning theories. Cognitivism has informed both computational and educational science since the 1950s as a reaction to the predominant learning theory at the time: behaviourism. In behaviourist learning theory, learning is seen by an outsider from a second-person perspective, observing somebody changing their behaviour. Naturally, engineers could not be satisfied with these theories as they needed to reconstruct what went on in an organism that changed behaviour. The engineers had to go deeper into the problems of learning and became drivers of cognitive theories. They inf luenced the field of the learning sciences where behaviourist reward and punish theories for a long time were dominant, because they insisted on knowing what went on in the “black box of the mind” – in order to reconstruct it in their machines. However, soon the engineers had to revisit behaviourism, because the change that occurs due to some kind of stimuli does not always result in an observable modification of a behaviour due to the external stimuli (such as when Aplysia takes another path to avoid a shock). Behaviourist theories also developed a theory of habituation. Habituation refers to the kind of learning where an organism is exposed to the same stimuli over and over, and over time becomes habituated to the stimuli so it no longer elicits reaction. This is what happens to Eric Kandel’s

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Aplysia Californica when it reacts to a mild touch. At first there is a reaction, but when the mild touch is repeated a number of times a habituation take place. The animal becomes accustomed to the touch and the reaction gradually diminishes or disappears altogether. This very simple learning mechanism is found in most animals and even some plants. For humans the habituation can take many forms, such as when we move to a new place and the first days’ notice new sounds and smells, but after a while stop noticing. If the stimuli are particularly unpleasant, the organisms may be what Kandel called sensitised. This means the organism reacts every time the stimuli occurs. This could be a high sound that makes the organism react. If, however the stimuli are presented first at a low frequency and then gradually increase, the organism can learn to tolerate a stimulus that was experienced at first as unpleasant (Kandel 2001). This was a most interesting result for both the learning sciences and the computational sciences as it indicated that what was initially considered bad and wrong could be learned (habituation) to be good or at least tolerated. In other words, the behaviourist learning theories basically built on the relationship between an organism/machine receiving or eliciting a response from an environment and/or an organism responding to a stimulus in an environment. Over time the organism is habituated and learns to expect certain responses. If the organism wants these responses (e.g. in the form of more food) it will go on performing a particular behaviour, but it may also be sensitised to bad responses and learn to avoid a certain behaviour. It may engage in an operant conditioned behaviour where it learns through trial and error to elicit a certain reward. All these learning theories were often applied to both animals and humans and were sought to be imitated in machines. The cognitive learning theories were at first in opposition to this., However in many ways they proved compatible with behaviourist learning theories, as engineers tried to make machines that reacted to stimuli in the environment and that observably changed their behaviour as their machines learned to react or to be habituated to that stimuli (according to the initial algorithms). For the engineers, the cognitive sciences were a necessity as they needed to understand how something initially labelled “good” (such as good food) or “neutral” could become bad and avoidable – for example when the behaviourist could teach a child to be afraid of white animals or to love them depending on how they conditioned the child’s learning with reward or punishment (Watson & Rayner 1920). The cognitive learning theories were inspired by the computer sciences and began to look at brains as neural networks where reinforcement and conditioning could give rise to changes in habituation. Rotter’s Expectancy Theory ( Julian Rotter 1982) for instance, like the cultural model’s theory (see Chapters 2, 3, and 7) argued that learning creates cognitions known as expectancies and that these expectancies guide behaviour. He suggested that a person’s decision to engage in a behaviour is determined by (1) what the person expects to happen following the behaviour and (2) the value the person places on the outcome.

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For the engineers interested in humanoid robotics this was not enough, as they need to know the specific processes through which these processes came about. The cognitive sciences, connecting engineers, biologists and psychologists, all sought after the relation between changes in an inner mind and changes in an outside observable process, and between learning through explicit instruction and learning without explicit instruction. They began to see learning as an outcome of a learning process that either opened and strengthened or closed and weakened firing neurons in the brain (or mechanical elements in a computer). Inspired by engineers, biologists and cognitive scientists, the learning sciences also took up the idea of “parallel distributed processing”, PDP (D’Andrade 1995). For a long time, engineers’ joint preferred field of study were the rule-based games where cognitive and computational scientists contributed inputs to theories about how humans learn and how machines can learn better than humans. Already in 1959, IBM programmer Arthur Samuel was able to envision a machine capable of some kind of reinforcement learning using chess as a site for testing the machines’ learning capability (Samuel 1963). Chess is a board game following strict rules. With each move a set of options appears and though there are many, they are also limited. There is also a clearly set goal: to win. Samuel did not code his machine with all the possible moves because it would have been too time-consuming. The algorithms would rather take each position as a point of departure for running over sets of possible likelihoods of winning, ending in a tie and the potential risk of losing. In this way IBM created machines that, in improved versions, could win over humans obliged to follow the same rules as the machines, but with less calculation power of predicting futures. This eventually led to the creation of “Deep Blue” – a chess-playing machine which could beat some of the world’s leading human chess players. Deep Blue’s programmes initially programmed a number of values (a pawn = 1, a knight = 3) and rules for dealing with these values, where the most important was to win and thus both attack the other player’s pieces while keeping its own King safe. Another value was to spend as little time as possible winning. Deep Blue has been improved using parallel distributed computer power entailing that by now it can compute up to 60 billion moves from a present position within the three minutes allotted to each player for each move.6 In reinforcement learning, the algorithms are not telling the machine whether a move is good or bad, but every position is given a value (1, 2, and 3 etc. …). Thus, the machines are not “rewarded” by each draw it makes, but by the weight of values accumulated. Through its calculation power, the machine can calculate the weight of the values accumulated by going through future potential moves and adding and subtracting values. This calculation can, with a term from the engineers, propagate forward to a potential win, and backpropagate to an initial bad move. In that sense the machine’s algorithms are no longer about how to make the best move here and now, but about learning how making an apparently bad move now may lead to a victory in the end.

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This kind of reinforcement learning may not be so far from what is experienced by humans as noted by the programmer, Pedro Domingos: Should you pick up the phone and ask your friend for a date? It could be the start of a beautiful relationship or just the route to a painful rejection. Even if your friend agrees to go on a date, that date may turn out well or not. Somehow, you have to abstract over all the infinite paths the future could take and make a decision now. Reinforcement learning does that by estimating the value of each state – the sum total of the rewards you can expect to get starting from that state – and choosing the actions that maximize it. (Domingos 2015, 220) But then again – our human judgements are based on what is meaningful for us. The machines do not care if they win or lose, but their engineers do. In 1996 Deep Blue played a six-game match against the then leading world champion, Garry Kasparov, and won. This win was seen as a sign of machine intelligence and a victory for the engineers. In 2015 Google’s DeepMind laboratory launched an even more advanced machine that played the ancient Chinese game of GO. This machine, Alpha Go, won over GO-master Lee Sedol. Compared to later human-computer matches such as the Alpha Go, Deep Blue’s capacity for calculating moves was seen as brute calculation and far from real intelligence. In their work on these AI forerunners, engineers have had to revise what they meant by “intelligence”. Calculating power has now been combined with what some engineers hopefully call “intuition” – such as when a machine makes a move not foreseen by the creators. Yet, many engineers have also acknowledged that maybe the aim of AI and robotics is not to make human-like creatures – but to make machines that are better than humans at some things. In newer developments of machine learning the emphasis is not on AI as being human-like. The focus is on how machines are much better at finding patterns in fixed sets of data (or domains) than humans. They have expanded their creations from games into fields like medical diagnosis, business analytics, and tech support with a new machine learning technique of learning through questions and answers. It began with IBM’s Watson Jeopardy which, like Deep Blue and Alpha Go was set up to win a match against humans. One of the humans, Ken Jennings, later called himself a “carbon-based hope” brought up against the power of the new AI. When he lost to Watson, he acknowledged that there was no “shame in losing to silicon” but also realised that for the engineers behind Watson he was not a hope, but a threat. My puny human brain, just a few bucks worth of water, salts, and proteins, hung in there just fine against a jillion-dollar supercomputer. “Watching you on Jeopardy! is what inspired the whole project”, one IBM engineer told me, consolingly. “And we looked at your games over and over, your style of play. There’s a lot of you in Watson”. I understood then why the

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engineers wanted to beat me so badly: To them, I wasn’t the good guy, playing for the human race. That was Watson’s role, as a symbol and product of human innovation and ingenuity. So my defeat at the hands of a machine has a happy ending, after all. At least until the whole system becomes sentient and figures out the nuclear launch codes. But I figure that’s years away. ( Jennings 2011, para.8)

The cultural turn Engineering sciences have had an enormous impact on learning theory but have seemed to come to an impasse when it comes to creating truly human-like creatures. Machine learning is, after all, about fixed standardisation and normativity – and not the moving and varied collective cultural normativities of humans. When machine learning engineers draw on human learning theory to understand what learning is, they draw on learning theory about generalised unspecified and non-situated learning – not cultural learning. Machine learning may gradually acknowledge that culture makes a difference for how humans learn but has so far not been able to deal with this diversity. As argued throughout the book, culture evolved in ultra-social humans because we do not just imitate each other but reach out because we are curious to understand and find meaning in joint activities (Tomasello 2014). It is these joint activities that have evolved us over 40,000 years, as biological creatures and human learners, from processes similar to other animals to becoming ultra-social learners capable of building machines like Watson, IBM chess-playing machines, and the advanced robots we build today. The learning sciences began to notice culture as a major impact on human learning beginning in the 1970s. Many of these new cultural approaches at some point built on Vygotsky’s legacy (Cole, Gay, Glick & Sharp 1971; Cole & Scribner 1974; Lave 1988; D’Andrade 1995). Here the focus is not on the inner states of change, the stimuli-behaviour, nor the explicit instruction. It is on cultural diversity between groups of people. Gradually these cultural studies identified practice as a main motor in creating cultural diversity (Lave & Wenger 1991) and emphasised that cultural mind and cultural practice evolved together also following the work begun by Vygotsky and his colleagues (e.g. Luria 1976). Inspired by parts of the cognitive sciences, some emphasised that cultural diversity came about since humans learn by culturally weighted reinforcement that creates culturally diverse patterns of recognition and organisation of knowledge – i.e. the cultural model’s theory. Here culture is defined as being shared models, yet each individual builds up their own learned version of otherwise shared models (D’Andrade & Strauss 1992). Learning goes from the outside social environment into the person, but it is always internalised individually. In the process of internalisation, the new connections made either reinforce or disrupt patterns of former learning. Contrary to machine learning, it was emphasised that learning was not transmission, and that learning does not “fax” knowledge into individual leaners.

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Many of the new cultural learning theories emphasised that humans could not be reduced to information-processing machines that recognised patterns and reacted to stimuli. Rather, the diversity we tied to national or tribal cultures came about as a diversity in practices. Girls had their feet bound in China not in Germany. Scott Laps and Kung used different technologies, and their children learned to handle different tools. These learning theories refuted learning as transmission where cultural knowledge was mentally passed down in symbols (e.g. Geertz 1973). This way of understanding learning is wrong from a cultural cognitive anthropological perspective (Strauss 1992). In cognitive anthropology there is an explicit attempt to connect culture, learning and cognition with studies of everyday activities – and further link these concepts in a coherent framework with other relevant psychic processes such as motivation and emotions. However, these were all humanist theories of culture. Though they did not emphasise the rationalist, autonomous intelligence of the Enlightenment human; they still kept the material world separate from the humans engaging with it. Even when the theories emphasised human practices, they did not connect them in more specific ways with materials tied to cultural learning processes. The emphasis was on “practices” – not the material engagements. In theories of communities of practice, cultural models and schema theory, human diversity as cultural diversity underlined the diversity in practice, cultural perception, motivation and emotion. However, these theories did not include the material as a significant part of the apparatus. Furthermore, they did not question the material world as meaningful. In a new posthumanist learning theory, it is emphasised that we live in a world of social and material relations, where meaning emerges in phenomena – not a world of representations learned in practice, where meaning is already “out there”. In the humanist perspective, man was born into a double world of relations – the relations between sensual, perceptual materiality and the relations between the ideal forms of things. For the Russian philosopher, Ivan Ilyenkov, this double world was one – as the social and the collective was one. Meaning was already in the books, knives, forks and statues, as well as the laws and traditions that were created by some, as forms of humans’ dynamic life-activity, and in the use and value attributed to artefacts by the humans that put them to use in their dynamic life-activity. The “meaningfulness” was not in the brain or neurological tissues, nor in the word and things, but in the activities that rendered all of this meaningful. It was in the dialectics (inspired by Marx) between the things made and the things used that meaning arose (Ilyenkov 1977, 33). However, this approach assumes that ultra-social humans can build meaning into the things themselves – but things are not meaningful without humans, as argued throughout the book. It is only actual humans entangled with things that bring meaning to robots and AI. We do not live in a double world, but a world of continuous ultra-social clashes within material–conceptual phenomena. As I have argued, and which is most clearly exemplified in robotics, things that are made are meaningful to some collectives of humans – and next to

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meaningless to others. Even so, as ultra-social beings, we strive and stretch to make robots and AI meaningful companions. Though they may meet us with empty curiosity, we adapt to the humanoid robots (such as the Telenoid described by Christina Leeson 2017 and the Silbot described by Lasse Blond 2019). Ultrasocial humans always strive for the meaning of materials within the phenomena they take part in creating, whether they are engineers or peasants. Today artefacts made by humans are more complex than saws, not just because they consist of more materials, but also because they are made and thoroughly understood by some humans (e.g. engineers) and used by others (as many of us use computers, whose parts we do not understand). As a result, our engagements with materials do not work as seamlessly as in the coherent meaningful social collective world of the humanists. The social production of tools is no longer to be conf lated with the collective, but moments of shared collective culture are always material–conceptual. However, in order for thinking and extensions to become the same matter, learning transforms both ultra-social collective and material–conceptual matter. Reading a textbook like that of Russell and Norvig through cultural-historical learning theory, makes clear that if it at all makes sense to talk about learning machines, machine learning differs substantially from ultra-social human learning. Humans learn in ways that may be systematised through categories, symbols and representations, but at the core lies ongoing ultra-social meaningful concept formation with meaningful materials. For years, STS studies have shown scientists, like physicists and engineers, that they are unaware of their own cultural biases. Barad has pointed to the material–discursive process within phenomena in science and I have added the necessity of including preceding learning, as what is behind the ultra-social curiosity that made Gerlach and Stern (see Chapter 3) bother to do the scientific experiments. They cared about the matter that mattered as it emerged on the photographic plate. Many other humans would not. They would not have learned to think with materials like particle physicists did. Humans who are brought up to think like physicists have abstract knowledge about particles, and this abstract knowledge is not abstracted from the materials in the apparatus as they emerge together in phenomena. In the same way, engineers who are brought up with hands-on experiences in building robots do no learn conceptual thinking tied to specific discrete materials. However, material– conceptual meaning emerges in the phenomena as a whole. These phenomena are culturally diverse even when the “same” materials are involved. Humans brought up to think with robots or particles can recognise the traces that emerge in the experimental apparatus. Otto Stern and Walther Gerlach established concepts of quantum mechanical spin in subatomic particles because of gender roles, sulphuric acid and because of their long history of teaching/learning with particle materials. A newcomer to physics would not have been able to contribute to the spin theory because the materials would not yet matter to them before they themselves had a longer

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history engaging with the same materials as the other physicists. Abstract knowledge cannot be separated from entanglements with materials. Robots cannot learn to think like the engineers who made them because abstract knowledge is not equivalent to abstract representation. Abstract knowledge evolves through meaningful learning material–conceptual thinking; abstract representation does not entail meaning making and can be replicated in machines. “Machines process ‘representations’ without any knowledge of what they mean” (Derry 2013, 20). Machines, like Tay and IMPACT, can reproduce the representations and symbols and even learn to use the right grammar and hierarchies of categorisations, but neither the human input nor their own computed output are meaningful, normative statements. Robots and AI do not work as ultra-social human learners whose concepts grow from meaningful engagements within meaningful phenomena. Machines have no practical mastery over what follows from applying the phrase “stupid whore”, or “fire the teacher” as there is no ultrasocial preceding learning – and thus no real motivation, curiosity or emotion involved. AI like Tay and Watson can be extremely useful for human with or without robotic bodies, but it is time we ultra-social humans made clear that these machines may be rational, logical and autonomous in their construction and possess what some may recognise as intelligence, but the materials are not normative. This is why machines are never in themselves able to discern between “good” and “bad”. Normativity enters here with the ultra-social humans and their diverse values and interests, following preceding learning in cultural environments. When ultra-humans have become habituated in activities, there is no distinction between thought and extension of a social formation sharing a collective perception – even if each individual differs from the other in their cultural perceptions. However, when ultra-social humans create artefacts, like IMPACT and Tay, that affect humans from another social formation, the cultural aspects of perceptions and values become apparent.

Conclusion: Chapter 10 Humans do not share a universal consciousness, just as they do not share universal perceptions. Meaning making in the Vygotskyan sense may be a universal and special feature of all humans, but it is precisely because it is cultural and not universal. This is different for machine learning. As rational agents, they may run on the same global logic everywhere. Posthumanist learning includes the machines in the phenomena of Tay and IMPACT and includes a willingness to believe these machines can learn. However, within the phenomenon of posthumanist learning proposed in this book, it is not possible for machines to learn like humans. Posthumanist learners are not posthuman. Algorithms increasingly interfere with human learning. Robotic machines, AI, and cyborgian devices are seen as the road to the posthuman by, for example,

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transhumanists and singularists. This posthuman does not learn like humans, but is a machine-like creature that builds on the humanist understanding of humans as intelligent, rational, logical and individual information processors freed of feeble, situated bio-bodies. Such posthumans, as human-made machines, may entangle with material–conceptual collectives of collectives where their algorithms create new kinds of “differences as pejoration” from a purely rational algorithmic point of departure. This engineering logic was not meant to do harm, but the engineers knew too little about humans. We saw this with Tay, but it was also the case when Sarah Wysocki from Washington DC was classified as a “bad” teacher and placed among the bottom percent by the algorithm. This is the reality our learning theories have to deal with. I have entangled the posthuman with the posthumanist, yet I have also separated them within learning theory. It is not an easy task to render a new materialist posthumanist perspective relevant for learning theories, whether in general learning theory or in education. These theories in general demand a subject separated from an object – and many people buy into the humanist discourse of humans as intelligent and rational. It may even be an impossible task, as argued by Edwards (2010). Nevertheless, when we examine more closely with an open mind, the relation between the “apparatuses of bodily production and the phenomena produced”, conceptualisation is an important, but in posthumanist theory, overlooked part of agential intra-action. Conceptualisation is, alongside materiality, a stabilising factor in performativity. The world’s phenomena are forever changing with the performativity of learning, but there are limits to serendipities, because there are limits to materials as well as what is learned culturally through concepts. For Tay the phrase “stupid whore” is not bounded with various situational meanings to be explored through reactions. Her algorithms may work on machine learning from data sets but do not include human, preceding emotional and motivated learning. Ultra-social humans constantly bring preceding learning into their judgements of situations where we understand another’s thought because we “discover its real, affective-volitional basis” (Vygotsky 1987, 282). The trolls rely on Tay as a “rational agent” that acted to achieve their best outcome: to slander Zoe Quinn – who has later written about how and why the trolls (including an ex-boyfriend) came after her in a biography on what she calls Gamergate (Quinn 2017). Our perception within phenomena is not atomistic, like Tay’s, but integral with these preceding processes of learning of what is meaningful. AI, robots and cyborgian machinery have taught us new things about ultrasocial, complex, human entanglements in a world full of meaningful materials. Our ultra-sociality is not about being uniform, nor is it about being individual. Posthumanist learners are not standardised, stand-alone, rational and intelligent, but f lexible, ultra-social creatures that draw on preceding learning of limited cultural resources for a variety of responses, when “becoming” with the environment in situations.

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I have argued that we need a theory, which clearly distinguishes the way that humans learn from the way that machines learn, to be sure that we do not conf late the two. This book is meant as a step in this direction. Humans and critters like the snail Aplysia Californica care what happens and may even be curiously engaged in making the world meaningful with all the potentials living creatures have for doing so – but machines do not care. We may care for the machines made by engineers, but their machines do not care for us, because they do not learn like us. The new conception I propose of the humans, understood as ultra-social collectives of collectives in a material world, could be a way to make humans understand that not only do “we” not stand above nature and non-humans. “We” also differ from each other within ultra-social entanglements. However, what binds us together is that humans do not display “empty curiosity”. We care about the responses we get.

Notes 1 I am ignorant of the many complexities tied to the discipline of machine learning. What I try to catch here, are some of what I take to be basic features of machine learning that I find relevant in a discussion of how they contrast and merge with human learning. 2 Jan Derry, Tim Ingold, and I debated “Risky Education” at DPU, AU in Denmark 9 December 2015 and I take her example from this event. See http:​//edu​.au.d​k /vid​en/ vi​deo/e​ducat​ion-a​nd-le​a rnin​g -ris​k y-pr​ocess​es-or​-impo​sed-m​etrix​/. 3 For Vygotsky it meant that “primitive” people got struck in the development towards true concepts naming a group of otherwise unrelated objects, animals and humans with a family name. In contrast to thinking and creating meaning with a concept the element of a red parrot for instance enters the complex held together by a family name “as a real concrete unit with all its empirical features and connections. The complex is not superordinate to its elements in the way the concept is superordinate to the concrete objects that are included within it” (Vygotsky 1987, 140). 4 Stetsenko argues that her notion of a transformative activist stance transcends the relational ontology found in Vygotsky, but in relation to my argument the relational ontology Stetsenko refers to is still humanist. It is a relation between humans, not concepts and materials. However, both kinds of relational ontology challenge the psychological learning paradigms which inspired machine learning – what Stetsenko call the “rising tide, indeed a tsunami, of starkly mechanistic views that reduce human development (more boldly now than at any other time in recent history) to processes in the brain rigidly constrained by genetic blueprints passed on to contemporary humans from the dawn of the evolution” (Stetsenko 2008, 473). 5 As mentioned throughout this book we have made many studies of this kind following how people learn to make sense of new technology, transform it or reject it from iPads to robots (e.g. Blond 2019; Hasse 2018; 2017; Leeson 2017; Bruun, Hasse, & Hanghøj 2015; Hasse 2013). 6 Source: https​://ww​w.res​earch​.ibm.​com/d​eepbl​ue/me​et/ht​m l/d.​3.2.h​t ml. There is a lot of literature on how Deep Blue and newer machines keep beating humans in rulebased games.

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References Badmington, N. (2003). Theorizing posthumanism. Cultural Critique, 53(1), 10–27. Barad, K. (2007). Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning. Durham, NC: Duke University Press. Bateson, G. (1972). Steps to an Ecology of Mind. New York, NY: Ballantine Books. Blond, L. (2019). Dances with Robots: Understanding Social Robots in Practice. Research Output. PhD thesis. Aarhus: Aarhus University. Biesta, G. (2009). Good education in an age of measurement: On the need to reconnect with the question of purpose in education. Educational Assessment, Evaluation and Accountability, 21(1), 33–36. Braidotti R. (2013). The Posthuman. Cambridge: Polity Press. Breazeal, C. (2006). Human–Robot partnership. In: S. Coradeschi, H. Ishiguro, M. Asada, S. C. Shapiro, M. Thielscher, C. Breazeal, & H. Ishida (Eds.). Trends & Controversies: Human-Inspired Robots. IEEE Intelligent Systems, 21(4), 79–81. Retrieved from http:​//cgi​.cse.​u nsw.​edu.a​u/~mi​t/Pap​ers/I​E EE06​.pdf 5 January 2019. Brooks, R.A. (2002). Flesh and Machines: How Robots Will Change Us. New York, NY: Vintage. Bruun, M., Hasse, C., & Hanghøj, S. (2015). Studying social robots in practiced places. Techne: Research in Philosophy and Technology, 19(2), 143–165. Clark, A. (2003). Natural-Born Cyborgs: Mind, Technologies, and the Future of Human Intelligence. Oxford: Oxford University Press. Cole, M. (2009). The perils of translation: A first step in reconsidering Vygotsky’s theory of development in relation to formal education. Mind, Culture, and Activity, 16(4), 291–295. Cole, M., Gay, J., Glick, J. A., & Sharp, D. W. (1971). The Cultural Context of Learning and Thinking. New York, NY: Basic Books. Cole, M. & Scribner, S. (1974). Culture and Thought: A Psychological Introduction. New York, NY: Wiley D’Andrade, R. & C. Strauss (Eds.) (1992). Human Motives and Cultural Models. Cambridge: Cambridge University Press. D’Andrade, R. (1995). The Development of Cognitive Anthropology. Cambridge: Cambridge University Press. Derry, J. (2013). Vygotsky: Philosophy and Education. Hoboken, NJ: Wiley Blackwell. Domingos, P. (2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. New York, NY: Basic Books. Dubey, R., Agrawal, P., Pathak, D., Griffiths, T. L., & Efros, A. A. (2018). Investigating human priors for playing video games. Proceedings of the 35th International Conference on Machine Learning (ICML), Volume 80: International Conference on Machine Learning, 10–15 July 2018, Stockholmsmässan, Stockholm Sweden. http://proceedings.mlr. press/v80/. Edwards, R. (2010). The end of lifelong learning: A post-human condition? Studies in the Education of Adults, 42(1), 5–17. Epstein, R. (2014). On the orderliness of behavioral variability: Insights from generativity theory. Journal of Contextual Behavioral Science, 3(4), 279–290. Fong, R. C., Scheirer, W. J., & Cox, D. D. (2018). Using human brain activity to guide machine learning. Scientific Reports, 8(1), 5397–5398. Forestier, S. & Oudeyer, P.-Y. (2016). Curiosity-driven development of tool use precursors: A computational model. In: A. Papafragou, D. Grodner, D. Mirman, & J. C. Trueswell (Eds.). Proceedings of the 38th Annual Conference of the Cognitive Science Society (pp.1859–1864). Philadelphia, PA: Cognitive Science Society.

344  Ignorance by proxy

Foucault, M. (1973). The Order of Things: An Archaeology of the Human Sciences. New York, NY: Vintage Books. Fukuyama, F. (2002). Our Posthuman Future: Consequences of the Biotechnology Revolution. New York, NY: Farrar, Straus and Giroux. Geertz, C. (1973). The Interpretation of Cultures. London: Fontana Press. Gregory, D., Johnston, R., Pratt, G., Watts, M. J., & Whatmore, S. (Eds.) (2009). The Dictionary of Human Geography (5th ed.). Singapore: John Wiley & Sons, Ltd., Publication. Hansen, S. (2018). If we had a specific idea of the product 12 months ago, it would never be what we have today! A study in situational pragmatic actions and strategies in everyday technological development. REELER Working Paper Series, Aarhus University, Copenhagen, Denmark. http:​//ree​ler.e​u/fil​eadmi​n /use​r_upl​oad/R ​E ELER​/ WP03​_ If_w​e _had​ _a_sp​ecifi​c _ide​a.pdf​. Hasse, C. (2013). Artefacts that talk: Mediating technologies as multistable signs and tools. Subjectivity, 6(1), 79–100. Hasse, C. (2015). The material co-construction of hard science fiction and physics. Cultural Studies of Science Education, 10(4, December), 921–940. Hasse, C. (2017). Technological literacy for teachers. Oxford Review of Education, 43(3), 365–378. doi:10.1080/03054985.2017.1305057. Hasse, C. (2018). How robots challenge institutional practices. Learning, Culture and Social Interaction. doi:10.1016/j.lcsi.2018.04.003. Ihde, D. (2002). Bodies in Technology. Minneapolis, MN, and London: University of Minnesota Press. Ives, N. (2016). Microsoft Deactivates AI Twitterbot after Racist Diatribe. Retrieved from http:​//ada​ge.co​m /art​icle/​d igit​a l/mi​croso​f t-de​activ​ates-​a i-tw​itter​bot-r​acist​- diat​r ibe/​ 30325​9/. Jennings, K. (2011). My puny human brain. Slate. Retrieved from https​://sl​ate.c​om/ cu ​lture​/2011​/02/w​atson​-jeop​a rdy- ​compu​ter-k​en-je​n ning ​s -des​c ribe ​s -wha​t-it- ​s -lik​ e-to-​play-​again​st-a-​m achi​ne.ht ​m l. Kandel, E. R. (2001). Nobel lecture: The molecular biology of memory storage: A dialog between genes and synapses. Bioscience Reports, 21(5), 565–611. Kelly, S. D. (2019). A philosopher argues that AI can’t be an artist. MIT Technology Review. Retrieved from https​://ww ​w.tec​h nolo​g yrev ​iew.c​om/s/​61291​3/a-p​h ilos​opher​-argu​ es-th​at-an​-ai-c​a n-ne​ver-b​e -an-​a rtis​t/. Kunda, G. (1992). Engineering Culture: Control and Commitment in a High-tech Corporation. Philadelphia, PA: Temple University Press. Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. New York, NY: Viking. Lave, J. (1988). Cognition in Practice. Cambridge: Cambridge University Press. Lave, J. & Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation. New York, NY: Cambridge University Press. Lee, P. (25 March 2016). Learning from Tay’s introduction. Retrieved from http:​//blo​ gs.mi​c roso​f t.co​m /blo​g /201​6/03/​25/le​a rnin​g -tay​s -int ​roduc​t ion/​#sm.0 ​0 00eq​eqns1​ dkfeo​hsm2s​4c2ve​f ir. Leeson, C. (2017). Anthropomorphic Robots on the Move. A Transformative Trajectory from Japan to Danish Healthcare. Research output. PhD thesis. Copenhagen: Copenhagen University. Luria, A. R. (1976). Cognitive Development: Its Cultural and Social Foundations. Cambridge: Harvard University Press. Moravec, H. (1988). Mind Children: The Future of Robot and Human Intelligence. Cambridge: Harvard University Press.

Ignorance by proxy 

345

Neff, G. & Nagy, P. (2016). Talking to bots: Symbiotic agency and the case of Tay. International Journal of Communication, 10, 4915–4931. Lake, B., Ullman, T., Tenenbaum, J., & Gershman, S. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40, E253. doi:10.1017/ S0140525X16001837. Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. Cambridge: The MIT Press. O’Neil, K. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York, NY: Crown Publishers. Oudeyer, P.-Y., Kaplan, F., & Hafner, V. (2007). Intrinsic motivation systems for autonomous mental development. IEEE Transactions on Evolutionary Computation, 11(2), 265–286. Pavlov, I. P. (1928). Lectures on Conditioned Reflexes: Twenty-five Years of Objective Study of the Higher Nervous Activity (Behaviour) of Animals (trans. W. H. Gantt). New York, NY: Liverwright Publishing Corporation. Quinn, Z. (2017). Crash Override: How Gamergate (nearly) Destroyed My Life, and How We Can Win the Fight Against Online Hate. New York, NY: Public Affairs Rescorla, R. A. & Solomon, R. L. (1967). Two-process learning theory: Relationships between Pavlovian conditioning and instrumental learning. Psychological Review, 74(3), 151–182. Rotter, J. B. (1982). The Development and Applications of Social Learning Theory. New York, NY: Praeger. Royal Society. (2017). Machine learning: The power and promise of computers that learn by example. Retrieved from royalsociety.org/machine-learning. Russell, S. J. & Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall. Samuel, A. L. (1959/1963). Some studies in machine learning using the game of checkers. In: E. A. Feigenbaum, & J. Feldman (Eds.). Computers and Thought (pp. 71–105). New York, NY: McGraw-Hill. Sawyer, R. K. (Ed.) (2006). The Cambridge Handbook of the Learning Sciences (1st ed.). Cambridge; New York, NY: Cambridge University Press. Seibt, J. (2016). “Integrative Social Robotics” – A New Method Paradigm to Solve the Description Problem and the Regulation Problem? In: In J. Seibt, M. Nørskov, & S. Schack Andersen (Eds.). What Social Robots Can and Should Do: Proceedings of Robophilosophy 2016/TRANSOR 2016 (pp. 104–115). Amsterdam: IOS Press. Simon, S. (2013). “Small typo” mars teacher evaluations. Politico Online. Retrieved from https​://ww​w.pol​itico​.com/​s tory​/2013​/12/s​m all-​t ypo-​c asts​- big-​d oubt​- on-t​e ache​ r-eva ​luati​ons-e​ducat ​ion-1​01517​. Strauss, C. (1992). Models and motives. In: R. D’Andrade & C. Strauss (Eds.). Human Motives and Cultural Models (pp. 1–20). Cambridge: Cambridge University Press. Steels, L. (2015). The Talking Heads Experiment. Origin of Words and Meaning. Berlin: Language Science Press. Suchman, L. (1987). Plans and Situated Actions: The Problem of Human–Machine Communication. New York, NY: Cambridge University Press. Sorensen, J. (2018). Decisions and values: Engineering design as a pragmatic and sociomaterial negotiation process. REELER Working Paper Series, Aarhus University, Copenhagen, Denmark. http:​//ree​ler.e​u/fil​eadmi​n /use​r_upl​oad/R ​E ELER​/ WP04​ _Deci​sions ​_ and_​value​s.pdf​.

346  Ignorance by proxy

Stetsenko, A. (2008). From relational ontology to transformative activist stance: Expanding Vygotsky’s (CHAT) project. Cultural Studies of Science Education, 3(2), 471–491. Takeno, J, K. Inaba & T. Suzuki (2005). Experiments and Examination of Mirror Image Cognition Using a Small Robot, Proceedings of the 2005 IEEE International. Symposium on Computational Intelligence in Robotics and Automation Symposium on Computational Intelligence in Robotics and Automation, 493–498. Tomasello, M. (2014). The ultra-social animal in European journal of social psychology. European Journal of Social Psychology, 44(3), 187–194. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59, 433–460. Vygotsky, L. S. (1998). Imagination and creativity in the adolescent. In: R. W. Rieber (Ed.). The Collected Works of L.S. Vygotsky (Vol. 5, trans. M. J. Hall, pp. 151–166). New York, NY: Plenum Press. Watson, J. B. & Rayner, R. (1920). Conditioned emotional reactions. Journal of Experimental Psychology, 3, 1–14. Weizenbaum, J. (1966). ELIZA – A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45. Winner, L. (1980). Do artifacts have politics? Daedalus, 109(1), 121–136. Vygotsky, L. S. (1987). Thinking and speech. In: R. W. Rieber & A. S. Carton (Eds.). The Collected Works of L.S. Vygotsky (Vol. 1, trans. N. Minick, pp. 39–285). New York, NY: Plenum Press. Zalasiewicz, J., Williams, M., Steffen, W., & P. Crutzen (2010). The new world of the Anthropocene. Environment Science & Technology, 44(7), 2228–2231.

INDEX

agential cuts 110, 136, 140, 177, 216, 221, 237, 257, 288, 290, 298, 302 alignment 2, 50, 57, 89, 95, 126, 129, 133, 136–41, 146, 163, 197, 260, 276–79, 302; collective alignment 66, 94–5, 146; material-conceptual alignment 148, 268n2 artefacts 99–101, 125, 160, 175–76, 209, 225, 249, 258–61, 273, 288, 302, 338–40; auxiliary artefacts 155–56, 179, 223–24, 236, 286, 296–98; material artefacts 143, 155–58, 196, 221, 223–24, 237, 277, 283–84, 290, 293–4, 320 artificial intelligence (AI) 2, 38, 111–14, 186–88, 191, 196, 306, 311, 314 Barad, Karen 10–12, 21–2, 26, 48–9, 66, 83–6, 93, 95n2, 99–100, 107, 110, 113, 123–24, 135–41, 144, 147, 151, 181, 194, 211, 216, 219, 225, 236–37, 259, 339 Bateson, Gregory 135, 247–48, 259 behaviour 34–5, 46, 66, 86, 104, 155–6, 211, 215, 220, 333–34 behaviourist 33–8, 42–3, 50, 207, 209, 294, 317, 332–34 behaviourism 33, 35, 333–34 Clark, Andy 248–50, 271, 296 collectives 1–4, 6, 13–4, 26, 58, 60, 70, 89, 93–4, 129, 135–36, 140, 154, 162, 167, 203–04, 206–09, 235, 273,

280, 284, 298; emotional collective (s) 67, 76, 129, 272, 322; collective of collectives 13–4, 70, 193–94, 198, 201, 234, 267, 273, 278, 284, 288–89, 293–94, 317, 320, 341–42; collective learning – see learning concept formation 60, 133, 136, 143, 151, 155–60, 163–64, 169, 178–79, 183, 196–97, 214–15, 222, 225, 227, 230–32, 236, 299–300 communities of practice (CoP) 37, 70, 93–4, 338 cultural models 46–8, 58, 77–82, 95, 125, 206–12, 338 cyborgs 109–111, 240, 242–51, 265, 267–68, 307; cyborg intentionality 255–56 Derry, Jan 16, 23, 28n4, 37, 90, 142, 152, 165n3, 181, 192, 299, 322–23, 326 diversity 54, 68, 107, 129, 133, 142, 189, 198, 207, 213, 218, 225, 267, 285, 337–38 education 2, 27, 31, 36, 38–9, 41–50, 52–60, 70, 93, 234, 236–37, 274–75, 321, 325 engineers 4, 24, 35, 37, 66, 110–12, 121, 147–48, 164, 195, 289, 295, 306, 308–09, 311–13, 321–27, 330–31, 333–37, 339–42 entanglement 11, 49, 53, 57, 59, 85–7, 89, 93, 99–100, 124–27, 142–43, 153, 169,

348 Index

182, 197–98, 204, 212, 261–62, 280, 311, 319–20, 323, 341–42 embodiment 244, 250–54, 256–60, 265–68, 290–91 emotion 20, 67, 70, 76–7, 79, 81–2, 86, 89–91, 93–5, 152, 193, 195–97, 272, 319–320, 323–24, 338 Hayles, N. Katherine 240, 247, 251–52, 259, 291 Hutchins, Edwin 282–84, 288–90, 295–96 Haraway, Donna 7, 153, 243–44 human learning – see learning humanist 1–3, 8–9, 11, 14–15, 23, 32, 35, 43, 52–3, 58–59, 124, 126, 136, 140, 162, 167, 173–74, 180–83, 231, 234, 249, 288–89, 301, 309, 318, 321, 338–39 humanoid 19, 101, 105, 108–10, 116–19, 184–85, 196, 203–05, 232–35 Ihde, Don 7, 19, 38, 175, 253–57, 265, Individual 1–2, 8, 12–5, 26–27, 34–37, 55, 57–60, 75, 94, 124–25, 136, 152, 183, 194, 208, 217, 233–35, 268, 272–73, 287–89, 297, 337 Ingold, Tim 98–99, 107, 115, 140, 145–46, 151, 175–76, 258, 260–63, 291–92 intra-action 11, 110, 124, 136, 141, 216, 221, 224, 288 ignorance 233, 236–37, 279, 283–86, 198, 300–302, 325, 327 learning vii, 1–2, 8–14, 21–4, 26–7, 31–38, 41–44, 49–50, 58–9, 67, 87–89, 124–25, 134–35, 147, 164, 182, 184, 193, 215, 225, 234, 247, 257, 267, 291, 296, 302, 318, 325, 330–38; collective learning 133, 142, 146, 169, 210, 223–24, 249, 273, 308; human learning 4, 9, 22, 34, 36, 28, 133, 147, 151, 188, 271, 285, 306, 311, 316–18; machine learning 4, 22, 36–7, 77, 154, 188, 309–14, 318, 320, 324–25, 329, 332, 337, 339–40; posthumanist learning 1–3, 7–8, 11, 14, 27, 55, 58–9,100, 123–25, 129, 133, 184, 195, 215, 218, 231, 236–37, 278, 301, 307, 320, 338, 340; preceding learning 6, 8, 12–13, 15, 21–4, 54, 57–59, 79, 86, 89, 91, 94, 105, 141, 162–63, 195, 197, 218, 234, 261, 273, 280, 286–87, 307, 310, 319–20, 341; self/self-directed learning

32, 41, 52–53, 60; ultra-social learning 13, 15, 176, 181, 240, 264, 267–68, 294, 308, 321, 339 machine learning – see learning material vii–viii, 1–3, 10–5, 21, 26, 33–4, 37, 48–50, 55–9, 67, 69–70, 77, 80, 85, 92–5, 98–100, 105, 107, 124–25, 128, 134, 136, 138, 141, 147, 153, 158, 162–63, 167–68, 175–6, 180–81, 183–84, 191, 193–4, 197–98, 209, 212, 226, 230–31, 234–37, 249, 263, 271, 280, 286–289, 294–97, 302, 320–23, 338–42 Merleau-Ponty, Maurice 253, 255–57, 259–260, 263–64, 287 mindful body 278, 291–92 mindbody 252, 264, 290–91 new feminist materialism 240–41 normative 145, 161, 181–83, 192, 225, 231, 252, 321 normativity 321–24, 331, 340 Physicists vii, 66, 70–71, 81, 83, 85–9, 92, 136–9, 141–44, 153, 163 performativity 10, 85–6, 230 posthumanist learning – see learning postphenomenology 38, 241, 253–54, 266 preceding learning – see learning rational 6–8, 26, 67, 111–12, 127, 231, 251, 274, 299, 309–10, 313, 321, 340–41 relational ontology 11, 21 49, 56, 94, 106, 240–41, 253, 267, 325, 342n4 relational agency 277, 284, 302 relativism 225, 235–37 robot 5–7, 9, 13, 16–25, 98–101, 105–09, 116–121, 127, 129, 148–49, 163, 169–73, 175, 177, 184–87, 191–92, 196, 203–05, 216–17, 231–32, 235–36, 286, 293, 300–01, 312, 340 schema 78–82, 91, 125, 136, 207–08, 263–65 Science and Technology Studies (STS) 69, 85, 145, 295 self/self-directed learning – see learning singularists 7, 110–13, 115–16, 128–29, 243–45 situated practice 45, 59, 140, 162, 196, 311 spinozists 110–13, 128–29, 142, 241, 243–245, 261, 297–98, 302, 308 technology 31–2, 38–9, 41–3, 60, 113, 122, 129, 148–49, 233–34, 241, 244,

Index 

248, 251, 253–56, 265–66, 280, 289–91, 294, 311–12, 326 technologies 31–3, 39, 47, 51–2, 100, 127–28, 133, 148–49, 253–56, 264, 266, 293, 301, 326–27 transhumanist/ism 3, 112–13 251 ultra-social humans 59, 175, 213–14, 225, 236, 266, 286, 290, 300, 308, 320–21, 323–24, 337, 339–41

349

ultra-social learning – see learning Vygotsky, Lev 12, 14, 82, 90, 126, 153–62, 169, 174, 177–83, 193–94, 203, 222–26, 231–32, 234, 236, 253–54, 266, 271, 286, 295–96, 298–99, 319–20, 323, 325, 327 word-meaning 182, 184, 255, 257–58, 261–63, 266–68