Cognitive Archaeology, Body Cognition, and the Evolution of Visuospatial Perception 0323991939, 9780323991933

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COGNITIVE ARCHAEOLOGY, BODY COGNITION, AND THE EVOLUTION OF VISUOSPATIAL PERCEPTION

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COGNITIVE ARCHAEOLOGY, BODY COGNITION, AND THE EVOLUTION OF VISUOSPATIAL PERCEPTION Edited by

Emiliano Bruner Research Group Leader in Hominid Paleoneurobiology at the National Research Center for Human Evolution in Burgos, Spain

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2023 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. ISBN: 978-0-323-99193-3 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

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Contents

Task-specificity and anatomical independence in perceiving properties of wielded objects 39 Task-specificity and anatomical independence in perception by means of wielded objects 41 What function(s) has the touch system evolved to serve? 42 What architectural configuration of the touch system coevolved to support this function? 44 Concluding thoughts: what to make of (the evolution of) tool use? 47 References 48

Contributors ix Biography xi Preface xiii

Section I VISUOSPATIAL COGNITION AND EVOLUTION 1. Somatosensation and body perception: the integration of afferent signals in multisensory cognitive processes

3. Evolutionary perspective on peripersonal space and perception

ROCHELLE ACKERLEY

MATHILDA FROESEL, SULIANN BEN HAMED AND JUSTINE CLÉRY

The sensory origin of body perception 4 Central processing and integration of somatosensory signals 12 Conclusion and future perspectives 17 References 18

Introduction 51 Functions and definition of the peripersonal space Brain expansion and evolution 56 Tool-use 62 Social and cultural societies 67 Conclusion 70 Acknowledgments 71 References 71

2. Perception by effortful touch and a lawful approach to (the evolution of) perceiving and acting JEFFREY B. WAGMAN, JULIA J.C. BLAU AND TYLER DUFFRIN

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4. The body in the world: tools and somato-centric maps in the primate brain

A (the) predominant approach to understanding how perceiving occurs 25 Assumption 1: The fundamental separation of animal and environment 26 Assumption 2: The primacy of animal-independent variables 27 Evolutionary puzzles and paradoxes and (brief) hints at resolutions 28 An ecological account of perceiving of, and behavior in, the surroundings 32 The ecological approach to perceiving by touch 36

BANTY TIA, RAFAEL BRETAS, YUMIKO YAMAZAKI AND ATSUSHI IRIKI

Introduction 85 The evolution of a biological substrate conducive to tool usage 87 Tool representation in the brain 91 Mapping the tool-usage space 96 Cognitive components of tool use 97 The tool with the body and the body in the world 99

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8. Parietal lobe expansion, its consequences for working memory, and the evolution of modern thinking

Conclusion 100 Funding 102 References 102

5. Parietal cortex and cumulative technological culture GIOVANNI FEDERICO AND FRANÇOIS OSIURAK

Introduction 109 Motor control 111 Visuospatial skills 115 Technical reasoning 119 Evolution of the parietal cortex and technical reasoning 123 Conclusion 125 References 126

6. Body-tool integration: past, present, and future LUKE E. MILLER AND MARIE MARTEL

Introduction 131 Body-tool integration during motor control Body-tool integration during sensing 139 Future of integrated technology 143 Conclusion 146 References 146

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Section II VISUOSPATIAL BEHAVIOUR AND COGNITIVE ARCHAEOLOGY 7. The evolution of the parietal lobes in the genus Homo: the fossil evidence EMILIANO BRUNER

Paleoneurology and functional craniology 153 Parietal endocasts 157 The fossil evidence on parietal lobe evolution in the human genus 162 Anatomy, cognition, and behavior 173 Acknowledgments 175 References 175

FREDERICK L. COOLIDGE

Working memory 182 Regions of the parietal lobes 185 The SMG, phonological storage, and the evolution of language 189 The parietal lobes and the default mode network of the brain 189 Egocentric and allocentric frames of reference and emotional regulation 190 The episodic buffer and the evolution of modern thinking 191 Do future simulations enhance prospective memory? 191 References 192

9. Experimental neuroarchaeology of visuospatial behavior DIETRICH STOUT

Introduction 195 Neuroarchaeology as evolutionary neuroscience Comparative evidence 198 Experimental evidence 201 Evolutionary interpretation 204 Conclusion 206 References 207

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9. Experimental neuroarchaeology of visuospatial behavior DIETRICH STOUT

Introduction 195 Neuroarchaeology as evolutionary neuroscience Comparative evidence 198 Experimental evidence 201 Evolutionary interpretation 204 Conclusion 206 References 207

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10. Cognitive archaeology, attention, and visual behavior MARÍA SILVA-GAGO AND EMILIANO BRUNER

Vision, attention, and human evolution 213 Eye tracking technology 214 Visual attention in cognitive archaeology 218 Vision and cognition in prehistory 231 Acknowledgments 232 References 232

11. Handling prehistory: tools, electrophysiology, and haptics ANNAPAOLA FEDATO AND EMILIANO BRUNER

A brain at hand: from haptics to cognition 241 Minds, hands, and stone tools 242 Perceiving tools: attention, activation, and emotional reaction 247 Electrodermal responses to Lower Paleolithic stone tool manipulation 253 Final considerations 255 Acknowledgments 257 References 257

12. A comparative approach to evaluating the biomechanical complexity of the freehand knapping swing ERIN MARIE WILLIAMS-HATALA AND NEIL T. ROACH

Introduction 263 Mechanics of the freehand Oldowan knapping swing 266 Nut-cracking mechanics in bearded capuchins 269 Discussion 270 Conclusions 273 References 274

13. Psychometrics, visuospatial abilities, and cognitive archaeology EMILIANO BRUNER, MARÍA SILVA-GAGO, ANNAPAOLA FEDATO, MANUEL MARTÍN-LOECHES AND ROBERTO COLOM

Psychometrics and cognition 279 Psychometrics and visuospatial ability 287 Psychometrics and archaeology 293 Human evolution: the body and beyond 299 Acknowledgments 300 References 300

Index 305

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Contributors Rochelle Ackerley Aix Marseille University, CNRS, LNC (Laboratoire de Neurosciences Cognitivese UMR 7291), Marseille, France

Mathilda Froesel Institut des Sciences Cognitives Marc Jeannerod, CNRS Université de Lyon, Bron Cedex, France

Suliann Ben Hamed Institut des Sciences Cognitives Marc Jeannerod, CNRS Université de Lyon, Bron Cedex, France

Atsushi Iriki Laboratory for Symbolic Cognitive Development, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan

Julia J.C. Blau Department of Psychological Science, Central Connecticut State University, New Britain, CT, United States

Marie Martel Royal Holloway University of London, Egham, United Kingdom Manuel Martín-Loeches Centro UCM-ISCIII de Evoluci on y Comportamiento Humanos, Madrid, Spain; Departamento de Psicobiología y Metodología en Ciencias del Comportamiento, Universidad Complutense, Madrid, Spain

Rafael Bretas Laboratory for Symbolic Cognitive Development, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan Emiliano Bruner Programa de Paleobiología, Centro Nacional de Investigaci on Sobre La Evoluci on Humana, Burgos, Spain

Luke E. Miller Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands

Justine Cléry Department of Neurology and Neurosurgery, The Neuro and ACAR, McGill University, Montreal, QC, Canada

François Osiurak Laboratoire d’Etude des Mécanismes Cognitifs (EA3082), Université de Lyon, Lyon, France; Institut Universitaire de France, Paris, France

Roberto Colom Facultad de Psicología, Universidad Aut onoma de Madrid, Madrid, Spain

Neil T. Roach Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, United States; American Museum of Natural History, New York, NY, United States

Frederick L. Coolidge Psychology Department, University of Colorado-Colorado Springs, Colorado Springs, CO, United States Tyler Duffrin Department of Psychology, Illinois State University, Normal, IL, United States

María Silva-Gago Programa de Paleobiología, Centro Nacional de Investigaci on Sobre la Evoluci on Humana, Burgos, Spain

Annapaola Fedato Programa de Paleobiología, Centro Nacional de Investigaci on Sobre La Evoluci on Humana, Burgos, Spain

Dietrich Stout Department of Anthropology, Emory University, Atlanta, GA, United States

Giovanni Federico IRCCS Synlab SDN S.p.A., Napoli, Italia; Laboratorio di Psicologia Sperimentale, Universita Suor Orsola Benincasa, Napoli, Italia; Dipartimento di Psicologia, Universita degli Studi della Campania “Luigi Vanvitelli”, Caserta, Italia

Banty Tia Laboratory for Symbolic Cognitive Development, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan Jeffrey B. Wagman Department of Psychology, Illinois State University, Normal, IL, United States

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Contributors

Erin Marie Williams-Hatala Department of Biology, Chatham University, Pittsburgh, PA, United States; Center for the Advanced Study of Human Paleobiology, The George Washington University, Washington, DC, United States

Yumiko Yamazaki Laboratory for Symbolic Cognitive Development, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan

Biography

Emiliano Bruner has a PhD in Animal Biology. Since 2007, he is the Research Group Leader in Hominid Paleoneurobiology at the National Research Center for Human Evolution in Burgos, Spain. He works in brain evolution, bridging anthropology and neuroscience. His research over the last 20 years has largely dealt with the

evolution of the parietal lobes in the human genus, with visuospatial cognition, and with the relationships between brain, body, and environment. He has published more than 150 scholarly articles, writes in several dissemination magazines, and is the editor of two books on paleoneurology.

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Preface

Touching minds: body, tools, and the evolution of a prosthetic consciousness When we set about designing the cover for this book, I proposed a schematized digital hand approaching a digital stone tool. After looking at the first sketch of the drawing, I had a subtle negative reaction, because it showed the left hand reaching for the handaxe. My mirror neurons fired awkwardly, because of my profound right-handedness. The geometry of the composition was really nice, but generated an unfortunate doubt: should we represent the right or left hand? To rely on an empirical approach, I sent the two mirror versions of the same cover to more than 60 friends and colleagues, with very distinct cultural backgrounds, and asked their opinion. The answers and rationales were diverse, suggesting multiple factors that went beyond such a simple visual preference. In many cases, the favored version was associated with handedness (right-handed persons prefer the right-hand version, and viceversa), probably because of the self-projection of the reader onto the image, in which embodiment and mirror neurons are comfortably pleased by a congruent simulation with one’s own perception. In other cases, the choice was justified because of more abstract and emotional feelings, such as “aperture” or “closeness,” “firmness” or “uncertainty,” “heaviness” or “lightness.” Finally, a third category of answers was associated with spatial, orientation, and geometric issues (the hand generates a diagonal

line, and the finger points toward a specific direction), including the left-to-right sense of reading, the side of the book aperture, or the position of the company logo. For few persons, these different criteria were valid at the same time, generating a conflictive final decision. Nonetheless, most of the time, people were pretty firm in their final choices and statements, which were accompanied by “surely,” “no doubt” or “certainly.” The final verdict was approximately 60% of the persons supporting the left-hand pointing right, which was, as you can see on the cover, the final decision. The message we can take from this improvised and naïve psychological survey is that the relationship between hand and vision is not an easy one, because so many factors can be involved, including somatic feedback, cultural influences, sensorial codes, and emotional components. We perceive the “world outside” mainly through our eyes, and then we interact with this world mainly through our hands. Primates largely invested, in adaptive terms, in vision and touch, through anatomical, physiological, and neural changes, and we should then expect that these two functional interfacesdthe eye and the handdmust have had a crucial role in shaping 70 million years of our cognitive evolution. That is, seeing and touching are not automatic and mechanical processes, but are embedded in a complex system of body experiences, perceptive filters, and neural circuitries, as part of a comprehensive flow of information between the brain, body, and environment.

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Preface

This flow of information is probably what we call “cognition” or “cognitive process,” and which is more generally labeled as “mind.” In the early 19th century, Edmund Husserl put perception at the center of the epistemological dialog concerning knowledge and cognition and, few decades later, Maurice Merleau-Ponty put the body at the center of this perspective. Then in the late 80s, open-minded neurobiologists and psychologists, like Francisco Varela, realized that many of the concepts put forward in this regard belonged to the ancient Buddhist tradition, proposing a bridge between Western neuroscience and oriental philosophy. The main rationale for this combination concerned the necessity, for science, to be integrated with a philosophical tradition that includes experimental and empirical feedback, when exploring and investigating the processes associated with cognition, consciousness, or awareness. Western philosophy is mainly rooted in logic and theoretical reasoning, while oriental tradition proposes meditation as a form of training and exploration of the mind-body relationships, by using the own body as an experimental laboratory. Following the evidence provided from both approaches (namely, the Western science and oriental philosophy), nowadays we suspect that the main cognitive domain linking perception and cognition is the attention system, a bottleneck factor which turns perception into awareness and consciousness, by virtue of a sustained executive control. However, in this case, we have to say that the use of the term “perception” in the title of this book is a general one, and it does not refer strictly to the sensorial decoding of the signals. Instead, it deals with the following cognitive process, which integrates the sensory inputs with attention and spatial abilities. Varela and his colleagues published the book entitled “The Embodied Mind” in 1991 and, in the same decade, we found other scholars, within the academic context, proposing that cognition is not a product of a brain, but instead a process associated with the interaction between body and environment. At that time, this perspective was largely supported on a theoretical and

philosophical basis, like in the Extended Mind Theory put forward by Andy Clark and David Chalmers. Later, these theories began to be investigated in neurobiology and, more recently, they also called the attention of archaeologist and evolutionary anthropologists, who tried to consider whether (and to what degree) extended cognition may be a crucial issue in hominid evolution. In more or less the same period, neurobiologists working with both living and fossil species were finally persuaded to discuss the outstanding differences in the parietal cortex between humans and nonhuman primates. The brain is a single and integrated system, so a functional dissection of its parts is but a conventional exercise that, however, is necessary to develop theories and analyses in a scientific framework. The functions of the parietal cortex depend intimately on the rest of the brain regions, and its reciprocal relationships with the frontal areas are undoubtedly a crucial issue for most of our complex mental skills. Nonetheless, the anatomical differences in this region between our species and the rest of primates are so apparent that they probably merit special consideration. Such macroscopic differences had been evidenced more than half century before, but largely neglected because of the poor interest toward the parietal lobes, a region that was long hypothesized to be involved in “basic” cognitive aspects. The management of the body was assumed to be a primitive issue, shared by most animals and hence scarcely interesting when investigating the complex functions associated with the evolution of our intelligent species. This was possibly one of the reasons why the noticeable development of our parietal cortex was left unattended, at least when compared to the amount of research dedicated to other brain regions. In our species, the anatomy of the parietal lobe is so complex that the homology with the areas of the other primates is still speculative. This fact should have been sufficient to attract attention regarding its derived features, but it was dismissed in most evolutionary perspectives on brain and cognitive evolution until very

Preface

recently. Interesting, also half century of cybernetics was suggesting that making a robot walk or sense (body management) is much more difficult than making it compute (calculations) or store data (memory), although only these two latter functions have been long interpreted as cues of the highest mental capacities. Apart from the anatomical differences, the functional analyses of these regions showed that the parietal cortex is a crucial node for visual imaging, body projection, tool use and eye-hand coordination, attention, and language. Experimental approaches in cognitive science also demonstrated that, as we can read in the chapters of this book, acting and sensing are the inseparable components of most cognitive domains, that the body is used as a metric unit for any spatial, temporal, or social projection, that tools are sensed as part of the body and included within the cognitive circuitry, and that vision and action are linked by a special kinds of simulation neurons that can blend inner and outer body experiences. All these are key features of general consciousness and are essential to most human-only mental abilities. Visual imaging, for example, is the very foundation of mental experiments, and of the capacity of past (memories) and future (predictions) projections. Eyehand coordination and tool-integration are also of special interest, mostly when considering that humans became, 300,000 years ago, obligatory tool-users, embedding their culture and cognition inside a complex network of extra-somatic peripheral elements called “technology.” In sum, it turns out that we humans have a complex parietal cortex devoted to a comprehensive and blurred list of functions that we can generally label as “body cognition,” and that these functions are not automatic and mechanical routines aimed at grasping a wooden stick properly, but are instead intimately associated with core aspects of the self, mental simulation, and consciousness. It is not clear whether the differences between humans and other animals, in this sense, are a matter of grade (that is, a disproportionate development of shared resources) or due to the specific evolution of brand-new

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capacities. Either way, the result is impressive, and the effects of these changes in humans support cognitive abilities that are incomparable with any other known form of life. We should admit that, at some point, these changes must have evolved, more gradually or more abruptly, within the lineage of our own genus, the genus Homo. The powerful imaging capacity was able to place perceptions and feelings within a very long timeline that we called “ego,” entrenching the present moment between the challenging demands of a harsh past and of an uncertain future. The possibility to include external elements within the cognitive networks may have allowed the selection and evolution of a “prosthetic capacity,” as the enhanced ability to extend and amplify cognitive skills beyond the spatial, physical, and physiological limitation of the body and of the brain. The following questions might be: Did this really happen? When and how? Within this process, what is the precise role of the brain, of the body, and of the tools? Can all this be tested? What are the consequences of this evolutionary background? Trying to investigate this topic is rather difficult, because any inference must be necessarily based on existing species only (which are not ancestors of the human lineage), and on the poor and fragmented fossil record (which cannot provide direct and consistent cognitive data). In addition, any issue concerning cognitive evolution needs a multidisciplinary expertise and knowledge, because it deals with all possible fields embracing anthropology, neuroscience, or ecology. Therefore, if we want to provide robust and consistent scientific hypotheses on these topics, we must rely on multiple and independent sources of information, and on diverse professional skills. Which is, ultimately, the aim of this book: an invitation to evaluate all these questions, and to pay more attention to our body, when taking into consideration the amazing possibilities of our own minds. Emiliano Bruner Burgos, September 2022

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S E C T I O N I

Visuospatial cognition and evolution

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C H A P T E R

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Somatosensation and body perception: the integration of afferent signals in multisensory cognitive processes Rochelle Ackerley Aix Marseille University, CNRS, LNC (Laboratoire de Neurosciences CognitiveseUMR 7291), Marseille, France

body, which includes the muscles, joints, connective tissue, fascia, viscera, and internal sensing. In early work, Sherrington described different senses, which included divisions into teloreception (vision, audition), proprioception (body position in space), chemoreception (olfaction, gustation), exteroception (touch, including mechanoreception, thermoreception, and nociception), and interoception (visceral sensing) (Sherrington, 1948). There have been debates on the distinction between exteroception and interoception, and it is clearly a complicated division, but it is generally agreed that exteroception concerns the relationship between the body and the external environment, whereas interoception is the representation of the physiological condition of the body (Craig, 2002). Hence, it is possible that some bodily receptors could be considered both exteroceptive and interoceptive, such as in affective sensations of pain and pleasure, which can be encoded directly by the skin, but produce emotional responses that impact homeostatic processes. In

Our skin encompasses the entire body and is our largest sensory organ. It not only holds us together and acts as a barrier, but it receives constant stimulation from the external world and gives us a sense of embodiment. Within our skin and body, we have a vast system of afferents that encode mechanical, thermal, and chemical stimuli and send them to the brain for processing and integration, which have developed for better adaptation to our environment through evolution over millions of years. Our afferent system has evolved to allow us to effortlessly interact with the world and can provide both warning (e.g., pain) and pleasurable (e.g., gentle caress) information. The somatosensory afferent system can be divided in many ways, such as differences between skin type, skin innervation, or between exteroception and interoception; however, it is highly complex and exhibits large differences between individuals. This chapter will deal mainly with information coming from the skin, but it is important to consider somatosensation from the whole

Cognitive Archaeology, Body Cognition, and the Evolution of Visuospatial Perception https://doi.org/10.1016/B978-0-323-99193-3.00007-6

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© 2023 Elsevier Inc. All rights reserved.

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this way, sensation refers to the feelings produced about states of the sense organs and nervous system, whereas perception is defined as the interpretation and awareness we gain from the activation of our sensory organs. In the present chapter, the complexity of the somatosensory system will be addressed, with the implications for central processing and its integration with other sensory and cognitive mechanisms.

The sensory origin of body perception The sensory input from our body comes in the form of many different channels, which are integrated centrally to give our sense of self and bodily perception. The source of these signals is primarily from the skin, but internal sensors in tissue such as muscles, joints, and receptors inside the core of the body also contribute. Further, our other senses can influence body perception, for example, seeing our body (e.g., looking at our feet to help us walk over rough terrain), hearing our body (e.g., when something touching our skin makes a noise), and even how our body normally smells and tastes, where we may feel different if these inputs change. Although the direct measurement of these processes can be challenging, often due to the complicated nature of our body and environment, there are a number of useful methods to approach these questions. For example, microneurography can be used to measure the activity in peripheral nerves (for a review, see Ackerley and Watkins, 2022), electrodermal analysis can tell us about emotional responses to touch (Ree et al., 2019; Fedato et al., 2020; see also Chapter 11), electromyography can be used to quantify the use of muscles (Mayo et al., 2018; Ree et al., 2019), and perceptual ratings help us to understand the feelings generated (Ackerley et al., 2014b; Sailer et al., 2020). Below, information from the skin will be first covered in detail and

then with consideration of the impact of other sensory inputs.

Somatosensation from the skin The skin contains numerous specialized receptors to sense mechanical, thermal, and chemical stimuli applied to the body (Fig. 1.1). A recent comprehensive review and analysis of body innervation density stated that there are >1 million fibers in the dorsal roots of the spinal cord in total, which include large-, medium-, and thinly myelinated fibers to unmyelinated fibers, and it was estimated that a young human adult body has w230,000 myelinated Ab mechanoreceptive afferents, although we lose about 5% of our afferents every decade of adulthood (Corniani and Saal, 2020). The capacity of the sensory afferent system is therefore vast; however, the brain processes this input efficiently and effortlessly. The skin itself is highly

FIGURE 1.1 Overview of different skin types, stimuli that impact on the body, and the classes of receptors that can encode this. Skin can be generally divided into glabrous skin of the ventral hands and feet, hairy skin, and mucocutaneous skin (e.g., mouth, nose, eyes, genitals). Three main types of stimuli can impact the body, namely of mechanical, thermal, and/or chemical sources, where these are encoded by different types of receptors.

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The sensory origin of body perception

heterogenous, where different skin regions have different sensitivities to stimuli, containing several afferent classes, in varying densities. Overall, the skin can be divided into glabrous (nonhairy), hairy, and muco-cutaneous skin (Fig. 1.1). The vast majority of the skin (90%) can be classed as hairy skin, as it contains hair follicles. The hairs can be very small and thin (e.g., vellus hairs) or thick and long (e.g., scalp terminal hairs), but this is all defined as hairy skin. Thus, the length and diameter of hairs can differ greatly, as well as hair follicle density, which can vary by more than an order of magnitude across the skin (Szabo, 1967), and vellus hairs account for 80%e90% of all hairs (Halata, 1993). It is clear that some skin regions contain many thick, terminal hairs (e.g., scalp, dorsal arms), but there is no significant difference between males and females in hair density itself (Szabo, 1967), only the type of hair differs (i.e., whether it is thick terminal hair or thin vellus hair). It is also noteworthy that the density of hair follicles in human skin is equivalent to that of a similar-sized animal (Schwartz and Rosenblum, 1981). Glabrous skin is defined as the nonhairy skin of the ventral hands and feet, which also has ridges (e.g., fingerprints) that are most obvious on the finger and toe pads.

Mucocutaneous skin can almost be classed as a type of border skin, which is the surface between the outer skin and inner bodily tissue. Mucocutaneous skin is often wet (e.g., eyes, mouth, genitals) and needs to be maintained in this state of higher water content. The borders between all skin types are not well-defined, where there is often a smooth transition between skin types, for example, the dorsal sides of the finger tips are classed as hairy skin, but there are virtually no hairs present. The skin can be divided into at least two layers that are somewhat different in thickness over the skin, and estimates of thickness can vary. The epidermis is the top, outer layer of the skin, which also includes the very most outer surface: the stratum corneum. Underneath is the thicker dermis, which is a supporting layer that contains connective tissue. Although the glabrous skin appears to be thicker, the general thickness of the skin is similar across the body, at around 2 mm thick; however, there is regional variation (Fig. 1.2). The hairy skin in general has an epidermis of w80 mm thickness (Robert et al., 1966; Mogensen et al., 2008), whereas the glabrous skin has a thicker epidermis, where the stratum corneum alone is w500 mm, and a relatively reduced dermal layer. The overall FIGURE 1.2 Different thicknesses of skin across the body. Adapted from Robert et al. (1966).

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thickness of the scalp skin is also reduced at w1.3 mm, and the eyelid is very thin at around 0.5 mm (Hwang, 2013), whereas the back has an extremely thick dermis of w4 mm (Robert et al., 1966) (Fig. 1.2). Depending on the individual, some of the skin on the foot sole can be around w5 mm thick also, for obvious reasons due to the impact of weight enforced on this skin. These characteristics of the skin show its high heterogeneity and complexity. This is mirrored in the receptors that are present, which can be generally classified as mechanoreceptive, thermoreceptive, or nociceptive. Although numerous receptors can respond to more than one stimulus type (mechanical/thermal/chemical), they typically show a preference (e.g., peak response) for one type of stimulus. Concerning mechanoreceptors, humans have many different types that are present at different densities across the skin. Low-threshold mechanoreceptive afferents can be generally classed as fast-conducting Ab and slowly conducting C-fiber. Vellus hairs may be innervated by more thinly myelinated (or unmyelinated) fibers, although this classification cannot be verified, as virtually no recordings from vellus hair afferents (Adriaensen et al., 1983) have been found in microneurography (i.e., peripheral axonal nerve recordings in humans; for an overview of the technique, see Ackerley and Watkins, 2022). There are a number of different types of Ab mechanoreceptive afferent, which can be classed as fast-adapting (i.e., when a mechanical stimulus is applied to the skin, held stationary, then lifted off, there are only responses to the onset and/or offset of touch) or slowly adapting (i.e., when the same stimulus is applied, there will be clear onset and/or offset responses, as well as firing during the sustained indentation). Four types of Ab mechanoreceptive afferent exist in the glabrous skin (Table 1.1), namely, fastadapting type I (FA-I, putatively connected to Meissner corpuscles), fast-adapting type II (FAII, putatively connected to Pacinian corpuscles),

slowly adapting type I (SA-I, putatively connected to Merkel disks), and slowly adapting type II (SA-II, putatively connected to Ruffini endings) afferents (Vallbo and Johansson, 1984). It is postulated that there are around 17,000 mechanoreceptive afferents in each human hand (Johansson and Vallbo, 1979), which along with the face, is one of the most densely packed areas of mechanoreceptors. This is why the hands are essential in exploring the world, where we use them for the dexterous manipulation of objects (Johansson and Birznieks, 2004) and to sense a multitude of different textures (Weber et al., 2013), which is encoded precisely with millisecond timing (Johansson and Birznieks, 2004; Mackevicius et al., 2012; Saal et al., 2017) and is essential in our modern-day lives. Further, in line with the development of skillful tool manipulation in humans, studies have shown that the biomechanical context of tool use and tool making has itself influenced the evolution of the human hand (Williams-Hatala et al., 2018). This is important to consider, as the way our body encodes interactions with our world has been highly shaped by our environment and the objects we interact with, whether these be things like tools or other humans. In hairy skin, the FA-I Meissner afferents are not present, but instead, fast-adapting hair follicle afferents (HFA) and field afferents are present (Vallbo et al., 1995), which are highly sensitive mechanoreceptors (Table 1.1). Low-threshold mechanoreceptive C-fibers are called C-tactile (CT) afferents (for a review, see Ackerley, 2022) that are intermediate-adapting and are abundant over the arm (Vallbo et al., 1999; Löken et al., 2009; Ackerley et al., 2014a) and face (Nordin, 1990), but are much more sparse on the leg (Edin, 1992; Löken et al., 2022). It is postulated that CTs are also abundant on the torso, but this has never been shown directly. In addition, CTs have occasionally been found on the glabrous skin of the hand (Watkins et al., 2021). Where Ab mechanoreceptive afferents have

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TABLE 1.1

Proposed division of types of cutaneous mechanoreceptor, thermoreceptor, nociceptor, and muscle proprioceptor. There is considerable variability within each type, where some types have subclasses. *Ctactile afferents were believed to only be present in hairy skin, but a recent study has shown a sparse projection in glabrous hand skin (Watkins et al., 2021).

Main class

Afferent type

Putative receptor

Low threshold Fast-adapting type 1 Meissner mechanoreceptor corpuscles

Thermoreceptor

Nociceptor

Axon type

Body region

Further information

Myelinated Ab Glabrous skin

Signal discriminative aspects of tactile interactions, including form.

Fast-adapting type 2 Pacinian corpuscles

Myelinated Ab All skin

Highly sensitive to all touch (including remote touch) and signals vibrations well.

Slowly-adapting type 1

Merkel disks

Myelinated Ab All skin

Signals discriminative aspects of tactile interactions, including pressure.

Slowly-adapting type 2

Ruffini endings

Myelinated Ab All skin

Usually considered to signal deeper pressure and skin stretch.

Field

Unknown

Myelinated Ab Hairy skin

Very sensitive touch afferents that likely signal minimal-force wetness interactions in hairy skin.

Hair

Hairs

Myelinated Hairy Ab, thinly skin myelinated Ad

Signals hair movements, from both terminal (thick hairs, myelinated axon) and vellus (fine hairs, thinly-myelinated axon) hairs.

C-tactile (CT; C-low Free nerve ending threshold mechanoreceptor, CLTM), type 3

Unmyelinated C

May signal more sub-conscious and affective aspects of touch. Responds preferentially to slow, gentle, stroking touch delivered at skin temperature.

Ad cool

Free nerve ending

Thinly All skin** Cool-sensitive with a maximum myelinated Ad discharge at temperatures around 27
C, believed to be the main neuronal population subserving innocuous cold sensations.

C-cold, type 2 (C2)

Free nerve ending

Unmyelinated C

All skin** Cooling, with no sensitivity to touch; can show activity at typical skin temperature and fire down to 0
C. May show paradoxical responses to heating.

C-warm

Free nerve ending

Unmyelinated C

All skin** Sensitive to warming, no sensitivity to touch. Subclasses: Low threshold warm receptors (LTWRs), high threshold warm receptors (HTWRs)

High threshold mechanoreceptor

Free nerve ending?

Myelinated Ab All skin** These have been recently demonstrated in hairy skin (Nagi et al., 2019). High speed sensing of sharp mechanical pain.

All skin*

(Continued)

I. Visuospatial cognition and evolution

8 TABLE 1.1 Main class

1. Somatosensation and body perception

dcont'd Afferent type Ad nociceptor

Proprioceptor

Putative receptor Free nerve ending?

Axon type

Body region

Further information

Thinly All skin** Very little evidence of their existence myelinated Ad (see Adriaensen et al., 1983), signals pain.

C-mechanosensitive Free nerve (CM), type 1A, ending polymodal

Unmyelinated C

All skin** Signals noxious touch (CM) and some chemical sensitivity. Subclasses also respond to noxious temperature, namely C-mechano-heat (CMH) and Cmechano-heat-cold (CMHC) nociceptors.

Cmechanoinsensitive (CMi or C-MIA), type 1B

Free nerve ending

Unmyelinated C

All skin** Noxious heat, little mechanical sensitivity within measurable limits. Some chemical sensitivity. Subclasses: C-mechanoinsensitive-heat-insensitive (CMiHi) nociceptors have little thermal sensitivity either, while Cmechanoinsensitive histamine-positive (CMi(Hisþ), also known as C-pruritic, C-itch) are pruriceptors and sensitive to histamine.

Primary muscle spindle, type Ia

Annulospiral endings

Myelinated Aa Muscle

Slowly-adapting response to muscle stretch, with higher dynamic sensitivity, signaling the degree of change in muscle movement.

Secondary muscle spindle, type II

Flower spray endings

Myelinated Aa Muscle

Slowly-adapting response to muscle stretch, with lower dynamic sensitivity, signaling the length of the muscle.

Myelinated Aa Tendon

Directly encodes the contraction of the muscle, i.e., muscle tension. Little response to muscle length changes.

Golgi tendon organ, Golgi organ type Ib Joint receptors

Ruffini, Golgi, Myelinated Aa Joint Pacini, ?

Responds well to joint movement, especially at the extremity. Some responses to joint rotation. Little response to mechanical joint pressure.

*C-tactile afferents were believed to only be present in hairy skin, but a recent study has shown a sparse projection in glabrous hand skin (Watkins et al., 2021). **The innervation of these afferents in human glabrous skin is unknown from microneurography, but can be inferred from psychophysical tests and animal work. For further reading: mechanoreceptors (Vallbo and Johansson, 1984; Vallbo et al., 1995; Corniani and Saal, 2020; Ackerley, 2022), thermoreceptors (Konietzny, 1984; Campero et al., 2001; Green, 2004; Schepers and Ringkamp, 2010; Ackerley and Watkins, 2018), nociceptors (Campero et al., 1996; Serra et al., 1999; Bostock et al., 2003; Green, 2004; Ackerley and Watkins, 2018; Nagi et al., 2019), and proprioceptors (Matthews, 1972; Hulliger, 1984; Burke et al., 1988; Macefield, 2005)

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The sensory origin of body perception

been implicated in the signaling of discriminative touch process, due to their fast conduction velocity, the CT information arrives after a delay, thus they are believed to reinforce gentle contact, such as in pleasant, positive affective touch (McGlone et al., 2014). Interestingly, it is accepted that there is a general decline in the acuity of the mechanosensory system with age (Stevens and Choo, 1996; however, see Skedung et al., 2018 who show that some participants do not decrease much in tactile capacity), although work has shown that touch actually becomes more pleasant with age (Sehlstedt et al., 2016); however, the reason for this is unknown. Mechanoreceptors primarily encode different types of mechanical events that are applied to the skin (e.g., FA-II afferents encode vibration well, slowly adapting mechanoreceptors encode pressure well); however, mechanoreceptors can show some sensitivity to thermal and chemical stimuli. Although these have been littleexplored, work has shown that SA-IIs may increase their response to cool touch (Konietzny, 1984; also unpublished observations from Ackerley et al., 2014a). Further, SA-Is may have decreased responses to cool touch (Bouvier et al., 2018), but animal work has also shown dynamic SA-I firing increases to cooling (Iggo and Muir, 1969; Duclaux and Kenshalo, 1972). CT afferents have been shown to have decreased firing to mechanical stimulation that is warmer or colder than skin temperature (Ackerley et al., 2014a), although mechanical skin cooling appears to be more complex, where sustained, additional lower-frequency firing can be found (Ackerley et al., 2018). It is also likely that HFAs show no sensitivity to thermal stimulation of the skin (Ackerley et al., 2014a), but it is clear that when the body is cold, the autonomic nervous system can induce piloerection of hairs, which would lead to afferent activation. Therefore, although mechanoreceptors always encode mechanical events, their responses can be modified by temperature and firing can even be induced with chemicals, such as the sensation

9

of tingling/buzzing induced by sanshool (which also activates thermoreceptors and nociceptors) (Bautista et al., 2008; Lennertz et al., 2010; Cataldo et al., 2021). The complexity of receptor encoding of mixed-modality stimuli again points to the specific adaptation of biological organisms to their environment and the interactions they have. The skin contains many receptors that primarily encode thermal and noxious stimuli. The thermoreceptive system has received less attention, although it is central in somatosensation: imagine touch without temperature? This is like vision without color. When something touches us, it is always accompanied by the encoding of its temperature. Only a handful of studies have looked at pure thermoreceptors, which can be putatively classed into cool Ad fibers, cold C-fibers, and warm C-fibers, although these classes are debatable, due to a lack of evidence of existence (Konietzny, 1984; Campero et al., 2001; Paricio-Montesinos et al., 2020). It is agreed that C-cold fibers exist and respond to cooling (Konietzny, 1984; Campero et al., 2001, 2009) and the application of menthol (Campero et al., 2009), but are generally not sensitive to touch. However, it could be that they are related to C-mechano-heat-cold (CMHC) nociceptors (Table 1.1), as C-cold fibers can also paradoxically respond to heating (Campero et al., 2009). It is also noteworthy that the bodily thermal representation is likely very different to the tactile representation. Contrary to the acuity of the hands and face in tactile discrimination, there is a general trend that we sense temperature changes more readily on the upper half of our body. Our face is the most sensitive, especially the lips, where throughout our whole life, we can sense w0.05
C change in lip temperature (Stevens and Choo, 1998). Temperature sensing on the lower leg is rather poor, especially the toe, which for young adults (65 years), this rises greatly to w10
C increase or 3
C decrease

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1. Somatosensation and body perception

in temperature (Stevens and Choo, 1998). Therefore, as well as the general decrease in touch capacity with age, the thermosensory system also has some degradation. Potentially noxious stimuli, as encoded by nociceptors, has been much more researched, with many publications demonstrating the diversity of C-fiber nociceptors (for overviews, see Bostock et al., 2003; Ackerley and Watkins, 2018). Although humans usually aim to decrease nociceptive input, it is essential, as it provides a warning that the skin could be damaged. This important source of afference can greatly shape our body perception, as a strong nociceptive input is difficult to ignore and causes negative affect, driving us to do something to alleviate it. There exist many different types of nociceptor, from ultrafast conducting nociceptors (Nagi et al., 2019) to very slow C-fibers (for a review, see Ackerley and Watkins, 2018) (Table 1.1). Nociceptors respond to all different types of stimuli, even those that are not particularly noxious, for example, C-mechanosensitive (CM) nociceptors can have low force activation thresholds, similar to low threshold mechanoreceptors, but only respond weakly to gentle touch (Nordin, 1990; Watkins et al., 2017). However, the optimal responses from nociceptors are normally in the painful range (e.g., heating over 42
C, cooling less than 20
C, strong mechanical force). Overall, it is evident that the skin somatosensory system is highly complex, where many different types of receptors encode bodily interactions, as well as there being large variability at all levels. Even giving someone a hug will activate numerous receptors from all classes, such as all types of low threshold mechanoreceptor, thermoreceptors, nociceptors (mechanoreceptive nociceptors that have lower force activation thresholds), and even muscle proprioceptors (these may fire due to pressure on muscles, as covered below). It is therefore the balance and synergy between the activation of all these different types of somatosensory

receptors that shape our bodily perception. For example, lots of input from low threshold mechanoreceptors, with little input from other types, would likely signal a pleasant contact; however, high activation in all classes of the receptor would likely be unpleasant (e.g., intense firing, addition of nociception). Further, the balance between the activation of fast-conducting afferents, which give temporally precise information about actual tactile events, is complemented by the activation of slower afferents (e.g., some thermoreceptors, nociceptors, and CT afferents) that provide “color” to sensations, such as by reinforcing specific aspects of contact (e.g., CTs may reinforce gentle interpersonal interactions).

Internal somatosensory sensing: muscles, joints, and viscera The division between the outside and inside of the body is rather vague, where there is not a sharp border between external skin and internal tissue. Rather, just like the borders between hairy and glabrous skin, the boundary between external hairy skin and mucocutaneous skin is imprecise. This can be readily seen in the change in the skin near mucocutaneous regions (e.g., although our lips seem rather defined, on inspection, there is no sharp border) and felt in that it is difficult to sense exactly where our internal sensation of touch ends (e.g., for rectal sensations, Rogers, 1992). If you concentrate on the border between sensing internal “touch,” such as when food goes down our esophagus, it is not easy to sense the point at which the sensation ends. However, it is clear that we can feel sensations such as pressure and vibration internally, but it is not like feeling external touch; we are aware that it comes from inside the body. Therefore, it is apparent that we can define external contact as touch, but internal sensations are more related to interoception, body schema, and body wellness.

I. Visuospatial cognition and evolution

The sensory origin of body perception

We have numerous receptors encapsulated in our deeper tissue, including in skeletal muscle, joints, and viscera. We gain our sense of selfin-space, proprioception, though four types of sensitive mechanoreceptive afferent: primary muscle spindles (group Ia, situated in the muscle), secondary muscle spindles (group II, situated in the muscle), Golgi tendon organs (group Ib, situated in tendons), and joint receptors (situated in joints) (see Table 1.1; and for a review, see Macefield, 2005). These proprioceptive afferents have thickly myelinated axons and send information very quickly to the brain. Muscle spindles have the peculiarity of being innervated by a sophisticated, descending, efferent system: the (gamma) g-fusimotor system. This efferent drive can change muscle spindles sensitivity, meaning that the encoding of muscle activity may be influenced by descending factors such as vision, attention, learning, and emotions (for a review on the effects of the g-drive on muscle afferent firing, see RibotCiscar and Ackerley, 2021). Proprioceptors are important in encoding the position of the body in space, as well as its movement, but they are also activated simply by pressing on muscle. Although tendon and joint receptors are quite insensitive to pressure, muscle spindle afferents will readily respond to pressure applied to the muscle belly. The exquisite sensitivity of muscle afferents is intriguing; they respond to rather gentle touch, including light tapping, pressure, and a range of vibration applied to the skin over muscle receptive field, and will even respond to more remote stimuli, such as via tendon manipulation or more remote vibration (Macefield, 2005). This implies that the input from muscle afferents during tactile interactions, even passive ones, can provide information about bodily contact. In a similar way, cutaneous afferents have also been shown to respond well to body movements, where it has been demonstrated that mechanoreceptive afferents, particularly type IIs, are tuned to joint orientation (Aimonetti et al., 2007).

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We also have receptors that have thinly myelinated axons (group III, Ad fibers, mainly mechanosensitive) and are unmyelinated (group IV, C-fibers, mainly metabosensitive) in our skeletal muscle, which sends information more slowly to the brain and can signal mechanical, thermal, and chemical changes in our muscles. Where muscle and joint proprioceptive afferents likely contribute at least to some direct awareness of the body in space (Macefield et al., 1990; Macefield, 2005), the group III and IV fibers are tentatively classed as nociceptors. They can signal neuromuscular fatigue and pain, as they are optimally activated through exercise contraction-induced mechanical, thermal, and metabolic stimuli (McCord and Kaufman, 2009; Amann et al., 2020). Thus, where proprioceptors can be classed as more exteroceptive, group III and IV muscle afferents are more interoceptive, as their direct stimulation can cause cardiovascular reflex adjustments. The majority of group III and IV muscle afferents are chemosensitive (around half), while around a third respond to mechanical stimulation, and a third to thermal stimuli (Jankowski et al., 2013). There are receptors deep in our bodies that contribute to our visceral sense of interception and internal sensing. Much less is known about these, as it is extremely challenging to conduct microneurography recordings from internal sources (cf. Dunham et al., 2018; Ottaviani et al., 2020). However, it is clear that we can receive mechanical, thermal, and nociceptive afferent information from our internal bodies. One type of mechanoreceptor, the Pacinian corpuscle (FA-II afferent in the skin), is found throughout the entire body, from the internal organs and nerves of the torso and pelvis, to within the connective tissue of joints, and in blood vessels (Roberts, 1959), although these Pacini end-organs can be of considerably different size and shape (Sheehan, 1933). Mechanosensing is important in our body, but especially in the gut, where we have other myelinated afferents (unencapsulated fibers, Merkel-like

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1. Somatosensation and body perception

enterochromaffin cells) and unmyelinated afferents that capture specialized and nonspecialized sensory signals in the gut (Sheehan, 1933; Mercado-Perez and Beyder, 2022), which often can be linked to visceral affective sensations, such as fullness after a meal and in painful pathologies. Although there is a lot to learn about human internal sensing and what it contributes to body representations, it cannot be overlooked, as it has evolved to give us an integrated sense of self and is essential to our well-being.

Central processing and integration of somatosensory signals Once a peripheral receptor has been activated, the signal is transmitted toward the central nervous system, where somatosensory signals are forwarded mainly to the parietal lobe. Although this pathway is simplified in textbooks, there is incredible complexity at all levels, with divergence and convergence of afference well before the information enters the brain. Below is an overview of this complex system and the potential points at which information changes along the afferent pathway.

Classic central pathways activated by somatosensory afference Our sense of tactile awareness is mainly subserved by myelinated Ab mechanoreceptive afferents, which is part of the so-called “discriminative touch pathway” (McGlone et al., 2014). The canonical view is that these afferents constitute a direct route or “labelledline” for touch information to be transmitted very quickly to the brain. Typically, the afference enters the spinal cord via the dorsal root, then ascends ipsilaterally up the dorsal columns in a topographically organized fashion. The input from the main body is relayed by the cuneate (input from the upper body) or gracile (input from the lower body) parts of the dorsal column

nuclei (DCN), then the signals cross the midline (decussation) to synapse in the thalamus, and then on to primary (S1) and secondary (S2) somatosensory cortical areas. These responses shape our reactions about touch and how we explore our world with millisecond precision. On the other hand, more slowly conducting information in the anterolateral system takes a different route. C-fiber input is typically seen as entering the spinal cord and then ascending 1e2 vertebral levels to make ipsilateral synapses in the dorsal horn. Secondary neurons at this site project over the midline (decussation) and the information ascend via lamina I of the dorsal horn, through the spinothalamic tract, to synapse in the ventral posterior thalamus, and then on to cortical areas such as the insular cortex, S1, and cingulate cortex. However, this canonical textbook view is far from the actual complexity of this pathway: the ascending information is not simply relayed; rather, there is the potential for processing and interaction at each step. There are multiple points where ascending information transmission becomes more complex. Even the peripheral nerve afferent is highly complex. The receptor end is very sensitive to stimulation, but the axon and dorsal root ganglion (DRG) cell body are also electrically excitable (Devor, 1999; Ackerley and Watkins, 2018). It has even been postulated that the DRG neuron serves as a source of afferent input, as some DRG cells are able to fire repetitively (Devor, 1999). For afferent fibers that ascend directly up the spinal cord (e.g., Ab mechanoreceptive afferents), the principle branch of the axon ascends in the dorsal column; however, collateral branches of the entering axon also terminate in the spinal cord locally, or within a few segments, adding to the potential for interactions (for an indepth overview, see Abraira and Ginty, 2013). Small-diameter afferents have an even more complex termination pattern, with a high degree of interaction, where different dorsal horn interneurons play a key role in integrating information and there are neurons that project these

I. Visuospatial cognition and evolution

Central processing and integration of somatosensory signals

signals from the vast majority of the whole dorsal horn (layers IeV). The information, which can come from a number of afferent types, is then sent via the dorsal columns, the spinothalamic tract, or the spinocervical tract (Abraira and Ginty, 2013). For signals that reach the DCN, each ascending main afferent contacts w1700 DCN neurons and each one receives input from w300 afferents (Johansson and Flanagan, 2009). The synaptic interruption in the DCN provides a processing step for ascending information. A single DCN neuron can have a similar receptive field and response properties to other DCN neurons, yet each neuron responds to a unique combination of convergent input, where it has been demonstrated that tactile and object features have already been computed, making their activity more similar to cortical responses than peripheral nerve input (Jörntell et al., 2014; Suresh et al., 2021). The vast majority of all sensory afference is relayed to the cortex via the thalamus, notably apart from olfactory signals. Some postural information is sent to the cerebellum, via the spinocerebellar tracts, and sympathetic afferents terminate in medullary nuclei and the hypothalamus, before entering cortical areas. The thalamic nuclei are thought to be the gateway to the brain, where incoming signals are relayed to cortical areas; however, the thalamus is a potential source of large integration of both incoming afference and reciprocal exchange between cortical areas via cortico-thalamo-cortical connections (Cappe et al., 2009). The circuitry of the thalamic nuclei will not be covered presently, but, in brief, specific areas have been identified that are associated with somatosensory input, such as the ventral posterolateral (VPL) nucleus of the thalamus for tactile bodily inputs, the ventral posteromedial nucleus for facial input, and the posterior part of the ventromedial nucleus (VMpo) and the basal part of the ventromedial nucleus (VMb) that are involved in affective and visceral sensing (for a review on

13

interoceptive pathways, see Craig, 2002). Studies have demonstrated that thalamic responses, like in the DCN, resemble more the firing patterns of somatosensory cortical neurons than the peripheral code, where tactile feature extraction has been demonstrated in VPL (Vazquez et al., 2013). Thus, the responses of neurons, both in the DCN and thalamus, are similar to somatosensory cortical activity, but they are not identical: there is processing and extraction of pertinent information in these subcortical structures. After passing through the subcortical nuclei, somatosensory afference is mainly sent to the parietal lobe, where Brodmann area (BA) 3 of the S1 receives a vast input from the thalamus (Kaas et al., 1984); however, mechanoreceptive, thermoreceptive, and nociceptive information are all represented in the S1, S2, and insula (Davis et al., 1998; Stanc
ak et al., 2006; Rolls et al., 2008; Peltz et al., 2011; Mancini et al., 2012; Panchuelo et al., 2020). There are multiple, precise body representations in S1 (Kaas et al., 1979; S
anchez-Panchuelo et al., 2013), but somatotopical relations are less evident in the S2 and insula. There is a wealth of research on tactile responses in S1, which will not be covered in-depth here, but it is clear that the human S1 responds to a broad spectrum of mechanosensory input, from vibration to pressure, and all types of tactile discrimination, yet it is also evident that activity in both the S1 and thalamus is highly related to the period of sensory activation, thus the processing of the direct somatosensory signal (Romo and Rossi-Pool, 2020). Therefore, it is believed that the S1 is the main tactile awareness detection center that then drives other areas in the parietal and frontal lobes to act and make decisions (Romo et al., 1998). However, S1 does not function alone and has, among others, dense connections with the primary motor cortex (M1), S2, and insula. The wide activation of cortical areas in “quantal touch” (the basic, elementary sensation provoked from stimulating a single afferent) has

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been demonstrated using a unique approach to stimulate a single mechanoreceptive afferent and measure the resulting cortical activity, i.e., using single unit intraneural microstimulation with brain imaging (Trulsson et al., 2001; Sanchez Panchuelo et al., 2016; O’Neill et al., 2019). This approach uses microneurography to identify and record from single Ab mechanoreceptive afferents, then the same peripheral neuronal axon is electrically stimulated using very low current pulses. It is possible to gain a “quantal” percept of a tactile sensation, where it is believed that the activity in one mechanoreceptor can be felt as a specific point on the skin, for example, an FA-I afferent feel like a small point of vibration, whereas an SA-I feels like a small point of pressure (Vallbo et al., 1984; Torebjörk et al., 1987; Watkins et al., 2022). Although it is clear that this percept is likely evoked by the activity of many thousands of neurons, there is conservation along the somatosensory path and the selective artificial stimulation of one mechanoreceptive afferent activates a network of cortical areas. This was demonstrated at 7 Tesla (T) functional magnetic resonance imaging (fMRI), where single unit intraneural microstimulation gave rise to a small area of contralateral S1 being activated, which was in correspondence with a larger area activated at the same cortical region by point-vibration at the skin receptive field site of the afferent. Further, activity to single unit intraneural microstimulation was also found bilaterally in the S2, M1, premotor cortex, insula, and posterior parietal cortex, as well as in contralateral prefrontal cortex and in the ipsilateral S1 (Sanchez Panchuelo et al., 2016). It is noteworthy that the M1 was coactivated during this pure tactile input, demonstrating the common, yet lesser-reported finding of S1/M1 synchrony, whether the source is motor or somatosensory. However, due to the close anatomical proximity of S1 and M1, as well as the strong links between somatosensation and movement, this codependence is to be expected. Further, many of the activations from intraneural microstimulation

were bilateral, even S1, showing the integrated nature of bodily touch processing. These findings show the need for constant interplay between movement and its feedback, which is especially important in complex manipulations, such as in tool use, and point to why these functions are difficult to separate. The S2 is very much implicated in the processing of more complicated aspects of touch, such as form, orientation, and pattern. Where S1 is closely related to somatosensory discrimination, S2 encodes task performance and influences decision-making, which is associated with knowledge of both present and past tactile events (Romo et al., 2002). There is often a contralateral predominance in S1 activity to touch, but both S2 cortices often respond, which is similarly seen with the insula (Olausson et al., 2002). Concerning the insula, this structure has been more implicated in the processing of homeostatic and affective stimuli, such as the perception of gentle touch, temperature, and pain in the posterior insula (Craig et al., 2000; Craig, 2002; Olausson et al., 2002). However, the insula is activated during all tactile interactions (Morrison, 2016). Other somatosensory association areas over parietal regions, such as Brodmann area (BA) 5 and 7, are typically also activated during somatosensory interactions (Ackerley et al., 2012), but these have been less studied as compared to the main areas and respond in the integration of somatosensory signals with other processes. Somatosensory association cortex is involved in multisensory, motor, and vestibular processing, where the more caudal areas, going toward visual cortex, have more been implicated in the integration of somatosensory and visual signals (Iwamura, 2003). Overall, it is evident that for each step of information being encoded peripherally and sent to be integrated centrally, there is the potential for complexity, where signals are filtered, processed, and integrated to better adapt the information and extract relevant features. This is

I. Visuospatial cognition and evolution

Central processing and integration of somatosensory signals

highlighted in a rare group of people who have sensory neuronopathy, losing large myelinated somatosensory fiber function below the level around their head. They have no fast-feedback from touch or movements, although they can complete some sensorimotor tasks, with the aid of slowly conducting fiber input, vision, and their residual motor memory (Cole and Sedgwick, 1992). This shows that damage to the afferent pathway can be compensated by other mechanisms. This complexity continues well into the cortex, where primary targets for somatosensory information represent stimulus characteristics well, but other activated areas, as covered below, are more implicated in the integration and understanding of what the input means to us.

Integration of somatosensory signals with other senses and with internal mechanisms As mentioned above, somatosensory stimuli can activate a widespread network of brain areas, including areas in all lobes, subcortical structures, and the cerebellum. However, these additional regions often involve more specific tasks, such as the engagement of cognitive and emotional mechanisms, which are wellingrained into everyday life and have thus evolved in complex ways to be coactivated at different levels. Concerning multisensory integration, some senses are heavily interlinked, such as taste and smell, yet touch is impacted by all our senses. Vision, audition, and proprioception are inherently highly associated with touch, and how we explore something tactually can be influenced by smell and even taste, such as when we eat something, the food has specific gustatory and olfactory qualities, but you also feel its texture. Touch is also very important in vestibular mechanisms, such as when you reach out to help balance, which becomes particularly evident with aging.

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Internal central mechanisms may engage the more typically activated somatosensory areas, for example, S1 that can respond to visual input, which includes seeing touch of objects and between humans. The discovery of mirror neurons in the motor system, which respond during a selfaction and on the observations of another’s action (for a review, see Rizzolatti and Craighero, 2004), has opened up many lines of research concerning the internal representations of others. The mere exposure to watching touch interactions between humans (i.e., vicarious touch) readily activates the S1, S2, and insula, as well as other parietalopercular areas (Blakemore et al., 2005; McCabe et al., 2008; Keysers et al., 2010; Ebisch et al., 2011; Morrison et al., 2011; Bolognini et al., 2014), although the specific network may depend on the exact situation. For example, there are differences in the somatosensory brain activity found during active self-touch and passive touch delivered at the same skin, where strong activity is provoked in self-touch in S1, but also that selftouch activates somatosensory association areas, including the precuneus (Ackerley et al., 2012). These findings demonstrate that networks exist to distinguish between self-touch and interpersonal- or object-touch, which include how we can cancel feedback from our own movements in a context-dependent manner (Blakemore et al., 1998; Ackerley et al., 2012). Further, it highlights the importance of our ability to actively manipulate tools and their incorporation into our body schema (Miller et al., 2018, 2019; see also Chapters 4 and 6), which has also been implicated in the expansion of the precuneus in our evolutionary development (Bruner et al., 2017; Chapter 7). In evolutionary terms, our social interactions have heavily shaped our development (Dunbar, 2010). This includes the propensity for touch, such as in grooming, to strengthen social bonds and conspecific relationships. Direct interactions with others and the development of our complex social network are underpinned by verbal and nonverbal communication, but in the case of

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touch, it is hypothesized that CT afferents form a basis to help reinforce and reward gentle interpersonal contact (Löken et al., 2009; Vallbo et al., 2009; Olausson et al., 2010; Ackerley et al., 2014a). CT-optimal touch has been shown to activate the insula well (Olausson et al., 2002; Morrison, 2016). Some studies have found activity in the superior temporal sulcus (STS) during affective touch stimulation, although it is likely that this area is more involved in the integration of touch with information from the other senses and ongoing integrative processes (Morrison, 2016). However, the premotor cortex and STS are often recruited during the observation of social touch, often more than when the person views an object being touched. The STS and surrounding cortical regions have been implicated in processing biological motion and the movements involved in social situations (Deen et al., 2015), as well as being activated in multisensory motion processing (Beauchamp et al., 2008), thus encode higher-level aspects about our everyday and interpersonal interactions. There is a clear importance of social context in our lives, which is heavily engrained in our contact with others. This is also reflected in processing the meaning of touch, where social significance can also impact central processing (Gazzola et al., 2012). The prefrontal cortex is often activated during situations of somatosensory comparison, where working memory is engaged, such as in the discrimination between two tactile tasks (Romo et al., 1999). There are clear attentional and context-dependent mechanisms that modulate activity in primary somatosensory areas, where information from various brain structures concerning memory, attention, motivation, and emotion will shape responses (Romo and Salinas, 2001). For example, task-dependent activity during a vibrotactile attention paradigm has shown many different cortical areas that can respond to help the task at hand, including regions in the frontal eye fields, premotor cortex, STS, and supplementary motor area (Burton et al., 2008). Another study found that sensory

stimuli that change and capture our attention and awareness recruit multimodal mechanisms, such as the additional activation of the temporoparietal junction, inferior frontal gyrus, cingulate, and supplementary motor areas (Downar et al., 2000). The recruitment of such diverse and distributed networks demonstrates the complex involvement of numerous cortical areas in the full processing of somatosensory stimuli, in line with the needs of the current situation, enabling us to act in appropriate ways. Frontal areas, including the orbitofrontal cortex, are recruited in the affective evaluation of touch, as in pleasantness processing (Rolls et al., 2003; Rolls et al., 2008). The OFC is also known to be activated by other senses in a similar fashion, for example, in the processing of gustatory pleasantness and even in bodily pain (Francis et al., 1999; Rolls et al., 2003; Rolls et al., 2008), thus represents a multisensory area for the evaluation of reward, be it for positive or negative reinforcement. The cingulate cortex has also been implicated in similar affective processing of stimuli from different senses and is subject to cognitive modulation (Francis et al., 1999; Rolls et al., 2003; McCabe et al., 2008). Overall, these higher-order brain areas are clearly modulated in different ways, depending on the task at hand and the behavioral situation, where decision-making and orientation toward goals play a large role in determining the processing. One further central area that makes a major contribution to somatosensory integration is the cerebellum and the precerebellar nuclei (e.g., pons, inferior olive). The cerebrocerebellar projection is one of the major pathways of the brain, linking many cerebral cortical regions, especially somatosensory, motor, and visual areas, with the cerebellar cortex. There is considerable integration in the cerebellum, where the afferent input greatly outnumbers the efferent output (ratio 40:1, Brodal, 1992). The cerebellum is highly involved in the modulation and regulation of behavior, and in the

I. Visuospatial cognition and evolution

Conclusion and future perspectives

acquisition of new skills, where timing and learning are important. Concerning touch and interactions with the environment, the cerebellum is believed to be important in generating feed-forward sensory prediction for the consequences of behavior. Thus, there is the ongoing comparison of motor output with sensory input and whether this matches or there is any error, where tactile pathways play a central role in shaping this (Blakemore et al., 2000; Bower, 2011; Sch€ afer and Hoebeek, 2018). The point at which information becomes truly “multisensory” is difficult to identify, as the brain is heavily interconnected, thus is it likely that somatosensory signals are influenced very early by the other senses, as well as by ongoing cognitive and emotional mechanisms, as outlined above. However, there are specific situations where somatosensory information is highly coprocessed with other sensory information. For example, in the case of tactile motion, as well as the STS that is involved in biological motion perception, area V5/MT, which plays a key role in visual motion perception, is activated when the skin is stroked (Hagen et al., 2002). However, a further study using vibrotactile stimulation of the skin found responses only in MST, where these were as strong as in S1 (Beauchamp et al., 2007). Further, in touch-audition interactions, sounds can modulate how touch is perceived, as clearly found in the classic parchment illusion and roughness perception (Jousm€ aki and Hari, 1998; Guest et al., 2002). In addition to any processing of incoming somatosensory information, the brain needs to act on that information. The mechanisms of this will not be covered presently, although it is noteworthy that the brain can directly adapt its own input, depending on the circumstances. As mentioned above, proprioceptive muscle afferents are innervated by the descending g-fusimotor efferent system, which can directly modify muscle spindle sensitivity to better adapt actions to the behavioral situation. This has been shown for cognitive mechanisms, such as attention and

17

learning, as well as the impact of emotion (for a review see Ribot-Ciscar and Ackerley, 2021). There has even been shown to be a direct influence of vision on muscle-afferent firing. A recent study has shown that when participants did not see their foot move, muscle spindle firing was slightly increased, as compared to when participants had congruent proprioceptive and visual information (Ackerley et al., 2019). This demonstrates a weighting of information, where proprioceptive signals may be augmented from a central command when visual feedback is not available. In another study, it was shown that when muscle afference feedback from a moving hand was coupled with incongruent visual information, proprioceptive sensitivity was reduced to resolve such bisensory discrepancy (Jones et al., 2001). In all, the descending g-fusimotor drive shows the influence of the brain on our own sensory feedback, where there are feedforward processes that enable rapid and efficient adaptation to an external situation, which helps us prepare responsive and appropriate action. This process is ongoing and is updated continually, showing the complex, intricate nature of our interactions with our environment.

Conclusion and future perspectives In all, this chapter has examined the sensory origin of bodily perception, by looking at the different types of somatosensory afference that are received and processed by the brain, and why these have been selected in evolutionary terms to help us encode interactions with our environment. It highlights the complexity throughout this process, where even at the receptor level, there can be an integration of signals (e.g., the encoding of mechanical and thermal signals by mechanoreceptors). Somatosensory processes clearly involve multisensory integration, as well as being influenced by ongoing cognitive and emotional/affective mechanisms. In the case of touch especially,

I. Visuospatial cognition and evolution

18

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vision is a key component in humans, where we are driven by the dominance of visual inputs, and it is evident that these interact with somatosensory areas to understand our contact with the environment (e.g., texture perception) and with others (e.g., social significance). However, our tactile sense provides us with the means to do many complex tasks, such as object manipulation, which has greatly shaped who we are. The progression from early occasional tool use 2 million years ago, to the habitual incorporation of tools in our lives, through to these tools now being obligatory, highlights how touch and our need to manipulate and explore things in our world has modified our own bodies over time (Shea, 2017). Rather, we now possess “prosthetic capacities” that allow us to treat tools as an extension of our own bodies (Miller et al., 2018, see also Chapters 6 and 13), which have been shaped by biological and environmental influences (Bruner, 2021). It seems that humans can integrate external objects, likely even other people, into body schema, meaning that these external entities can become temporarily integrated into our self. There are many avenues yet to explore in bodily perception, and it is of interest to use increasingly sophisticated techniques, such as high-field fMRI, to probe mechanisms in humans precisely, especially subcortical structures. It is also of interest to investigate the relevance of the expansion of the human parietal cortex (Bruner et al., 2023), especially in comparison with other animals and their differences in object manipulation capacities and social structures. Further, it is of importance to define tasks and protocols to unravel the exact processes underlying cognitive influences on somatosensation, where open science and the availability of data will mean access to a vast amount of information that can be used to gain further, meaningful insights into somatosensory processes, with increased power to enable the reliable generalization of findings. This would advance our understanding

of bodily perception, both on a fundamental level, and also in combating somatosensory diseases.

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anchez-Panchuelo, R.-M., et al., 2013. Regional structural differences across functionally parcellated Brodmann areas of human primary somatosensory cortex. Neuroimage. https://doi.org/10.1016/j.neuroimage.2013.03.044. Sanchez Panchuelo, R.M., et al., 2016. Mapping quantal touch using 7 Tesla functional magnetic resonance imaging and single-unit intraneural microstimulation. Elife 5, e12812. https://doi.org/10.7554/eLife.12812. Sch€ afer, C.B., Hoebeek, F.E., 2018. Convergence of primary sensory cortex and cerebellar nuclei pathways in the whisker system. Neuroscience 368, 229e239. https:// doi.org/10.1016/J.NEUROSCIENCE.2017.07.036. Schepers, R.J., Ringkamp, M., 2010. Thermoreceptors and thermosensitive afferents. Neurosci. Biobehav. Rev. 34 (2), 177e184. https://doi.org/10.1016/j.neubiorev.2009.10.003. Schwartz, G.G., Rosenblum, L. a, 1981. Allometry of primate hair density and the evolution of human hairlessness. Am. J. Phys. Anthropol. 55 (1), 9e12. https://doi.org/ 10.1002/ajpa.1330550103. Sehlstedt, I., et al., 2016. Gentle touch perception across the lifespan. Psychol. Aging 31 (2), 176e184. https:// doi.org/10.1037/pag0000074. Serra, J., et al., 1999. Activity-dependent slowing of conduction differentiates functional subtypes of C fibres innervating human skin. J. Physiol. 515, 799e811.

Shea, J.J., 2017. Occasional, obligatory, and habitual stone tool use in hominin evolution. Evol. Anthropol. Issues News Rev. 26 (5), 200e217. https://doi.org/10.1002/ EVAN.21547. Sheehan, D., 1933. The afferent nerve supply of the mesentery and its significance in the causation of abdominal pain. J. Anat. 67 (Pt 2), 233. Sherrington, C.S., 1948. The Integrative Action of the Nervous System. Cambridge University Press, Cambridge, UK. Skedung, L., et al., 2018. Mechanisms of tactile sensory deterioration amongst the elderly. Sci. Rep. 8 (1), 5303. https://doi.org/10.1038/s41598-018-23688-6. Stanc
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Vazquez, Y., Salinas, E., Romo, R., 2013. Transformation of the neural code for tactile detection from thalamus to cortex. Proc. Natl. Acad. Sci. USA 110 (28), E2635eE2644. https://doi.org/10.1073/pnas.13097 28110. Watkins, R.H., et al., 2017. Optimal delineation of single Ctactile and C-nociceptive afferents in humans using latency slowing. J. Neurophysiol. 117 (4), 1608e1614. https://doi.org/10.1152/jn.00939.2016. Watkins, R.H., et al., 2021. Evidence for sparse C-tactile afferent innervation of glabrous human hand skin. J. Neurophysiol. 125 (1), 232e237. https://doi.org/ 10.1152/jn.00587.2020.

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Watkins, R.H., et al., 2022. Slowly-adapting type II afferents contribute to conscious touch sensation in humans: evidence from single unit intraneural microstimulation. J. Physiol. 600 (12), 2939e2952. https://doi.org/ 10.1113/jp282873. Weber, A.I., et al., 2013. Spatial and temporal codes mediate the tactile perception of natural textures. Proc. Natl. Acad. Sci. U.S.A. 110 (42), 17107e17112. https://doi.org/ 10.1073/pnas.1305509110. Williams-Hatala, E.M., et al., 2018. The manual pressures of stone tool behaviors and their implications for the evolution of the human hand. J. Hum. Evol. 119, 14e26. https://doi.org/10.1016/J.JHEVOL.2018.02.008.

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C H A P T E R

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Perception by effortful touch and a lawful approach to (the evolution of) perceiving and acting Jeffrey B. Wagman1, Julia J.C. Blau2, Tyler Duffrin1 1

Department of Psychology, Illinois State University, Normal, IL, United States; 2Department of Psychological Science, Central Connecticut State University, New Britain, CT, United States

A (the) predominant approach to understanding how perceiving occurs

properties of that surrounding environment. Bringing things full circle, (3) the ability of any given individual member of that species to both perceive properties of and perform everyday behaviors with respect to the surrounding environment isdlargely, though perhaps not entirelyddependent on and supported by the evolution of that animal species. What often goes unappreciated, however, is that mutually dependent and mutually supportive processes of perceiving and evolving require mutually dependent and mutually supportive scientific theories of how such processes occur. Our goal in this chapter is to examine one side of this mutually dependent and mutually supportive relationship. Specifically, we examine whether predominant theories of how perceiving occurs are up to the task of being mutually dependent on and supportive of theories of evolution, and if not, whether there are viable

It seems irrefutable that the process by which members of a given animal species perceive properties of the surrounding environment and the process by which that species evolves are mutuality dependent and mutually supportive. At the risk of oversimplifying: (1) The evolution of given animal species isdlargely, though perhaps not entirelyddependent upon and supported by the ability of individual members of that species to successfully perform everyday behaviors with respect to the surrounding environment. (2) The ability of any given individual member of that species to successfully perform everyday behaviors with respect to the surrounding environment isdlargely, though perhaps not entirelyddependent upon and supported by that individual’s ability to perceive

Cognitive Archaeology, Body Cognition, and the Evolution of Visuospatial Perception https://doi.org/10.1016/B978-0-323-99193-3.00004-0

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2. Perception by effortful touch and a lawful approach to (the evolution of) perceiving and acting

alternatives that would better serve this role (see Reed, 1996). For the past several centuries (and continuing to the present day), the predominant theoretical approaches to understanding the process by which perceiving (and behaving) occurs can aptly be described as cognitive, representational, or computational. The specifics of such approaches vary. However, without exception, all such approaches to understanding perceiving and behaving have at least two unstated but firmly entrenched assumptions in common. We contend that these assumptions ultimately create unsolvable mysteries and thus create insurmountable barriers to scientific (read: lawbased) explanations of how any given animal perceives properties of and performs everyday behaviors with respect to the surrounding environment. Thus, we conclude that predominant theories of how perceiving occurs are incapable of being mutually dependent on and supportive of theories of evolution. Furthermore, we propose that Gibson’s ecological approach (Blau and Wagman, 2022; Gibson, 2015/1979; Turvey, 2019) is the most suitable alternatived perhaps the only suitable alternativedand we use the achievements of the touch system to help make this case.

Assumption 1: The fundamental separation of animal and environment The first unstated but firmly entrenched assumption of the predominant approaches to understanding how perceiving (and behaving) occur is that animals are fundamentally and logically distinct from their surroundings. In other words, where the animal stops, the surrounding environment begins, and vice versa. This animalenvironment dualism is broader in scope thand and, in fact, encompassesdthe more familiar mind-body dualism (Turvey, 2019). The assumption of animal-environment dualism necessarily entails that the surroundings of a given animaldwhich

are located outside the animaldare fundamentally distinct from the animal’s perception of those surroundingsdwhich are located inside the animal. In short, the animal’s surroundingsd including the energy forms that impinge on that animal’s sense organsdare described as physical states or processes that occur outside of or around that animal. In contrast, the animal’s perception of those surroundings is described as a mental state or process that occurs inside of or within the animal. This fundamental and logical separation of animal and environment means that an animal never experiences its surroundingsdor even the energy that impinges on its sense organsd directly. This is because the various energy forms outside the animal that impinge on that animal’s various sense organsdelectromagnetic, chemical, mechanical, and thermal activityd must be converted to an entirely different energy form inside the animaldneural activitydto be of any use in the perceptual process. The next step in the perceptual process is to convert this neural activity into yet another fundamentally different formda mental state that reflects the properties of the surroundings and is experienced by that animal. This conversion of neural activity to a mental state is only possible because there are set of mediating mechanisms, entities, or processes also occurring inside the animald typically representation and computationdthat bridge the epistemic and ontological gaps between animal and environment and between physical and mental. Performing everyday behaviors with respect to the surrounding environment requiresdmore or lessdrunning this process in reverse. The mental states occurring inside the animal are converted back into a pattern of neural activity by an additional set of mediating mechanisms, entities, or processes occurring inside the animald typically motor commands, motor programs, body schemasdthat bridge the epistemic and ontological gaps in other direction. This neural activity is then converted to yet another completely

I. Visuospatial cognition and evolution

Assumption 2: The primacy of animal-independent variables

different energy formdmechanical forcesdthat originate inside the animal (as muscular activity) but are ultimately directly applied to the surroundings outside the animal. The fundamental separation of animal and environment means that an animal’s perception of properties of the surrounding environment is limited to a mental experience of such properties, mediated by internal mechanisms, entities, or processes. While this mental experience can be related in some way to the energy forms impinging on the sense organsdand hence to the properties of the surroundings themselvesd it cannot be directly related. In other words, perceiving properties of the surrounding environment is necessarily an indirect process (Blau and Wagman, 2022; Turvey, 2019).

Assumption 2: The primacy of animalindependent variables The second unstated, but firmly entrenched, assumption of the predominant approaches to how perceiving (and behaving) occur is that the surrounding environment, the energy impinging on the sense organs, and the perceptual process itself are all best described using the frameworks of Newtonian physics and Euclidean geometry. This has two far-reachingdyet often unacknowledgeddimplications for theories of how perceiving occurs. The first of these is that the most appropriate description of the surrounding environment (including the energy impinging on the sense organs) and of perceiving the properties of that environment is in terms of (lowerorder) physical or geometric variables (e.g., size, shape, distance, angle, mass, color, among many others). The second of these is that the most appropriate description of the processes by which perceiving and behaving occur is a linear and mechanistic causal chain of events. The process by which perceiving occurs, for

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example, consists of the following chain of events: (1) low-level energy forms (e.g., light or sound waves) impinge on the relevant sense organ, (2) such energy is converted to neural activity, and (3) this neural activity is fed through the relevant mediating mechanism, entity, or processes located inside the animal, resulting in mental experience of those properties. Given this description of the perceptual process, the degree of congruence between the mental experience inside the animal and the geometric and physical properties of the surrounding environment outside the animal is informative about the mechanisms, entities, or processes inside the animal that generated the mental experience of those properties. Over the centuries, investigations have repeatedly shown that the low-level energy patterns impinging on the sense organs are ambiguous at bestdand completely uninformative at worstdabout the geometric and physical properties of the surrounding environment. For example, patterns of light impinging on the retinal surface are ambiguously related to object size or shape. Hence, two objects of the same shape can appear to be different shapes, and two objects of different shapes can appear to be the same shape, depending on context. Likewise, patterns of pressure on bodily tissues are ambiguously related to object mass. Hence, two objects of equal mass can feel unequally heavy, and two objects of unequal mass can feel equally heavy, again depending on context. In short, there is potential for both one-to-many and many-to-one relationships between the geometric and physical properties of a particular energy pattern impinging on a sense organ and perception of the geometric or physical properties of the surrounding environment (Blau and Wagman, 2022; Turvey, 2019). Yetddespite such potential ambiguityd everyday experience is decidedly unambiguous, even if it is occasionally inaccurate. And the relative wealth of everyday experienceddespite the

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relative poverty of the stimulationdhas provided insight into the internal mechanisms, entities, or processes by which perception of such properties of the surroundings occurs. Again, the specifics vary, but in general, thesed typically, representational or computationald mechanisms, entities, or processes serve the purpose of disambiguating the ambiguous energy patterns. But this is no small task. Rather, it seems unavoidable that to perform this task, such mechanisms, entities, or processes must be infused or endowed with at least some degree of intelligence or knowledge. In the most genericdand best knowndexample, this disambiguation occurs by means of a process of unconscious intelligent inference (Fodor and Pylyshyn, 1981; Gregory, 2009). Such inferences are aided by factors such as knowledge about which geometric or physical properties of the surroundings were most likely to have generated this particular (ambiguous) energy pattern impinging on the sense organs (see Clark, 2013). Consequently, the bulk of empirical and theoretical efforts in the predominant approaches to understanding the process of perceiving properties of the surroundings is directed toward elucidating the specifics of this intelligent disambiguation process.

Evolutionary puzzles and paradoxes and (brief) hints at resolutions Despite their predominance, there are many reasons why these traditionaldcognitive, representational, or computationaldapproaches to understanding the process by which perceiving occurs are untenable (see Blau and Wagman, 2022; Turvey, 2019; Wagman et al., 2019). At least some of these reasons are based on the numerous puzzles and paradoxes that emerge when such approaches are considered in an evolutionary context (see Blau and Wagman, 2022; Reed, 1996). In what follows, we will briefly describe some of these puzzles and paradoxes anddeven

more brieflydhint at a possible resolution, which will require reconsidering the two fundamental assumptions of the predominant approaches to perceiving and behaving described above. Then, we will provide a description of the ecological approach to perceiving and behaving (which we will rename “perceiving and acting”) (Gibson, 2015/1979; see Blau and Wagman, 2022; Turvey, 2019). This approach not only rejects both problematic assumptions but also develops a law-based explanation of perceiving and acting with an evolutionary context in mind from the start. Finally, we will provide an overview of the ecological approach to perceiving and acting by touchdknown as effortful or dynamic touch. We will argue that (1) the touch system has evolved as a taskspecific and anatomically independent smart perceptual device embedded in lawfully generated structured energy arrays, (2) the embodiment of such a device is a biotensegrity system, and (3) such a system is sensitive to lawfully patterned tissue deformation, which provides information about affordances. Evolutionary Puzzle #1: What is it that evolves? As we have described, in the predominant approaches to understanding the process of perceiving properties of the surrounding environment, animals are assumed to be necessarily distinct and separate from their surroundings. Whereas the surrounding environment is physical, objective, and external, the process of perceiving properties of the surrounding environment and resulting experience of those properties is mental, subjective, and internal. Yet, at the same time, the behaviors of that animal within and with respect to those surroundings aredlike the surroundings themselvesd physical, objective, and external. In other words, the environment and behavior exist on one side of an epistemological and ontological divide, and the perceptual process and the resulting mental experience exist on the other (see Fig. 2.1). This creates a paradoxical state of affairs in which an animal directly behaves

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Evolutionary puzzles and paradoxes and (brief) hints at resolutions

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FIGURE 2.1 In the predominant approaches to perception, the environment in which behavior occurs is physical, objective, and external, but the resulting experience of that environment is mental, subjective, and internal.

with respect to the properties of the surrounding environment but indirectly perceives such properties. For example, an animal can directly walk on the ground surface but can only indirectly perceive the properties of that ground surface (Turvey, 2013). In short, the animal is separate from the environment, the environment is separate from mental experience of that environment, and mental experience of the environment is separate from behaving in that environment (see Fig. 2.1). But behaving in that environment is not (and cannot be) separate from the environment itself. And, if all of this is true, then what is it that evolves? If the animal is separate from the environment, does the animal evolve independently from or perhaps in response to the environment? Clearly, evolution selects for the successful performance of everyday behaviors. Yet, behavingdwhich is directdis separate from perceivingdwhich is indirect. Does this mean that evolution selects for behaving independently from perceiving? And why would animals have evolved to have direct behavior with respect to the surroundings but indirect perception of the environment? Brief hint to the solution of Evolutionary Puzzle #1. As we will see, the key to solving this puzzle is replacing the assumption that animals and environments are fundamentally separate and

distinct with the assumption that animals and environments (and hence perceiving and behaving) are fundamentally continuous and symmetric. Consequently, animals and environments exist and evolve as an inseparable animalenvironment system. If so, rather than being a mental state or experience inside the animal, perceiving the surroundings (like behaving with respect to those surroundings) is a particular kind of relationship between animal and environment. Evolutionary Puzzle #2- What is perceived? What is perception for? In the predominant approaches to understanding perception of the environment, it is assumed that since geometric and physical properties provide the best (read: most objective) description of the environment, they must also provide the best (read: most objective) description of perceived properties of the environment. Therefore, it is assumed that mental experience of the surrounding environment primarily or exclusively consists of geometric and physical properties of those surroundings such as size, shape, distance, angle, mass, color, intensity, among many others (see Fig. 2.2, left). In other words, the perceptual process results in mental experience of the very same set of properties that are measured by various artificial measuring devices. For example, by means of vision, animals

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2. Perception by effortful touch and a lawful approach to (the evolution of) perceiving and acting

FIGURE 2.2 In the predominant approaches to perception, it is assumed that perceptual experience consists of objective and context-independent properties of the environment (left). In the ecological approach, it is assumed that perceptual experience consists of relational and context-dependent properties of the animal-environment system (right).

have mental experiences of the very same properties that are measured by devices such as a meter stick (distance), a photometer (light intensity), or a protractor (angle). And by means of touch, animals have mental experiences of the very same properties that are measured by devices such as a scale (weight) or a dynamometer (mechanical force) (see Fig. 2.2, left). But why would animals have evolved in this way? Why would the visual system have evolved to measure the same properties as a meter stick, a photometer, or a protractor? And why would the touch system have evolved to measure the same properties as a scale or a dynamometer? Why would mental experience consist primarily or exclusively of wholly objective and contextindependent physical and geometric properties? Evolution selects for the successful performance of everyday behaviors. And everyday behaviors are performed by individual animals (i.e., members of a particular species) in a

particular context. That is, behaviors are (1) relationalddepending on a particular fit between a given animal and the environment (its niche), and (2) context-dependentddepending on a particular set of circumstances (in that niche). But geometric and physical properties are completely objective and completely context independent! They are independent of any given animal and any given set of circumstances in any given niche. So how does mental experience of animal- and context-independent properties lead to the performance of successful animaland context-dependent behaviors? How can evolution select for a fit between animals and their environment when mental experience is of properties that exist in the absence of animals? Brief hint to the solution of Evolutionary Puzzle #2. As we will see, the key to solving this puzzle is replacing the assumption that the perceptual process yields awareness of animal- and

I. Visuospatial cognition and evolution

Evolutionary puzzles and paradoxes and (brief) hints at resolutions

context-independent geometric and physical properties of the environment with the assumption that the perceptual process yields awareness of relational and context-dependent ecological properties of the animal-environment system (see Fig. 2.2, right). Consequently, the very same context-dependent relations between animal and environment that support behaving in the surrounding environment support perceiving of the surrounding environment. Therefore, rather than being primarily or exclusively aware of the set of objective properties that describe the surroundings, animals are primarily or exclusively aware of the set of behaviors possible in those surroundingsdknown as affordances (Gibson, 2015/1979). Evolutionary Puzzle #3dHow is it that perception occurs? As we described above, in the predominant approaches to understanding the process by which perceiving properties of the surroundings occur, the low-level energy patterns impinging on the sense organs are ambiguous at bestdand completely uninformative at worstdabout physical and geometric properties of those surroundings. Consequently, these energy patterns must eventually be disambiguated by mediating mechanisms, entities, or processes that are infused withdat least some degree ofdintelligence, broadly defined. However, these internal mechanisms, entities, or processes have an important limitation in that they cannot operate on the energy patterns impinging on the sense organs directly. They can only operate on the (internal) mental representations of these ambiguous energy patterns (again, broadly defined). As described above, this is problematic because animals can only indirectly perceive the properties of their surrounding environment. But just as problematic is thatdby definitiondrepresentations bear no necessary (read: lawful) relationship to that which they

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represent. And herein lies the problemdwhile inserting representations into the perceptual process is a necessary step in the predominant theoretical approaches to understanding how perceiving occurs, doing so makes it impossible to provide a law-based scientific theory of how it is that an animal perceives the surrounding environment (Blau and Wagman, 2022; Turvey, 2019). And herein lies the (even bigger) problemdto the extent that scientific theories of perceiving and evolution must be mutually dependent and mutually supportive, the predominant theoretical approaches to perceiving undermine a law-based scientific theory of evolution. Why would animals have evolved to be sensitive to ambiguous (or even uninformative) energy patterns that would need to be represented and then disambiguated? Why would evolution have been selected for direct behaving with respect to the environment but indirect perceiving of that environment? And are different theories of perceiving required for different speciesd depending on the specifics of their perceptual machinery, neural sophistication, cognitive capacity, and hence, their ability to intelligently disambiguate ambiguous energy patterns? Brief hint to the solution of Evolutionary Puzzle #3. As we will see, the key to solving this puzzle is replacing the assumption that variables borrowed from Newtonian physics and Euclidean geometry are sufficient to describe the energy patterns of relevance to perceiving and behaving with the assumption that variables developed within an ecological physics and an ecological geometry are necessary to do so. Such variables are relational, context-dependent, and (most importantly) unambiguously related to the set of behaviors that are possible in those surroundingsdthat is, affordances. If so, then such patterns would only need to be detected (and exploited), not represented and disambiguated.

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An ecological account of perceiving of, and behavior in, the surroundings American perceptual psychologist James J. Gibson (1950, 1966, 1979/2015) developed an alternative approach to perception of and behavior in the environment. This approach not only explicitly rejected the problematic assumptions of the predominant approaches to perceiving and behaving described above but also provided the theoretical foundation for a law-based explanation of both processes. Moreover, Gibson developed this approach with an evolutionary context in mind (Reed, 1988, 1996). Consequently, Gibson’s ecological approach differs in almost every way from the more traditional approaches to perceiving and behaving (see Blau and Wagman, 2022; Turvey, 2019; Wagman and Blau, 2020). After spending the first few decades of his career doing research steeped in a traditional approach to perception, Gibson eventually arrived at the conclusion that such approaches would never provide a satisfactory explanation of how animals successfully perform everyday behaviors. This was especially problematic because Gibson recognized that successfully performing everyday behaviors is a challenge faceddand metdby all animal species. Moreover, he recognized that meeting this challenge was the most importantdif not the onlyd evolutionary problem that the various perceptual abilities of animals evolved to solve (Gibson, 2015/1979; Reed, 1996; Swenson and Turvey, 1991; Turvey, 2013; Wagman et al., 2019). So, Gibson made a bold choice. He created an entirely new approach to understanding awareness of the surroundings and behavior within those surroundings. That is, rather than providing new answers to old questions, Gibson posed entirely new questions and started with entirely new assumptions (Blau and Wagman, 2022; Heft, 2001; Michaels and Carello, 1981; Reed, 1988). Rather than assuming that the

variables of Newtonian physics and Euclidean geometry provide the best (or perhaps the only) description of the environment and then developing a theory of perception of such properties that may or may not apply to successfully performing everyday behavior, Gibson flipped the script. He started with the plainly observable fact that all animals successfully perform everyday behaviors and then developed a description of the environment and a theory of perceiving that fit with this observation. For Gibson, it was a mistake to use the objective and animal-independent laws and properties of Euclidean geometry and Newtonian physics as the a priori basis from which to develop an understanding of how animals successfully perform everyday behaviors (see Fig. 2.3, top). Rather, the successful performance of everyday behavior ought to serve as the a priori basis from which to develop an understanding of the relational and animal-dependent geometry and physics that make such performance possible (see Fig. 2.3, bottom; Wagman, 2020). This endeavor resulted in the development of an “ecological physics” and “ecological geometry”da description not of the environment-as-such, but rather a description of the environment-for-animals or the environment-as-setting-for-behavior (Nonaka, 2020; Pagano and Day, 2020; Turvey, 2004). Gibson was convinced that it was only necessary to describe the perception of the surrounding environment as intelligent disambiguation of energy patterns if both the surrounding environment and the energy patterns of relevance to perception were described at an inappropriate scale. Once the environment and the energy patterns were described at an appropriate scaled the scale of relational and animal-dependent ecological physics and ecological geometrydit would reveal higher-order energy patterns that were unambiguously (lawfully) related to the set of behaviors that are possible in those surroundingsdi.e., affordances. Such patterns would provide information about affordances.

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An ecological account of perceiving of, and behavior in, the surroundings

FIGURE 2.3

In the predominant approaches to perception, the animal-independent laws of (standard) physics and geometry are used to understand the process of perceiving and behaving (top). In the ecological approach, the everyday success of perceiving and behavior are used to develop an ecological physics and geometry (bottom).

Thus, awareness and behavior could be described, respectively, as the detection and exploitation of such informationdno more and no less (Wagman and Blau, 2020; Wagman et al., 2019). In this way, Gibson’s ecological approach provides the theoretical foundation for a law-based explanation of perception and behavior that applies to all animals regardless of their perceptual machinery, neural sophistication, or cognitive capacity. Consequently, it supportsdrather than underminesda lawbased explanation of evolution (Reed, 1996; Swenson and Turvey, 1991; Wagman et al., 2019), and at the same time, it resolvesdor avoids entirelydthe puzzles and paradoxes that emerge when traditional approaches to

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perception and behavior are considered in an evolutionary context. Thus, Gibson’s ecological approach is a theory of how perceiving occursd or perhaps the only theory of how perceiving occursdthat is capable of being a mutually dependent and supportive partner to theories of evolution. This approach fundamentally changes the answers to the questions posed abovedWhat is it that evolves? What is perceived? What is perception for? How is it that perception occurs? In what follows, we provide the ecological answers to these questions and then describe how such answers apply to the ecological approach to perception by touch. What is it that evolves? As we have described, in the predominant approaches to understanding awareness of the surroundings, the separation of animal and environment creates a paradoxical state of affairs in which behavior is direct, but perception is indirect (Turvey, 2013). And this creates difficult (perhaps insurmountable) challenges in explaining how and why these processes evolved in this paradoxical way. But for Gibson, it is a mistake to separate the animal from the environment. Animals are continuous with environments. Animals and environments are reciprocal, logically inseparable, and mutually constrainingdjust like the surface of a Möbius strip is a continuous surface (see Fig. 2.4; Turvey, 2013, 2015). It is this reciprocal and mutually constraining relationship

FIGURE 2.4 In the ecological approach to perceiving and behaving, animal and environment are continuousdlike the surface of a Möbius strip.

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between animal and environment from which the “ecological approach” gets its name. Thus, for Gibson, when it comes to understanding perception of and behavior with respect to the surroundings, the fundamental unit of analysis is (and must be) the animal-environment system, not (any part of or process within) the animal or the environment in isolation. Critically, dissolving the separation between animal and environment also dissolves the separation between mind and bodydand with it, the separation between perceiving and behaving. Instead, perceiving is continuous with behaving. Perceiving and behaving are also reciprocal, logically inseparable, and mutually constraining (see Blau and Wagman, 2022; Turvey, 2019, 2013). This reciprocity or mutuality between animal and environment (and between perceiving and behaving) means that it is not the animal that evolves, either independently from or in response to the environment. Rather, it is the animal-environment systemd relationships between animal and environment in an ecological nichedthat evolves (Lewontin, 2001; Wagman and Miller, 2003; Withagen and van Wermeskerken, 2010). What is perceived? Gibson’s ecological physics and geometry is a redescription of the environment-for-the-animal (the environmentas-setting-for-behavior). The environment-forthe-animal primarily consists of various combinations of animal-relative properties of substances, surfaces, and media (air and water for most terrestrial animals) that comprise an animal’s niche. Critically, these combinations determine the set of behaviors that are available to that animal in the current situationdthe affordances for that animal in that situation. Thus, a niche is a set of affordances for a given (individual or species of) animal. As such, affordances differ greatly for members of two different species of animals and more subtly for two different individual members of a given species. Affordances emerge from the fit between an animal and its environment. But a given

affordance almost never emerges from a simple (uni-dimensional) fit between animal and environment. Rather, affordances almost always emerge from a complex (multi-dimensional) fit between animal and environment (Blau and Wagman, 2022; Wagman et al., 2019; Wagman et al., 2016). For an object to afford picking up by a given animal, for example, that object must be sufficiently close (relative to the animal’s body size and movement abilities), sufficiently narrow (relative to the size of the animal’s grasping limbdbe it hand, foot, mouth, claw, beak, or tail), sufficiently detached from the support surface (relative to the animal’s mass and strength), and have a sufficiently textured surface (relative to the texture of the surface of the grasping limb) (see Fig. 2.5). Successfully performing everyday behaviors require that animals perceive and behave with respect to affordancesdnot animalindependent properties (e.g., size, shape, distance, angle, mass, color). Moreover, given the unambiguous (lawful) relations between a given energy pattern and a given affordance, animals ought to perceive a given affordance as-such. That is, they ought to perceive a singular complex multidimensional fit between animal and environment (e.g., graspable-with-one-hand-whilestanding-and-leaning) rather than (1) multiple singular fits between animal and environment (e.g., stand-on-able, reach-from-able, graspable) or (2) multiple geometric or physical properties of animal or environment that comprise those fits (e.g., grasping limb length and object distance, grasper width and object width). A large body of research has confirmed both of these intuitions. Animals of many different species perceive affordances for behaviors such as reaching, intercepting, catching, fitting through, and getting across, among many others (for a review, see Wagman et al., 2019). And animals perceive affordances as a “complex particular.” That is, perceiving affordances for a given behavior neither reduces to nor requires perceiving isolated geometric or physical

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An ecological account of perceiving of, and behavior in, the surroundings

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FIGURE 2.5 A given affordance (e.g., “pickup-able”) almost always emerges from a complex (multidimensional) fit between animal and environment.

properties of animal or environment (see Thomas and Riley, 2014; Thomas et al., 2017; Wagman and Stoffregen, 2020). How is it that perceiving occurs? The continuity of animals and their environments (and hence the continuity of perceiving and behaving) fundamentally changes the description of how it is that animals perceive (and behave with respect to) their surroundings. This is because it fundamentally changes the description of the energy patterns of relevance to such processes. Specifically, there is a shift from lower-order energy patterns that are passively received by (the receptor surface of) a given sense organ of a stationary animal to higher-order energy patterns that are actively obtained by the perceptual systems of an animal in a niche. The former are best described using the variables of Newtonian physics and Euclidean geometry and serve as “information” in the syntactic sense, in thatd with respect to perceiving and behavingdthey are meaningless data and must be interpreted to have any meaning for perceiving and behaving animal. The latter are best described using the variables of an ecological physics and

an ecological geometry and serve as “information about” in the semantic sense in that they are meaningful (with respect to perceiving and behaving) and need only be detected to be useful to a perceiving acting animal. Such higher-order energy patterns are (potentially) lawfully related todthey provide information aboutdthe set of behaviors available to that animal. In Gibson’s ecological approach, properties of the surrounding environment lawfully structure surrounding patterned energy distributions, creating a unique distribution of structured energy at each possible point of observation in that energy distribution. For example, substance and surface properties lawfully structure the ambient optic arraydthe surrounding pattern of structured reflected light. At any given point of observation in the optic array, there is a unique distribution of structured reflected light. The points of observation at which an animal encounters a given surrounding patterned energy distribution are determined by that animal’s size, shape, and mass, but also by how and where that animal moves through the environment. Thus, a large animal will encounter a

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different pattern of structured light (from a different point of observation) in the optic array than a small animal. And a fast animal will encounter a different changing pattern of structured light in the optic array (a different pattern of optic flow) than a slow animal. Therefore, the patterns in structured energy distributions that an animal actively encounters as it moves through the surrounding environment are lawfully determined by the relationship between the substance and surface properties of the surrounding environment and that animal’s various action capabilities. In other words, these patterns are informative about the affordances for that animal. Therefore, perceiving affordances is not a matter of intelligently disambiguating ambiguous stimulation patterns. Rather, it is a matter of detecting lawfully structured (informative) stimulation patternsdno more and no less (Blau and Wagman, 2022; Wagman and Blau, 2020).

The ecological approach to perceiving by touch For better or worse, touch (along with its close relative, movement) has an undeserved underdog or misfit status in psychology (see Rosenbaum, 2005). In part, this stems from the two unstated assumptions of the predominant approaches to perception (and behavior) outlined above that (1) animals are separate from their environments and (2) perception of (and behavior in) the environment is best described using Newtonian physics and Euclidean geometry. As a reminder, these assumptions lead to a description of the process of perceiving as a linear causal chain of events in which low-level energy patterns travel from the environment to the relevant sense organ, into the brain (as a representation), and are then intelligently disambiguated by a mediating mechanism, entity, or process. It is likely no coincidence that this description of the perceptual process seems to apply most

readily to visual perception, given certain longstanding intuitions about vision, the anatomy of the visual system, and the phenomenology of visual experiences including (but perhaps not limited to) the following: 1. A single and centralized sense organ (the eye) is sensitive to and passively receives a specific energy form (light) and creates a localizable representation (the retinal image) 2. The perceiver and that which is perceived are separated in space and in phenomenal experience 3. Perceived properties are (generally) at the forefront of phenomenal awareness. 4. The process by which perception occurs (“input”) is entirely separable from the process by which behavior occurs (“output”). Consequently, vision and the visual system have achieved favored status among the multiple means by which animals (particularly humans) perceive their surroundings. In fact, vision and the visual system became the paradigmatic models by which to understand how animals perceive and behave with respect to their surroundings. By contrast, this description of the perceptual process does not as readily apply to perception by touch, given that these same set of intuitions do not seem to apply as well (or at all) to touch, the anatomy of the touch system, and the phenomenology of touch experiences. Specifically: 1. The touch system spans the skin, muscles, and connective tissue of the entire body and actively obtains a variety of mechanical energy forms. The identity and location of representations are unclear (there is no analog to the retinal image). 2. To a large extent, the perceiver and that which is perceived are neither separated in space nor in phenomenal experience. 3. Perceived properties of touched objects and of the body itself are generally in the background of phenomenal awareness

I. Visuospatial cognition and evolution

The ecological approach to perceiving by touch

(unless a given part of the body is explicitly being used to perceive a given property of a touched object) 4. Touching cannot be separated from movementdthere is no clear input or output. Consequently, touch and the touch system have achieved underdog or misfit status among the various means by which animals perceive their surroundings. Philosophers, psychologists, cognitive scientists, evolutionary biologists, and other scholars interested in the links between perception, behavior, and evolution have (at least) two paths forward. The first path forward is to accept the two unstated assumptions and the resulting description of the perceptual process as facts, overlook the fact that intuitions about the visual system do not apply as readily to the touch system (or overlook the touch system entirely!), and move forward using visual perception and the visual system as the paradigmatic means by which animals perceive and behave with respect to their surroundings. The second path forward is to recognize that the misfit nature of touch is at odds with its fundamental role in the evolutionary process and in daily life. In the evolutionary process, touch (along with the chemical senses) preceded vision as a means by which early organisms linked everyday behavior to surrounding energy patterns (Gibson, 1966). And in daily life, touch provides the background support for behaving with respect to the environment by stabilizing the postures from which the movements of the body are performed (Bernstein, 1967; Cabe, 2018; Cole and Waterman, 1995). At the same time, the touch system also provides foreground support for behaving with respect to the environment by coordinating the movements of the body. Gibson’s ecological approach follows this second path to its logical conclusions. Namely, given the fundamental role of the touch system in both the evolutionary process and in daily life, (1) the lack of fit between longstanding

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assumptions and intuitions about perception and the touch system is an indication that those assumptions and intuitions are flawed, if not completely incorrect and (2) the touch system (and not the visual system) ought to be the paradigmatic means by which animals perceive and behave with respect to their surroundings. Developing an ecological approach to perception by touch requires uncovering how properties of objects held by or attached to the body unambiguously (lawfully) structure a surrounding patterned energy distribution, creating a unique distribution of structured energy at each point of observation in that distribution. But what are the relevant properties of objects held by or attached to the body? What is the surrounding patterned energy distribution of relevance for touch? And what is the relevant point of observation in that distribution? We have answered the first question in a few different ways, in a few different places already. In Gibson’s ecological approach, the properties of relevance to objects held by or attached to the body are relational, rather than objective. They are animal- and behavior-relative rather than animal- and behavior-independent. In short, they are the affordances of that objectdthe set of behaviors that are possible for that animal with that object in those surroundings. We can answer the second question by considering what the touch system doesdespecially its role in moving the body and objects attached to it. Moving a part of the body or an object attached to it requires doing work (the “effort” in effortful touch). It requires applying the muscular forces required to move that body part or object in the various ways required to perform a given behavior. Such movements include combinations of pushing, pulling, poking, lifting, wielding, hefting, shaking, and twisting, among others. Moving any given body part (or any object) in these ways requires producing a particular pattern of muscular forces. Specifically, it requires applying different amounts of force in different directions at different times.

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So, the patterned energy distribution of relevance to perception by touch ought to be lawfully related to the pattern of muscular forcesdthe amounts and directions of forcesd required to control a body part or an object in the context of a given behavior. Decades of research have shown that this patterned energy distribution is the mass distribution of the object described as an inertial arrayda concept analogous to the optic array that describes the pattern of forces required to move an object in different directions (see Fig. 2.6; Carello and Turvey, 2017, 2015). We can now answer the third question. A structured energy array is always encountered at a particular point of observation. In the case of the optic array, the point of observation is the animal’s location among surrounding surfaces relative to the distribution of reflected light (see Fig. 2.6, left). In the case of the inertial array

of an object held by or attached to the body, the point of observation is the animal’s location on that object relative to the distribution of mass of that object. In other words, it is their grasp location on that object (see Fig. 2.6, right). Grasp location on an object establishes the point about which that object is moved. And the distribution of mass of the object relative to that point lawfully determines the patterndthe amounts and directions of forcesdof muscular forces required to control that object. Just as there is a unique pattern of structured light at every possible point of observation among surrounding surfaces, there is a unique pattern of required forces and torquesda unique inertial arraydat each grasp location on a given object. The inertial array at a given grasp location can be quantified with a multidimensional quantity known as a tensordin this case, an inertia tensor (I) (see Fig. 2.7). In general, a tensor describes a

FIGURE 2.6 The patterned energy distribution of relevance to perception by vision is the optic array at a given point of observation (left). The patterned energy distribution of relevance to perception by touch is the inertial array at a given point of rotation (right).

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Task-specificity and anatomical independence in perceiving properties of wielded objects

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FIGURE 2.7 The inertia tensor (I) describes the differences in resistance to (rotational) movement in different directions about a particular grasp location. It describes how forces are transformed into movements.

quantity that varies along multiple dimensions simultaneously. Therefore, by definition, a tensor is a “higher-order” variable. An inertia tensor (represented by a 3  3 matrix of values) describes the simultaneous differences in resistance to (rotational) movement in different directions about a particular grasp location. Therefore, the inertia tensor at a given grasp location on a given object describes how muscular forces applied at that location on that object are transformed into the movements of that object (see Fig. 2.7). Many things change in the context of pushing, pulling, poking, lifting, wielding, hefting, shaking, and twisting a given object. However, two things remain unchanged or invariant (1) I at a particular grasp location on that object and (2) the perceived geometric and functional properties of that object. In other words, I is relational quantitydI is defined only when the object is grasped; I is a context-dependent quantitydthe specific values of I depend on the where the object is grasped; and most importantly, I is unambiguously related to the set of behaviors that are possible with a given objectdit is lawfully related to the forces required to control the object. This makes I an ideal candidate for the structured energy pattern that provides information about the possibilities for movement withdthe affordances ofdobjects attached to the body. Decades of research have confirmed this intuition (see below).

Task-specificity and anatomical independence in perceiving properties of wielded objects Task specificity The lawful relationship between I and the patterns of muscular forces required to control a wielded object provide the foundation for two kinds of flexibility exhibited in perception by effortful touch. The first kind of flexibility is task-specificitydthe fact that a multitude of functional or geometric properties of a given object can be perceived when that object is wielded by a given anatomical component about a given joint. In other words, many different properties of a given object can be perceived within and across a given bout of wielding. For example, when wielding a hand-held object about the wrist, people can perceive properties such as whole length, partial length, grasp position, width, shape, orientation, and heaviness. While these properties are not affordances per se, they stand proxy for affordances of the object such as reaching with, fitting into, orienting with, and manipulating. People can also perceive affordances of that object per se, such as whether and how it might be used for tasks such as striking, poking, and reaching with, among others (see Carello and Turvey, 2015, 2017 for reviews).

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Decades of experiments have shown not only that the ability to perceive geometric and functional properties of a wielded object is supported by sensitivity to I, but that the ability to selectively attend to a particular property of that object is supported by selective sensitivity to a particular component of I (see below, see Carello and Turvey, 2015, 2017 for reviews). Each of these components quantifies resistance to movement in a particular direction. In general, the perception of the properties of the object itself is supported by sensitivity to the moments of inertia (i.e., Ixx, Iyy, Izz, see Fig. 2.7). These values quantify resistance to rotations about the three main orthogonal axes of rotationdx, y, and z, respectively. Hence, the moments of inertia quantify the symmetrical forces required to move the object about these axes. Accordingly, perception of the length and the width of a wielded object is primarily supported by sensitivity to Ixx and Izz, respectively (Fitzpatrick, Carello, and Turvey, 1994; Turvey et al., 1998). Perception of properties of the body relative to the object and perception of properties of the object relative to the body are each primarily supported by sensitivity to the products of inertiadIxy, Ixz, Iyz (see Fig. 2.7). These values quantify resistance to rotation about axes perpendicular to the x, y, and z axes, respectively. Hence, the products of inertia quantify the asymmetrical forces required to move the object. Accordingly, perception of the location of the hand relative to a wielded object and perception of the orientation of the object in the hand are each primarily supported by sensitivity to Iyz (Pagano et al., 1994, 1996), and perception of the partial length of a wielded object extending to one side of the hand is primarily supported by sensitivity to Iyz but also to Ixx (Carello et al., 1996). Whereas components of I provide information about how an object can be moved in a specific way, relations among components of I provide information about how that object can be moved in a more general way. Perception of heaviness of a wielded object, for example, is supported by

sensitivity to the absolute and relative values of the three moments of inertia (Ixx, Iyy, and Izz) (Amazeen and Turvey, 1996). In other words, perceived heaviness depends on both the overall amount of force required to move the object and the relative amounts of force required to move that object in different directions. All other things being equal, as larger and more diverse sets of forces are required to move an object, that object becomes both more difficult to move in any direction and more difficult to move in some directions than in others. And when this occurs, the heavier that object feels (Shockley et al., 2001; Turvey et al., 1999; Wagman, 2015; Wagman et al., 2007). Such findings necessitate a reinterpretation of the classic findings that two objects of equal mass can feel unequally heavy, and two objects of unequal mass can feel equally heavy (see above). In the context of the ecological approach to perception by touch, such findings are not an indication that perception of heaviness is a matter of (sometimes incorrectly) intelligently disambiguating ambiguous input. Rather, they are an indication that perception of heaviness is much better characterized as perception of moveableness and that conventional animal- and context-independent properties (such as “heavy” or “light”) may be inappropriate for a principled understanding of perception by touch (Shockley et al., 2004; Wagman, 2015).

Anatomical independence The second kind of flexibility is anatomicalindependencedthe fact that a particular functional or geometric property of an object can be perceived when that object is wielded in a multitude of ways, by a multitude of anatomical components, about a multitude of joints. For example, people can perceive the length of a given object regardless of whether that object is freely wielded, held relatively still, or additionally supported by another limb or object (Burton et al., 1990; Carello et al., 1992a). They can also

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Task-specificity and anatomical independence in perception by means of wielded objects

do so regardless of whether that object is wielded with the hand about the wrist, the elbow, or the shoulder (Pagano et al., 1993), and regardless of whether the object is wielded by the hand, foot, torso, or head (Hajnal et al., 2007a,b; Palatinus et al., 2011; Wagman et al., 2017). And, they can do so regardless of whether the object is wielded in air or in water (Mangalam et al., 2017, 2018; Pagano and Cabe, 2003), whether it is wielded with the preferred or nonpreferred hand (Carello et al., 2006), and whether it is wielded with a fully functioning limb or one that has reduced sensoryemotor capabilities due to aging, or conditions such as peripheral neuropathy, stroke, or spinal injury (see Carello et al., 2009). In each and every one of these cases, perception of length is supported by sensitivity to Ixx (see Fig. 2.7), though such sensitivity may be reduced in some circumstances more than others. Not only is the perception of properties of a wielded object supported by (analogous) general sensitivity to I across anatomical components, wielding environments, and tactile sensitivities, but the ability to selectively attend to and differentiate among properties of a wielded object is supported by selective sensitivity to the same components of I across these contexts (see Carello and Turvey, 2015, 2017 for reviews). For example, people can selectively attend to either the whole length of an object or the partial length of that object to one side of the grasp location regardless of whether that object is wielded by hand, by foot, by torso, or by head. And the ability to do so is supported by the selective sensitivity to the same components of I (Ixx in the case of length and Iyz and Ixx in the case of partial length, see Fig. 2.7) across anatomical components (Palatinus et al., 2011; Wagman et al., 2017). Similarly, people can selectively attend to either the grasp location relative to the object length or the partial length to one side of the grasp location regardless of whether that object is wielded by the hand or

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by the head. And the ability to do so is supported by the selective sensitivity to the same components of I (Iyz in the case of grasp location and Iyz and Ixx in the case of partial length, see Fig. 2.7) across anatomical components (Wagman and Higuchi, 2019). Finally, people can perceive object heaviness regardless of whether that object is lifted with the arm or the leg. Moreover, the perception of heaviness is supported by the same relationship between muscle activity and movement in each case (Waddell and Amazeen, 2018; Waddell et al., 2016). Although such studies did not explicitly test the hypothesis that perception of heaviness is supported by analogous sensitivity to the absolute and relative values of the three moments of inertia across the two anatomical components, the results are consistent with this possibility.

Task-specificity and anatomical independence in perception by means of wielded objects The upshot of the preceding section is that (1) people can perceive (and differentiate among) many different properties of a wielded object and (2) people can perceive a given property of a wielded object in many different contexts including those in which the object is wielded by different (combinations of) anatomical components. That is, the perception of the properties of a wielded object exhibits both task-specificity and anatomical independence. Yet, in addition to perceiving properties of a wielded object itself, people are also capable of perceiving properties by means of a wielded object. That is, people can perceive a multitude of functional and geometric properties of an unseen surface by exploring itd tapping, probing, prodding, or scraping itdwith a wielded object. For example, when exploring surfaces with a hand-held object in this manner, people can perceive whether an inclined surface

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can be stood on (Fitzpatrick, Carello, Schmidt, and Corey, 1994), whether an obstacle can be stepped over (Wagman and Taylor, 2005), whether a gap can be stepped across (Burton, 1992), and whether an aperture can be passed through (Favela et al., 2018). Just as the ability to perceive the properties of a wielded object is supported by sensitivity to components of the inertia tensor (I) of that object, so too is the ability to perceive the properties of a surface explored by means of a wielded object. For example, perception of whether a gap can be stepped across or whether an obstacle can be stepped overdboth properties of the probed surface relative to the bodydare each primarily supported by sensitivity to a product of inertia (Iyz) of the wielded object (see Fig. 2.7, Burton and McGowen, 1997; Wagman and Taylor, 2005). In addition, when exploring a surface with a probe, people can selectively attend to and differentiate between a property of the probed surface (i.e., egocentric distance) and a property of the probe (i.e., length). This ability is supported by selective sensitivity to different invariant mechanical variables in each caseda moment of inertia (Ixx) of the wielded object in the case of perceiving probe length and the angle of inclination of the probe at contact with the surface in the case of perceiving surface distance (Carello et al., 1992b). Finally, just as people can perceive a particular property of a wielded object when that object is wielded in a multitude of ways, by a multitude of anatomical components, about a multitude of joints, they can also perceive a particular property of a surface explored with an object that is wielded in a multitude of ways, by a multitude of anatomical components, about a multitude of joints. For example, people can perceive whether an inclined surface can be stood on regardless of whether they probe that surface with an object wielded with the preferred or nonpreferred hand, with one or both hands, or with different two-handed grasps (Wagman and Hajnal, 2014a). They can

also do so regardless of whether they probe that surface with an object attached to the foot or with an object attached to the head (Hajnal et al., 2018; Wagman and Hajnal, 2014b; Wagman and Hajnal, 2016; Wagman et al., 2017). Similarly, when probing a surface with an object, people can selectively attend to and differentiate between the egocentric distance of a probed surface or the length of the probe regardless of whether they probe the surface with an object held in the hand or attached to the foot. And the ability to do so is supported by the same selective sensitivity to invariant mechanical variables across anatomical components (Carello, Fitzpatrick, and Turvey, 1992; Wagman et al., 2020).

What function(s) has the touch system evolved to serve? As we discussed previously, the description of the perceptual process that follows from the longstanding assumptions of predominant approaches to perceiving and behaving does not readily apply to perceiving by touch. There are, of course, many reasons for thisdsome of which stem from fundamental characteristics of touch, the anatomy of the touch system, and the phenomenology of touch experiences. Two such characteristics can help shed light on what function(s) the touch system has likely evolved to serve and what anatomical configuration of the touch system likely coevolved to support this function. The first characteristic is that the process of touching cannot be meaningfully separated from the process of moving. This suggests that the movement system and the touch system coevolved for the purposes of supporting the performance of everyday behaviors through exchanges of lawfully structured mechanical energy with the surrounding environment (see Fultot et al., 2019;

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What function(s) has the touch system evolved to serve?

Godfrey-Smith, 2016; Keijzer, 2015; Keijzer et al., 2013). The second characteristic is that the movement system and the touch system consist of an overlapping (if not identical) set of numerous and heterogeneous anatomical components and tissue types that are distributed across the entire body. A large subset of these components and tissue types are involved in most, if not all, instances of everyday movingdstanding, walking, running, jumping, reaching, grasping, and throwing, among many othersdand everyday touchingdpushing, pulling, poking, lifting, wielding, hefting, shaking, and twisting, among many others. Together, these two fundamental characteristics suggest that the movement system and the touch system coevolved to coordinate an overlapping, if not identical, set of numerous and disparate anatomical components into functional units used in the service of performing everyday behaviors (Carello and Turvey, 2017; Reed, 1982, 1996; see Aimonetti et al., 2007). Such functional units have been described as task-specific control units in the case of moving the body with respect to the surrounding environment and task-specific detection units in the case of perceiving properties of the body and the surrounding environment (Wagman and Hajnal, 2014a,b). In what follows, we describe the essential (analogous) properties of each of these functional units and then describe a proposal for the architectural configuration of the touch system that coevolved to support such functional units.

Synergies as task-specific control units The functional unit of relevance in performing a goal-directed movement is known as a synergy. A synergy is a grouping of potentially independent anatomical components that work together as a functional unit to achieve a movement goal by exploiting lawful coordination patterns (Bernstein, 1967; Latash, 2008). Importantly, synergies are flexibly constrained by functiondnot rigidly

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constrained by anatomy. In typical bipedal walking, for example, the relevant synergy is (minimally) comprised of the skeletal, muscular, connective, and neural tissues of the torso, hips, legs, and feet. While the movement trajectories of each of these individual body parts will vary on each step, the functional relationships across those body parts that support bipedal walking will be preserved over the course of multiple steps. In addition, synergies flexibly, spontaneously, and temporarily form and reform as the goal or the context changes. The functional relationship among the components of the hips, legs, and feet established for walking, for example, will be temporarily modified and then reestablished after an accidental stumble. And finally, synergies can include components from any part of the body as well as components that are external to the body. If the ground surface becomes steep or unstable, for example, the synergy for bipedal walking may be modified to additionally include components of the upper bodydthe torso, shoulders, arms, and handsdor even external objectsda railing, a walking stick, or even the body parts of another person offering assistance.

Smart perceptual devices as task-specific detection units The functional unit of relevance in perceiving a given property by touch (or any other means, for that matter) is known as a smart perceptual device. A smart perceptual device is a grouping of potentially independent anatomical components that are constrained to work together as a functional unit to achieve a perceptual goal by detecting lawful stimulation patterns (Carello, Fitzpatrick, Domaniewicz, et al., 1992; Runeson, 1977). Like synergies, smart perceptual devices are flexibly constrained by function, not rigidly constrained by anatomy. In perceiving a given property of a wielded object, the smart perceptual device is (minimally) comprised of the skin, muscles,

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and joints of the shoulder, upper and lower arm, wrist, and hand. While the movement trajectories of each individual body part will vary within and across bouts of wielding, the functional relationships across those body parts that support the detection of the relevant component(s) of I will be preserved (Solomon and Turvey, 1988; Turvey et al., 1990). In addition, smart perceptual devices flexibly, spontaneously, and temporarily form and reformed as the goal or the context changes. The functional relationship among the skin, muscles, and joints of the shoulder, upper and lower arm, wrist, and hand established for perceiving a given property of a wielded object will be modified to perceive a different property (Arzamarski et al., 2010; Michaels and Isenhower, 2011; Riley et al., 2002) or if wielding movements are restricted in some way (Carello, Fitzpatrick, Domaniewicz, et al., 1992; Solomon and Turvey, 1988; Solomon et al., 1989). Finally, smart perceptual devices can include components from any part of the body as well as components that are external to the body. The smart perceptual device established for perceiving a particular property of or by means of a wielded object can include the shoulder, arm, wrist, and hand, but also the head, neck, torso, and trunk, hips, legs, ankles, and feet (Hajnal et al., 2007a,b; Palatinus et al., 2011; Wagman and Hajnal, 2014a,b; Wagman et al., 2016; Wagman et al., 2017), external objects (Peck et al., 1996), or even another person who helps to lift and maneuver the object (Amazeen, 2014; Richardson et al., 2007).

What architectural configuration of the touch system coevolved to support this function? We have argued that the movement system and the touch system coevolved for the purposes of supporting the performance of everyday behaviors. More specifically, we have argued that these systems have coevolved to coordinate an

overlapping, if not identical, set of numerous, heterogeneous, and disparate anatomical components into functional units used in the service of performing such behaviors. In particular, the touch system evolved for the purpose of flexibly, spontaneously, and temporarily coordinating the various skeletal, muscular, connective, and neural components of the body (as well as external objects) into task-specific, but anatomically independent, measurement devices that capitalize on lawful relations in mechanical stimulation patterns. As we have described, such lawful relations in mechanical stimulation patternsdthose between I and the amounts and directions of muscular forces required to control a wielded objectdprovide the necessary foundation for the task-specificity and anatomical independence exhibited by the touch system. But what kind of anatomical configuration of the touch system might have evolved to support the detection of such lawful relations? What kind of anatomical configuration of the touch system might have evolved to support task-specificity and anatomical independence? What kind of anatomical configuration of the touch system might have evolved to support the function of the touch system?

Biotensegrity and the misfit nature of the touch system One promising conjecture is that the overlapping set of numerous, heterogeneous anatomical components and tissue types that comprise the touch and movement systems are best described as a nested biotensegrity structure (Turvey and Fonseca, 2014; see Scarr, 2014). In general, tensegrity (an amalgam of ’tension’ and ’integrety’) is a structural system consisting of a set of discontinuous, inflexible compression components that are held together with continuous, elastic, tension components (see Fig. 2.8, left). The finely tuned opposing tension and compression forces stabilize the entire structure and maintain the shape of that structure in the

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What architectural configuration of the touch system coevolved to support this function?

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FIGURE 2.8 A tensegrity system consists of a set of discontinuous, inflexible compression components held together with continuous, elastic, tension components (left). Applied forces are immediately redistributed across the entire system, changing its shape but maintaining its structure (right).

absence of any external forces. These opposing forces also provide strength, resilience, and flexibility in the presence of external forces (Ingber, 1993). When an external force is applied to any part of a tensegrity structure, those forces are immediately redistributed across the entire system, changing its shape but maintaining its structural equilibrium (see Fig. 2.8, right). That is, the application of a unidimensional, local force creates a multidimensional, global pattern of forces. In a biotensegrity model of the touch and movement systems (Turvey and Fonseca, 2014), the various anatomical components and tissue types that comprise the touch and movement systems form a singular, complex, interconnected, nested tensegrity structure in which the bones serve as the primary continuous tension elements and the muscles, tendons, ligaments, and other connective tissue serve as the primary intermittent compression elements at all levels of scale. As in any tensegrity structure, the opposing forces provided by these elements stabilize the entire system and maintain its structure in the absence of external forces. Importantly, this accounts for one of the misfit features of the touch systemdits “background” role in both perceiving and moving. In fact, a biotensegrity architecture guarantees that the touch system is perpetually poised, allowing it to provide the necessary

background support for any and all subsequent behavior by stabilizing the postures from which the movements of the body are performed (Turvey and Fonseca, 2014). Also as in any tensegrity structure, when forces are applied (or generated by) a given part of the system, they are immediately redistributed across the entire system, creating a multidimensional global deformation field within and across the various nested, heterogeneous, and interconnected components of the system (see Fig. 2.9). This accounts for another of the misfit features of the touch systemdthe fact that the touch system spans the skin, muscles, and connective tissue of the entire body and is responsive to a variety of (mechanical) energy forms. In fact, a biotensegrity architecture guarantees that any given instance of perceiving by touch involves the entirety of the nested, interconnected, and heterogeneous components of the touch system. A biotensegrity model sits in contrast to more traditional anatomical models in which the touch and movement systems each consist of independent groupings of components, tissue types, and receptor types that differ in both function and distribution across the various parts of the body. For example, in the touch system, the various kinds of mechanoreceptors are

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FIGURE 2.9 In a biotensegrity model of the touch and movement systems, movement of any part of the body (or of the entire body) creates a multidimensional deformation field across the nested, heterogeneous, and interconnected components of the touch system across the entire body.

embedded in different kinds of tissue (muscles, ligaments, tendons, skin), are sensitive to different kinds of mechanical stimulation (touch, pressure, vibration, movement), and are distributed in different concentrations in different parts of the body. Thus, the stimulation patterns of relevance occur locally and are specific to tissue type, receptor type, and location. Therefore, such patterns reflect the idiosyncrasies of the bodily location at which the contact occurs but not necessarily the source of the contact itself. And, in part, it is this potential ambiguity about the source of the mechanical contact that needs to be intelligently disambiguated by mediating mechanisms, entities, or processes.

Biotensegrity and the ecological approach to perception by touch One of the key features of a biotensegrity system is that forces that are applied locally are redistributed globally in a deformation field (see Figs. 2.8 and 2.9). The nature of this global deformation field is consistent with the ecological approach to perception by touch. One of the primary claims of the ecological approach is that describing both the environment and the energy patterns at an appropriate scale would reveal higher-order energy patterns that are unambiguously (lawfully) related to the set of behaviors that are possible in those surroundingsd affordances. As we described above, the inertia

I. Visuospatial cognition and evolution

Concluding thoughts: what to make of (the evolution of) tool use?

tensor is an example of such a higher-order variable. It describes the lawfully generated multidimensional set of forces required to control a given object at a particular grasp location. Analogously, this global deformation field describes the lawfully generated multidimensional pattern of tissue deformation that occursdacross the nested components of the touch systemdwhen such forces are being applied (see Figs. 2.8 and 2.9). The nature of the global deformation field also supports both task-specificity and anatomical independence. The lawful relations between I and the amounts and directions of muscular forces required to control a wielded object provide the potential for the taskspecificity and anatomical independence exhibited by the touch system. However, such characteristics can only be manifest by the touch system to the extent that such lawful relations can be detected. To the extent that I lawfully structures the global deformation field, these lawful relations can be detected. I is lawfully related to the forces required to move an object and is unaffected by the idiosyncrasies of the bodily component used to move the object. Therefore, the global multidimensional deformation field will be lawfully related to the source of the deformation and will be largely unaffected by idiosyncrasies of the bodily location at which the contact occurs. And, given that the deformation field is distributed globallyd across all levels and components of the touch system, all such levels and components have potentially equivalent roles in detecting such lawful relations. Thus, task specificity and anatomical independence are jointly supported by (1) the invariant nature of the stimulation pattern(s) of relevance to the haptic system and (2) the nature of the tissues that register such patterns. In fact, task-specificity and anatomical-independence may be fundamental characteristics of the touch system described as a biotensegrity system embedded in lawfully structured energy arrays.

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Concluding thoughts: what to make of (the evolution of) tool use? One of the everyday behaviors that the touch system and the movement system coevolved to perform is tool use (see Mangalam et al., 2022). While some animal speciesdmostly, but not exclusively, birds and primatesdregularly use tools, tool use is relatively rare in the animal kingdom (Hunt et al., 2013). And of course, tool use by humans far exceeds that of any other species. It is tempting to conclude, therefore, that the disparity in tool use between humans and other species is a direct consequence of fundamental differences in the brain and nervous systemdand therefore in cognitive, representational, or computational capacitiesdbetween humans and other species. Likewise, it is tempting to conclude that the evolution of humans from “occasional tool users” to “habitual tool users” to “obligatory tool users” over the last 2 million years (Shea, 2017; see Bruner, 2021) is a direct consequence of the evolution of these structures and these capacities. However, such conclusions may be imperiled by the very same puzzles and paradoxes that bedevil all approaches that rely on cognitive, representational, or computational structures or capacities. Avoiding such puzzles and paradoxesdand therefore providing support for a law-based explanation of perceiving, acting, and evolvingdrequires explaining this disparity in tool use between humans and other species as an emergent consequence of differences in their respective ecological niches. In other words, the sophistication (and proliferation of) tool use by a given animal species may depend on the sophistication of the ecological niche of that species. In fact, animals not known for their tool use abilities, including rodents and fish, perceive and exploit affordances for tool use when their niche is artificially modified to include opportunities to do so (Crawford et al., 2020; Givon et al., 2022). Therefore, the evolution of human tool

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use may be better explained as an emergent consequence of the coevolution of humans and their ecological niche. This process likely not only created new affordances for tool use but also new opportunities to detect and exploit information about such affordances.

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C H A P T E R

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Evolutionary perspective on peripersonal space and perception Mathilda Froesel1, Suliann Ben Hamed1, Justine Cl
ery2 1

2

Institut des Sciences Cognitives Marc Jeannerod, CNRS Universit
e de Lyon, Bron Cedex, France; Department of Neurology and Neurosurgery, The Neuro and ACAR, McGill University, Montreal, QC, Canada

Introduction

engaging in positive in-group interactions or negative out-group interactions such as fights or war), its emotional organization (safe home or wild environment), and our goals within this environment (feeding, manufacturing, nurturing, or protecting from life-threatening conditions). These diverse factors play a direct role in how we may perceive the space around us and in particular the space directly around us. Indeed, this space is critical as it not only represents an interface maximizing interactions with our immediate environment (e.g., with objects, conspecifics, animals, etc.) but also an interface maximizing risk on body integrity (e.g., attacks by predators, collision to the body, injury during negative social interactions, etc.). This space immediately around our body is thus important as it is the space which we can act upon, where we can reach and protect ourselves, where we feel safe and comfortable, and where we can socially interact with others. This space is often referred to as the peripersonal space (PPS) (mostly in the field of neurosciences) or personal space (mostly in the field of psychology and social

The human brain has one of the most complex structural and functional architecture of all living species. The dramatic expansion of the human brain has been accompanied by the development of specialized brain areas. This is the result of a strong evolutionary pressure constrained by the acquisition of language, rich bimanual coordination patterns, as well as complex social interactions, culminating in highly organized social hierarchical societies embracing specific normative cultural frameworks. In this context, we hypothesize that the perception of our environment as humans and the way we interact with it is fully determined by our evolution, our ecology, and our culture. As a result, it depends on numerous external and internal factors rangingdnon-exhaustivelydfrom the physical organization of our direct environment (working on a computer or walking in cities rather than hunting in the endless Savana or in dense temperate forests), its social organization (living in large social groups or in small family nuclei,

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sciences). In the following section, we will stick to the PPS terminology. Its functions have been associated with defensive and avoidance behaviors, approaching and interactive social behaviors, object and tool manipulation, and has been strongly linked with its multisensory and sensorimotor characteristics (Cl
ery et al., 2015; Cl
ery and Ben Hamed, 2018; de Vignemont and Iannetti, 2015; Hunley and Lourenco, 2018; Noel et al., 2021). Importantly, this space does not have a rigid barrier but acts more as a bubble or a buffer between us and the external world, that varies in size depending on the environmental and social context as well as on the internal state of the subjects (Bufacchi and Iannetti, 2018; Cl
ery and Ben Hamed, 2018, 2021). In this chapter, we will first present the notion of PPS as a general functional construct to several animal species, then we will focus on PPS in humans and nonhuman primates, describing the specific functional networks involved in this PPS representation. Secondly, we will present evidence and speculate on how PPS may have evolved from nonhuman primates to humans, in parallel with brain expansion, prolonged postnatal development, postural change, and higher complexity in emotional and social regulation. Third, we will discuss how tool-use and PPS might have concurrently evolved (as a case of coevolution). Finally, we will describe how the social and cultural specificities of human societies have contributed to single out human PPS relative to PPS in other species.

Functions and definition of the peripersonal space From an evolutionary perspective, the first function of the PPS is a protective function. Indeed, the first objective to meet by any living organism is the survival of the species, otherwise termed the principle of fitness maximization. This comes with the ability to avoid/escape danger as well as with the ability to protect oneself. As a result, having a mechanism allowing

predictively monitor what may happen in the vicinity of the body is important to detect potential threats and anticipate the optimal behavioral response (de Vignemont et al., 2021b).

Peripersonal space as a common function in the animal world Behavioral evidence Heini Hediger observed in his zoo in Zurich that the animals were not processing space uniformly, and described a flight response that is species-specific (Hediger, 1934, 1950). The flight distance is strongly linked to environmental constraints and changes. The imposed interaction with humans impacted flight distance in numerous species (Cooper et al., 2014; Darwin, 1839, 1868; Hediger, 1934, 1950; Samia et al., 2015). Animals like dogs, horses, cows became “tamed” and reduced their specific flight distance as their fear of humans decreased. Inversely, some species like some insular birds who had short flight distance due to the absence of predators, and thus weak fear reactions, disappeared after the arrival of humans (Møller, 2021). Interestingly, observations on birds have shown that their PPS (defined here as the flight distance) may vary during the breeding season, or during abnormal weather conditions where prey (small birds) can be seen in close proximity to predators (raptors) until the situation was back to normal, resulting in the death for some of them (Gilbert et al., 2010; Møller, 2011, 2021). The response to looming stimuli that signal impact on the body is also an important component for animal survival. Accordingly, looming visual stimuli, with a threatening aspect (e.g., predators), trigger defensive behaviors such as freezing or fleeing in both natural and laboratory conditions (Blanchard et al., 1998; Eilam, 2005), in multiple and diverse animal species ranging from rodents (De Franceschi et al., 2016; Shang et al., 2018; Wallace et al., 2013; Yilmaz and Meister, 2013), to insects (Card and Dickinson, 2008; Fabian et al., 2022; Gibson et al., 2015; Rind and Santer, 2004; Tammero and Dickinson,

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2002), nonhuman primates and humans (Ball and Tronick, 1971; Schiff et al., 1962). Studies on impact prediction have shown its strong link with PPS functions and its strong multisensory component (Brozzoli et al., 2012b; Cl
ery et al., 2015; Cl
ery and Ben Hamed, 2018, 2021; Noel et al., 2021; Serino, 2019). Neural bases Impact prediction and the processing of looming stimuli involve a parieto-frontal-occipital network and areas that mainly overlap the PPS network. In particular, the ventral intraparietal area (VIP) and premotor area F4 show strong activations in response to looming visual stimuli predicting a tactile stimulation on the skin (Cl
ery et al., 2015, 2017, 2018). The pulvinar and the superior colliculus (SC) also seem to play a role. Both in rodents (Sahibzada et al., 1986) and macaques (DesJardin et al., 2013), stimulation of the SC can induce defensive-like behaviors. In rodents, the SC has looming-sensitive neurons (Westby et al., 1990) and coordinates this defensive behavior by transferring a threat signal to the parabigeminal nucleus or the lateral posterior thalamic nucleus to activate a fleeing or freezing response (Shang et al., 2018). In marmosets, neurons of the SC show direction selectivity and have been suggested to play a role in the processing of looming stimuli (Tailby et al., 2012). The SC is strongly connected to the pulvinar (Ghahremani et al., 2017; Kwan et al., 2019; Stepniewska et al., 2000), the primate homologous of the lateral posterior thalamic nucleus, where we also observed activations (Cl
ery et al., 2020b). This thus suggests a possible link between the processing of looming stimuli in nonhuman primates and freezing behavior, either as a genuine adaptive behavior or as a consequence of the fact that in most nonhuman primate experiments, animals are restrained and cannot escape. These findings suggest a potentially preserved subcortical networking underlying freezing responses in rodents and primates. Interestingly, while primates rely

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essentially on the visual modality, rodents rely more on the olfactive modality. We may expect that the cortical network subserving PPS in other species is mainly driven by the main sensory modality used in this species. Further studies in nonprimate species need to be performed as the research in the PPS field mainly focuses on humans and on nonhuman primates, the species where it has first been defined.

Peripersonal space in humans and nonhuman primates Despite early works and observations reporting that the processing of space is not uniform (Hall, 1966; Hediger, 1950), the term “PPS” has been coined by Rizzolatti and colleagues (1981a, 1981b). His team recorded neuronal responses within the periarcuate cortex located in the frontal lobe, in macaque monkeys. They observed that a large part of the neurons which were coding for visual stimuli occurring close to the body was also responsive to somatosensory stimuli presented at the same location (e.g., neurons responding both to a visual stimulus presented near the hand and to a tactile stimulus applied to the hand). These visual neurons have thus been characterized as being bimodal and some as trimodal as they were also responding to auditory stimulations close to the subject. Interestingly, similar neurons have been discovered in other brain regions, in particular the parietal cortex, the premotor cortex, and the putamen (Colby et al., 1993; Fogassi et al., 1996; Graziano and Gross, 1993; Graziano et al., 1997, 1999; Graziano and Cooke, 2006). Thus, the PPS has been defined as a multisensory space (Cl
ery et al., 2015; Cl
ery and Ben Hamed, 2018; de Vignemont and Iannetti, 2015; Noel et al., 2021) encoded by bimodal or trimodal neurons who have their receptive fields anchored on specific body parts which respond to tactile stimulation on these specifics body parts and to visual and/or auditory stimulations occurring close to the body.

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Neural bases and cortical networks In the parietal cortex, these bimodal and trimodal neurons are found in the VIP and in area 7b (Hyv€ arinen, 1981; Hyv€ arinen and Shelepin, 1979). In the premotor/prefrontal cortex, these neurons are found in areas F4 and F5. This evidence has led to the identification of two frontoparietal networks involved in PPS representation (Cl
ery et al., 2015; Cl
ery and Ben Hamed, 2021; de Vignemont and Iannetti, 2015; Jeannerod et al., 1995; Luppino et al., 1999; Rizzolatti et al., 1998; Rizzolatti and Fogassi, 2014; Rizzolatti and Luppino, 2001; Rizzolatti and Matelli, 2003; Sakata et al., 1998). First parietofrontal network: VIP-F4

The first parietofrontal network is constituted of the parietal area VIP and the prefrontal area F4 (Avillac et al., 2005; Bremmer et al., 2013; Cl
ery et al., 2018; Duhamel et al., 1997, 1998, 1997; Fogassi et al., 1996; Graziano et al., 1994, 1999; Guipponi et al., 2013; Matelli and Luppino, 2001; Rizzolatti et al., 1981b; Schlack et al., 2005). These two areas have strong functional homologies and anatomical interconnection (Matelli and Luppino, 2001; Rizzolatti and Luppino, 2001). The cortical area VIP responds to visual information in a head-centered frame of reference (Avillac et al., 2004; Duhamel et al., 1997), shows a strong convergence and integration of visuotactile information (Avillac et al., 2005; Duhamel et al., 1998; Guipponi et al., 2013, 2015), and encodes space information relative to the head and shoulders (Bremmer et al., 2000, 2002). By its involvement in impact prediction (Cl
ery et al., 2015, 2017) and the coding of PPS (Cl
ery et al., 2015, 2018; Cooke and Graziano, 2004), VIP area highlights the importance of the multisensory component of the PPS. It has recently been suggested that the human homolog of nonhuman primate VIP might have evolved into three specialized homolog parietal areas (Foster et al., 2022). The second node of this network is the

prefrontal area F4. Contrary to bimodal neurons in VIP, bimodal neurons in F4 respond to visual information in a limb (arm/hand) centered frame of reference. In addition, some neurons of this area respond to face tactile stimulations (Fogassi et al., 1996; Gentilucci et al., 1988; Guipponi et al., 2015; Rizzolatti et al., 1981a; Wardak et al., 2016), with a subportion of neurons exclusively responding to the center of the face (Wardak et al., 2016). What is the function of this VIP-F4 network? Stimulation of the bimodal VIP and F4 neurons described above elicit complex defensive behaviors such as head motion, guarding response with the fast withdrawal of the arm or hand behind the back, squinting eyes, facial skin contraction, lift upper lip, ears folding back against the head . (Cooke et al., 2003; Cooke and Graziano, 2004; Graziano et al., 2002; Graziano and Cooke, 2006). Overall, the consequences of these stimulations indicate a strong link between sensory information associated with specific parts of the space near the body, and protective actions. The aim is to reduce the body exposure, protect the hands and soft abdomen, protect the neck and the eyes, the two lasts being the most vulnerable body parts to predation (Graziano, 2021). Cooke and Graziano (2003) showed that after several repetitions, the defensive responses that persist are the ones linked to the face (Graziano, 2021). Interestingly, the functional face touch parieto-temporoprefrontal network is larger than the shoulder touch network (Cl
ery and Ben Hamed, 2021; Wardak et al., 2016). This is corroborated by the somatosensory homunculus along the central sulcus which also shows stronger representation dedicated to the face (Huang et al., 2012, 2017; Huang and Sereno, 2007; Sereno and Huang, 2006, 2014). This enlarged cortical network associated with a rich defensive movement repertoire should be considered from an evolutionary perspective. Indeed, the head is one of the most vulnerable parts of our body,

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where our central nervous system is located as well as the majority of our sense organs and communication functions. As a result, it is crucial to protect it (Cl
ery and Ben Hamed, 2021). This VIP-F4 network is thus associated with a defensive network defining a safety margin around the body for localizing objects and potential threats related to the body in this space (Brozzoli et al., 2013, 2014; Chen et al., 2014; Cl
ery and Ben Hamed, 2021; Graziano and Cooke, 2006). Second parietofrontal network: AIP/7b-F5

The second parietofrontal network is constituted of parietal areas AIP and 7b, and the prefrontal area F5 (Caprara et al., 2018; Cl
ery et al., 2018; Fogassi et al., 2001; Gallese et al., 1994; Iriki et al., 1996; Matelli and Luppino, 2001; Murata et al., 2000; Rizzolatti and Luppino, 2001; Rizzolatti and Matelli, 2003). The three cortical regions AIP, 7b, and F5 have functional homologies and a strong anatomical connection (Matelli and Luppino, 2001; Rizzolatti and Luppino, 2001). Area 7b has a strong tactile component but a third of its tactile neurons responding to tactile stimulation applied to the face or arm also respond to visual stimuli presented near those body parts (Hyv€ arinen, 1981; Hyv€ arinen and Shelepin, 1979) as well as to motor activity, from simple grasping movements to more complex action sequences such as “bring this fruit in my mouth” (Fogassi et al., 2005; Fogassi and Luppino, 2005; Hyv€ arinen and Poranen, 1974; Hyv€ arinen and Shelepin, 1979; L Leinonen, 1979; Leinonen and Nyman, 1979; Robinson et al., 1978). Area AIP has neurons selective for 3D objects and/or 2D object components depending on where the visual stimulus occurs (Durand et al., 2007; Murata et al., 2000; Romero et al., 2012, 2013; Sakata and Taira, 1994; Srivastava et al., 2009; Theys et al., 2012; Verhoef et al., 2010). Premotor area F5 has canonical neurons responding to handgrasping, containing highly overlapping

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movement representations of the mouth and hand, and visual selectivity matching their motor selectivity (Fogassi et al., 2001; HeppReymond et al., 1994; Murata et al., 1997; Raos et al., 2006; Rizzolatti et al., 1988). Another category of F5 neurons is called “mirror’ neurons. If the graspable object is presented close enough to the body (i.e., at reaching distance), those neurons respond both to the execution of a given action, “I grab my mug” and to the observation of the same action performed by another conspecific or nonconspecific agent, “I’m looking at the agent grabbing his mug” (Bonini et al., 2014; Brozzoli et al., 2014; Caggiano et al., 2009; Gallese et al., 1996; Rizzolatti et al., 1996). Overall, this AIP/7b-F5 network is thus closely associated with approaching behavior and action space and more specifically with goal-directed reaching or grasping actions within the PPS (Cl
ery et al., 2015; Cl
ery and Ben Hamed, 2018; de Vignemont and Iannetti, 2015). Subcortical areas The neural bases of PPS are mostly explored at the cortical level. Knowledge of the contribution of subcortical structures to this function is still sparse. However, three subcortical areas seem to play an important role in PPS representation: the putamen, the superior colliculus, and the pulvinar. The putamen has numerous neurons with tactile receptive fields located on the face, the hand, or the arm. Part of these neurons also responds to visual stimuli when they are presented close to the tactile receptive fields anchored to the face, the hand, or the arm. These visuo-tactile neurons are somatotopically organized. Interestingly, arm and hand neurons have visual receptive fields encompassing visual space up to 5 cm around the arm/hand while face neurons respond to visual stimuli presented up to 20 cm from the tactile receptive field (Graziano and Gross, 1993, 1996; Brozzoli et al., 2012b). This again highlights the importance of the face and its protection; it is essential

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to detect objects and potential threats before they are too close to the face. Accordingly, the putamen is well-known for its role in motor planning and executive functioning (Gentile et al., 2011). We suggest it closely interacts with both parietofrontal networks for goaldirected actions such as reaching or grasping, and for protective actions such as avoidance of a looming stimulus. The SC and the pulvinar also seem to play a role in these functions. As mentioned above, they are both involved in impact prediction. Both these structures are characterized by their multisensory properties. They represent two hubs where stimulus information from different sensory modalities such as visual, tactile, or auditory modalities converge (for review, see Froesel et al., 2021). In addition, the SC is involved in enacting defensive responses across species (Pereira and Moita, 2016) and has multimodal response properties (Triplett et al., 2012) while the pulvinar has a role in proprioception and posture and combines multisensory information to determine “what is where” (for reviews, see Froesel et al., 2021; Worden et al., 2021). Both structures have output connections toward parieto-frontal areas (Clower et al., 2001; Gitelman et al., 2002; Makin et al., 2012) but also exhibit functional coactivation during visual looming stimulation with those parietofrontal areas (Cl
ery et al., 2020b; de Borst and de Gelder, 2022) that are also involved in near space encoding (Cl
ery et al., 2018) and impact prediction (Billington et al., 2011; Cl
ery et al., 2017). Most of the neurophysiological bases of the PPS have been investigated in monkeys providing precious insights into the PPS functions. Numerous studies have found a similar organization in humans with the involvement of these parieto-frontal networks (Bremmer et al., 2001; Brozzoli et al., 2013, 2012a, 2011; di Pellegrino and L adavas, 2015; Gentile et al., 2013; Grivaz et al., 2017; for reviews, see Cl
ery et al., 2015; de Vignemont and Iannetti, 2015; Foster et al.,

2022; for book, see de Vignemont et al., 2021a). However, humans and nonhuman primates do not interact in the same way with their environment as humans exhibit much more complicated and sophisticated interactions. Are those networks preserved across humans and nonhuman primates or do they evolve?

Brain expansion and evolution Humans diverged from marmosets about 35 million years ago, from macaques about 25 million years ago (Miller et al., 2016) and from chimpanzees about 6e7 million years ago (Goodman et al., 1998; Perelman et al., 2011). The frontal and parietal cortex are important in all primates relative to other species. However, humans have a large parietal cortex, i.e., larger parietal bones and lobes involved in visuospatial integration (Bruner, 2017). While the proportions of the frontal lobes are comparable to living apes, humans have a prefrontal connectivity to the rest of the brain that is more diversified than in other species (Garin et al., 2022). This is expected to result in important functional differences. This expansion and functional specificities are suggested to be due to a strong positive selection pressure on the neural systems that support abilities such as action understanding, tool-making, or social learning (Ferrari and Rizzolatti, 2015). It is hard to study the evolution of the brain with direct methods, but some indirect methods exist. First, by studying endocasts (physically casted reproductions of the interior surfaces of skulls), one can learn information on sulcal and gyral anatomy or size expansion (Falk, 2014; see Chapter 7). Secondly, one of the great characteristics and abilities associated with humans is toolmaking, a collection of artifacts from millions of years to today that can help to identify the methods used to produce those artifacts and better understand human evolutionary history (Stout and Hecht, 2015; see Chapter 9). For example, using FDG-PET studies, an imaging

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technique allowing to associate brain activation with behavior occurring outside the scanner, it has been possible to demonstrate that the Oldowan technology (i.e., the early mode of toolmaking technology such as sharpening stone) activates a distributed fronto-parieto-occipitotemporal network (Frey, 2007; Stout et al., 2008; Stout and Chaminade, 2007). The same network is activated by Acheulean toolmaking (i.e., intentional stone shaping into large tools) but with additional regions activated (Stout, 2011; Stout et al., 2008) showing that the execution of more complex actions following an organized sequence has a direct impact of the brain organization and activation. Interestingly, by training subjects in Paleolithic stone toolmaking over a period of 2 years, Hetch and colleagues observed structural changes in their brains, notably in the ventral premotor cortex and the intraparietal cortex (Hecht et al., 2015). This metabolically expensive reallocation of structural resources in white and gray matter, occurring in part in frontoparietal circuits in modern humans, suggests a positive selection pressure on our ancestors who had smaller brains for a rapid and enhanced plasticity and adaptation of these circuits along human evolution (Ferrari and Rizzolatti, 2015). Homo sapiens experienced an expansion and specialization of the parietal cortex, when compared with all other non-human primates. For example, in marmosets (a small primate), the areas 1 and 2 have not been well-delineated yet, the existence of an area 2 is even being questioned for marmosets (Cl
ery et al., 2020a, Kaas, 2019; Kaas et al., 2018) while those two areas have been well described in Old World macaques and humans who exhibit much bigger and complex anterior parietal regions, whereas the anterior parietal regions are much bigger and complex in Old World primate and humans. In particular, the evolution of the parietal cortex is extensively studied by human paleoneurologists, cognitive archeologists, and neuroanatomists, to collect paleontological and

57

neontological data for testing evolutionary hypotheses on brain functions such as visuospatial functions and brain-body-tool integration (Bruner, 2021; Bruner et al., 2016; Bruner et al., 2018; Bruner et al., 2018a, 2018b; Bruner and Iriki, 2016; for review, see Bruner et al., 2023). In addition, the training of Japanese macaques on using tools they do not use in the wild leads to changes at the neurophysiological, morphological, and molecular genetics levels that approach the ones observed in humans (Iriki and Sakura, 2008). Last, in a recent comparative review of the literature on the function of area VIP (parietal node of first fronto-parietal network, see above) in humans and nonhuman primates, we show that human VIP research has consistently identified three bilateral parietal areas encoding VIPlike properties as identified in macaques, presumably as a result of parietal cortex expansion in humans relative to macaques (Foster et al., 2022). We show that each of these parietal areas appears to subsume some, but not all, of macaque area VIP functions. In particular, we propose that human VIP’s differentiation of head and self-related processing may be linked to the emergence of human bodily selfconsciousness. In other words, we propose that human PPS is endowed with functional specificities that are yet inexistent or merely embryonic in the macaque brain. By studying cognitive functions such as tool use and space representation, in parallel with the frontal and parietal expansion, in nonhuman primates, we can gain more insights into the evolutionary trajectory of these cognitive mechanisms.

Posture One of the important physical changes that occurred in human evolution is the change in posture. About 2 million years ago, a switch occurs from generalist species able to brachiate and walk (Australopithecus) to a walking only genus (Homo) as the development of a typical human

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inner ear from Homo ergaster would suggest. Indeed, the inner ear is related to balance. More specially, the vestibule has the function of registering body and head movements to ensure permanent bipedal posture. By becoming bipedal, the relationship between the posture, the body, and the surrounding space has dramatically changed. The torso is much more exposed as it is not directly protected by the arms anymore, and the head and the neck are more vulnerable too. While the bipedal posture may not first look adequate from a survival point of view, this posture gives us other advantages but how and when this change occurred is still debated (Niemitz, 2010). Indeed, the biped posture allows us to build tools and throw weapons, carry food, watch the surroundings or wade in the water (Niemitz, 2010). We believe that globally the PPS from the postural change to Homo sapiens did not drastically change over the evolution of the Homo species. However, the visuospatial abilities have evolved in convergence with the parietal lobe expansion, leading, for example, to the possibility of using projectile tools in Homo sapiens (Bruner, 2021; Bruner et al., 2016, 2018, 2018a, 2018b; Bruner and Iriki, 2016). As the PPS representation is strongly linked to visuospatial perception and integration, this suggests that some gradual-related changes could have occured during evolution. By projecting tools, projecting themselves into virtual reality (VR) or using artificial equipment or objects, the PPS has extended its boundary for interacting with a wider surrounding environment. The link between peripheral vision and posture is well known. Vision notably helps to calibrate and maintain body position (Wade and Jones, 1997). As head and gaze direction influence more strongly postural sway than trunk position, it is assumed that the peripheral vision depends on a viewer-centered frame of reference rather than a body frame of reference (Berencsi et al., 2005). As described earlier, intraparietal bimodal neurons involved in PPS have a head/ eye-centered frame of reference (Avillac et al.,

2004; Chen et al., 2014; Duhamel et al., 1997). The bipedal posture could provide a visual advantage to scan the environment for predators and anticipate the arrival of objects toward the body, in the manner of suricates (Dart, 1959) and subsequently allow an enlargement of the visual-PPS as described in Cl
ery and Ben Hamed (2021). Indeed, we propose that this has the effect of expanding the PPS by enlarging the field of view, and the anticipation humans can have of what could come from the environment. Complementarily, feeling to be taller, which is the main difference between quadrupeds and bipeds, has been proven to enlarge PPS (D’Angelo et al., 2019). Accordingly, a study from Biggio and colleagues (2019) directly shows the link between posture and the modulation of defensive PPS. The authors measured the Hand Blink Reflex (HBR) in boxing athletes, with different levels of experience, and nonboxing athletes. The strength of the HBR has been associated with the size of PPS (Biggio et al., 2019; Sambo et al., 2012a, 2012b). Consequently, the authors observe a suppression of the HBR in the boxing group and they show that this suppression is highly correlated with the years of practice (Biggio et al., 2019). This study suggests that the sensorimotor experience acquired by previously learnt protective posture shapes the PPS representation. Thus, we suggest that the evolution of the human posture from a quadruped to a biped fully standing position has led to a different sensorimotor experience that has shaped the PPS representation (Fig. 3.1). Interestingly, Serino and colleagues (2015) show that PPS involves at least three differentiated body-part representations. One is centered in the hands/arm, one in the face, and one in the trunk. The PPS size for the trunk is the largest and more constant, the PPS for the head is smaller, and the one for the hand is even smaller but also modulated by its relative position to the trunk (Serino et al., 2015). The biped posture has impacted the position of the hand and head relative to the trunk allowing them to reach and

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FIGURE 3.1 Evolution of peripersonal space representation as a function of the evolution of human posture. The discrete evolutionary step of the human postural change from brachiation to a biped fully standing position has led to different sensorimotor experiences that have shaped the different body part representations of PPS (face, hand, torso) but also enlarged the visual PPS allowing to better predict the potential incoming visual stimuli into PPS.

grasp further away. We thus suggest that the PPS has evolved accordingly (Fig. 3.1). Indeed, reaching and grasping are some of the key functions of the PPS. These functions are useful for interacting with our direct surrounding environment but also for social interactions. Importantly, tool use has strongly influenced human evolution. Humans became occasional, then habitual (before 0.3 million years ago) to obligatory tool users (Shea, 2017). Today, we cannot find living humans who do not use tools at all. This strong dependency on tool use is the result of our evolution under a strong, sustained, and directional selective pressure. Tools are essential for human survival as we are not skilled anymore to face life-threatening situations we may have met in the environments of our evolutionary origins (Shea, 2017). Over time, through the complexification and specialization of the parietal lobe as well as of the

cognitive skills of humans (Bruner, 2021), we may suggest that by increasing their ability to integrate tools into the body scheme (prosthetic capacity), the human representation of space has evolved to adapt to this new way of interacting with the environment as reflected by PPS tool use extension. Tool-use and social cognition are extremely linked to human evolution as these abilities are considered unique to humankind. In the next two sections, we will discuss how tool-use and social cognition are linked to the PPS and its dynamic components but first we will discuss the PPS from a developmental view and the emotions linked to PPS.

Development of the peripersonal space Recent studies investigated if PPS is something innate or acquired. At birth, motor abilities

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such as reaching, grasping, or manipulating nearby objects are not present in human infants (Berthier, 2011). Developing spatial representations for the control of action requires the ability to efficiently integrate sensory information coming from multiple modalities relating to the link between the acting limbs and the nearby external world but varying as a function of limb position and body posture (Bremner et al., 2008). Across development, posture rapidly changes due to the relative sizes and shapes of the head, body, and limbs (King, 2004) and the number of new postures that a baby acquires with age and by learning new actions (Bayley, 1969; Carlier et al., 2006; Holmes and Spence, 2004; L adavas, 2002; Morange and Bloch, 1996; Provine and Westerman, 1979; Rochat, 2019; Van Hof et al., 2002). Findings converge toward two distinct developmental mechanisms of sensory integration for representing PPS. One, called “visual spatial reliance,” is primarily relying on visual information and prior experience to probabilistically locate the hand; and the second, called “postural remapping” is dynamically incorporating information about body posture to this previous multisensory spatial mapping. The first mechanism has been observed in the first 6 months of life and then the second mechanism develops after 6.5 months of age to increase the reaching and grasping abilities toward more fine-grained and goal-directed actions that can also be performed without seeing the acting limb (Bremner et al., 2008; Clifton, 1994; Clifton et al., 1993; Juett and Kuipers, 2019; Lockman et al., 1984; Robin et al., 1996; von Hofsten and Fazel-Zandy, 1984). Before 4 months old, young infants do not show eye blinking reaction in response to looming visual stimulation in PPS (Yonas et al., 1977). However, recent studies show that newborns can differentiate visual events based on their motion direction, with a preference for objects moving toward them versus away from them (Orioli et al., 2018a, 2018b). They also integrate audiotactile and visuo-tactile information at an early age (few hours after birth) and the efficiency for integrating these multisensory information

increases with age (Orioli et al., 2019, 2020; Ronga et al., 2021). Furthermore, looming stimuli toward infants are shown to trigger stereotypical defensive behaviors (Ball and Tronick, ~ ez, 1988). 1971; N
an These studies suggest that a raw version of PSS and primitive coding of body/selfboundaries are present at birth, with the main function toward defensive space, and that this PPS develops rapidly and progressively with age, thanks to the postural changes, experience, and brain maturation and all the important developmental changes occurring during the first year of life (Begum Ali et al., 2015; Rigato et al., 2014; von Hofsten, 1991, 1980; von Hofsten and Fazel-Zandy, 1984). Studying the PPS evolution in infants and children may give us precious insight into the evolutionary trajectory of the PPS representation. Experiments can be performed by assessing the PPS size along the postural changes and the different learning process (e.g., grasping a toy) across the development. Using eye-tracking systems (Silva-Gago et al., 2021), electrodermal activity measurements (Fedato et al., 2019, 2020b; Silva-Gago et al., 2022), and specific tools, e.g., mimicking the tools used through human evolution (Fedato et al., 2020a; Silva-Gago et al., 2019), one would be able to better characterize the mechanisms and dynamics subserving the development of PPS functions. For example, in the first months, babies mainly use their mouths to interact with their environment, then they progressively use more and more their hands. Interestingly, some studies have shown the use of the mouth as a third hand in Neandertals (Bruner and Lozano, 2014, 2015). The authors suggest that the frequent use of mouth and teeth to integrate praxis may have been the result of a brain organization that had not completely adjusted to the actual degree of cultural complexity, and thus required additional body interface elements recruited from other functions, as observed in babies (Bruner and Lozano, 2014, 2015). The fact that Homo sapiens rapidly switch from mouth to hands as the main interface to interact with their environment suggests

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a different brain organization that may have played a key role in their survival.

Evolution of emotions linked to PPS When discussing PSS, it is often referred to reaching and grasping spaces the outcome of which is a somatosensory stimulation to some body part, or touch. Interestingly, touch is at the same time part of the exteroceptive, proprioceptive, and also interoceptive (insula and emotion) systems (see also Chapter 11). The PPS is thus not only linked to tracking in body configuration but also to emotional processing. Indeed, another characteristic of the PPS is its dynamic plasticity linked to emotional states. Numerous studies have shown that PPS increases with anxiety or threatening stimuli (de Haan et al., 2016; Ferri et al., 2015; Poliakoff et al., 2007; Taffou and Viaud-Delmon, 2014; Van Damme et al., 2009) suggesting the role of the limbic cortex in PPS representation (Cl
ery and Ben Hamed, 2021). Consequently, the positive or negative emotions elicited by a stimulus have a direct impact on how we perceived the relative distance between us and this stimulus and how one will adapt his/her PPS by increasing his/her safety margin if we feel discomfort or threat. But where do our emotions come from? Are they innate or acquired? Following Darwin’s hypothesis (Darwin, 1872), emotions have been suggested to be innate and biology-based process (Ekman and Friesen, 1971). This theory is based on the idea that the perception and experience of basic emotions such as happiness, anger, fear, sadness, anger, and disgust are encoded and modulated by distinct neural responses that differentiate one emotion from another (Ekman and Cordaro, 2011; Murphy et al., 2003; Vytal and Hamann, 2010). In opposition, a constructionist theory has been developed in more recent years, stating that emotions are constructed mental states that are built and shaped by the acquisition of

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conceptual knowledge that is derived from prior experience and reenacted during perception (Barrett, 2006; Barrett et al., 2007; Barsalou, 2008; Niedenthal, 2007). In 2020, Bertoux and colleagues (Bertoux et al., 2020) performed a study on healthy participants and in patients with a neurodegenerative disease (the semantic variant of primary progressive aphasia) where a deficit in emotion recognition has already been described. They observe deficiencies in emotion recognition, emotion concept knowledge, and valence errors in patients, supporting the constructionist view of emotion recognition and valence processing. Emotions are thus strongly linked to our own culture and shaped by our positive and negative experiences in our everyday life. However, newborns and babies also express how they are feeling at the present moment (i.e., pleasure or displeasure), without necessarily understanding why there are feeling these states. This suggests that while the first emotions may be innate, their complexity, meaning, and recognition evolve with age and experience. Interestingly, Graziano recently developed some hypotheses on emotions’ evolution and their possible link to the PPS. He describes how smiling, laughing, and crying may have evolved, based on the body and face movements that are used by humans during the expressions of these emotions (Graziano, 2021). He suggests that “the specific, quirky, physical actions by which we communicate internal emotional states have been profoundly influences by peripersonal space and defensive movements.” With respect to social cues, evolution shapes the receiver first and then the sender (Graziano, 2021). Graziano explains that “the receiver evolves to react in a specific way when it observes a specific stimulus. As a result of that first evolutionary step, the sender has been given a lever by which to manipulate the behavior of the receiver. The sender then evolves to control or exaggerate that triggering stimulus in a strategic way.” For the origin of smile, he suggested that when a potential predator approach the sender, the

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peripersonal neurons of this last one respond by monitoring the predator characteristic (trajectory, location, danger) to adjust one’s selfprotection posture, including the shape of the mouth and lips as described previously. The upper lip is pulled up and exposed the upper teeth, indicating to the receiver that the sender is nonaggressive toward him. By associating this posture with being perceived as a nonthreatening individual probably lead to a greater advantage in evolutionary terms. As a result, by exaggerating this facial mimic including before potential danger, the sender gives a strong social signal increasing the chance of not being attacked. The smile would thus correspond to the evolutionary selection process of an exaggerated “no-danger” facial expression. For example, when you walk into the street and cross other people, you may have this unconscious tendency to smile at them to show that you are a nonthreatening person to avoid any potential altercations. Similarly, using the same mimic behavior, i.e., using a defensive reaction but even in the absence of real danger or urgency, Graziano describes the origin of laugher by the intrusion of another person into our PPS and use the example of tickle-evoked laughter (Graziano, 2021). By laughing at the approach of the tickler, you send a social signal rewarding the tickler who (might) stop his ongoing action. Lastly, he proposed that the origin of crying might have been driven by the transformation of the defensive reaction to a mimic behavior (squeezing muscle around the eyes, leading fluid out of tear ducts) to seek comfort, stop or shorten the inevitable aggression, or avoid being hurt (Graziano, 2021). Crying is a social signal but not just a simple way to express sadness but also to seek comfort and manipulate the interlocutor (e.g., kids crying on demand to obtain what they want or stop a fight). Overall, this evolutionary perspective on emotions and associated facial expressions propose a strong link with defensive behavior, social communication, and PPS function.

Tool-use In addition to the physiological limitation that can impact the PPS, another component to be considered is the environment and the use we make of it. Object manipulation has been observed in a wide range of species such as mammals (Mann et al., 2008; Root-Bernstein et al., 2019), birds (Boire et al., 2002), and nonhuman primates (Breuer et al., 2005; van Schaik et al., 1999). However, humans present a unique wide range of complex tool used for many different functions that go beyond food search or reaching something far away. Indeed, primates and, particularly, humans, have developed the ability to manipulate objects, notably thanks to the evolution of opposable thumbs (Kivell, 2021), bimanual manipulation, and the bipedal posture that allow dexterity and refined prehension essential for the manipulation of complex objects of different sizes and shapes. In this context, a distinction has been proposed between object-use and tool-use, the latter implying three conditions (Bruner, 2021; Bruner and Gleeson, 2019). According to this theory, a tool should be a part of the body schema, it should be integrated into a productive chain, and it should be integrated culturally (Plummer, 2004). Based on this definition, very few examples of tool-use can be reported in nature. The Iriki and Sakura’s experiment with Japanese macaques (2008) represents an important step in understanding how tool use can change the brain. They were able to produce neurophysiological, molecular genetics, and morphological changes by teaching Japanese macaques to use tools that they would not have used in the wild. This effect, which they called “intentional niche construction” has been suggested to be an extension of natural selection and shows the strong effect that the environment can have on the brain (Iriki and Sakura, 2008). In this case, the macaques have been taught by another species how to use the tool. The human ability to use one tool to create another one is unprecedented in the

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animal world or at least extremely rare looking at the conditions defining tool use. Notably, stone knapping (Muller et al., 2017), together with learning by instruction or active teaching (Renfrew et al., 2008), is considered to be a unique human feature, although there is recent evidence that this might also exist in other hominoid primates such as chimpanzees (Estienne et al., 2019). It is considered that the stone technology threshold has been the starting point of human technological evolution (Ambrose, 2001). This movement can be considered very simple for a human of today, but this know-how imposes planning and fine motor skills requiring bimanual coordination that have been possible, thanks to postural changes and the emergence of handedness (Roux and Brill, 2005). One human strength would be, in addition to visuomotor coordination, the capacity to plan complex actions with several steps following a hierarchical order (Stout et al., 2008), i.e., a productive chain. This is the foundation of the coconstitution of mind and things theory. Humans manage to create refined tools with other tools, they carry the tools and restrict their usage for themselves and for their own social group and manage to teach their children how to use them. It is assumed that children that best mastered these tools and these objects and developed this cognitive ability were those who had the highest chance of survival and reproduction. Humans have surrounded themselves with tools for thousands of years now, such that they are now considered essential to our survival and for some, extensions of ourselves. Human culture has managed to keep modifications overtime and presents a cumulative cultural evolution. This so-called “ratchet effect” is the result of social learning focusing on process more than on product: learning by instruction and by social positive feedback provided for conformity and negative feedback for noncompliance (Tennie et al., 2009; Whiten et al., 2009). The use of these accumulated tools, their storage, and improvement over time is an important factor

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marking our physical and societal evolution. The gradual increase in the complexity of tools and techniques in human populations over generations is referred to as cumulative technological culture (Dean et al., 2012). To realize an action from the conception level to the production level, we need to control our body parts to reach, grasp, and manipulate objects (Chapter 5). The parietal cortex being strongly involved in this process, we may expect an impact on the PPS. Indeed, as the PPS size is modulated by tool use and social context we suggest that the social environment in which the use of a tool is learnt or the concept behind its use will directly affect one’s relation to the tool and the PPS (a positive or negative reinforcement learning will increase or decrease the PPS affected by this specific tool use learning) as well as the way this learning will be transferred. While human tool use is specific in several respects, notably by the complexity and the wide range of tools and tool usages, basic mechanisms describing the effect of physical tool use on the extension or contraction of the PPS have been uncovered.

Plasticity of peripersonal space with tool use One effect of this tool use is the incorporation of the tool into the body schema, leading consequently to an extension of PPS (Maravita and Iriki, 2004). This effect has first been observed in macaques (Iriki et al., 1996). After using a rake during 5 min, some neurons of the intraparietal cortex of these macaques, responsive to both somatosensory stimuli on the hand and visual stimuli near the hand, showed an extension to their receptive field to include the rake. This embodiment of the tool is representative of the PPS expansion (Iriki et al., 1996). With tool use, the tool is integrated into the multisensory representation of the body. Similarly in humans, reaching far away objects with a 1m long tool

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has been shown to extend PPS along the tool axis but also the body representation with a feeling to have a longer forearm (Canzoneri et al., 2013b). This effect was only present when the tool was used to reach and not just to point, as observed in another study that adds the very interesting fact that PPS extension is more linked with the functional length of the tool than to its real length (Farne et al., 2005). Importantly, a similar extension of PPS around the hand, following the cane used to navigate, contracted backward after a resting period showing the dynamic character of PPS size (Serino et al., 2007). In the frame of PPS linked with tools, there is the notion of reaching distance and grasping related more to goal-directed actions than to a secure space. It should be noted that a recent study suggested that the PPS and the arm-reaching space are two distinct spatial representations that need to be considered when using arm-reaching tasks for studying PPS (Zanini et al., 2021). This is supported by the use of tools as a substitute for the hand for a potentially risky action (Povinelli et al., 2010). A typical example would be using a spoon to stir a hot soup or a stick to touch an unidentified and potentially dangerous thing. It would imply that even when using a tool, the object is not completely incorporated in the body schema or does not result in an extension of the defensive PPS as it is used to protect the body from potential hurt. This phenomenon has been reported in chimpanzees (Povinelli et al., 2010). The remapping of far space to near space has also been observed in human patients. For example, a patient who was exhibiting a neglect effect only in near space (i.e., inability of the patient to process and perceive stimuli in this near space) extended this neglect effect to far space after the use of a stick, remapping the far space reached by the stick to the “new” near space (Berti et al., 2001; Berti and Frassinetti, 2000). A similar effect was observed in patients with tactile extinction (i.e., failure to detect a tactile stimulus specifically when presented with another stimulus on certain parts of the body),

who showed an expansion of this extinction after the use of a rake (Bonifazi et al., 2007; Farne and Ladavas, 2000). Overall, these studies demonstrate a dynamic representation of PPS that can be modulated by objects and the nature of the action we perform with it, whether it is grasping or pointing (Brozzoli et al., 2009, 2010) (Fig. 3.2). Note that both actions had the same effect at their onset but grasping produced further enhancement than pointing on the visuo-tactile interaction during the execution phase. These results and previous studies cited above link the remapping of PPS with planning voluntary action and motor control (Patan
e et al., 2019). Quite surprisingly, it has been shown that passive use of a wheelchair, with the patient being pushed by another person, provoked a PPS extension, whereas the active use of the wheelchair did not produce PPS size change nor when participants were blindfolded. This suggests that the PPS extension may also depend on the interaction between the self and the environment and thus that this extension and dynamic of PPS is due to the integration of information coming from both body and environment (Galli et al., 2015).

Tool use and PPS in handicap Some disabilities, such as blindness, lead to the use of tools for long-term support, for example, handling a cane for navigating safely in a sidewalk and cross a street. This has the effect of producing a sustainable extension of the PPS (Serino et al., 2007). This is also the case for patients with a prosthesis following an arm amputation. PPS directly extends to the prosthetic hand when carried and retracts to the stump without the prosthesis (Canzoneri et al., 2013a). As for the cane, the regular and longterm use of the object allows the immediate extension of the PPS to integrate them into the body. In other words, two different PPS representations could be interchangeably instantiated

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FIGURE 3.2 Peripersonal space is strongly modulated by tool-use and the context of interaction. (a) PPS extends along the length of the spear or stick following its use. (b) PPS projects toward the computer when the keyboard is used. (c) PPS shifts entirely to the virtual body.

depending on the context. Progress in prosthesis elaboration is aiming at getting the best embodiment of artificial members. The creation of percutaneous osseointegrated interfaces that allow continuous and long-term sensory feedback demonstrates better motor control of the prosthesis regardless of the environmental conditions (Ortiz-Catalan et al., 2014). Playing with the sensory feedback and the tactile inferences

is at the core of the embodiment and should reinforce the incorporation of the prosthesis in the PPS by enhancing visuo-tactile integration (Ackerley and Kavounoudias, 2015). One could assume that this process leads not only to a projection of the action PPS but also to the instantiation of a defensive PPS. The patient would thus not consider the prosthesis as an object we use to protect the body anymore (as the spoon to stir

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hot water) but as an integrated part of the self that can be used for the protection of the body. By being able to imagine and create more sophisticated and adapted tools across the human evolution, humans can improve their daily life quality with more autonomy and increase the overall survival rate. Years ago, when someone was injured or disabled, his chances of survival were not as high as nowadays. Globally, the PPS associated with tool use mainly relies on hand-object interaction and is thus mainly defined as a peri-hand space or hand-centered PPS. However, other body parts are also strongly linked to PPS.

Body illusion and self-representation PPS can be modified not only by the bias induced by tool use but also by changing the body representation. PPS, while representing the self as distinct from others and the environment, highly depends on the context and can contribute to higher-level cognitive functions mediation and physical self-consciousness (Serino, 2019). For example, PPS varies when the body height perception is manipulated (D’Angelo et al., 2019). It is interesting to note that feeling taller extend action PPS, whereas feeling smaller does not modify its size. To go further, as PPS seems to depend on body ownership, providing an illusion of owning an external body extends the PPS to this altered body (Botvinick and Cohen, 1998). The full body illusion (FBI) can be created by making the participant view a virtual body being stroked, while receiving synchronous tactile stroking on their own physical body. This multisensory process helps them to project themselves into the virtual body (Fig. 3.2). Following the induction of FBI in this experiment, the PPS, defined here by an audioetactile interaction paradigm, drifted toward the virtual body in front of the participant while it was reduced in the participant’s back (Noel et al., 2015). PPS

can thus extend to a manipulated object but also project to virtual body perceived as self even if multisensory inputs allowing this effect are unconscious (Salomon et al., 2017). Along the same idea of self-projection and PPS extension into a virtual body, PPS can also shift toward the body of another person (Maister et al., 2015a). This is observed during the fascinating enfacement illusion, which can induce the perception that someone else’s body is our own body (for review, see Porciello et al., 2018). Here, participants reported tactile stimuli faster when sounds were presented close to the other’s body than to their own body, showing thus a complete drift of the PPS.

PPS and new types of virtual technological tools Our society is increasingly turning to new technologies. For example, we all use almost daily computers and smartphones. As sticks and other less technological tools extend PPS, holding or using a computer mouse, extends our auditory PPS toward the computer screen (Bassolino et al., 2010). However, although this extension persists without the active use of the mouse, the PPS does not cover the screen if the participant is not holding the mouse, showing that the direct link between the object and the PPS remains (Fig. 3.2). Nevertheless, new technologies create tools that are no longer just possible physical extensions of our body to perform actions but vectors of self-projection. This is particularly the case when using VR or particularly immersive video games. Similar to physical tools, these new high-technology tools can have a drastic effect on the PPS. During their projections into another world, subjects have the same type of reaction to virtual agents as to a real person (Giles, 2006). During these games, a relocation of self-location is at play having as effect a modification of the PPS (see previous paragraph). Games do not only involve action but

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Social and cultural societies

also emotions and social interactions. It has been shown that personal space, i.e., space defined by the relative distance from another person where one feels comfortable, is larger when several virtual agents showing angry faces versus happy faces are approaching or when multiple virtual agents are approaching compared to only a single one (Bönsch et al., 2018). Personal space here can reflect the secure PPS which has been proved to depend on emotions and social interactions (Bogdanova et al., 2021; Markman and Brendl, 2005; Pellencin et al., 2018; Teneggi et al., 2013; Valls-Sol
e et al., 1997). VR has been successfully used as a treatment for different types of phobia, such as cynophobia, arachnophobia, and claustrophobia all known to be linked with PPS distortion (Hunley et al., 2017; Lourenco et al., 2011; Rabellino et al., 2020; Taffou and Viaud-Delmon, 2014; Vagnoni et al., 2012). By exposing patients to repetitive (4 times) and long last (90 min) sessions in front of their fear via VR, they manage to reduce their anxiety and phobic fear and enhance their selfefficacy to cope with the threatening stimuli. This positive effect of the treatment was observed in both adults and children (Bouchard et al., 2002). In recent years, VR has also been developed for helping burn patients during the care and healing of their wounds. The acute pain, that is an important signal from an evolutionary perspective, makes the life of these burned people harder during the care. By playing video games through VR in an icy and snowy virtual environment, their attention is drifted away from the pain during the care and helps release part of this pain (for review, see Bermo et al., 2020). By virtually projecting their body into this virtual world, we may expect a modification of their PPS boundaries after care using VR relative to before. While this technological advance may have an impact on our PPS representation, it is also a powerful tool that can be used to study the PPS from an evolutionary perspective. Indeed, using VR, we should be able to create virtual worlds

resembling the ones of our ancestors and thus reproduce similar interaction with objects and the environment from this period. Two distinct timescales in the remapping of PPS would be at play. A very rapid timescale remaps more dependent of upcoming sensorial information (Noel et al., 2020) and a more “long-term” remap due to internal feelings, repetitive social (bitten woman or abused children) or nonsocial (using a computer mouse or a cane for blind people) feedbacks. In the same way that PPS can be permanently modified by the frequent and regular use of an object, as seen with the cane used by blind people, we can hypothesize that a similar drift of the PPS will be observed after a regular immersion into a virtual world. This hypothesis seems to be confirmed by the widespread use of VR in psychotherapy these last years (see for review, see Liu and Tang, 2020). While these studies focus on the positive impact of VR, important questions pertain to its possible negative impact. How pervasive is the impact of this new technology on our selfbody consciousness and our PPS? Does someone playing an immersive game but in an aggressive virtual world such as war games will exhibit a body margin extended in real life and be more sensitive to external stimulations? Or on the contrary, will he/she develop some habituation that will reduce the PPS as observed in fear experiments? While the plasticity and dynamism of the PPS are strongly linked and influenced by tool-use, the emotional and social components also play an important role.

Social and cultural societies Interactions between people are greatly influenced by culture, habits, and social norms that vary according to the country, region, or even the social class of the individual. A simple example is the way saying “hello” can be done without contact, a simple handshake, hug, or

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kiss. The very regular use of this practice according to the various uses will accustom individuals to more or less regular intrusion in their PPS, potentially modifying it in the long term. As PPS is highly linked with body representation, it is also bounded by social interactions and emotion perception. Can we assume that people from very tactile nations (e.g., who will have the habit of touching each other during a conversation, who will speak to another person at a close distance, or who systematically sit next to another person in an empty room) could have a smaller PPS than people from nations who show much more distances in their interactions? Is this only an individual effect or does it go beyond, toward a population and cultural effect? Are these differences only related to the interpersonal space, i.e., the comfort-distance space between yourself and another individual, or are it related to more specific functions of the PPS?

Culture A rising concern when we talk about studies on culture or social aspects is that most psychological and behavioral research data are coming from Western, Educated, Industrialized, Rich, and Democratic societies (Henrich et al., 2010). For example, in 2008, Arnett described that research published in APA journals mainly focuses on Americans who represent less than 5% of the world’s population (Arnett, 2008). Consequently, most of these studies are not representative of all humanity as they only concern a single subpopulation of the human population. Living conditions differ from one society to another and those differences lead to different cultures, beliefs, and ways to see the world and interact with other people. Those differences are strongly anchored by our roots and history but can also give us insights into human evolution. A scale of comparison between different cultures has been proposed. It englobes six different

cultural dimensions: the power distance index (i.e., acceptance degree of authority), masculinity versus femininity, uncertainty avoidance index (i.e., how the nations avoid the unknown), long-term orientation versus short-term normative orientation (i.e., tradition vs. modern dilemma), indulgence versus restraint index (i.e., focus on long-term benefit vs. present joy), and finally individualism versus collectivism (Hofstede, 2011; Hofstede et al., 2010). Linked with our topic, it has been suggested that individuals raised in an individualist versus a collectivist society have different self-constructions. This would impact emotions, cognition motivation, and thus behavior (Wang, 2000). The characteristics of the independent self and interdependent self, respectively, bounded with individualism and collectivism can have an impact on personal space and more generally on PPS. People that are used to collaborate with others would be more trustful and more empathic than individualists. As PPS is linked with empathy (Fossataro et al., 2016), do these nations have a different PPS? Knowing that PPS is more expended when we face a person considered moral than when we face a person considered immoral (Pellencin et al., 2018), in nations with a high degree of morality or religiosity, would this remap of PPS be larger than in a country that gives less importance to morality? Interestingly, Høgh-Olesen (2008) looked at how personal space expands or shrinks depending on context and spatial relationships in subjects from six countries situated in four different climate zones. He found that northern “low-contact cultures” (Greenland, Finland, Denmark) keep a larger interpersonal distance than southern “contact-cultures” (Italy, India, Cameroon). This result confirms that culture and living conditions greatly influence the way we interact with our environment and other people. In addition, it has been shown that the perception of touch on the face can be modulated by seeing other people being touched and more

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particularly by the familiarity and the social proximity of the other person (Serino et al., 2009). In this study, Caucasian and Maghrebian subjects present more enhanced tactile perception when they were watching faces from their own ethnic group than when they were watching the other ethnic group being touched (Serino et al., 2009). The authors observed similar behavior in relation to the political group participants belongs to. According to these findings, we suggest that shared ethnicity and shared beliefs also strongly influence our PPS representation. However, some studies have shown that perceptual, bodily representations involved in body ownership and the evaluative, conceptual representations involved in implicit social attitudes may be linked together depending on the context. For example, when changing to an outgroup that is less similar to us, it will lead to less implicit social biases via a process of selfassociation, first through the physical domain (perceived physical similarity between self and outgroup member), and then through the conceptual domain (generalization of positive selflike associations outgroup) (Maister et al., 2015b).

Social PPS, when your PPS become my PPS In recent years, several studies looked at how social behavior and context may influence PPS. First, Ishida and colleague (2009) found that neurons from the macaque VIP area responded both when the macaque’s own body was touched or approached by a visual stimulus, and when the corresponding body part of an experimenter who was facing the animal was touched or approached by a visual stimulus. This study brought the first insight into the use of selfrepresentation for perceiving other’s body parts. Subsequent studies showed similar results in humans (Brozzoli et al., 2013; Maister et al., 2015a; Teramoto, 2018) and went further by suggesting that other individual’s PPS is remapped

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onto self-representations. Teramoto (2018) observed that participants detected faster a target when a visual stimulus was approaching their hand in the near space or approaching the near space of a partner (for similar or different body parts from the participant), corresponding to the participant’s far space. The relation and shared experience between the subject and partner are also influencing this shared PPS representation (Maister et al., 2015a). Other behavioral studies have shown that the size of the PPS representation can be modulated by the social context. Indeed, PPS plays an important buffer in adjusting our social interactions. To interact with others, we need to minimize our interpersonal distance without invading other’s PPS (Kennedy et al., 2009). For example, PPS representation will shrink or merge with another individual PPS depending on whether they behaved cooperatively or not (Gigliotti et al., 2021; Hobeika et al., 2019; Rocca et al., 2019; Teneggi et al., 2013; for review, see Coello and Cartaud, 2021). Our own actions are thus dependent on other actions but also on the reward and motivation linked to those actions (Coello et al., 2018; Gigliotti et al., 2021). The presence of others, familiarity, and personally traits will also directly impact the PPS representation depending on if they are partners, conspecifics, confederates, or intruders (for an extensive review, see Bogdanova et al., 2021). For example, the facial emotions and expressions will influence this PPS representation (Cartaud et al., 2018; Ellena et al., 2020; Ruggiero et al., 2017) but also lead to an increasing electrodermal activity or heart rate when presenting in near space (Cartaud et al., 2018; Dureux et al., 2021; Ellena et al., 2020).

Peripersonal space within a world pandemic The COVID-19 pandemic that hit the world in 2019 led to restrictions and rules strongly

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impacting our lives. By wearing a mask and practicing social distances, we have changed the way we perceived our space, others, and how we socially interact. We don’t know yet the long-term effect of these restrictions, but some studies looked at how they may impact the PPS. Basically the action mode of the virus has changed the way we perceive space as “safe.” If the defensive PPS helps to protect against frontal attacks on our bodies, e.g., from objects looming toward us, the threat of the virus has changed the rules. The virus could “attack” us through the air projected by our fellow human beings which is by definition invisible but also by being on everyday life objects. One of the most striking consequences of this period was the confinement and social isolation that many experienced. Surprisingly, while we could predict an increase in PPS to protect the body from the potential aggression, it has been shown that the PPS has been reduced following the lockdown (Serino et al., 2021). The authors interpret this shrinkage as some kind of repercussion due to the implementation of social distancing than a decrease in the potential contacts. After the lockdown, the participants presented stronger near-far segregation, associated with less interaction at a far distance. This effect correlates with “perceived vulnerability to disease” scale, i.e., the more participants were afraid of being contaminated by pathogens, the stronger was the differentiation between near and far space and the more they were prone to engage in protective avoidance behavior with others. The habituation of social isolation due to the repeated lockdowns would have led to a sharper distinction between our own space and external space leading to less anticipation of what comes from outside as well as a higher impact of PPS violation. Indeed, the physical distancing had an impact on the perception of interpersonal distance that was enlarged during lockdown but also varied with the incidence of the virus (Cartaud et al., 2020; Welsch et al., 2021). This distance was also modulated by the fact that

people could be wearing protective equipment or not (Cartaud et al., 2020; Lee and Chen, 2021; Lisi et al., 2021). Furthermore, this effect is also influenced by the regions where people live and thus their culture (Cartaud et al., 2020; Lee and Chen, 2021) These results suggest a strong socio-cultural adaptation to the distance requirements and barrier gestures for protection that may lead to some change in our current evolution. Actually, some preliminary results show that the pandemic is strongly affecting babies and child development. The development of gross motor, fine motor, and personal-social subdomains (Shuffrey et al., 2022) as well as time screen exposure (Bergmann et al., 2022) or vocabulary development (Kartushina et al., 2021) is different in babies born during the pandemic and young children compared to prepandemic cohorts. The wearing of masks is limiting facial interaction and recognition that babies use for decrypting their parents’ emotional cues and regulate their responses toward them or to potential dangers (for discussion, see Green et al., 2021). We hypothesize this will strongly impact the way the next generation will apprehend and interact with our world and thus impact their PPS representation.

Conclusion Since our birth, we are solicited by numerous stimuli in our surrounding environment. By being conscious of our body and thus of our selfboundaries, we are able to manage those different stimuli to sort them, anticipate their potential consequences, such as an impact or an attack, close to our body. This PPS representation is dynamic and shaped by our everyday life and constant interaction with our world whether physical, emotional, or social. Across human evolution, several factors have influenced this space. The discrete evolutionary step of postural change from brachiation to full bipedal posture

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References

about 2 million years ago is proposed to have been crucial in the evolution of PPS. As the torso and the neck became much more exposed, the PPS increased. These postural changes have to co-occur with other gradual changes that have enhanced visuo-spatial perception and integration have led to a progressive increase of tool use from occasional, habitual, and to now obligatory use with more and more sophisticated and artificial tools built and created by humans, requiring an adaptation of PPS fitting better tool manipulation and integration to body scheme. Last, human history is showing a constant cultural and social evolution that is progressively differentiating across populations and is expected to impact the PPS in more subtle yet distinctive ways.

Acknowledgments We would like to thank Emiliano Bruner and an anonymous reviewer for their positive and constructive feedback on this chapter. MF and SBH were funded by the French National Center for Scientific Research (CNRS). JC is funded by The Neuro, McGill University, and the Azrieli Center For Autism Research.

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The body in the world: tools and somatocentric maps in the primate brain Banty Tia, Rafael Bretas, Yumiko Yamazaki, Atsushi Iriki Laboratory for Symbolic Cognitive Development, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan

Introduction

gradual (Ambrose, 2001; Shea, 2017). Obligatory tool use (i.e., compulsory reliance on tools for survival) is dated around 0.3 Ma, which coincides with near-modern human brain size (Shea, 2017). Throughout this process, tools granted their users new abilities that were not easily performed by the body alone. Tool usage created novel social, cognitive, and ecological niches which influenced subsequent developments (Iriki and Taoka, 2012). New cognitive capacities supported by the emergence of new functional brain regions allowed hominids to develop and incorporate tools and technologically advanced behaviors within their habitats, driving rapid changes in their ecological niches. The first tools were unsophisticated objects manufactured from raw materials that initially resembled the shape of the finished product. Similar to the unmodified tools many animals use, they provided the body with new functions beyond those available from the preexisting physical framework. However, making and using these basic implements required the presence of prior biological adaptations. Specific anatomical features (e.g., mouth, limbs, and proboscis)

The early usage of tools by hominids coincided with an accelerated increase in brain size (Fig. 4.1). Archaeological evidence in extinct species, as well as examination of current primates, supports the view that brain size coevolved with manipulation complexity, innovation, and social learning (Heldstab et al., 2016; Reader and Laland, 2002). However, this process was not linear in hominids, and it likely involved distinct events and lineages (Bruner, 2019). The earliest evidence of stone tools (i.e., simple sharpedged slivers, lumps of stones, hammers, and anvils) is known as the Oldowan culture and is attributed to Homo habilis, starting approximately 2.5 Ma. It was followed by the Acheulean culture, which saw the emergence of more symmetrical stone tools produced by the larger-brained Homo erectus/ergaster and Homo heidelbergensis starting approximately 1.7 Ma. The slow pace of technological advances during these periods suggests that these taxa were not habitual tool users and that the transition from occasional to habitual tool use may have been

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FIGURE 4.1 The increase in cranial capacity in hominids (ordinates) followed the early use and manufacturing of tools by the Paleolithic stone industries (upper part of graph). Tool usage marked an accelerated increase in cranial capacity (arrow), creating novel social, cognitive, and ecological niches (Iriki and Taoka, 2012). Modified from Matzke (2006) and Iriki et al. (2021).

are required to effectively interact with tools, and primates have an ideal physical setup for tool manipulation, as their hands are free while sitting and, in the latest stages of human evolution, while standing or walking. Furthermore, hominids are equipped with a neural apparatus supporting the representation and use of tools. It has long been known that the self-body is mapped consistently in the brain. Motor and sensory responses are localized in an arrangement that generally reflects the physical body’s spatial organization (Leyton and Sherrington, 1917; Penfield and Boldrey, 1937), which is the source (and destination) of these neuronal responses. In certain cases, this arrangement may also reflect a functional workspace around the body, as with the adjacent representations

of the face and hand, which occasionally overlap in somatomotor maps (Schieber, 2001). Longduration simulation trains applied to specific sites of the motor cortex evoke ethologically meaningful actions, such as mouth opening and hand shaping into a grip posture moving toward the mouth (Graziano et al., 2002). This indicates that sensorimotor maps are arranged to support physiologically relevant brain circuits for behaviors of critical ethological value, such as concurrent actions of the hand and mouth (Desmurget et al., 2014). It seems logical that the neural mechanisms for sensorimotor mapping are limited to the body’s boundaries, where somatosensory sensing and motor commands terminate. However, the use of tools also evokes neuronal responses to stimuli at the tool

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The evolution of a biological substrate conducive to tool usage

boundaries, causing a literal remapping of the body by incorporating the tool into the body representation in the brain. Cognitive skills are also required for tool usage. Learning to use a natural, unmodified tool can stem from the mere observation of an external object interacting with another (e.g., a stone breaking a nut). However, making and using manufactured tools have different demands. Manufactured tools are not only gathered but also modeled. Dexterous limbs, complex sensory-motor skills, visuospatial analysis of available materials, and the ability to plan a detailed sequence of movements are required (Bruner et al., 2023). As tool complexity evolved, more knowledge on the creation and use of these tools had to be transmitted to individuals through cultural learning. The care necessary to maintain these tools also increased when they became nonperishable properties of an individual or group. This level of integration between the brain, hand, and tool required not only the development of new behavioral abilities but also a reorganization of the whole cognitive structure (Bruner and Iriki, 2016). Approximately 2 million years ago, concurrent with these behavioral and cultural changes, the hominid brain underwent an explosive increase in size (DeSilva et al., 2021; Dunbar, 1993). In particular, the parietal lobe, which is central to cognitive functions at the core of material culture, was suggested to have undergone significant expansion during human evolution (Bruner et al., 2023). Recent work linking archaeology and neuroscience indicates that the Oldowan technology, which consisted of basic stone percussion, relied on posterior parietal cortex activation, whereas the later Acheulean technology, which produced more symmetrical shapes with thin cutting edges, required parietal and prefrontal activation, presumably for behavior planning and executive functions (Bruner et al., 2023; Stout et al., 2015). While these findings exemplify the parallel evolution of neuronal substrate, cognitive functions, and tool complexity, the mechanisms

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involved in making, using, and sensing tools rely on partly different networks which could have evolved independently and led to distinct skill combinations among primate species.

The evolution of a biological substrate conducive to tool usage Throughout their evolutionary process, primates accumulated biomechanical and neurophysiological traits that allowed them to efficiently manipulate objects. A primary feature of this process was the vertical rotation of the body axis, which became perpendicular to the direction of support and locomotion. The orientation of the body axis is crucial to determine the ways in which an individual perceives, analyzes, and acts upon its environment (Bruner and Iriki, 2016; see also Chapter 3). A premise for the acquisition of a body axis was the emergence of symmetrical body designs. Five major body plans (i.e., asymmetrical, spherical, cylindrical, radial, and bilateral) are present in metazoans, possibly originating from an ancestral cylindrical geometry (Chen, 2009; Manuel, 2009). These body plans are associated with various functionalities, and bilateral symmetry has been functionally linked to directional locomotion. In many of these species, the anteroposterior axis corresponds to the main body axis (often along the direction of locomotion), while the dorsoventral axis is oriented with respect to gravity. This organization was deemed to provide an advantage in terrestrial environments due to the rapidity and efficiency of movements along the body axis toward the head, which resulted in the dominance of these species on land. Directional locomotion implicitly involves an intention to move toward a certain direction and possibly drew the first emphasis on intentional, purposeful motor behaviors. Throughout evolution, there has been a shift from more primitive species, for whom the locomotor apparatus allowed whole-body

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movement in the direction of the mouth (e.g., worms and fish), toward species with more complex praxis involving the flexible neck or arm reaching (e.g., certain birds and primates; Iriki and Taoka, 2012). Along this path, arboreal locomotion greatly enhanced the diversity of postures and the range of arm movements in primates, which further promoted the development of visuospatial functions (Ross and Martin, 2007; Schmidt, 2011). Among primates, hominids were uniquely characterized by a transition from horizontal to vertical body axis, which culminated in constant bipedalism in humans. Terrestrial bipedalism was a crucial step in human evolution for at least two reasons. First, it offered hominins motility in vertical posture, namely, the maintenance of an upright, head-lifted posture with locomotion perpendicular to the trunk axis. This facilitated long-distance travel in open woodland habitats and more efficient foraging of widely dispersed foods (Lieberman, 2010). Second, bipedalism diverted the forelimbs from their postural and locomotor function and facilitated the specialization of the hands as apparatuses for dexterous object manipulation (Iriki and Taoka, 2012; Kimura, 2019). Questions remain regarding the links between posture and hand usage (Hashimoto et al., 2013) and between posture/locomotor mode and brain activity (Thibault and Raz, 2016; Tia et al., 2021; Tia and Pifferi, 2021). Still, the Homo lineage displays a special association between vertical posture and increased manual dexterity. The evolutionary origin of terrestrial bipedalism has long attracted scientists’ interest. Although, for decades, humans’ genetic proximity with chimpanzees favored the assumption that bipedalism evolved from “knucklewalking” quadrupedalism, recent competing hypotheses suggest that it may have arisen from a suspensory or brachiatory locomotor mode, vertical climbing, or arboreal bipedalism of an orthograde arboreal ancestor (Alm
ecija et al., 2021; Crompton et al., 2010). Indeed, although

all great apes occasionally walk bipedally, it is the most suspensory one, the orangutan, that most frequently uses bipedal locomotion. During upright gait, its vertical ground reaction force curves most overlap with those of humans, achieving highly efficient energy conversion (Crompton et al., 2010). However, the mosaic of fossil hominid morphologies makes it challenging to reconstruct the evolution of locomotor modes. Some authors posit that bipedalism could have evolved from an orthograde body plan, without an intermediate stage of advanced suspension or specialized knuckle-walking but proficient at vertical climbing (Alm
ecija et al., 2021). The recently discovered Ardipithecus (Ar.) ramidus (w4.4 Ma), and Pierolapithecus (w12 Ma) were described as orthograde but lacking suspensory adaptations. Based on fossils of Ar. ramidus, palmigrade compressive orthogrady (i.e., hand-assisted arboreal bipedalism) was proposed to be the oldest hominoid locomotor adaptation, which later led to specializations for knuckle-walking in chimpanzees and gorillas, suspension/brachiation in orangutans, and bipedalism in humans (Crompton et al., 2010). Indeed, from a more generalized (i.e., non-suspensory) morphology, only a few structural modifications of the human hand would have been required to allow better dexterity and tool usage. Although verticality is linked to increased manual dexterity in the Homo lineage, human hands and feet may have evolved partly independently (Hashimoto et al., 2013). Comparative neuroscience experiments demonstrated that, whereas humans and macaques share a similar hand representation in the primary somatosensory cortex, their feet representations strongly diverge. This pattern suggests that the human hand could have evolved as an adaptive reuse of an existing structure, whereas the human foot is a more recent development that is tightly linked to terrestrial bipedalism. In both humans and macaques, the somatotopic separation of the fingers is functionally supported by independent

I. Visuospatial cognition and evolution

The evolution of a biological substrate conducive to tool usage

finger control, coherent with a functiondependent somatotopic representation. In regard to foot representation, humans, who rely on the big toe for pressure application and sensory feedback during bipedalism, show a separate representation of the big toe from the other four toes. By contrast, macaques, who lack independent toe motor control and precise tactile discrimination, show a fused-toe representation. Since the human foot may have evolved to its modern shape and function independently from, and more recently than, hand dexterity, factors other than bipedalism should have promoted the evolution of hand dexterity and eventual tool use. Therefore, we hypothesize that other postures rotated the body axis vertically prior to bipedalism, (e.g., the sitting posture for dexterous hand actions; Christel and Billard, 2002; Whishaw et al., 1998) and triggered a series of evolutionary processes along tool usages. Although a major asset for visually guided actions, the emergence of vertical posture was subject to specific requirements, among which was the ability to translate body-part information between coordinate systems (i.e., body-, eye/ head-, and hand-centered systems; Graziano,

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2001; Iriki and Taoka, 2012). In the vertical posture, the visual axes are directed parallel to the base of support and perpendicular to the body/trunk axis. As a result, various axes (e.g., body, hand, and eye/head) are dissociated. The sitting posture represents an ideal condition to experimentally assess the transition from horizontal to vertical body axis (Fig. 4.2). This posture is frequently observed in primates during dexterous hand usage in tasks such as grooming, object manipulation, and tool use (Christel and Billard, 2002). It raised the body axis vertically and facilitated diverse and complex reach-to-grasp movements around the body. Other orders (e.g., rodents and scandentia) also show a preference for sitting during precursory use of skilled forelimb movements (Whishaw et al., 1998), and they may rely on similar mechanisms, although body-eye-hand coordinate transformation was little documented in nonprimates. One potential difference between primates and nonprimates is that object reaching and handling could involve different sensory modalities, such as chemosensation in mice (Gali~ nanes et al., 2018) versus vision in primates (Heesy, 2009). Another distinction is that

FIGURE 4.2 The transition from a horizontal to a vertical body axis when switching from quadrupedal (a) to sitting (b) to bipedal (c) postures facilitates reaching and grasping behaviors. Simultaneously, bipedal walking allowed hominids to expand their reach area through locomotion while keeping their hands free. Modified from Iriki et al. (2021).

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primates uniquely combined vertical posture with other task-related adaptations, such as binocular vision for 3D/depth perception, high visual acuity (Heesy, 2009), and forelimb musculoskeletal features associated with graspingrelated functional properties (e.g., independent finger movements and opposable thumbs; Molnar et al., 2017). These gave way to greater manipulation complexity and visuomotor processing skills, which had to be translated into multiple coordinate systems. Representing and translating object locations in multiple spatial coordinate systems required the emergence of novel neural resources specialized for highly developed spatial-information processing (Cohen and Andersen, 2002; Piserchia et al., 2016). Although primates are arguably not as unique as previously thought in regard to their brain size, the brain-expansion process and its results were unique in this taxon. In most species, the brain increases via the proportional growth of preexisting areas, with larger brains being structurally analogous to smaller ones and featuring enlarged primary areas tasked with handling somatosensation and motor control of a larger body (Krubitzer and Dooley, 2013, Fig. 4.3). In contrast, in primates, the brain expanded with the addition of new cortical areas conducive to the development of novel cognitive functions (Holloway et al., 2003; Krubitzer and Dooley, 2013). This process is linked to the expansion and emergence of new regions in the parietal cortex, which may have served as preadaptations for more complex visuomotor functions (Iriki et al., 2021; Peeters et al., 2009). Structural and functional changes in the parietal cortex could also explain some differences observed between Neanderthals and modern human populations. In paleoanthropology, identifying morphological differences in cranial endocasts (i.e., size and proportions, sulcal patterns, and traces of meningeal vessels) can either reflect functional variations between

FIGURE 4.3 The brain of primates is organized differently from those of other mammals, with primary sensory areas (blue, visual; red, somatosensory; yellow, auditory) occupying proportionally less space as the body size increases in different species. Modified from Krubitzer (2009) and Krubitzer and Dooley (2013).

I. Visuospatial cognition and evolution

Tool representation in the brain

species or structural constraints (Bruner, 2021). By examining Neanderthal remains, scientists have established that their brain retains a pattern in which parietal areas are constrained between frontal and occipital regions, with a large brain volume imposing a spatial flattening of the parietal outline (Bruner et al., 2014). Supernumerary ossicles, reflecting an imbalance between brain and skull growth during development, further highlight these structural constraints. Morphological differences with modern humans were suggested to be associated with specific parietal regions involved in visuo-spatial integration (e.g., the precuneus or intraparietal sulcus). Hence, the fact that Neanderthals had an overall shorter and less globular parietal cortex (and perhaps secondary somatosensory cortex; S2) compared to Homo sapiens, could have implied lower visuospatial processing skills (PereiraPedro et al., 2020). In relation to this idea, archaeological records indicate that Neanderthals, contrary to modern humans, made extensive use of the mouth to support object handling, although both groups shared otherwise similar traits (e.g., vertical body axis and cultural complexity; Lozano et al., 2008). That is, Neanderthals needed additional support from the mouth, acting as a “third hand,” in alignment with the body axis to support a given task. Since the parietal cortex is fundamentally involved in integrating eye-hand coordination, this behavior could indicate a mismatch between Neanderthals’ cultural complexity (e.g., healthcare, language, and art; Langley et al., 2008) and biological system. Namely, the eye-hand system may have been insufficient to integrate bodyeartifact relationships (Bruner and Iriki, 2016). Indeed, the mouth is the classical support to hand praxis in primates. This hypothesis of limitations in visuospatial processing in Neanderthals deserves further investigation of archaeological records, as well as developmental studies of mouth-hand exploratory behaviors,

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neuroscience, and morpho-functional studies of mouth-hand relationships (Bruner et al., 2018).

Tool representation in the brain The acquisition of novel motor skills is not limited to evolutionary changes; it is also associated with structural and functional brain changes over short time spans (Dayan and Cohen, 2011). The link between motor skill acquisition and neuronal plasticity at cortical and subcortical levels evolves over time. Changes in activation can reflect the recruitment of additional brain networks during practice or the use of fewer neuronal resources as learning proceeds. To address the question of structural brain changes following tool use, Quallo et al. (2009) subjected macaques that had been trained to use a rake to retrieve distant food items to magnetic resonance imaging and voxel-based morphometry. These authors observed an increase in gray matter volume that started as early as after 1 week of training and correlated with behavioral performance in the task. This effect was localized in brain regions then known to be involved in actual tool use or related behaviors, such as the superior temporal sulcus and intraparietal sulcus (Fig. 4.4). Gray matter expansion was unexpectedly also localized in S2, which had not previously been associated with these functions (Fig. 4.5). A few years later, new findings linked S2 to the tool use network by clarifying its role in the tool-mediated self/ world map, as described in the following sections (Bretas et al., 2020). Recent evidence suggests that S2 integrates somatosensory and visual information to build and manipulate a body image that accounts for both a tool and the surrounding world. A reliable indication of an area having a sensory (i.e., visual, somatosensory) role is the presence of receptive fields, which are specific locations in space where the presentation of

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FIGURE 4.4 Changes in the macaque brain in areas related to tool usage or tool-related behaviors after being trained to use a rake to collect food. Gray matter increase (in color, indicating the t-score) was observed in the intraparietal sulcus (a), superior temporal sulcus (b), and secondary somatosensory cortex (c). From Quallo et al. (2009).

FIGURE 4.5 Location in the brain of the primary sensory areas, somatosensory association area, and secondary somatosensory cortex (S2). The S2 is located in the upper bank of the lateral sulcus (LS), which is shown opened here.

stimuli evokes neuron activation. Spatial locations are anchored by the respective sensory organs that perceive the stimuli (Avillac et al., 2005). Experiments have shown visual and somatosensory receptive fields in various areas of the parietal cortex (Avillac et al., 2005; Blatt et al., 1990; Duhamel et al., 1998; Hihara et al.,

2015; Taoka et al., 2000). The neurons in these areas are multimodal (i.e., responsive to one or more sensory modalities) and spatially congruent. For example, a neuron may respond to both somatic and visual stimuli directed toward the same area in the body (Bretas et al., 2021; Duhamel et al., 1998; Hihara et al., 2015). Moreover, these responses are topographically organized, with adjacent areas of the body being represented by adjacent positions in the brain, similar to a map of the space around the body. Multimodal sensory neurons could be necessary for accurate motor and sensory predictions (Avillac et al., 2005; Deneve et al., 2001; NeppiM odona et al., 2004), combining visual and proprioceptive feedback with low latencies and high spatial accuracy to guide motor responses and react to moving objects near the body (Culham et al., 2006; Reichenbach et al., 2014). However, beyond sensing, cognitive processes such as intention and attention are also spatially encoded in the brain (Andersen and Buneo, 2002; Avillac et al., 2005). Although apparently straightforward, visually guided hand movements while manipulating complex objects require more than simple spatial coordination. Attention, exploration, and object identification contribute to tool manipulation, and these cognitive functions also share the parietal cortex as a substrate

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Tool representation in the brain

(Andersen and Buneo, 2002; Gottlieb and Snyder, 2010; Hsiao et al., 1993; Ishida et al., 2010; Romo et al., 2002; Yamazaki et al., 2009). Moreover, observation of object-manipulation activates the superior temporal sulcus in macaques (Rizzolatti and Luppino, 2001), and tool recognition activates the left posterior temporal cortex in humans (Johnson-Frey, 2004). Some brain functional properties supporting tool use can be acquired after training, in nonnaturally tool-using primates. For instance, tool use can modulate neuronal responses in task-

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related brain regions. In an early study, Iriki et al. (1996) showed that bimodal visual/somatosensory neurons in the anterior bank of the intraparietal sulcus undergo rapid changes in their visual properties during a toolmanipulation task. Performing the rake task for 5 min was sufficient to induce an expansion and elongation of the visual receptive fields along the axis of the tool (Fig. 4.6). Conversely, 3 min of food retrieval with the hand led to a contraction of previously expanded receptive fields. These findings brought strong support

FIGURE 4.6 Bimodal distal-type neurons (a) with somatosensory receptive fields at the hand (sRF) responding to visual stimuli close to the hand (pink area, b), with the responses extending to the tip of the tool when actively manipulating the tool (c), but not when only passively holding it (d). The same happened with proximal-type neurons (e) before (f) and after (g) tool use. From Maravita and Iriki (2004).

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to the notion that tool use induces a rapid update of the body representation that combines visual and somatosensory information. This idea was further strengthened by behavioral studies showing that tool use modulates movement parameters such as grasping kinematics, as well as spatial judgment and tactile perception which can translate into an elongated representation of arm length (see Chapter 6; Cardinali et al., 2009; Miller, Longo, et al., 2019). Miller et al. (2018) showed that human subjects can accurately localize the contact points along the surface of a handheld tool in a coordinate system intrinsic to the space of the tool itself with near-perfect accuracy. Higher performance during active sensing (i.e., self-generated action) compared to passive sensing (i.e., passive reception of impact) indicates that both somatosensory and motor signals are involved in this process. Mechanoreceptors of the hand were found to be crucial to extract sensory information from the tool and demonstrated similar accuracy and speed with the tool as with the body itself (Miller, Fabio, et al., 2019). Overall, forelimb and tool processing seem to rely on common mechanisms in sensorimotor cortex, consistent with the view that tool use can extend somatosensory processing beyond the limits of the body, to represent handheld tools as extensions of the body. The earliest stages of information processing during tool handling were localized in the hand region of the primary sensorimotor cortex and later spread to the posterior parietal cortex, which represents hand and tool actions in a shared coordinate system. While reasonable knowledge has been gained on these regions, these mechanisms are less known in S2. It is assumed that, by integrating visual and somatosensory information, S2 has a role in establishing the body schema to which the tool is incorporated during training (Bretas et al., 2020), but the actual plasticity of body representation in S2 has scarcely been investigated. In humans, tool use modulates components of

somatosensory evoked potentials whose cortical generators have been mapped in S2 and in the posterior parietal cortex, indexing the construction of multisensory models of the body and possibly the reshaping of higher-order sensorimotor models of the body (Miller, Longo, et al., 2019). Studies on frontal motor regions provide another example of tool-use-induced modulation of neuronal properties. Umilta et al. (2008) demonstrated that hand-grasping neurons in the ventral premotor cortex generalize their activity to tool handling after training (Orban and Caruana, 2014; Umilta et al., 2008). Moreover, neurons that discharged during finger extension with normal pliers fired during finger flexion with reverse pliers that required an opposite pattern of finger movements. Premotor neurons, therefore, appeared to encode the temporal organization leading to the distal goal formed during tool handling. Overall, these studies support the idea that information processing in toolrelated brain regions is capable of plastic and dynamic changes depending on the situation and task requirements. A significant part of the aforementioned results was obtained in macaques, which represent the most extensively studied nonhuman primate brain model. However, macaques rarely spontaneously use tools in the wild (but see Proffitt et al., 2018) or transfer this skill to other individuals. Although they are equipped with the basic neural machinery for tool use (Iriki and Taoka, 2012), it is thus reasonable to assume that macaques differ from humans in certain neural mechanisms. In a comparative review, Kastner et al. (2017) proposed that, over the course of evolution, several homologous regions of macaque posterior-medial intraparietal sulcus (e.g., area V6A and lateral intraparietal area) and anterior-lateral intraparietal sulcus (e.g., anterior intraparietal area and ventral intraparietal area) were spatially separated in the human posterior parietal cortex to support functions that do not have counterparts in macaques

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Tool representation in the brain

(e.g., tool use abilities). Indeed, Peeters et al. (2009) described a uniquely human activation in a rostral sector of the left inferior parietal lobule, solely during observation of actions done with tools. This area, referred to as the anterior supramarginal gyrus (aSMG), is related to the association of hand actions with the functional use of tools. During evolution, the aSMG, which putatively arose from the duplication of the anterior intraparietal cortex, may have become controlled by posterior parietal cortex afferents carrying tool-related signals (Orban and Caruana, 2014). Peeters et al. (2009) linked aSMG to the uniquely human causal understanding of tool properties. This notion of causality implies an understanding of a mechanism binding two events, such that subjects can use this mechanism to predict, control, and adjust these events (Visalberghi and Tomasello, 1998). To date, no species have demonstrated the extent of understanding and flexibility in task adaptation that is observed in humans. A classic task to evaluate primate understanding of physical causality is the trap-tube task, where the subject must insert a stick into a tube from which it can push the reward out of the tube (Fig. 4.7).

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Depending on the side in which the tool is inserted, it can either push the reward into a trap or out of the tube. While human children over 5e6 years old reliably succeed at this task, capuchins only perform at a chance level and scarcely display an understanding of the physical forces involved (Horner and Whiten, 2007; Visalberghi and Limongelli, 1994). Similarly, apes show a limited degree of causal understanding in tool use (Povinelli, 2003). Further findings support the existence of a tool-specific brain network in humans. According to Orban and Caruana (2014), humans have two parietal systems involved in tool behavior: one dedicated to grasping objects, and the other specific to tool use (e.g., aSMG). The intraparietal sulcus would provide visual features of the target object to the putative human homolog of macaque anterior intraparietal cortex (for hande object interaction) and aSMG (for tooleobject interaction). Other afferents, providing information relevant to tool use (e.g., technical reasoning and semantic information) would converge to aSMG. Imaging experiments have found that hand and tool action categories are represented separately at early processing stages in parietal

FIGURE 4.7 Apparatus of the trap-tube task used to evaluate primates’ understanding of cause-effects relations. Depending on the side of the tube in which the tool is inserted, it can either push the reward into a trap (top panel) or out of the tube (bottom panel). Note that the insertion of the tool on the side nearest the reward results in pushing it into the trap, whereas insertion on the side farthest from the reward results in retrieving the reward, which gives the possibility to build a distance-based associative rule (rather than causal reasoning), as observed in one capuchin (Visalberghi and Tomasello, 1998). From Horner and Whiten (2007).

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and occipitotemporal regions and are later integrated into frontoparietal regions (Gallivan et al., 2013). Areas with common patterns of activity were associated with high-level cognitive and action-related processing, rather than the movements or effectors needed to achieve the action. To our knowledge, no evidence of brain response uniquely related to tools has yet been described in macaques.

Mapping the tool-usage space Peripersonal space is defined as the area around the body that can be reached (see Chapter 3). In addition to being the area where motor control is performed and somatosensation is received, it is where defense against threats is most critical and where social interaction is most meaningful (de Haan et al., 2016; Graziano and Cooke, 2006; Ishida et al., 2010; Teneggi et al., 2013). The peripersonal space is encoded as a map in various locations within the brain, often by the multimodal visual-somatosensory neurons mentioned in the previous section (Fig. 4.6). A primary feature of this nearby space is visual and somatosensory stimuli being able to overlap when the subject sees something touching or being touched by its own body or sees its own body movements. Tool usage, however, effectively stretches peripersonal space beyond the body’s physical limits, which is further increased by the primate’s advantage of prehensile limbs, which can extend and rotate without compromising the grip while reaching around the whole body. The reach of tools, as manipulated by human and nonhuman primates, is thus further extended when a limb is stretched, with little change in the somatosensory feedback received through the tool or in the range of motor responses allowed, as the grip on the tool is kept stable. Each tool also transmits different tactile feedback, while requiring various motor skills to be manipulated. This transience and flexibility

inherent to tools are mapped in the brain and supported by elastic representations of the nearby space that can swiftly adjust when a tool is used (Farne and Ladavas, 2000; Iriki et al., 1996). Peripersonal space depends on the integration of multisensory cues surrounding the body by a specialized neural system (Cl
ery and Ben Hamed, 2018). It is dynamically reshaped by action components, experience, and learning to define an action-based or reachable space for goal-directed actions. Considering the aforementioned tool-mediated changes in the body schema, this suggests that complex interactions occur between tool-induced reorganization of peripersonal space and the body schema. Altogether, the integration of the experienced sensory feedback, namely, the temporal synchrony between visual inputs in the far space and tactile inputs arising from object manipulation, may have a major role in shaping the functional reorganization of peripersonal space. Contrary to previous assumptions of a unique representation, recent work in humans suggests that the space around us is separated into functional regions defined by the body part they mostly relate to. The hand, face, and trunk are different components of the peripersonal space (Cl
ery and Ben Hamed, 2018). While the trunk representation size is relatively constant, those of the hand and face vary, not only according to their position relative to the rest of the body, but also relative to the trajectory of stimuli relative to the body. The verticalization of the body axis in hominids, by modifying the relations between joint angles and limb movements, thus presumably influenced peripersonal space construction. Moreover, the vertical posture during sitting in primates and, ultimately during standing in humans, provides a constant view of the hands and tools through various angles and distances. When an object is rotated and explored with the hands, its visual and tactile properties converge to create a three-dimensional representation to guide complex manipulation and object recognition (Gauthier et al., 2002; Grefkes et al.,

I. Visuospatial cognition and evolution

Cognitive components of tool use

2002; Hsiao, 2008; James et al., 2002; Maule et al., 2015; Murata et al., 1996). Combining these factors requires elaborate interactions between verticalization, peripersonal space, and tool manipulation. While exploratory manipulation is important for object identification, tool usage is a goaldirected behavior performed with the intention of modifying the condition of an object through the use of the tool (Parker and Gibson, 1977). This is evident in the brain: in macaques and humans, the tool is only incorporated in the body image when intended to be used with a purpose, not when held as a simple object (Iriki et al., 1996; Obayashi et al., 2001; Witt et al., 2005). While the reasons for this remain unclear, they likely derive from other tool-related processing constraints. The remodeling of the body schema during tool use depends not only on the tools themselves but also on the motor pattern necessary to complete the action. Hence, different tool use tasks requiring specific motor patterns have different effects on body representation. Romano et al. (2019) found that, in human subjects, reach-to-grasp movements lead to a distalization of the perceived midpoint on the arm, whereas swing-to-hit movements lead to a proximalization. A similar reasoning applies to perceived object affordances. Depending on the subject’s intention, a rock can be seen as a projectile or as a nutcracker, which are associated with different motor plans. The tool is therefore not solely represented as an object, but also as an intended action, such that a subject anticipates the consequences of their actions on the environment to rescale the body schema and peripersonal space (Witt et al., 2005). In most protocols, an action purpose or intention emerges from the instructions given to participants or from the task to which the macaque was trained (Iriki et al., 1996; Witt et al., 2005). An action purpose can be more abstract than an object-directed goal; for example, it can involve communicative content (e.g., waving a hand to greet someone). An important distinction has been made between

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transitive (object-directed) and intransitive (nonobject-directed) actions in comparative psychology and neuroscience, especially in the imitation literature, where these actions are thought to rely on partly different neuronal processes (Bonivento et al., 2014). It is still to be determined to what extent tool-related transitive (e.g., reaching distant objects) and intransitive (e.g., communicative) actions are comparable in terms of neural mechanisms and integration in the body schema.

Cognitive components of tool use At some point in the process of incorporating the tool into the body image, an intended object is created, which is a representation of the result of the premeditated interaction between the tool and the target, perhaps not as an idealized physical shape but rather as a conceptual goal. This representation is updated after the use of the tool, and the result is compared to the intended-object with the support of memory systems (Kastner et al., 2017). Among neuronal substrates subserving these functions, a region that has been well-described in humans and macaques is the lateral intraparietal cortex in the posterior parietal cortex. This region, in addition to carrying 2D information about objects, is involved in higher-order functions such as spatial attention, decision-making, and working memory. It can therefore integrate visual cues with cognitive information relevant to the behavioral context and provide this information to separate brain regions for motor planning. In humans, the dorsal visual system expanded and complexified over the course of evolution to better integrate object information with working memory systems in the parietal lobe and enable the emergence of tool-specific object representations. These considerations converge to the point that, in human lineages, the neuronal substrates involved in sensorimotor or functional

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properties of tool usage did not develop alone, but rather in parallel with other cognitive functions to handle the intended use of tools, the processes of tool-making, and the behavioral context in which a tool is used (Kastner et al., 2017). The complexity of tool usage and tool making can be approached through the concept of action hierarchies (Stout, 2011). In this view, superordinate levels, which represent abstract or spatially and temporally distant goals, are divided into simpler subgoals and actions. This process allows for task flexibility and context-specific adaptation and could represent a basis by which early modern humans developed complex technologies. However, a prerequisite for building such action hierarchies is the capacity for space/time and goal abstraction, working memory, and other functions required to define action sequences for complex tool-related actions. In humans, the high level of sophistication of tool use and tool making was attributed to marked differences from other primates in cognitive capacities deemed crucial to these functions, including but not limited to eye-hand coordination (i.e., sensorimotor coordination during complex behavior), executive functions (e.g., foresight, inhibition, and working memory), causal understanding, social learning, and language (Seed and Byrne, 2010; Vaesen, 2012). Some authors emphasized the capacity to plan ahead and anticipate actions as a key component in the tool manufacture process (Seed and Byrne, 2010). Moreover, temporally enduring artifacts in primates expand the contribution of the social context by allowing the youngest individuals to reuse tools manufactured by the older ones (Fragaszy et al., 2013). Combining these cognitive assets could explain the unique complexity and diversity of human culture in the animal kingdom (Stout, 2011). To further examine the notion of causality, substantial research was led in the field of comparative cognition, highlighting the fact that human and nonhuman primates use different mechanisms to understand the

functional relationship between an action and its outcome. As previously discussed, humans seem to have a better understanding of the physical mechanisms mediating interactions between events, and to flexibly manipulate these events (Visalberghi and Tomasello, 1998). Although not to the same extent, chimpanzees and capuchins display a certain degree of causal understanding of action features, as shown by behavioral selectivity (i.e., choosing or fashioning tools to suit specific goals) and flexibility (i.e., using different approaches to achieve the same goal; Seed and Byrne, 2010). However, nonhuman primates tend to rely more on perceptual features than on the physical processes by which a tool produces a result. For example, a previous study examining chimpanzee performance in the trap-tube task reported the case of an individual who had learned to push a piece of food away from the trap and continued to do so even after the tube had been inverted and the trap had become nonfunctional (Povinelli, 2003). However, counterexamples exist and show some degree of physical process prevalence over perceptual features in nonhuman primates, for instance, in capuchins that can effectively select the heaviest stone tool for nut cracking, even when presented with lighter similar-looking stones (Visalberghi et al., 2009), and choose the ideal anvil with the depth and width to crack the nuts (Liu et al., 2011). What emerges from these results is that causality understanding is not an all-or-nothing issue, but rather a matter of degree, with an overall understanding of the general rules in humans and an understanding dependent on perceptual conditions in nonhuman primates. In addition, a major difference between humans and chimpanzees pertains to the learning process of cause-effect covariances, which in chimpanzees occurs through associative learning rather than through causal reasoning or inference of the physical consequences of actions (Vaesen, 2012). This process clearly contrasts with humans, who readily seek causal explanations

I. Visuospatial cognition and evolution

The tool with the body and the body in the world

from an early age. Human cognitive components for tool usage are subtended by expanded parietal areas that are considered to represent a direct extension of macaques’ parietal areas.

The tool with the body and the body in the world Although the body map adapts to incorporate the tool, the target object of the tool may be located outside the body’s limits. Movements toward external objects and the spatial location of these objects are also mapped when an intentional movement is involved, despite the object being physically disconnected from the body (Andersen and Buneo, 2002). The vertical rotation of the body axis, including the sitting posture, further challenges the limits of this representation by allowing primates to expand the

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space of their volitional actions while making active use of a tool. In a similar fashion, the same areas that map stimuli in the subject’s peripersonal space also show responses to stimuli near others’ bodies (Blakemore et al., 2005; Ishida et al., 2010; Keysers et al., 2004). Since tools function identically both when manipulated by the self and by others, they become a point of convergence between egocentric and allocentric representations. Tools also have permanence, as they exist in their own time dimension independent from the user (and can be time-shared between users or stored for later usage). By seeing the same object that was coded as part of their own body being manipulated by another while producing the same results in the world, the agent may situate itself as an individual within a social world through self-objectification (Fig. 4.8; Iriki, 2006; Ishida et al., 2010). The perspective switching

FIGURE 4.8

A neuron in the secondary somatosensory cortex of a macaque responds to seeing a hand approaching its body (top). The same response is elicited when the approaching hand can only be seen through a mirror (bottom), indicating that the macaque is capable of the self-objectification necessary to recognize itself as the subject in the mirror image. Modified from Bretas et al. (2021).

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necessary to use an egocentric reference frame for mapping the other’s body parts may involve a form of spatio-social coding between the self and others (Ishida et al., 2010). The need for a social representation of space arises from the process of learning how to manipulate a tool. Neurons in the macaque’s mirror systems respond to seeing other individuals manipulating a tool. While these responses are not initially present, but rather appear after the macaque repeatedly watches a tool being used, the same motor goal can be generalized across tools, indicating that action understanding can encompass tool usage (Ferrari et al., 2005). In humans, the learning and transmitting of knowledge are more complex, and language plays an intrinsic role. Language also appears to have similar motor and sensory representations as those of tool usage (Rizzolatti and Arbib, 1998); thus, it may not be a coincidence that human language and tool usage evolved simultaneously with the support of the same brain machinery (Bretas et al., 2020; Thibault et al., 2021).

Conclusion Primates display a large range of biological adaptations for tool usage that, in humans, encompasses a vertical body axis, specialized forelimbs, and expanded neural resources for high-level information processing. The transitory nature of tools also requires a plastic representation of the self-body in the brain in the form of topographical maps arising from preexisting multimodal motor-sensory representations of the body in the parietal and frontal cortices. The specificity, plasticity, and continuity of these maps all have functional implications for motor control, somatosensation accuracy, and higherorder processing (Amedi et al., 2003; Diamond et al., 1999; Kalisch et al., 2009; Liu et al., 2021; Yamazaki et al., 2010). It is therefore natural to assume that the same applies to tool usage, given

that it is similarly represented in the brain as an extension of the body, with sensory deficits causing a disarrangement of these maps in the brain (Saadon-Grosman et al., 2015). Locating the external source of a sensation felt in the body through a tool and producing the appropriate motor response requires linking the feeling produced at the surface of the body to the action observed in the environment at the end of the tool. As the tool modifies this process by interposing between the individual and the environment, it spatially disconnects the sensation in the body from the place where the action occurs, and the motor responses performed from the movement produced by the tool. However, tool users are capable of correctly accounting for a tool by incorporating their body into an internally constructed representation of the world that includes a social and sensorimotor space (Bretas et al., 2020). Several recent neuroscience and archaeological findings have illustrated the complexification of tool use throughout human evolution. Current research supports the view of a coevolution of the human brain, body, and environment subtended by significant enhancement of visuospatial competences. This is partly reflected by the greater symmetry of stone tools, which was likely paralleled by morphological brain changes in hominids (Bruner et al., 2018). Successive periods were identified based on stone tool complexity, including the Oldowan industry (simple flakes obtained from stone percussion; approximately 2.5e1.7 Ma), the Acheulean industry (greater symmetry and larger flakes; approximately 1.7e0.3 Ma), and the more recent Paleolithic periods (smaller and sharper tools). Throughout the Oldowan and Acheulean periods, the rate of change in stone tools was relatively slow and comprised long periods of little innovation, with tens to hundreds of thousands of years occasionally separating successive stone tool occurrences (Shea, 2017). Authors have proposed that hominids might have gradually shifted from a tool-assisted to a tool-dependent

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Conclusion

foraging mode throughout these periods, thereby triggering new cognitive capacities to create a greater variety and complexity of tools (Bruner et al., 2018). Lithic evidence prior to 1.7 Ma is considered to reflect occasional stone tool use, whereas evidence younger than 0.3 Ma is thought to reflect obligatory tool use. Intermediate stages (1.7e0.3 Ma), roughly corresponding to the Acheulean period, would thus reflect a transition through which tool usage acquired a habitual character. This period coincides with the first appearance of Homo ergaster and Homo erectus, who differed from earlier hominids by enhanced features of bipedal locomotion and carrying behavior, thus favorable to tool carrying. After 0.3 Ma, the hypervariability of stone tools sharply contrasts with the minimal differences observed earlier. This milestone presumably marks the onset of obligatory tool use, where more frequent and diversified lithic assemblages illustrate mixed technological strategies and tool intensification. The criteria for obligatory tool use, which were only fulfilled by Homo sapiens, imply that tools do not simply assist individual needs but are necessary for the survival of individuals in an ecological and cultural niche (Bruner and Gleeson, 2019; Shea, 2017). Parallel to the evolution of lithic technology, paleoanthropological analyses of skull morphology suggest anatomical differences between extant and archaic humans (Bruner et al., 2018). Compared with early hominids, both Neanderthals and modern humans display a significant enlargement of the parietal lobe, albeit in different proportions. While Neanderthals show a lateral bulging of the dorsal parietal region, Homo sapiens demonstrate, in addition, a longer parietal outline (Bruner, 2021). Compared to Neanderthals, the modern human brain was hypothesized to show an expansion of inferior parietal lobule regions between the supramarginal and angular gyrus and the parieto-occipital boundary on the superior parietal lobule.

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Although fossil evidence suggests that significant expansion of the parietal lobes could have occurred in modern humans, some of these morphological changes were not described at the onset of Homo sapiens appearance (approximately 0.3e0.1 Ma) but would likely have appeared later, coinciding with archaeological records of complex tools (e.g., projectile technology) and graphic culture (Bruner and Gleeson, 2019; Neubauer et al., 2018). Conversely, extinct human species that lacked a pronounced parietal bulging did not show comparable technological advances and relied on the mouth as a support for hand praxis (Bruner, 2019). Additionally, information derived from endocasts suggests a distinctive pattern of association between parietal, frontal, and temporal cortex in modern humans. The parietal lobes contain the intraparietal sulcusddirectly implicated in eye-hand coordination, tool use, and tool makingdand the precuneus, which is a major node of visuospatial integration networks. These functions are crucial in establishing relationships between the brain, body, and environment. The precuneus also belongs to a fronto-parietal network thought to be involved in the shift from emulation (i.e., primate-general ability to reproduce the results of observed actions) to imitation (i.e., ability to reproduce the process of observed actions, described in humans and to a minor extent in chimpanzees; Bruner et al., 2018; Hecht et al., 2013; Whiten et al., 2009). Other regions of the inferior parietal lobule that are involved in language comprehension and calculations were hypothesized to be derived in modern humans (Bruner, 2021). Further evidence is needed to determine whether these changes in brain morphology were gradual or discrete in modern humans and the extent to which genetic influences and environmental feedback facilitated them. Eventually, human cognitive advancement was supported by not only the neural system, but also the capacity to rely on tools or external technology for cognitive functions,

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thereby exceeding the limits of the biological system (Bruner and Gleeson, 2019). Tools should thus not only be considered as by-products of human cognitive and neurological processes but also as parts of these processes (Bruner, 2021). The emergence of a social dimension of tool use was encouraged by the need to share and transmit knowledge on tool manufacture and use. It was suggested that obligatory tool use coevolved with spoken language, based on molecular studies which revealed that genes involved in language production could date back to 0.2e0.3 Ma (Shea, 2017). Obligatory stone tool use has important fitness consequences since it has a direct impact on survival and should create strong selective pressures for greater investment in tool production, increased tool-complexity, and social pressures for teaching. Without efficient means for social transmission, the initial costs of creating new complex tools, in terms of time and energy, might not have matched their benefits. Language and imitation are essential means to teach and learn manufacturing techniques. Moreover, in primates, brain size and especially the association cortex, is proportional to group size, and this particularly applies to the parietal cortex, which contains some of the main association regions and supports functions related to selfrecognition, self-other perception, and bodycentered simulation (Bruner and Gleeson, 2019). Hence, we can hypothesize that changes in the parietal cortex of Neanderthals and modern humans were possibly associated with changes in the size and organization of their social groups. Eventually, by seeing another individual manipulate a tool previously coded as part of one’s own body, an agent could become able to situate itself as an individual in a social world through self-objectification. This process may have promoted the emergence of other cognitive functions, such as perspective switching and self-consciousness.

Funding This work was supported by JSPS Grant-in-Aid for Scientific Research on Innovative Areas JP19H05736 (A.I.) and JSPS Standard Postdoctoral Fellowship for Research in Japan (B.T.).

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C H A P T E R

5

Parietal cortex and cumulative technological culture Giovanni Federico1,2,3, François Osiurak4,5 1

IRCCS Synlab SDN S.p.A., Napoli, Italia; 2Laboratorio di Psicologia Sperimentale, Universit
a Suor Orsola Benincasa, Napoli, Italia; 3Dipartimento di Psicologia, Universit
a degli Studi della Campania “Luigi Vanvitelli”, Caserta, Italia; 4Laboratoire d’Etude des Mecanismes Cognitifs (EA3082), Universite de Lyon, Lyon, France; 5Institut Universitaire de France, Paris, France

Introduction One of the essential cognitive functions of the human brain is to allow the individual to perform voluntary actions. By voluntary, we mean that these actions are goal-oriented, that is, guided by a new state of the world that the individual seeks to achieve. To do so, the individual must conceive of action helpful in reaching this new state of the world. For instance, if someone aims to hang up a painting on a wall (i.e., the goal), they can conceive of pounding a nail with a hammer. Conceiving an action to modify the state of the world is not enough to cause the modification in the world. The production of body movements (or motor actions, see below) is required. Thus, the individual must control their own body (parts) to reach, grasp, and manipulate external objects to make them interact together to realize the action that has been conceived. The concepts of goal and intention have generated intense debate in the literature (e.g., Jeannerod, 1994). Here we will define

Cognitive Archaeology, Body Cognition, and the Evolution of Visuospatial Perception https://doi.org/10.1016/B978-0-323-99193-3.00001-5

a goal as the new state of the world that an individual seeks to achieve. By contrast, we will refer to an intention as an individual’s internal state, which can be satisfied by achieving a goal. For example, the goal of hanging up a painting on a wall can be guided by different intentions, such as the pleasure of seeing the painting each morning before going to work or the pleasure of helping a friend who is not assured in do-ityourself activities. The distinction drawn here between a conception level and a production level has been commonly described in the neuroscientific literature on the action, particularly in patients with tool-use disorders (e.g., Buxbaum, 2001; Gonzalez Rothi et al., 1991; Heilman et al., 1982). This literature has also stressed that the parietal cortex is deeply involved in both levels, confirming its crucial role in the action. In parallel with the neuroscientific literature on the involvement of the parietal cortex in action, the anthropological literature has identified a phenomenon known as cumulative

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technological culture (CTC; Boyd et al., 2011; Boyd and Richerson, 1995; Henrich, 2015). CTC refers to the gradual increase in the complexity of tools and techniques in human population over generations (Dean et al., 2012). This phenomenon has allowed humans to expand across the globe and beyond by carrying out actions that they cannot perform only from their biomechanical capacities (e.g., going to space). CTC is indubitably a social phenomenon, given that the generation of tools and techniques that are too complex to be invented by a single individual necessarily requires learning from their conspecifics (i.e., social learning). In this respect, some have stressed the importance of specific social cognitive skills (e.g., theory-ofmind or mentalizing skills) in our lineage (e.g., Dean et al., 2012; Herrmann et al., 2007; Tennie et al., 2009; Tomasello et al., 2005, 1993), which could have increased our social-learning skills (e.g., imitation and teaching) and, as a result, favored the emergence of CTC. Nevertheless, it can be assumed that the social transmission of technical content could not have been as effective if our predecessors had not developed a specific ability to conceive more complex actions or, more precisely, more complex mechanical actions (Osiurak et al., 2022). In broad terms, two lines of evidence coexist in the literature. The first mentions the critical involvement of the parietal cortex in action, and the second highlights the existence in our lineage of increasing complexity of actions over generations (i.e., CTC). Consistently, most recent studies in paleoneurology highlighted how an increased globularity of the braincase and bulging of the parietal region in modern humans (i.e., the increased cortical complexity in both the superior and inferior parietal lobules) might play a critical role in the emergence of functions that are at the root of CTC (e.g., Bruner et al., 2023; Bruner and Gleeson, 2019). In this chapter, we attempt to articulate these lines of evidence at a theoretical level, with the question of the role of the parietal cortex in the evolution of human

technologies. We will focus on three motor/ cognitive functions associated with the parietal cortex: motor control, visuospatial skills, and technical reasoning. As shown in Fig. 5.1, these three motor/cognitive functions are distributed differently within the parietal cortex. We will discuss, in turn, how each of these functions can potentially contribute to the increasing complexity of actions. This will lead us to propose a hypothetical scenario of the evolution of the human parietal cortex in relation to CTC.

FIGURE 5.1 The involvement of the parietal cortex in the motor-control system (yellow), visuospatial skills (green), and technical-reasoning skills (red). As illustrated here, the involvement of the parietal cortex differs according to the motor/cognitive function. Whereas the motor-control system is mainly supported by the superior parietal lobes and intraparietal sulci, visuospatial skills might be more distributed in recruiting the superior parietal lobes, intraparietal sulci, and inferior parietal lobes. Technical-reasoning skills are mainly associated with the activity of the left inferior parietal lobe, particularly the area PF.

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Motor control

Before beginning, we would like to present a theoretical distinction that will be useful for understanding the rest of the chapter. This distinction concerns the nature of actions. We will reserve the terms “motor actions” for the actions at the interface between the body and the environment and “mechanical actions” for the interactions between external objects. This distinction is not easy to grasp, notably because individuals usually need motor actions to realize mechanical actions. Therefore, we will illustrate it with an example. Imagine someone using either a screwdriver or a knife to drive a screw. In either case, the mechanical action corresponds to the rotation of the screw. However, the motor actions performed to realize this rotating action differ according to the tool used. In the case of the screwdriver, the motor action is a wrist rotation. In the case of the knife, the motor action is a lateral wrist motion. Note that if the individual eventually uses a screwgun, the motor action becomes a flexion of the index finger. In all these cases, although the mechanical action remains the same (i.e., rotation of the screw), the motor actions differ. Conversely, roughly the same motor action (e.g., lateral wrist motion) can be helpful in performing different mechanical actions. In other words, mechanical actions can be described as tooleobject or, more generally, objecteobject interactions while motor actions as handeobject or, more generally, bodyeobject interactions (Osiurak et al., 2017). This distinction mirrors the above distinction between the conception and production systems, respectively. This distinction can sometimes be confusing, given that the individual’s body can also be considered an object in the case of mechanical actions directed toward the body. Even in these cases, the action can be described as mechanical, not motor. For instance, if someone uses a hairbrush to brush their hair, the mechanical action concerns the interaction between the brush and the hair, as if the hair were an external object. Nevertheless, the movements performed by

the hands to manipulate the hairbrush can be described as motor actions.

Motor control Function The motor-control system, which can be viewed hereafter as equivalent to the production system, is supported by parietal and frontal/premotor structures. Here we will focus on the parietal contributions to the motor-control system (Fig. 5.1). The critical role of the motor-control system is to select, plan, and control online the motor actions that can be useful for achieving a goal (e.g., Rosenbaum, 2010). The motor-control system is blind to the goal pursued by the individual (e.g., object transport, tool use). For instance, if someone decides to move an object from one location to another (e.g., moving a bucket) either in a context of object transport (e.g., to make place) or in a context of tool use (e.g., to collect water leaking from the roof), the only problem that the motor-control system aims to solve is how to achieve this goal with the most economical motor action (Osiurak and Badets, 2017). Said differently, in either case, the problem is the same for the motor-control system, and it has no need to be informed by the conception system about the reasons why such goals are pursued. The idea that the motor-control system is dedicated to helping the individual to perform motor actions in the most economical way possible is supported by how the neuropsychological literature has traditionally described action-related disorders. For instance, optic ataxia is a deficit in visually guided reaching movements without primary motor or sensory deficits (e.g., Andersen et al., 2014). The reaching errors typically occur for peripheral targets. Patients with optic ataxia can correct these errors by transporting the hand to the correct object location after having transported it to the incorrect location. In a way, the goal is fulfilled (i.e., to reach and grasp the object). However, this goal is not achieved most

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economically at a motor level, which characterizes the presence of an action-related disorder. Another important aspect is that the motorcontrol and conception systems can interact dynamically. As explained more extensively below, the idea of the mechanical action to be performed is generated by the conception level (e.g., driving a nail into a wall with a hammer). This idea constrains the motor-control system in the planning, selection, and online control of the most economical motor actions. However, if a technical problem occurs (e.g., the nail cannot be driven into the wall), the conception system can provide new solutions (e.g., selecting a new tool or changing the way of grasping the hammer to increase the percussion power), which will constrain the motor-control system in the selection, planning, and online control of new motor actions. In addition, the motor-control system can also impose some constraints on the conception system, sometimes even before any overt motor actions. Thus, even if someone comes up with the idea that a hammer with a big head would be the most appropriate tool to drive a nail into a wall, the difficult manipulation of the hammer because of the weight of its head can lead the individual’s conception system to generate new solutions. Therefore, we briefly summarized here a kind of motor-to-mechanical cascade mechanism between the conception of mechanical actions and the motor control system (Federico et al., 2021a,b). Interestingly, most recent behavioral evidence has shown how the effects of this cascade mechanism may reverberate in the temporal allocation of visuospatial attention to tools and objects (Federico et al., 2021a,b, 2022; Federico and Brandimonte, 2019, 2020, 2022; Pilacinski et al., 2021; Tamaki et al., 2020).

From object manipulation to objecteobject manipulation Whereas the frontal/premotor cortex is in charge of producing appropriate motor

commands, the parietal cortex has been repeatedly considered to play a crucial role in the generation of motor intentions, which refer here to future internal models of motor actions (e.g., Wolpert et al., 1995; but see also Tunik et al., 2007). When someone aims to reach and grasp an external object, a motor intention is created, which corresponds to the state of the position and location of the hand grasping the object. The gap between this future state and the current state of the position and location of the hand is essential to guide the appropriate motor commands produced by the frontal/premotor cortex (e.g., Schwoebel et al., 2002). Two sources of information must be integrated within the parietal cortex to perform this computational analysis. The first source is visual information, which allows computing distances between the location and position of external objects and the position and location of the body. The second source is proprioceptive information, which allows computing distances between the location and position of different body parts. The dynamic representation of the location and position of the different body parts is also called body schema (e.g., de Vignemont, 2010; Schwoebel and Coslett, 2005). The description provided so far about the role of the parietal cortex in the motor-control system has mainly concerned how people can reach, grasp, and manipulate single external objects. In other words, this description has focused on object manipulation, which can be defined as the transport of a single object in different locations of the environment. However, the ability to perform object manipulation is relatively common in the animal kingdom. As a result, this ability is far from sufficient to account for the increasing complexity of actions that characterize our lineage (i.e., CTC), which corresponds to creating voluntary interactions between external objects. For this reason, it is necessary to shift the focus to objecteobject manipulation, which can be viewed as the ability to produce an interaction between at least two external

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Motor control

objects by actively manipulating one of them. Tool use is undoubtedly the most representative behavior of objecteobject manipulation, which also encompasses construction behavior (see below). Shumaker et al. (Shumaker et al., 2011) defined tool use as follows: The external employment of an unattached or manipulable attached environmental object to alter more efficiently the form, position, or condition of another object, another organism, or the user itself, when the user holds and directly manipulates the tool during or prior to use and is responsible for the proper and effective orientation of the tool (Shumaker et al., 2011).

Tool use has been repeatedly considered a particular sort of object manipulation. For instance, Gibson stressed that tool use demonstrates that the boundary between the individual and the environment is not fixed at the skin’s surface but can shift (Gibson, 2014). Said differently, tool use seems to involve a phenomenon of incorporation so that the tool becomes a sort of nonneural extension of a “natural” end-effector (e.g., hand, beak), thereby shifting the boundary between the individual and the environment beyond the surface of the skin. A significant body of neuroscientific evidence has confirmed the existence of this incorporation phenomenon, which is made possible by the plastic nature of body schema (e.g., Farn
e et al., 2005; Iriki et al., 1996; Maravita and Iriki, 2004; Miller et al., 2018; see Chapter 4 and Chapter 6). More specifically, tool incorporation is characterized by a mechanism of distalization, which refers to the idea that, once a tool is grasped appropriately to be used, the natural end-effector is no longer the end-effector (Osiurak and Federico, 2021). The end-effector becomes the part of the tool used to interact with another object (i.e., the active part). This implies an attentional shift from the natural end-effector to the active part of the tool, although it is still the natural endeffector that must be controlled. This mechanism of distalization may be the key mechanism that distinguishes tool use from object manipulation.

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Note that, in the classical definition of tool use proposed by Shumaker et al. (Shumaker et al., 2011), the mechanism of distalization is not necessary to characterize behavior as tool use (“prior to”). Thus, according to this definition, throwing a stone to break an egg (a behavior observed in vultures) can be considered tool behavior, even if, in such cases, the tool is not manipulated during its use. Other authors have preferred to label tooling as the specific instances of tool use in which the mechanism of distalization is needed, thereby distinguishing tooling and other tool-use actions such as throwing actions (e.g., Fragaszy and Mangalam, 2020). Interestingly for our purposes, differences between “object use” and “tool use” have also been highlighted in the archaeological literature. Indeed, an object to be a tool should be part of a technological network, essential to cognition and culture, and cognitively embedded. Thus, object-use and tool-use are seen as distinct processes (Bruner and Gleeson, 2019; Bruner, 2021).

Tool use and CTC The question that arises is whether tool-use skills can be the necessary condition for the emergence of CTC in our lineage. Some arguments suggest the answer may be yes. Tool-use skills undoubtedly give individuals many opportunities to produce mechanical interactions between external objects. This can offer the discovery of new interactions, which can be learnt, improved, and progressively led to complexify the actions on the environment. The specific case of New Caledonian crows is informative in this regard. New Caledonian crows are reputed for being skilled tool-users (e.g., Bayern et al., 2018; Danel et al., 2017; Hunt, 1996; Kacelnik et al., 2009; Weir et al., 2002). For instance, New Caledonian crows can construct tools by combining two or more nonfunctional elements, an ability observed only in humans and great apes. Thus, while CTC is largely admitted as

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unique to our lineage, some evidence indicates that they can show some signs of CTC (Rutz and Hunt, 2020; Shumaker et al., 2011). Therefore, a direct link may be drawn between tooluse skills and the ability to increase the complexity of actions in the environment. However, some caution must be exercised when drawing such a link. In this section, we have focused on the role of the motor-control system in action, suggesting that this system can allow an individual to perform tool incorporation. At this point, it is important to remember that this tool incorporation phenomenon does not describe how individuals can learn and/or understand the mechanical interactions they produce with external objects. Instead, this phenomenon describes how the body can control and manipulate one external object to act on another. Said differently, even if a link can be drawn between the presence of tool-use skills and signs of CTC in some species, an alternative interpretation is that this link does not result from the ability to perform tool incorporation (i.e., the production level) but rather from the ability to learn and/ or understand the mechanical actions produced (i.e., the conception level). One way of testing this alternative interpretation is to explore whether some tool-using species do not show CTC. As reported by Shumaker et al. (2011), many species, including mammals, birds, and insects, can show tool behavior. Nevertheless, no sign of CTC has been reported in these different species. Another note of caution is that the focus on “general” tool-use skills might be too restrictive (Hansell and Ruxton, 2008; Osiurak and Heinke, 2018), thus stressing how the distinct aspects associated with tool use can be selectively investigated. For instance, it has been proposed how tool sensing, making, and using are different processes that involve distinct neural correlates (e.g., Bruner, 2021). Also, as mentioned above, tool use is the most representative behavior of objecteobject manipulation. However, objecte

object manipulation also encompasses construction behavior, which can be defined as follows: Two or more tools and/or objects physically linked to make a functional, semipermanent thing that, once completed, is not held or directly manipulated in its entirety. A construction itself is therefore not a tool. Nor is it tool manufacture, because the product is not a tool (Shumaker et al., 2011, p. 19).

Although the phenomenon of incorporation is not thought of as necessary for construction behaviordeven if this remains possibledthe fact is that this behavior involves the production of interaction between external objects. Thus, given that CTC describes the increasing complexity of actions on the environment, nothing prevents a species from showing signs of CTC based only on the increasing complexity of their construction behavior. Obviously, we are aware that the evolution of our constructions does not reflect only an increasing complexity as if our tools and constructions had evolved in a parallel way. Instead, they also reflect how the coevolution of our tools and constructions has generated progressively more and more complex tools and constructions (i.e., using a tool to improve the efficiency of a construction, which then improves the efficiency of new tools, and so on). In broad terms, we conclude that tool-use skills could have contributed to the emergence of CTC in our lineage, favoring the production of new interactions between external objects. Nevertheless, even if tool-use skills could have been a favorable condition for CTC, they may not be sufficient or even necessary. It is one thing to control the body to perform tool-use actions, but another to learn and/or understand the effects these actions produce on the environment. To sum up, even if a species with a plastic motor-control system is certainly better equipped for interacting with the environment and producing more and more complex actions than a species devoid of it, the fact remains that the crucial condition for the emergence of

I. Visuospatial cognition and evolution

Visuospatial skills

CTC may instead lie in the presence of a specific conception system. We will discuss this possibility in the following two sections.

Visuospatial skills Function Visuospatial skills belong to the conception system and are more widely distributed within the parietal cortex than the motor-control system (Fig. 5.1). The reason why they are a part of the conceptual system is that they allow the individual to mentally generate a new state of the environment, which orients the activity of the production system (i.e., the motor-control system). The terms visuospatial skills are generic and encompass a wide range of concepts that have been linked to the ability to create mentally spatial transformations of external objects, such as visual/mental imagery (Kossyln et al., 1979), visuoconstructive skills (Scott and Schoenberg, 2011), spatial reasoning (Tversky, 2005), and visuospatial working memory (Miyake et al., 2001) (see Chapter 13). Interestingly, the degree of complexity of these spatial transformations can vary from imagining the motion or the rotation of a single external object to organizing external objects in correct spatial relationships so that they form an entity (e.g., drawing, construction). In a way, this distinction mirrors the object manipulation versus objecteobject manipulation distinction addressed above. In the previous sections, we have presented the specific constraints that objecteobject manipulation imposes on the motor-control system compared to object manipulation (i.e., tool incorporation and distalization). The difference here is that we will discuss how visuospatial skills can contribute to conceiving a mental simulation of the actions performed by external objects, thereby justifying that visuospatial skills are involved at the conception level and not at the production level.

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The term visuospatial representations will be hereafter used to describe the representations mentally manipulated by visuospatial skills.

Visuospatial transformations As stressed by Tversky (2005), a first tricky question is to understand what makes visuospatial representations spatial. Visuospatial representations are spatial because they capture the spatial properties of the environment by preserving, at least partially, the spatial structural relations of visuospatial information. This information can concern static properties of external objects (e.g., shape, size) or between external objects and reference frames (e.g., distance, direction) as well as dynamic properties (e.g., path, direction). Thus, visuospatial transformations are based on the use of visuospatial information. Here, we will focus on spatial information acquired visually, even if it has been shown that many of the static and dynamic properties of external objects can also be available from modalities other than vision. The most famous example of what can be a visuospatial transformation is the mental-rotation study designed by Shepard and Metzler (1971), in which they showed that the time to judge whether two figures in different orientations are the same or mirror images correlated linearly with the angular distance between the orientation of the figures. Visuospatial transformations can concern either the mental manipulation of a single object (i.e., mental object manipulation) or the spatial organization between two external objects (i.e., mental objecteobject manipulation). We will begin by presenting how the visuospatial transformations, when applied to a single external object, can be involved in planning motor actions. Said differently, we will discuss how visuospatial skills interact with the motorcontrol system to produce goal-directed actions. The end-state comfort effect offers a good illustration of this interaction.

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The end-state comfort effect was initially discovered in 1990 by Rosenbaum et al. with the bar transport paradigm (Fig. 5.2; Rosenbaum et al., 2019). In this paradigm, the participant has to pick up a black-and-white dowel positioned horizontally and place the black (or white) end squarely on black (or white) targets positioned on each side of the dowel. A comfortable final

posture corresponds to positioning the dowel squarely with the thumb pointing up (and not pointing down). Rosenbaum et al. reported that participants preferred using an initial overhand grip when asked to place the dowel on the black target (Fig. 5.2a) and an initial underhand grip when asked to place the dowel on the white target (Fig. 5.2b; Rosenbaum et al., 2019). In

FIGURE 5.2 The bar transport paradigm. In the bar transport paradigm, participants had to pick up a black-and-white dowel positioned horizontally and place the black (or white) end squarely on the black (or white) target (panels a and b). As illustrated here, a comfortable final posture corresponds to the thumb pointing up. An initial overhand grip is preferred when the black end has to be placed on the black target (panel a). When the target is white, an initial underhand grip is preferred (panel b). In both cases, these grips are selected because they allow a comfortable final posture. To select these initial grips, participants need to think of the expected perceptual effect: The dowel with either the black or the white end pointing up. Participants also have to represent the link between the final orientation of the dowel and its current orientation to decide which initial grip to perform. However, as shown in panel c, if the dowel becomes colored only after it is grasped, participants cannot decide which grip to select, even if they can know what the comfortable final posture to perform is. Adapted from Osiurak and Badets (2017).

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Visuospatial skills

both cases, these choices allowed the participants to adopt a comfortable final posture with the thumb pointing up. The crucial result is that the initial underhand grip is uncomfortable, which suggests that the participants, when asked to place the dowel on the white target, favored a comfortable final rather than initial posture. This end-state comfort effect nicely demonstrates that postural end states are generated before the motor action begins (as discussed above with the notion of future internal models). However, this end-state comfort effect also demonstrates the interaction between visuospatial skills and the motor-control system (Osiurak and Badets, 2017). Indeed, imagine now that the paradigm is modified so that the participants are instructed about the color of the target (e.g., black) while they cannot see how the dowel is positioned horizontally (Fig. 5.2c). In this case, they cannot know whether the black end of the dowel is oriented toward the right or the left. For the motorcontrol system, the constraint always remains the same, that is, favoring a comfortable final posture with the thumb pointing up. Nevertheless, if the participant grasps the dowel without being informed about how the black end of the dowel is oriented, this can lead them to adopt an initial posture that induces an uncomfortable final posture (with the thumb pointing down). The only way to systematically select the appropriate initial posture requires to be informed about how the dowel is initially oriented. Then, visuospatial skills can come into play to simulate the motion of the dowel mentally to the target and, thus, bias the selection of the appropriate initial posture within the motor-control system. To sum up, the motor-control system’s selection and planning of the most economical motor actions are guided by the visuospatial transformations of the targeted external object. Interestingly, several studies have shown how the interactions between visuospatial skills and the motor-control system improve during the first months and years of life, progressively

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allowing infants and children to produce more and more economical motor actions when manipulating external objects (e.g., Connolly and Dalgleish, 1989; Greer and Lockman, 1998). Let us turn now to the case of visuospatial transformations when applied to the organization of several external objects in correct spatial relationships so that they form an entity (e.g., drawing, construction, i.e., mental objecteobject manipulation). These kinds of visuospatial transformations can be needed when, for instance, we have to decide whether a wheelbarrow can pass between two trees or whether two puzzle pieces can be arranged together. The ability to perform such transformations can be assessed by some visuoconstructive tests such as the Kohs block design test (Kohs, 1920). In this test, individuals have to reproduce figures from models shown to them. To do so, individuals are presented with painted cubes with different colors or color combinations for the six sides of each cube. The main difference with mental object manipulation is that mental objecteobject manipulation requires performing visuospatial transformations concerning the interaction between at least two external objects. The high demands that these visuospatial transformations place on the individual have led to the progressively developed concept of visuospatial working memory, which can be viewed as part of a mental workspace in which visually presented material is maintained and manipulated (e.g., Logie and Sala, 2005; Miyake et al., 2001). A significant body of evidence has indicated an important role of the parietal cortex, particularly the posterior parietal cortex (i.e., superior parietal cortex, intraparietal sulcus, and inferior parietal cortex), in visuospatial skills (Fig. 5.1; see also Trojano and Conson, 2008). For instance, Sack et al. (Sack et al., 2005) showed that both parietal lobes were recruited in a mental imagery task even if each lobe could play a different role: generation of mental images for the left parietal lobe and spatial comparison for the right parietal lobe. They also showed that the right

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parietal lobe could take the function of the left parietal lobe (i.e., generation of mental images) in case of dysfunction of the left one (see also Formisano et al., 2002; Trojano, 2000). Other studies have corroborated the involvement of both parietal lobes in visuoconstructive tasks (e.g., Ino et al., 2003; Makuuchi et al., 2003; for a review, see Trojano and Conson, 2008).

Visuospatial skills and CTC The question is whether visuospatial skills are the crucial cognitive skills allowing the emergence of CTC. As for tool-use skills, some arguments suggest the answer may be yes. Indeed, given that CTC describes the increasing complexity of actions made on the environment, the ability to perform mentally visuospatial transformationsdparticularly in the case of mental objecteobject manipulationdappears to be essential to produce new mechanical interactions between external objects. As discussed above, CTC is characterized not only by the constant improvement of tools but also of constructions, which place higher demands on the organization of external objects to form an entity. The ability to produce such reorganization mentally provides an adaptive value to produce more complex mechanical actions. Nevertheless, as for tool-use skills, a certain number of arguments question the idea that a certain level of visuospatial skills is sufficient for the emergence of CTC. First, it is noteworthy that many other species may perform mental object manipulation. At a scientific level, it is very challenging to investigate this ability by using mental imagery tasks as in humans. However, if we consider that the bar transport paradigm is appropriate for examining visuospatial skills, particularly in the context of mental object manipulation, then it becomes easier to explore the visuospatial skills of nonhuman animals. Interestingly, this paradigm has been adapted to several nonhuman species,

such as cotton-top tamarins (Weiss et al., 2007) and lemurs (Chapman et al., 2010; Horvath et al., 2008), which are not known for being skilled tool-users nor for showing any sign of CTC. Tamarins and lemurs are also thought to have diverged from the hominid line about 40 and 65 million years ago, respectively (Horvath et al., 2008; Rosenbaum et al., 2012). The findings have indicated the presence of an end-state comfort effect in these species, suggesting that the ability to perform visuospatial transformations on single external objects is not specific to our lineage and not sufficient to provoke CTC. Second, it may be suggested that the ability to perform visuospatial transformations on single external objects (i.e., mental object manipulation) is not the appropriate level of analysis. Indeed, if visuospatial skills are involved in CTC, it is perhaps better to place the focus on the ability to perform visuospatial transformations to organize external objects to form an entity (i.e., mental objecteobject manipulation). There is no evidence ruling out the possibility that, for instance, a potential enhancement of visuospatial working memory in our lineage could have favored the emergence of CTC (for more discussion on this possibility, see below). Nevertheless, this possibility is confronted with a theoretical limitation at the cognitive level. Indeed, even if visuospatial transformations appear to be critical to conceive spatial interactions between external objects, these interactions are restricted to the spatial dimension. Of course, the spatial dimension is fundamental when an individual engages in activities in which several external objects interact. For instance, if someone intends to drive a screw, they must consider the shape of the head of the screw to select the appropriate screwdriver (e.g., cruciform, slotted). Nevertheless, the increasing complexity of our actions is also characterized by the ability to produce mechanical actions between external objects that go beyond the spatial dimension. After all, at a spatial level of analysis, a slotted screwdriver with a plastic blade can be appropriate if the

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Technical reasoning

blade fits in the slot of the screw. Yet, the blade will not be rigid enough to produce the rotation of the screw. In broad terms, the ability to perform visuospatial transformations is insufficient to generate a wide variety of mechanical actions between external objects. Instead, generating a greater variety of mechanical actions requires the cognitive processing of physical dimensions other than the visual one, particularly “material dimensions” such as solidity, rigidity, or weight. A possibility to overcome this theoretical limitation is to assume that visuospatial skills can be extended to cover nonspatial dimensions (e.g., Tversky, 2005). The question is why these skills should still be labeled spatial since they would not be specific to the spatial dimension. As explained in the next section, this explains why the term technical reasoning can be preferred. This possibility is also difficult to justify based on the neuropsychological and neuroimaging literature, which has identified two different networks involved in either visuospatial skills or technical reasoning, which are characterized by different involvements of the parietal cortex: A bilateral recruitment of the superior parietal lobes, intraparietal sulcus, and the inferior parietal lobes for visuospatial skills versus a specific involvement of the left inferior parietal lobe for technical reasoning (Fig. 5.1; see below for a more extensive discussion on this aspect). To sum up, even if the mental production of mechanical actions between external objects certainly needs to integrate the spatial dimension and, as a result, the involvement of visuospatial skills, it remains unlikely that these skills are sufficient to explain how CTC could have emerged in our lineage.

Technical reasoning Function Technical reasoning belongs to the conception level and can be defined as the ability to reason

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about physical object properties, particularly those that go beyond the spatial dimension (e.g., solidity, rigidity, weight; Federico et al., 2021a; Osiurak et al., 2010, 2020c). It is causal (i.e., prediction of the effects on the environment) and analogical (i.e., transfer of what is acquired from one situation to another). It is based on mechanical knowledge, which refers to knowledge about physical principles (i.e., know-how) such as cutting or lever actions. This knowledge is acquired through experience and is nondeclarative. Thus, it is difficult for many of us to clarify what we understand about the physical principle at work. The outcome of technical reasoning is a mental simulation of the mechanical action to be performed, which helps orient the activity of the motor-control system. In this regard, the interaction between technical reasoning and the motor-control system is similar to that between visuospatial skills and the motor-control system (Osiurak et al., 2020b). In both cases, the motor-control system aims to select, plan, and control online the most economical actions to realize the mental simulation of an action performed by external objects.

Neurocognitive bases The technical-reasoning hypothesis has been initially developed in the field of neuropsychology with the study of left brain-damaged patients with tool-use disorders, also called apraxia of tool use. Those patients struggle to select and appropriately use familiar tools, such as a hammer or a knife. A significant body of evidence has demonstrated a strong link in these patients between the ability to select and use familiar tools and the ability to select, use, and even sometimes make novel tools to solve mechanical problems (e.g., folding a wire to create a hook useful for extracting a target stuck inside a box; Goldenberg and Hagmann, 1998; Goldenberg and Spatt, 2009; Hartmann et al., 2005;

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Heilman et al., 1997; Jarry et al., 2013; Osiurak et al., 2020c). These findings, particularly the difficulties these patients met in novel tool-use tasks, highlight that their tool-use disorder might be due to the inability to reason about physical object properties. These patients can also show perplexity when asked to use tools (Osiurak et al., 2013). For instance, it was observed in a novel tool-use task that these patients initiate few mechanical actions between external objects and are less prone than individuals without tool-use disorders to follow trialand-error strategies by inspecting the potential effects of mechanical actions. This result may appear counterintuitive because it may be suggested that impaired technical reasoning should lead these patients to adopt trial-and-error strategies excessively. Instead, this behavioral pattern stresses that the impaired ability to produce mental simulations of potential mechanical actions prevents these patients from generating partial solutions, which can be adjusted subsequently based on the effects they create on

the environment (i.e., reasoned trial and error; Vaesen, 2012). It is long known in the neuropsychological literature that tool-use disorders may occur preferentially after damage to the left inferior parietal lobe. More recently, several lesion-mapping studies have confirmed this conclusion in reporting that tool-use disorders in both novel and familiar tool-use tasks are preferentially observed after damage to the left inferior parietal lobe and particularly the area PF within the supramarginal gyrus (Fig. 5.3a; Goldenberg and Spatt, 2009; Martin et al., 2016; SalazarL opez et al., 2016). The involvement of the area PF in the processing of mechanical actions (i.e., the interactions between external objects) and not of motor actions (i.e., the interaction between the end-effector and an external object) has also been confirmed by neuroimaging studies. Reynaud et al. (2016) conducted a meta-analysis of neuroimaging studies interested in tool use in which they distinguished between studies wherein participants had to focus on the

FIGURE 5.3

Evidence for the technical-reasoning hypothesis. (a) Lesion sites reported in voxel-based lesion-symptom mapping studies investigating familiar and novel tool use in LBD patients (Goldenberg and Spatt, 2009; Martin et al., 2016; Salazar-L opez et al., 2016). The area PF of the left inferior parietal lobe is the only brain area commonly found in all the studies, suggesting that a common neurocognitive process is at work, whatever the familiarity of the tool-use activity. (b) A key finding of a recent neuroimaging meta-analysis on tool use (Reynaud et al., 2016). The analysis included studies in which healthy participants had to focus on the appropriateness of mechanical actions (i.e., tool-object relationships). Results revealed activation of the left area PF (in red in the zoomed picture), confirming that the left area PF is involved explicitly in mechanical-action understanding. (c) A key finding from a recent neuroimaging meta-analysis on tool-use action observation (Reynaud et al., 2019). The results concern the contrast: Observation of tool-use actions minus non-tool-use actions. Again, the activation of the left area PF is found (in yellow in the zoomed picture), indicating that people reason technically not only to conceive their own mechanical actions but also when watching others using tools. I. Visuospatial cognition and evolution

Technical reasoning

mechanical actions (e.g., judging whether the orientation of a screwdriver relative to a screw is correct) and those in which participants had to focus on the motor actions (e.g., judging whether a specific hand posture is appropriate to grasp a screwdriver). They found a preferential activation of the left area PF when the focus was on mechanical actions and of the intraparietal sulcus when the focus was on motor actions (Fig. 5.3b). Activation of the left inferior frontal gyrus was also found when the focus was on mechanical actions (Reynaud et al., 2016, 2019), suggesting that technical reasoning can involve a specific frontoparietal network. The outstanding question is still to understand the role of the left inferior frontal gyrus in technical reasoning, which appears crucial also to specify the role of the left area PF. Other activations outside the parietal cortex were also reported when the focus was on motor actions. However, we will not discuss these findings further here, which fall beyond the scope of the present chapter. The involvement of the left area PF in the processing of mechanical actions was also found in the context of tool-use action observation. In another meta-analysis of neuroimaging studies concerning action observation, Reynaud et al. (2019) distinguished between studies in which participants had to watch someone performing a tool-use action and those in which participants had to watch someone performing a non-tooluse action (e.g., grasping an external object). In both cases, there was a bilateral recruitment of the fronto-temporo-parietal action observation network. Notably, the contrast between observation of tool-use actions minus the observation of non-tool-use actions indicated a preferential activation of the left area PF as well as of the left inferior frontal gyrus (Fig. 5.3c). These findings indicate that the same network and notably the left area PF is engaged when people perform, understand, or observe others carrying out mechanical actions. Said differently, people may reason technically also when they observe others performing mechanical actions.

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Technical reasoning and CTC CTC is characterized by the increasing complexity of actions performed on the environment. This increase implies that the mechanical actions between external objects are not restricted to the spatial dimension but also involve other physical properties, which can be viewed as more material (e.g., rigidity, solidity). In this regard, any cognitive skills that are recruited to process these other dimensions appear to be an appropriate candidate to explain how CTC can be supported at a cognitive level. As technical reasoning is thought to be mainly concerned with these other physical dimensions, it becomes a plausible candidate to explain how CTC could have emerged in our lineage. Experimental psychology provides support for this possibility. Microsociety paradigms have been commonly used to investigate cultural evolution in laboratories (Caldwell and Millen, 2009; Wasielewski, 2014). One of the most frequently used paradigms is the transmission-chain paradigm. The task can be, for instance, to optimize the speed of a wheel that descends a track by moving the four weights placed along each spoke of the wheel (Fig. 5.4; Derex et al., 2019). The first participant of the chain performs the task. The second participant can scrutinize the product of (i.e., reverse-engineering), observe the action made by, or communicate with the first participant before performing the task themselves. Then, the third participant does the same with the second one, and so on. It has been shown that cumulative performance can be observed in these different conditions, notably in reverseengineering conditions where participants do not interact at all (Caldwell and Millen, 2009; Derex et al., 2019; Zwirner and Thornton, 2015). Given the absence of social interaction, technical-reasoning skills appear to be a viable hypothesis to explain how an individual can at least copy and, at best, improve the product of their predecessor by merely scrutinizing it. This

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FIGURE 5.4 Parallel improvement of a wheel system and of its understanding. (a). Derex et al. conceived an original experiment in which participants had to improve a physical system (Derex et al., 2019). The system was a wheel that descended a 1-m-long track. It had four radial spokes. One weight could be moved along each spoke on 12 positions. In their experiment, participants were organized into transmission chains (b). Each participant had five trials to improve the physical system by modifying the wheel configuration. Only the last two trials (configurations and associated speeds) were transmitted to the next participant in the chain (trials in gray in b). Their understanding of the wheel system was also assessed with a multiple-choice task consisting in presenting different wheel configurations and in selecting the fastest one. We recently partly replicated this experiment (Osiurak et al., 2021) and found, as Derex et al. (Derex et al., 2019), that the speed wheel increased over generations (d). This finding confirms that CTC can be studied in laboratories. In some cases, the wheel did not descend (i.e., failures). Contrary to Derex et al. (Derex et al., 2019), we observed (e) an increase in understanding over generations (for an extensive discussion about this discrepancy, see Osiurak et al., 2021). A strong association was also obtained between wheel speeds and understanding scores (c). The participants’ understanding scores were compared to those of a control group, who did not experience the system. As shown in (e), the participants in the experimental group outperformed the control group, including the participants in the first generation. This demonstrates that the experience of the wheel system (i.e., first generation), which was progressively associated with the social transmission of technical content (i.e., second-to-fifth generations), led participants in the experimental group to improve their understanding of the physical system gradually. These findings support the technical-reasoning hypothesis by showing that an increase in its understanding accompanies the improvement of a physical system over generations.

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Evolution of the parietal cortex and technical reasoning

possibility has been confirmed by a series of experimental studies (Osiurak et al., 2021, 2022), in which it has been shown that learners’ technical-reasoning skills (assessed separately with psycho-technical tests) are the best predictor of cumulative performance (de Oliveira et al., 2019; Osiurak et al., 2016, 2020a). Other evidence indicates that the progressive improvement of a physical system in a reverse-engineering condition is accompanied by a better understanding of the system (Fig. 5.4; Osiurak et al., 2021). In other words, technical-reasoning skills may be crucial to select what is relevant and what is irrelevant from the product/the demonstration of the predecessor, as well as to explore the technical solution space in a reasoned way (i.e., convergence toward the most technically plausible solutions). The fact that the left area PF is preferentially activated in tool-use action observation is consistent with these findings, suggesting that individuals could spontaneously reason at a technical level when observing others performing mechanical actions. To sum up, technical reasoning might be an important cognitive mediator in learning, understanding, and predicting mechanical actions performed between external objects in both asocial and social contexts.

Evolution of the parietal cortex and technical reasoning An evolutionary scenario In the previous sections, we have discussed the potential role of the motor-control system (notably the mechanism of distalization), visuospatial skills, and technical reasoning in CTC. Our conclusions are as follows. Although the mechanism of distalization, which is critical to perform tool-use actions, can favor the interactions between external objects, this mechanism seems insufficient to explain how our actions on the environment could have progressively increased in terms of complexity. Instead, it seems more plausible that any increase in complexity relies on the conception system and

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not on the production system. This has led us to turn to visuospatial and technical-reasoning skills. Visuospatial skills allow an individual to manipulate external objects mentally. Nevertheless, the fact that they focus on the spatial dimension does not offer them sufficient characteristics to envisage them as a support for an increase in terms of complexity. By contrast, technical reasoning appears to be better equipped to support this increase. The increase in cognitive sophistication and in the ability to perform complex actions may have followed a trajectory consistent with the evolution of tool use in humans. In fact, it has been proposed that the use of tools may have evolved as a phenomenon that was first occasional, then habitual, and, finally, obligatory (Shea, 2017). In this sense, changes in neurocognitive functions may reflect such an evolutionary path. Therefore, the effects of the neurocognitive changes in the human brain driven by the evolution of tool use might have reverberated in different functional specializations of the parietal areas. While being suggestive, this hypothesis needs further study. However, by moving the focus from the neurocognitive systems associated with motor control and visuospatial skills to the ones related to technical reasoning, most recent studies highlighted how functional and/or structural changes within the left inferior parietal lobe and particularly the area PF could have supported the emergence of technical-reasoning skills in our lineage, thereby favoring CTC. We recently conducted a structural MRI study (Federico et al., 2022), which has led us to refine this scenario. In this study, we asked participants to complete two psycho-technical tests. The first placed higher demands on technical-reasoning skills (e.g., selecting the easiest to hammer among four different nails). In contrast, the second placed higher demands on visuospatial skills (e.g., selecting among four threedimensional geometrical shapes the one corresponding to a specific two-dimensional pattern). Then, we extracted the cortical thickness of 10 inferior parietal areas (i.e., PGp, PGs, PGi, PFm,

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PF, PFt, PFop, IP0, IP1, and IP2) for both the left and right hemispheres. We found a positive correlation between both tests as well as positive correlations between the left PF cortical thickness and both visuospatial and technical-reasoning skills, and between the right PF cortical thickness and visuospatial skills. There were no other significant links between both skills and the different parietal areas. We also used a mediation analysis, which revealed that visuospatial skills fully mediated the link between the left PF cortical thickness and technical reasoning. These findings are interesting in several respects. First, the link reported between visuospatial and technical-reasoning skills suggests that technical reasoning might not be completely independent of visuospatial skills. This possibility is viable at a theoretical level if we consider that it is difficult to reason on nonspatial physical properties without considering the spatial dimension. In a way, technical reasoning might be viewed as a sort of elaborated visuospatial skill that includes processing nonspatial physical object properties (i.e., an adaptation). One may suggest that this proposal is inconsistent with the conclusions drawn above. However, it is not. Indeed, whereas visuospatial skills recruit a significant number of areas within the parietal cortex of both hemispheres (i.e., superior parietal lobes, intraparietal sulcus, inferior parietal lobes), technicalreasoning skills mainly involve the left area PF within the supramarginal gyrus. In this way, even if technical-reasoning skills can be viewed as a kind of adaptation of visuospatial skills, both skills are not superposable at a neural level, and it remains relevant to distinguish them at a cognitive level. It is also noteworthy that right brain-damaged patients with visuospatial deficits can show difficulties when using tools with external objects (Goldenberg and Hagmann, 1998). However, these difficulties are characterized by errors concerning the spatial orientation of the tool according to the object, which can be rapidly corrected from feedback. Crucially, these patients are not impaired when selecting appropriate tools based on nonspatial dimensions,

which correspond to the specific deficit reported in left-brain damaged patients with tool-use disorders. In other words, our evolutionary scenario suggests that technical-reasoning skills might originate in visuospatial skills and that their emergence has been supported by an adaptation of some of the parietal areas used for visuospatial skills, notably the left area PF.

Palaeoneurology and cognitive neuroscience The evolutionary scenario proposed here makes precise predictions that can be examined from the paleoneurology literature on the evolution of the parietal cortex. This prediction is that structural and/or functional changes could have occurred within the left inferior parietal cortex and, notably, the left area PF in our lineage, thereby supporting the emergence of technical reasoning and favoring CTC. Paleoneurology refers to the study of endocasts, which are extinct species’ casts of cranial cavities. This study offers some inferences from endocranial morphology on brain anatomy (Bruner, 2018, 2020; Holloway, 1981). Holloway (Holloway, 1981) was the first to conduct an endocranial shape analysis on fossils and living hominoids. This analysis revealed a great parietal variability among different species. This was confirmed later by Bruner and colleagues (Bruner, 2004; Bruner et al., 2003, 2018), who have found that larger parietal lobes characterize modern humans. Interestingly, the evolution of the inferior parietal lobe has been long hypothesized in the context of technological evolution (e.g., Bruner et al., 2023), and most recent evidence suggests morphological changes in the inferior parietal lobe (e.g., Pereira-Pedro et al., 2020). While the field of paleoneurology is relatively recent but well-equipped to support the link between technical reasoning and the evolution of the parietal cortex, an effort should be made to specify alternative scenarios that can plausibly explain how CTC could have emerged without focusing on such a kind of neurocognitive link. At least two alternative scenarios can be

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proposed, which we will quickly discuss in turn. The first one is based on the idea that CTC could have emerged in our lineage because of an enhancement of working-memory capacities (e.g., Haidle, 2010; Wynn and Coolidge, 2008). The rationale we have adopted so far has been that the emergence of CTC requires a qualitative increase in complexity concerning the actions performed with external objects. This qualitative increase has been suggested as resulting from the shift from processing spatial dimensions to processing nonspatial physical dimensions. However, another possibility is that the emergence of CTC has resulted from a quantitative increase in complexity, which can be characterized by the development of longer sequences of actions. A good illustration of this aspect was given by Haidle (Haidle, 2010), who described the sequences of actions needed to make a simple spear (about four key actions) versus a spear with a split-based bone point (about 12 key actions). The longer the sequence, the higher the demands on working memory. A large body of evidence has demonstrated the prefrontal cortex’s important contribution, particularly its dorsolateral portion, to working memory (see Chapter 8). Taken together, these findings support the idea that enhanced working-memory capacities could have favored the quantitative increase of actions in terms of complexity. It is also noteworthy that some findings have also shown the potential role of the precuneus in working-memory capacities. These findings are consistent with the expansion of the precuneus in our lineage, thus providing converging evidence from paleoneurology and cognitive science. The second alternative scenario is based on the idea that CTC originates in specific social cognitive skills. So far, we have deliberately put aside the social dimension of this phenomenon to focus on the cognitive skills required to perform actions with external objects. This could give the impression that we conceive the emergence of CTC as a nonsocial phenomenon, which

is only driven by nonsocial cognitive skills, as if individuals can acquire knowledge about their environment simply based on their own interactions with the environment (i.e., asocial learning). Of course, we do not deny the critical importance of social learning, which remains the best way for individuals to acquire a significant amount of knowledge about their environment. Instead, we proposed that some noncognitive skills and particularly technicalreasoning skills could have been crucial for our predecessors to learn from others in less elaborated forms of social learning such as reverse engineering or even observation, thus favoring the emergence of signs of CTC (Osiurak and Reynaud, 2020; Osiurak et al., 2022). Nevertheless, an alternative scenario is that CTC can emerge only because of more elaborated forms of social learning, such as teaching or imitation, which are supported by specific social cognitive skills (e.g., theory of mind, mentalizing; Dean et al., 2012; Herrmann et al., 2007; Tennie et al., 2009; Tomasello et al., 2005). These skills involve cerebral structures other than the parietal cortex (e.g., frontal and temporal lobes; Gallagher and Frith, 2003; van Overwalle and Baetens, 2009). According to this perspective, our focus on the role of the parietal cortex in CTC may appear inappropriate because none of the cognitive functions it supports might be necessary or sufficient for the emergence of CTC. We have already discussed the limitations of this scenario elsewhere, thus suggesting how even less elaborated forms of social learning in concert with technicalreasoning skills could have been sufficient for the emergence of signs of CTC, without ruling out the potential boosting role of social cognitive skills in social transmission (Osiurak and Reynaud, 2020; Osiurak et al., 2022).

Conclusion The parietal cortex is the seat of action in the human brain. In this respect, the increasing

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complexity of actions in our lineage could have resulted from structural and/or functional changes in this brain region. We have proposed an evolutionary scenario in which the adaptation of visuospatial skills to nonspatial physical dimensions of external objects could have led to the emergence of technical-reasoning skills, thereby favoring CTC. Given that technicalreasoning skills notably require the recruitment of the area PF within the left inferior parietal lobe, we have hypothesized that this brain could be characterized by more pronounced evolutionary structural and/or functional changes. Finally, we have proposed two alternative scenarios that introduce the involvement of other cognitive skills (i.e., working memory and social cognitive skills) and move the debate beyond the parietal cortex.

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Body-tool integration: past, present, and future Luke E. Miller1, Marie Martel2 1

Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands; 2 Royal Holloway University of London, Egham, United Kingdom

Introduction Tool use is often considered one of the defining features of our species Homo sapiens. Whereas many species of primates (and other animals) manipulate objects to interact with the environment in a goal-directed manner (St Amant and Horton, 2008), it is a matter of debate whether these abilities qualify as tool-use (Bruner and Gleeson, 2019). Indeed, they are vastly outweighed by the skill displayed by humans (Vaesen, 2012). We as a species have a unique propensity to develop tools that overcome the physical limitations of our bodies (e.g., size, strength, etc.), and then iteratively improve on their design to increase optimality (Osiurak et al., 2010). We use cutlery to cut our foods, grabbers to extend our reach, saws to chop down trees, and prosthetics to replace missing body partsdjust to name a few. As is clear from these examples, tools serve many functions and augment their human user by extending the body, both physically and functionally. This raises the following question: in what ways does tool use require the sensorimotor system to adapt its representations?

Cognitive Archaeology, Body Cognition, and the Evolution of Visuospatial Perception https://doi.org/10.1016/B978-0-323-99193-3.00010-6

This question can be addressed on multiple temporal scales, the first being the evolutionary time scale of tool-related adaptations to the human biomechanical and neural systems. Human hand morphology shows the evolutionary hallmark of progressive adaptations for tool use, including the opposition of the thumb and fingers that allows for the necessary precision and power grips. Tool-ready bone structure appears to already be present in Australopithecus africanus (3 to 2 Mya) (Skinner et al., 2015), though it was long thought that the first stone tool makers were Homo habilis (2.4 Mya) (Tobias, 1965). There is also evidence for changes to the distribution of mechanoreceptors in the human hand (Jones and Lederman, 2006), which likely reflect adaptations that promote the extraction of sensory information during tool use (Johansson and Flanagan, 2009). Furthermore, human-specific regions within the parietal lobe likely reflect neural specialization for tool use (Peeters et al., 2009; Reyes et al., 2022; see Chapter 9). This time scale is addressed in many chapters of the present book, as well as throughout the many sections of this chapter.

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There is also a much shorter time scale encompassed by the demands of daily tool use, which requires the sensorimotor system to dynamically adapt to the physical demands of a variety of tools. Every time we use a tool, the physical properties of our limbs (e.g., their lengths, inertia, torque, endpoint velocities, etc.) change. Successfully controlling a tool requires taking these changes into account and updating sensorimotor representations of the body, often called “body schema.” Yet, these representations take years to achieve a stable metric configuration during development (Cardinali et al., 2019). The fast sensorimotor plasticity required by tool use thus needs to coexist and adapt to growing dimensions of the limbs and the body itself (Kording et al., 2007). Furthermore, toolobject contact produces sensory feedback that must be attributed to the tool and not the body. How does the sensorimotor system solve these problems? When wielding a tool, the forelimb and tool can be thought of as a functionally integrated sensorimotor systemdan armþtool system. One prominent proposal in neuroscience is that sensorimotor representations of the forelimb are updated such that they do not just reflect the current state of the arm but instead reflect the state of the armþtool system. Several terms have been used to refer to this phenomenon, such as embodiment (Makin et al., 2017), incorporation (Cardinali et al., 2016a), and assimilation (Nishide et al., 2011). In the present chapter, we will refer to it as body-tool integration, in order to stress the functional combination of limb and technology. This body-tool integration has been proposed to reflect a fundamental component of tool use (Bruner and Gleeson, 2019). Furthermore, it is likely the product of evolutionary adaptations whose function is to merge body and tool in the brain, reflecting an evolved “prosthetic capacity” (Bruner, 2021).

The British neurologists Henry Head and Gordon Holmes were among the first to comment on this proposal scientifically (Head and Holmes, 1911), stating that it is because we have “body schemata” that “we owe the power of projecting our recognition of posture, movement, and locality beyond the limits of our own bodies to the end of some instrument held in the hand.” (p. 188). Evolutionary theories of projection nicely agree with this (Iriki et al., 2021), encompassing a first-order projection to create a “world-aroundthe-body” map, and a second-order one to create a “self-in-the-world” map. This last projection is more cognitive and important for the “selfobjectification” of tools (Iriki, 2006). To put this another way, body-tool integration for motor control and sensing involves repurposing bodyrelated sensorimotor processes to extend functionality beyond the body. This does not need to just be the case for the simple tools we use in our daily lives. The ability to integrate technology with our “body schemata” is also of the utmost relevance for more futuristic technologies, such as additional limbs. In the present chapter, we will review the empirical validity of the claim that our sensorimotor system integrates the technology we use with its body representations. We will first focus on behavioral and neural evidence that controlling a tool updates body representations in a way that suggests an integration process over and above basic motor learning. We will then discuss how the somatosensory system is repurposed to sense with tools, much like a blind person does with a cane. Finally, we will turn our attention toward the future and discuss evidence for the integration of robotic devices, such as brainemachine interfaces (BMIs) and wearable extra limbs. Throughout the chapter, we will also turn our attention back in time and discuss how evolution may have shaped the sensorimotor mechanisms we discuss.

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Body-tool integration during motor control

Body-tool integration during motor control Tools are used daily by human beings to interact with their environment, whether they are simply using a pen or a ruler but also in the kitchen or sports. Despite the multitude of shapes and functions of these tools, one common feature is that they elongate our functional capabilities and lengthen the limbs. Thus, when using a tool, our brain has to integrate information originating from the environmentdsuch as the object one interacts withdwith information from the body, such as its posture or metrics (Shadmehr and Krakauer, 2008). Specific metrics of the body slowly change from birth to adulthood, suggesting that the sensorimotor representations used for action and perception are plastic and can be updated based on our actions and their sensory consequences. For instance, when using a tool that elongates reaching capabilities, there is an abrupt functional lengthening of the effector that needs to be accounted for by the brain. This lengthening taxes our sensorimotor system on different levels (for reviews, see Maravita and Iriki, 2004; Martel et al., 2016). Historically, the earliest work on this domain found that tool use extends the boundaries of peripersonal space, a multisensory representation of the space surrounding the body (see Chapter 3). This was often interpreted as a change in body representation. However, there is not one-to-one correspondence between peripersonal space and body representation, which have distinct sensory inputs and functional properties (Cardinali et al., 2009a). Indeed, recent work has found that their plasticity is dissociable (Bassolino et al., 2015). Additional evidence was therefore needed to directly implicate tools in a change of body representation. Using a tool requires adapting sensorimotor representations of the tool-using limb. A typical way to measure the effect of tool use on these representations involves evaluating how a brief period of tool use changes a specific behavior,

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such as an action or tactile localization (for a review, see Martel et al., 2016). The rationale behind these pre-post paradigms is that if tool use does indeed lead to a change in sensorimotor representations, its effects should be observed in sensorimotor behaviors and in the geometric representation of the effector itself. The present section will review these findings, their rules, and what they tell us about the processes involved in sensorimotor plasticity.

Effects of tool use on reaching behavior The seminal study from Cardinali et al. (2009b) revealed for the first time that the kinematics of free-hand movements were modified after using a mechanical grabber, attesting to an updated sensorimotor representation. When comparing the kinematics before and after a short 15-min session of tool use (Fig. 6.1a), participants’ reaches had smaller amplitudes and increased latencies (Fig. 6.1b). These effects have been replicated numerously since (Cardinali et al., 2012, 2016a; Baccarini et al., 2014; Martel et al., 2022) and were found independent of the direction of reaching (Martel et al., 2019) and of the movement performed (grasping and pointing) (Cardinali et al., 2009b). Crucially, using the hand to reach for an object with a weight on the wrist does not alter postuse kinematics (Bahmad et al., 2020), indicating that this is not only a question of added weight on the joint during tool use. Instead, the pattern of kinematic change is consistent with an increase in how long the user’s arm is represented (Cardinali et al., 2009b; Martel et al., 2019), suggesting a form of integration between the body and tool in the sensorimotor system. We have argued that the above sensorimotor generalizability is a key signature of body-tool integration during motor control (Martel et al., 2017). Crucially, these effects do not reflect the typical adaptation aftereffects as observed in sensorimotor learning studies (e.g., field force),

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FIGURE 6.1 (a) The reaching task was performed before and after using a tool. (b) Illustration of a velocity profile typically seen pre (blue) and post (red) tool-use. Using a tool decreases the peak amplitude of the velocity. (c) Illustration of the effect of tool use on peak velocity for children (right), adolescents (center), and adults (right).

but rather an update of the length parameter of the used effector (but see, Bell and Macuga, 2022). This is underscored by the persistent kinematic changes long after the cessation of tool use, demonstrating that a general arm representation has been updated. No traces of motor learning have been observed when healthy adults perform tool use under normal visual conditions: The amplitude and latency peaks during reaching are similar throughout the tool use session (Cardinali et al., 2009b). In contrast, children do not display the typical signatures of bodytool integration and show significant motor learning during tool use (Martel et al., 2021). Traces of motor learning have also been observed in both blindfolded sighted and blind participants. Importantly, blindfolded participants displayed the key kinematic signature of tool-driven plasticity while blind participants did not (Bahmad et al., in prep). However,

more studies are needed to fully understand the apparently weak relationship between motor learning during the tool session and kinematic changes observed after tool use.

Effects of tool use on tactile object perception Whereas reaching provides an indirect way to measure represented body size, tasks that measure tactile perception provide a more direct window into how the brain represents the body’s geometry (Longo et al., 2010). Using tools has been shown to have effects on body size that are convergent with the reaching results (Fig. 6.2a). When asked to localize touches applied on the elbow, wrist, and middle fingertip of their tool-using arm (Fig. 6.2b), the points representing elbow and wrist were

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feedback (Tajadura-Jimenez et al., 2012). Several studies have used these paradigms to identify tool-driven changes to represented body size and shape (Miller et al., 2014, 2017a, 2017b; Canzoneri et al., 2013). For example, Miller et al. (2014) found that tool use increased the length of the arm and decreased its width, which significantly altered the perceived size of objects touching the arm surface.

Emergence and development of sensorimotor plasticity during tool use

FIGURE 6.2 (a) The tactile localization task was performed before and after using a tool. (b) Illustration of localization task where participants reach toward a tactile object. Here, the object is a fly for illustrative purposes; in actual experiments, it is typically a vibrotactile or point-like stimulus. (c) An illustration of the effect of using a tool on where the object is localized in space. The magnitude of the change (dashed lines) is used to derive a change in the length of the arm representation (shown in gray).

perceived as further apart, consistent with a longer representation of arm length after tool use (Cardinali et al., 2009b, 2011). We illustrate this in Fig. 6.2c, which shows how tool use changes where touch on the wrist is localized in space. No difference was found on the distance wrist-fingertip, reflecting the finding that the hand representation does not change size. A similar approach used to characterize the geometry of body representation consists in using tactile distances judgment tasks. These paradigms have found that body representations do not accurately reflect body size and shape but are instead distorted (Longo and Haggard, 2011; Miller et al., 2016). Furthermore, they reveal the plasticity of body representations and can be updated through altered sensory

Humans are tool users from early infancy, mastering the use of a hammer for instance by the age of three years old and displaying behaviors distinct from nonhuman primates (Kahrs et al., 2013, 2014). Infants around 18 months display the ability to use a rake to retrieve distant toys (Rat-Fischer et al., 2012) and become proficient tool users by 6 years of age (Caçola and Gabbard, 2012). Does this also imply that the plastic capability of integrating tools into sensorimotor representations emerges early in life? In a recent study, we investigated this question by probing tool use-induced plasticity in children, adolescents, and young adults aged 7.5e21 years old as a function of their puberty score (Martel et al., 2021). The rationale for this choice was that puberty directly indexes body growth, thus considering the fact that body representations need to be updated during development. Participants were required to reach for an object before and after reaching for the same object with a mechanical grabber, similarly as in adults’ studies, with the only change that the tool weight and length were adapted to children’s height. Despite their proficiency in using tools, we showed that tool use did not have the same consequences in children, adolescents, or adults. Indeed, the kinematic pattern observed after a few minutes of tool use was opposite to what is typically observed in adults (Fig. 6.1c, right):

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children displayed increased amplitude and decreased latencies (Fig. 6.1c, left), which could be translated as representing their arm as shorter after tool use. Adolescents around their growth peak did not display any kinematic changes at all (Fig. 6.1c, center), suggesting an absence of sensorimotor representation plasticity. Thus, a growth spurt would be more difficult to track for the brain, as compared to the constant growth rate during childhood. Interestingly, this period was also associated with transient clumsiness, stressing the link between efficient motor control and the correct update of sensorimotor representations (Assaiante et al., 2014). This suggests that the plasticity allowing tools to be controlled as extensions of our limbs requires years to develop fully, is linked to pubertal development, and needs the body to be stable in size. Once it has fully emerged, it is, however, stable and specific, as it is observed in healthy blindfolded (Martel et al., 2019) and elderly (Bahmad et al., 2020) participants. It is interesting to note that adolescence has been proposed to be a uniquely human ontogenetic stage of growth (Bogin, 1999; Thompson and Nelson, 2011). The above findings may therefore have interesting and important implications for the evolution of tool use. Body-tool integration has been proposed to be a critical component of proper tool-use (Bruner and Gleeson, 2019). The extended stage of development in the Homo lineage may have been necessary for the hominin transition from object-users to tool-users. From this perspective, even modern human childrendwho do not display bodytool integrationdshould perhaps be thought of as object-users, becoming true tool-users once the ability to integrate tools fully develops.

Drivers of this plasticity Several factors have been described as driving or constraining this sensorimotor plasticity (and more broadly space), such as the nature of

sensory feedback, but also more functional characteristics of the tool. Perhaps the biggest driver of plasticity is the use of the tool, which is typically thought to be necessary for the effects of body-tool integration to occur. Many studies indeed showed that active tool use (but not passive holding) is typically required for updating body and peripersonal space representations (Iriki et al., 1996; Farn
e et al., 2005; Miller et al., 2017b). Tool use observation while passively holding a tool was sometimes enough to trigger extension of PPS (Costantini et al., 2011), and sometimes not (Galigani et al., 2020); noteworthy, in the first case, Costantini et al. (2011) stressed the importance of compatibility between the held tool and the observed tool, which suggests that participants may have imagined using the tool themselves or may have had the intention to use it unconsciously. Indeed, both intention to use the tool (Witt et al., 2005) and imagination of previous use (Baccarini et al., 2014) appear sufficient to trigger plasticity. Once the tool is used, tool-induced plasticity has been measured after only a few minutes (Holmes et al., 2007) but also almost immediately after tool use (Ganesh et al., 2014). This plasticity occurs whether or not the participant has used or seen the tool before and is independent of the direction (Martel et al., 2019) or type of movement performed (pointing/grasping; Cardinali et al., 2009). The available sensory feedback during tool use is also important in constraining plasticity. Proprioception is known to be crucial to indicate the state of the body, already since the seminal study by Head and Holmes (1911). Healthy humans are indeed able to use proprioception to estimate the length of rods from wielding them (Solomon et al., 1989; Debats et al., 2012). When tested in the typical tool paradigm, deafferented patients do not show the typical kinematic signature following tool use (Cardinali et al., 2016b), attesting to proprioception being crucial for the plasticity of the state estimation. Proprioceptive feedback during tool use is

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Body-tool integration during motor control

sufficient to induce kinematic changes in blindfolded sighted individuals who had never seen nor used the tool before (Martel et al., 2019). Interestingly, proprioceptive acuity correlates with the kinematic signature of tool-induced plasticity (Bahmad et al., 2020). Vision is also an important part of body-tool integration. The amount of kinematic changes occurring under visual guidance is greater than when blindfolded (Martel et al., 2019), further suggesting that vision has an important complementary role in adults. Indeed, a visual illusion of tool use has been observed to drive plasticity in tactile perception on a stationary limb (Miller et al., 2017). Participants used a tool while looking into a mirror, which provided the reflection that their nonetool-using arm (stationary behind the mirror) was indeed wielding the tool. We observed changes in tactile perception on both arms of similar magnitude, suggesting effects of body-tool integration can cross over when driven by visual feedback. Vision has been suggested to be crucial to build sensorimotor associations for new tools (Ganesh et al., 2014) and seems crucial during development. With the development of their multisensory integration abilities taking a decade (Nardini et al., 2008) and transient proprioceptive neglect during a growth spurt (Assaiante et al., 2014), children seem to mostly perform a vision-based tool use, as opposed to the proprioception-based typically observed in adults which leads to the typical update of the sensorimotor representation (Martel et al., 2021). The importance of developmental vision to develop proper sensorimotor plasticity was further demonstrated in our newest study on blind individuals. Here, we did not find the key kinematic signature of body-tool integration in both early and late individuals, despite being present in blindfolded sighted controls (Bahmad et al. in prep). This finding indicates that extended visual experience matters for integration. However, this finding may be specific to the plasticity of motor behavior. Indeed, blind .

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individuals are proficient at using a cane to sense the environment (Serino et al., 2007), an important form of body-tool integration. Plasticity is also constrained by the morphology of the tool, and its functional role (Jovanov et al., 2015): kinematic changes are, for instance, specific to the reaching component after the use of an arm-extending mechanical grabber, in line with the idea that the used tool only affected the arm representation of the participants (Bahmad et al., 2020; Cardinali et al., 2009b; Martel et al., 2019; Miller et al., 2014). The grasping component is instead immune, while it is affected after tool use involving a change of the hand/finger representation (Cardinali et al., 2016a), while the arm representation is left unaffected. Interestingly, this specificity is not present during development: late adolescents indeed display kinematic modifications in both their reaching and grasping component after using a tool (Martel et al., 2021), suggesting that this process develops with experience and needs the body to be stable in size. This rule seems to apply to tactile localization tasks as well: after using a mechanical grabber, localization of touch indicated a longer arm representation on the arm (Cardinali et al., 2009b, 2011), while after using an exoskeleton hand-shaped tool, the tactile perception was altered on the hand and not the arm (Miller et al., 2014, 2017a).

Neural evidence of sensorimotor plasticity Many studies have investigated the neural bases of tool use in humans, with a specific interest in whether tool representations overlapped with neural body representations, and how tool use might affect these representations (JohnsonFrey, 2004). These studies showed activation of frontoparietal cortices (Gallivan et al., 2013), with notable involvement of the superior parietal lobule, left intraparietal sulcus (IPS), or

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posterior parietal cortex (Tomasino et al., 2012; Valyear et al., 2007). The parietal lobe thus appears to be a crucial area of integration between the tool, the body, and the environment, in line with recent work suggesting the main role of the PPC is to estimate the state of the body with its rostral (anterior) body-related pole, while its caudal (posterior) pole would be limb unspecific and related to the environment (Medendorp and Heed, 2019). Using fMRI and multivoxel pattern analysis, Knights and collaborators (Knights et al., 2021) showed that visual areas that selectively process hand movements in parietal and occipital cortices also automatically process tool movements, in keeping with the idea that tools can be considered as an extension of body parts. In contrast, when viewing actions, hand and tool areas were observed to be distinct, even more in expert tool-users as compared to novices (Schone et al., 2021). As discussed above, active tool use or intention to use it is crucial to trigger the integration of the tool in sensorimotor representations. Actual movement was only present in Knights et al. (2021), potentially explaining why the two studies had different results. Moreover, segregation of representations is not a priori opposed to the hypothesis of body-tool integration, which is a functional claim about repurposing sensorimotor processing. This reuse could happen in distinct populations, which nevertheless perform the same neural computation. Such is actually the case when performing a movement with distinct body parts, such as hands and feet. Segregation between tool and body representations is at best agnostic in regard to functional integration. We believe a better approach to investigate neural bases of body-tool integration consists in measuring and comparing brain activity before and after tool use, a design more closely related to our behavioral approach. To this aim, Miller and colleagues (Miller et al., 2019b) recorded event-related potentials of participants before and after they used a hand-shaped exoskeleton,

known to change hand representation (Miller et al., 2014), or their hand to pick up an object. While using the hand did not modify brain activity, tool use modified the amplitude of the P100, which has been shown to underline multisensory body representation in the brain (Cardini and Longo, 2016) and has also been localized in the secondary somatosensory cortex (SII) and the PPC (Barba et al., 2004). This further confirms that tool use modifies sensory processing in SII and or the PPC in humans, in keeping with the studies in monkeys (see below). The neural evidence for tool integration raises the question of its evolutionary origins, especially given that humans and nonhuman primates show different tool use abilities. In a seminal study, Iriki et al. (1996) trained macaques to retrieve pieces of food in extrapersonal space using a rake. The visual field of bimodal visual-tactile neurons from the anterior IPS expanded after tool use, to include the rake. The IPS is responsible for integrating spatial information between the environment and the body, thus at the core of eye-hand interactions. Moreover, tool training as short as 2 weeks in nonhuman primates was sufficient to lead to the expansion of the gray matter in parietal areas (Quallo et al., 2009). Similarly, hand-grasping premotor neurons of macaque monkeys also became active after extensive training with pliers, as if pliers were now the fingers (Umilta et al., 2008). These studies demonstrate empirically that body-related neural processing can be coopted to include a tool. Interestingly, rhesus monkeys did not show any inferior parietal lobule activation when observing tool actions, whether they had been extensively trained to use the tools or not, while human participants did (Peeters et al., 2009). This suggests that the anterior supramarginal gyrus (aSMG) has evolved to be specific to human tool use (Orban and Caruana, 2014). The parietal cortex, crucial in tool and body interactions, has undergone remarkable expansion and specialization in primates. These

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changes likely reflect a functional shift toward an increased “prosthetic capacity” underlying the propensity of humans to integrate tools with their body representations (Bruner, 2021). Interestingly, the dorsal parietal regionsd particularly the precuneus (Bruner et al., 2017)dare also much larger in Homo sapiens (i.e., modern humans) compared to other extinct human species (Bruner et al., 2023; see Chapter 7). The precuneus has been linked to integrating visual and somatosensory information (i.e., visuospatial integration) (Cavanna and Trimble, 2006), a critical component of situating the body in the world (Bruner, 2018). It is therefore interesting to again note the important link between visual signals and body-integration (Miller et al., 2017b). The modern capacity for body-tool integration may be a more recent advancement in our evolutionary history, perhaps coinciding with the transition to being obligatory tool-users roughly 300 Kya (Shea, 2017). In general, it is almost certain that the neural ability to form tool-body relationships (particularly via visual-somatosensory integration) was selected through evolution and had a major role in the described expansion of the parietal cortex (Bruner and Iriki, 2016).

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focusing most of our attention on the spatial aspects of haptically sensing the environment with a tool; we believe it provides an additional convincing case in favor of body-tool integration. As with sensing through the physical body, tactile information about environmental surfaces can be extracted from tools, such as their texture. Imagine, for example, writing on a piece of paper with a pen. As you slide the pen tip across the paper, its texturedand that of the underlying surfacedcan be readily perceivable by you. This ability extends beyond this example to include the surface texture of a variety of materials (Klatzky et al., 2003; Lamotte, 2000). Furthermore, when probing a material with a tool, the sensation of its texture is not localized to the hand, despite mechanoreceptors being the entry point of this information into the nervous system. Instead, the brain projects the texture sensation out into the world and onto the tip of the pen (Vaught et al., 1968), consistent with having a self-in-the-world map that has integrated a tool into its spatial representation (Iriki et al., 2021).

Localizing touch on the surface of a tool Body-tool integration during sensing Another way that a tool is integrated into the sensorimotor system of its user is as an active sensor. The classic example of sensing with a tool is the blind cane-user, who has learned to augment their lost vision with the haptic information produced by the cane. However, this ability is not limited to the blind. Monkeys can be trained to see the environment via a camera attached to the end of a rake, using it as an externalized eye (Yamazaki et al., 2009). Furthermore, there is behavioral and neural evidence suggesting that the somatosensory system can represent tools as somatosensory extensions of the body. The present section will review this evidence,

The presence of this projection underscores the importance of space when integrating a tool with the somatosensory systemdthe experience of sensing with a tool is explicitly about the state of the tool and not the body. For this reason, several studies have investigated spatial perception during tool sensing, with a particular focus on tactile localization. In a recent series of experiments, we measured a user’s ability to accurately localize where a hand-held rod was touched (Miller et al., 2018). To do so, we took a common task used to measure tactile localization on the body (Mancini et al., 2011) and applied it to the case of tool sensingdnamely, participants localized where they felt a tool was touched (Fig. 6.3a) on a drawing of the rod.

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FIGURE 6.3

(a) Our localization paradigms typically have multiple possible locations of touch, which are shown by the purple circles overlaid on the tool. (b) Illustration of performance that is often observed in our experiments. Here, a regression (purple line) models the sensed location as a function of the actual touch location. For reference, the gray line shows perfect performance. (c) The weight of the first four resonant modes of vibration as a function of impact location. Lower modes reflect lower frequencies of vibration, which increase proportionally with the mode number.

We found that participants could do so extremely accurately (Fig. 6.3b), but that this was dependent upon whether the rod was actively wielded or not. When participants actively contacted an object with the rod, the location of contact on the tool’s surface was near-perfect. When the object instead contacted the rod as it was passively held, accuracy dropped but was still much higher than expected by chance (see also, Miyazaki et al., 2010). This accuracy was independent of prior experience, as it was just as high when participants never saw or used the tool prior to the start of the localization task. It is likely that this is because our

participants have had a lifetime experience using rod-like tools and have therefore developed internal models of their dynamics (Kilteni and Ehrsson, 2017; Imamizu et al., 2000). The ease by which humans can sense objects with a tool also suggests that it reflects a fundamental behavior that our nervous system has evolved to perform. If the tool is integrated into the user’s somatosensory system, one might predict that localizing touch on its surface involves similar computations as localizing touch on the body. One particularly important computation is a reference frame transformation (for a review, see Heed et al., 2015). Touch is initially localized in coordinates related to the body part that was touched. However, to act on that touch (e.g., reaching to swat a fly off the arm), this tactile information must be transformed into a reference frame centered on another body part (e.g., the other hand), or in external space. Remapping touch into external space requires integrating cutaneous input about the touch with proprioceptive information about body posture. One of the most popular methods to measure this reference frame transformation is using a temporal order judgment task (Yamamoto and Kitazawa, 2001a). In the classic version of this task, touch is applied sequentially to the right and left hand and participants have to report which hand was touched first. Interestingly, this task becomes significantly harder when the hands are crossed (crossed hands deficit), likely due to spatial conflicts between multiple concurrent reference frames (for discussion, see Maij et al., 2020); That is, when a touch occurs on the crossed left hand (left skin-based coordinates) placed in the right external space, it must be remapped into right external coordinates. In a series of studies, Yamamoto and colleagues (2001b, 2005) also observed a crossedtools deficit. When participants crossed two sticks across their midline, their ability to report which stick was touched first diminished, even though their hands remained uncrossed.

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Body-tool integration during sensing

However, this was not driven by a spatial conflict between hand location and tool location. When participants simultaneously crossed their hands and the sticks (which retains the handtool conflict), the deficit disappeared. This suggests that the crossed-tools deficit is likely tied to spatial representations specifically about the tool. For example, it might arise from a conflict between an abstract spatial representation of the tool side (e.g., the left tool) and the side of space the tool tip is on (e.g., the right side of space, in the case of a crossed tool). In all, these studies suggest that tool sensing also involves reference frame transformations similar to what is observed for the body.

Neural processes underlying body-tool integration To what extent does tool-sensing repurpose neural mechanisms dedicated to localizing touch on the body versus using novel tool-specific mechanisms? This question has been a major motivator of our recent research. Whereas the above studies highlight similarities between touch on tools and limbs, there are also crucial differences. These must be appreciated if we want to understand the neural mechanisms underlying the ability. Let us start with an obvious difference and discuss its implications: Unlike limbs, tools are not innervated. Touch on a limb activates a subset of mechanoreceptors that are informative about touch location; even directly stimulating a single mechanoreceptor leads to a percept of location on the skin. But this would not be possible in the case of sensing with a tool. How then is the location of touch on a tool encoded in the periphery? We propose that there must be mechanical information that the user’s peripheral sensors can tune into during sensing to extract where the tool was touched. Contacting an object with a tool likely produces sources of location

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information. This proposal is based on other forms of active haptic exploration that involve nonneural components, such as rodent whiskers (Prescott et al., 2011) and a spider’s web (Japyass u and Laland, 2017). For example, the location of an object that is contacted by a whisker is preneuronally encoded by the mechanical consequences of its morphological changes, which can be read out by receptors in the follicle (Bagdasarian et al., 2013). One sensory variable that we have explored is the vibrations resulting from tool-object contact (Fig. 6.3c). When a tool strikes an object (or is struck by an object), it resonates at specific frequencies (called modes) whose amplitudes depend upon the location of striking (see Supplementary Information in Miller et al., 2018). For most stiff rods (i.e., those made of wood, carbon fiber, pvc, etc.), the frequencies of several modes are typically within the range of 50e1000 Hz. Crucially, these frequencies are within the bandwidth of a type of mechanoreceptor called the Pacinian corpuscle (PC) (Bell et al., 1994; see Chapter 1). PCs have very precise temporal spiking (Mackevicius et al., 2012) that could faithfully reproduce the location-specific vibrations during tool use. We hypothesized that users could tune into the spike-timing information from PCs to extract location. In an initial test of this idea, we used a skin-neuron model called TouchSim (Saal et al., 2017) to model the spiking of PCs in response to vibrations that we recorded during tool use. Using support vector classification, we found that we could classify where the rod was touched at near 100% accuracy from only w25 ms of spikes. Smoothing this signal with a Gaussian kernel caused decoding to drop as a function of its width, demonstrating that the location information was encoded in the timing of PC spikes. This collection of findings is consistent with our hypothesis and with previous proposals that PCs are important for encoding toolrelated sensory information (Johnson, 2001). It is worth noting that the distribution of PCs in

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the human hand is thought to have undergone the greatest change during human evolution (Jones and Lederman, 2006). It is possible that this reflects an evolutionary adaptation for better extracting sensory information from a tool’s vibrations during both use and construction. That is, even at the earliest stages of sensory encoding, evolution has shaped the human nervous system to use a tool as if it were an extended somatosensory organ. While location encoding in the periphery is quite different between touch on limbs and tools, what about how it is processed in central brain regions? Does the central somatosensory system localize touch on a tool using separate neural computations that it does with the body? Or does it resolve these peripheral differencesdat some stage of processingdand repurpose its body-based computations? If the brain has evolved to treat the space of body parts and tools similarly, one would hypothesis that it represents them both similarly. To address this proposal, we used EEG to compare location-based neural processing for touch on a hand-held wooden rod and the arm of the same participants (Miller et al., 2019). This allowed us to not only compare the time course of brain responses but also directly compare their information content. We adopted a repetition suppression paradigm in order to identify location-specific somatosensory brain responses and analyzed our data according to whether the touch was in the same or different location as the previous touch. For touch on the tool and the arm, we observed significant suppression over contralateral sensorimotor channels within w50 ms and lasting beyond 100 ms after touch. The shapes of the somatosensory evoked potentials as well as the scalp topographies were similar for both surfaces. This provides initial, yet superficial, evidence that similar neural processing occurs for both rods and arms. To demonstrate that the observed brain responses for both tool and arm were more than

just a superficial resemblance, we turned to several types of machine learning approaches. For example, we trained a classifier to decode brain responses in the arm dataset and tested the same classifier on its ability to decode the tool dataset (and vice versa). All methods converged on the same finding: brain responses starting as early as w50 ms were surfaceindependent. That is, what mattered was whether or not location was repeated, not whether touch occurred on the arm or on a tool. Furthermore, source reconstruction for both the tool and arm led to a large overlap in both primary somatosensory and posterior parietal cortex, regions that have evolved to represent both body and tool similarly (Bruner and Iriki, 2016). These results not only strongly suggest that the brain re-uses body-based mechanisms to localize touch on a tool, the speed of convergence between body and tool during neural processing suggests an incredible (evolved) propensity for body-tool integration in the human brain. The neural oscillations following touch on a rod are also similar to touch on the body. Several studies have found that different oscillatory bands are involved in different touch-related reference frames (Heed et al., 2015). Power in the Beta band (15e30 Hz) reflects mapping in a skin-based reference frame; Power in the Alpha band (7e14 Hz) reflects mapping in an external reference frame. Inspired by the previous results on reference frame transformations during tool sensing (Yamamoto and Kitazawa, 2001b; Yamamoto et al., 2005), we investigated whether one or both of these oscillations reflect the mapping of touch on a tool (Fabio et al., 2021). We found that only the oscillatory power of the Alpha rhythm reflected location processing for touch on a rod. The sources of this modulation were found to be throughout the frontal and parietal cortices, largely overlapping with a network of regions involved in the external mapping of touch on the body (Buchholz et al., 2011). These findings together highlight both a

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Future of integrated technology

similarity and a difference with touch on the body. Specifically, whereas the alpha rhythm also reflects spatial mapping of touch on a rod, the beta rhythm does not. This suggests that an external reference frame is the default for mapping touch beyond the body. The results of the above studies demonstrate that some body-based neural processes are repurposed for mapping touch on a tool. This means that peripheral differences between encoding touch on body parts and tools are overcome at some as-of-yet unknown level of the somatosensory hierarchy. However, the ability to overcome the differences underscores the incredible capacity of the human nervous system to treat tools like extended body parts. We believe that sensing with tools thus reflects a rich and underexplored case study by which to probe the evolution of body-tool integration. When did our hominin ancestors evolve the ability to use sensory information from tools to construct spatial models of the world? What adaptations were necessary to make this possible or to optimize it? One promising approach to addressing such questions is via measuring brain and behavior during haptic exploration with different types of stone tools (Fedato et al., 2019, 2020; see Chapter 11). The chapter up to this point has discussed body-tool integration when both controlling a tool and when sensing with a tool. It is therefore natural to ask whether these are the same phenomena and, if not, how they are related. Framed more evolutionarily: is there a single body-tool integration capacity whose emergence during human evolution underlies controlling and sensing? Or do these phenomena reflect separate integration capacities with distinct evolutionary trajectories? Unfortunately, there are currently no straightforward answers to these questions. This is partly because the biological bases of body-tool integration are not very well understood (see above). It is currently unknown whether changes to the morphology and tactile afferents of the hand evolved

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separately, though we think it is likely. If this is the case, it would suggest that at least some evolutionary changes important for controlling and sensing were distinct. Whereas evolutionary changes to the parietal cortex likely underlie the emergence of the body-tool integration capacity, it is unclear whether distinct changes were needed for control and sensing. Though both behaviors involve the parietal cortex, whether they rely on similar regions and neural computations are currently unknown. Comparing integration for control and sensing, and their evolutionary origins, is a fruitful avenue for future research.

Future of integrated technology Tools provide an interesting case study for how the sensorimotor system adapts to functional and geometric body extensions. Over the last few decades, robotic technologies that also extend what the body can do have been developed. These intelligent sensorimotor technologies are rapidly becoming an increasing part of society and will likely play a role in the daily lives of future humans. For example, robotic sensorimotor interfaces have been developed for medical applicationsdsuch as prosthetics and telerobotic surgerydand for augmenting the body’s natural capabilitydsuch as enlarging the hand or adding fingers. Like tools, controlling and sensing with these technologies requires the user’s sensorimotor system to adapt to their properties. These technologies must thus be integrated with the brain and body and are thus prime targets for further exploring the limits of sensorimotor plasticity. The advent of these technologies raises several important questions: will these technologies become integral parts of a person’s sensorimotor system in the future? While answering them is beyond the scope of this chapter, and our current knowledge, it is worth discussing the neuroscientific progress being made in these domains. The present section will focus on two

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forms of body-technology integration that are current receiving significant attention: (1) restoring the damaged body with neural prosthetics (NPs) and (2) augmenting the body with additional robotic limbs.

Body restoration: prosthetics and brainemachine interfaces The structure and function of the sensorimotor system can drastically change when there is bodily injury, such as an amputation or paralysis. Dating back to the ancient Egyptians, humans have been attempting to augment these changes with prosthetics (Nerlich et al., 2000). Historically these prostheses were often cosmetic; that is, they lacked functionality but looked similar to the missing limb. With the advent of electronics, prostheses were able to be designed to also replace (to some extent) the missing function. The myoelectric is the most common and oldest functional prosthetic (Geethanjali, 2016). For these prostheses, the prosthetic hand is controlled by reading the activity of residual muscles of the upper arm using EMG electrodes on the skin (Farina et al., 2014). However, these prostheses often lack robust control and the feeling that they are part of the user’s body. Modern prosthesis development has seen a large technological shift over the last 2 decades with the development of NPs (Bensmaia and Miller, 2014). NPs have a potential advantage over other forms of prostheses, such as EMGcontrolled myoelectric, in that they directly interface with the user’s biology (Farina et al., 2021). This is made possible because amputees can still volitionally control motor signals that would have otherwise been sent to the missing limb. These control signals still activate residual portions of the missing limb’s nerves, making them a perfect target for bio-integration (Kuiken et al., 2007b). Take the case of an upper limb amputee. To interface their control signals with

a prosthetic hand, the residual median and ulnar nerves can be reinnervated with muscles in their upper arm and chest. EMG electrodes implanted in these muscles can read hand-related patterns of muscle activation and use them to align the prosthetic hand with the intentions of its user (Vu et al., 2020). In doing so, the NP becomes integrated with the user’s sensorimotor system, and can thus be used as an arm, also leading to an increased feeling of being integrated with the wearer’s body (Marasco et al., 2011). Other approaches have also proven fruitful in restoring control to NPs, including directly implanting electrodes in the user’s motor cortex (see below). Whenever we move our limbs, mechanoreceptors in the skin, muscles, and joints provide sensory feedback about the change in the limb’s state. This feedback is necessary to successfully control the body and manipulate objects. In contrast to biological limbs, most prosthetic limbs, including NP, are deafferented; they do not provide any somatosensory feedback to their user. Instead, the user must monitor the prosthetic with vision. A substantial amount of recent work has attempted to restore touch to an NP via direct bio-interfaces (Bensmaia and Miller, 2014). Stimulating electrodes implanted in residual nerves of the hand (e.g., median, ulnar) cause localized sensations of touch in the phantom hand (Kuiken et al., 2007a; D’anna et al., 2017). Such sensations can be used to restore tactile signals via sensorized prosthetic hands, where electrodes in the nerve are enslaved to readouts of these sensors (Raspopovic et al., 2021). This substantially increases the ability of the user to interact with objects. Furthermore, the more bio-realistic the stimulation patterns are, the better the user’s finegrained manipulation and bionic integration (Valle et al., 2018; George et al., 2019). Prosthetic limbs with peripheral interfaces require the residual ability to volitional control muscles via an intact spinal cord. This is not possible in tetraplegics, who are paralyzed from the neck down. Restoring sensorimotor

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Future of integrated technology

function to these patients instead depends upon BMIs, a device that directly interfaces a robotic arm with neural signals that can be volitionally controlled. This is usually done via electrodes implanted in the user’s motor cortex since it provides the control signals for muscles (Hochberg et al., 2012). However, cognitive signals in the parietal cortex have also been successfully used to control BMIs (Andersen and Cui, 2009). Along with electrodes that read control signals, electrodes implanted in S1 can be stimulated to restore touch to BMIs (Armenta Salas et al., 2018; Flesher et al., 2016). A recent study showed that doing so led to significantly increased performance in object manipulation, speeding up the user’s ability to grasp, lift, and transport objects (Flesher et al., 2021). Closed-loop BMIs hold promise for bio-realistic integration of the user and robotic arm.

Robotic body augmentation Beyond restoring body function, there has been significant progress in developing robotic technologies that functionally augment the intact human body. Unlike neural prostheses, these devices do not (yet) integrate directly with the biology of their users. They are instead wearable on the user’s body is typically used to fulfill a specific task, much like tools. For example, robotic devices have been developed that increase balance, aid in lifting heavy objects, and perform additional tasks while the user’s biological hands are busy (for a review, see Eden et al., 2022). One important way that robotic wearables can augment a human user’s performance is through object manipulation. It is therefore crucial that they be under the user’s volitional control, be integrated into sensorimotor control loops like a body part, and impose minimal interference on orthogonal motor tasks. Several approaches for control have been developed to address these issues. It is common to yoke

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control of the robot with the action of another body part or task-irrelevant muscle activation (Guggenheim et al., 2020), though this often limits actions that can be done in parallel. More success in the domain of sensorimotor multitasking has come from BMI interfaces with EEG signals (Penaloza and Nishio, 2018). Furthermore, like neuroprosthetics, adding tactile feedback to an extra robotic finger improved object manipulation, though this requires more research (Hussain et al., 2015). Using a tool changes how the sensorimotor system represents the physical body. This also appears to be the case when learning to use an additional robotic limb. In a recent study, the sensorimotor representation of fingers was measured using fMRI before and after they learned to use an additional thumb (Kieliba et al., 2021). After several days of training, participants became extremely proficient at using the thumb for a wide variety of object manipulation tasks. Interestingly, the separation between the neural representations of the biological fingers decreased, almost as if the motor cortex were making room for the robotic thumb. These findings raise the question of whether learning to use an additional robotic limb always interferes with body representations (Dominijanni et al., 2021). Is it possible for technological limbs to be represented orthogonally to biological limbs? One recent proposal suggests that the answer to this question may be “no.” As the brain’s functional and structural resources are limited, additional limbs may always compete with biological limbs. Indeed, given that even tools interfere with body representation, it is plausible that additional appendages always compete for resources. Whether it is possible to design wearables that do not interfere is an open question. It is tempting to attribute the ability to control robotic limbs to the permeability of the human sensorimotor system that evolved to use tools (Bruner and Iriki, 2016). Such a hypothesis, though intriguing, requires much more work

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and refining of questions. Indeed, monkeys that are not tool-trained can still readily learn to control a robotic arm (Velliste et al., 2008), though this requires significantly more training time than humans. Neuroprosthetics and BMIs are also linked directly to body control signals in M1 and PPC. Neural evidence that the same is true of tools is currently sparse, though we find it likely in some cases (Miller et al., 2019; Umilta et al., 2008). Furthermore, there is evidence to suggest that a prosthetic is not represented by the brain like a tool by its users (Maimon-Mor and Makin, 2020). Another possibility is that the permeability of the sensorimotor system to technology is due to preexisting computational abilities of parietal neurons. For example, the same bimodal parietal neurons in tool-trained macaques whose visual receptive fields expand to encompass a rake (Iriki et al., 1996) also expand to include video images of the macaque’s hand (Iriki et al., 2001). It appears that they readily project the self beyond the boundaries of the body in many contexts. Therefore, perhaps the evolutionary adaptation of body-tool integration was built on the preexisting plasticity of the boundaries of the self. While both possibilities are speculative at this point, we propose that the ability to learn to use additional robotic limbs is enhanced by having a brain that readily integrates tools into its sensorimotor representations.

Conclusion In the present chapter, we have reviewed neural and behavioral evidence for body-tool integration in both general tool use and robotic limbs. While most research has focused on identifying tool-induced changes in sensorimotor perception and action processes, more research is needed to specify the actual neurocomputational mechanisms underlying this plasticity. Further advancing the field indeed requires

that we move beyond the classic pre-post paradigm and start to investigate sensorimotor representations during tool control and sensing. Future work should also address the extent to which integrating tools and robotic limbs rely on similar mechanisms. We propose that sensorimotor body representations have a general permeability that has its roots in evolutionary adaptations for proficient tool control in humans.

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The evolution of the parietal lobes in the genus Homo: the fossil evidence Emiliano Bruner Programa de Paleobiología, Centro Nacional de Investigacion sobre la Evolucion Humana, Burgos, Spain

Paleoneurology and functional craniology Paleoneurology, simply speaking, deals with the study of brain evolution in extinct species (Holloway et al., 2004; Bruner, 2017). The term “extinct species” indicates that the target taxa no longer exist, and its biology can only be inferred through the fossil record. This necessarily implies an important array of limitations. First, in fossil species, the only information available generally comes from their gross skeletal anatomy. “Gross” means that, in general, only the macroanatomical features can be considered. In some cases, smaller details are available (like the imprints of osteoblasts and osteoclasts; Martínez-Maza et al., 2006), although this is the exception rather than the rule. Moreover, as mentioned, such macroanatomical traits only deal with the skeletal system, namely, the cranium and the postcranial elements. Inferences about soft tissues are, necessarily, indirect, and, most often, speculative. The second limitation concerns variability and variation, which are key aspects of evolution. Variation refers to the degree to which a feature differs from one individual to another in a

Cognitive Archaeology, Body Cognition, and the Evolution of Visuospatial Perception https://doi.org/10.1016/B978-0-323-99193-3.00006-4

sample, while variability refers to its capacity and potentiality to exhibit interindividual differences. Both aspects can be only roughly estimated in paleontology, because not all the extinct taxa are represented in the fossil record. Some species fossilize more easily because of their ecological niche or behavior, while others leave no trace in the geological layers. This means we generally do not have a comprehensive scenario of the phylogenetic variation and variability of a zoological group. The fossil record, in terms of species composition, is hence partial (only a few species fossilize) and biased (only species with some specific ecological features fossilize). The foundation of evolutionary biology is the comparative approach, in which trends, schemes, and patterns behind the evolutionary adaptations are extrapolated through quantitative analyses of the observed diversity (Martin and Barbour, 1989). Therefore, if the phylogenetic landscape is poorly represented, the resulting numerical modelsdnecessary for any evolutionary inquirydwill be inevitably fragile and unstable. The third limitation concerns the sample size. Even for those species that do fossilize, in

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general, the fossil record is scant, including but a few individuals. Furthermore, these few individuals are generally represented by scattered skeletal parts, which are usually fragmented and, in some cases, deformed. Consequently, even for those species for which we have fossil remains, the small sample does not fit the assumptions of most statistical tests. We have to consider that statistical inferences and numerical models are necessary to move from a pure descriptive approach to a quantitative and experimental framework. Descriptive approaches are fundamental in paleontology, but are not generally sufficient to provide a validation of theories and hypotheses, which are the foundation of the scientific method. Numerical approaches are essential to any heuristic exploration (aimed at identifying rules and patterns associated with biological diversity) and to hypothesis testing (aimed at verifying if and how much the observed data fit with a given prediction). In paleontology, the small sample drastically limits the power of any statistical analysis, namely, the capacity to detect real differences or patterns, and the capacity to recognize false signals associated with random effects. A final limitation, also implicit in the field of paleontology, is the difficulty to allocate a fossil to a species. In primates, the skull is often a poor source of information on taxonomy, even more misleading when taking into account the never-ending debates on the species concept (see Bruner, 2013). Despite these caveats, species naming has, in human paleontology, an important lucrative aspect, because of its association with academic profits and mass-media attention. The difficulties in assessing a proper taxonomic scenario and the confounding social influences can severely hamper a reasonable perspective of the phylogenetic diversity. These limitations must be seriously considered when dealing with paleoanthropology, probably much more than is commonly

discussed in the literature. If neglected, they can profoundly influence the interpretation of the available evidence. Nonetheless, at the same time, these limitations should not lead to discarding fossil information. In evolutionary anthropology, an alternative that can be used to investigate human evolution is to study living species, using chimpanzees or macaques as models for ancestral character states. The main problem, in this case, is that most of the information comes from a few species, of the roughly 300 currently named for living primates. Working on a few of them (humans, chimps and macaques) does not allow a robust comparative scenario. However, in the case of neontological analyses (that is, when using living taxa), the main issue deals with the fact that any living species belong to an independent evolutionary lineage, and it is as much derived as any other living group, including humans. This means that living primates can only show the product of the evolutionary process, and not the process itself (Bruner, 2019). To investigate the process, we need the taxonomic sequences associated with the phylogenetic lineages, that is, we need fossils. This is why, although partial, biased, and scanty, the fossil record still supplies precious and crucial biological information. Considering this context, we can see that the term “brain evolution,” in the above definition of paleoneurology, is definitely narrower than it can sound at first glance. In fact, it strictly refers to anatomical evolution. And, it does not deal with all the anatomy, but just with those aspects that are macroscopic (namely, associated with large anatomical elements) and superficial (that is, located on the external regions of the brain). The definition of what paleoneurology is, also indicates what paleoneurology is not. Paleoneurology is not, for example, about cells, or rather it cannot supply direct evidence about neurons

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Paleoneurology and functional craniology

and axonal fibers. Neither, of course, can it provide information on neurotransmitters or other biochemical features. Paleoneurology is not even about functions. In some cases, functions can be extrapolated when they have some direct association with geometry, like in the case of some spatial aspects of thermoregulation (Bruner et al., 2012) and connectivity (Bruner, 2022). But, even in these cases, most information is partial and must be intended as complementary to other sources of evidence. Most importantly, paleoneurology is not about behavior. As far as we know, the association between macroanatomical aspects of the brain and cognition is blurred and unclear, and behavioral speculations based on gross cortical morphology require a consistent dose of caution. The correspondence between cortical morphology and brain functional areas is uncertain and largely influenced by idiosyncratic features, and the association between areas and cognition is not linear or simple at all (Van Essen and Dieker, 2007; Van Essen and Glasser, 2014; Amunts and Zilles, 2015). If we consider these limitations and the limitations of the fossil record previously mentioned, we can easily understand that paleoneurologists should resist the temptation to supply firm and stringent conclusions on cognitive evolution, unless the anatomical evidence is sufficiently integrated with other kinds of information. Even so, the anatomical evidence itself should be properly interpreted, particularly if we consider that we do not have the brains of the fossil species, and we can only handle their skulls.

Skulls and endocasts The brain is not directly contacting the internal cranial surface, because of the interposed meningeal layers, namely, three connective sheets that are aimed at protecting and anchoring the brain into the cranial cavity. These sheets are anyway pretty thin and, therefore, the brain and the bone exert a direct and reciprocal

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spatial and biomechanical influence on each other, which leads to a pretty good correspondence between the endocranial and the brain curvature and surface. The endocranial cavity is, therefore, a good negative mold of the brain that, during the growth and development of an individual, has shaped its morphology (Fig. 7.1). Of course, the morphological information we can extract in this sense only concerns the external and general features of the brain that formed the cavity. The easiest feature to measure is its overall volume (e.g., Van Sickle et al., 2020), taking into account that, as a rule of thumb, endocranial volume is 10% larger than the actual brain volume. However, this value is very general and does not provide information, in a comparative frame, on what brain parts (regions, areas, lobes) or cellular components (neurons, axons, glia, blood vessels) have undergone size changes (expansion or reduction) during evolution. On the internal bone surface, we can also identify the impressions of some major sulci and gyri (Van Minh and hamada, 2017; de Jager et al., 2019; Dumoncel et al., 2021), and this can give a clue regarding the general cortical proportions of the brain, evidencing possible changes in the relative volumes of lobes and gross cortical regions. We can also observe the traces of some major vessels, arteries, and veins that ran into the meningeal layers (Fig. 7.2) and that are possibly associated with functions such as oxygenation, hydrostatic skeleton and protection, or thermoregulation (Bruner et al., 2011; Písova et al., 2017). Importantly, as primates, we have difficulties in mentally working with “a cavity,” that is an empty space that cannot be handled or visualized. Accordingly, we prefer to make a positive mold of that cavity, as to have a physical (or at least a digital three-dimensional) object to study, touch, and watch. This positive mold of the negative mold of the brain from the inside of the skull is called endocranial cast, or endocast.

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FIGURE 7.1 Major cortical elements visible on endocranial casts, and their correspondence with parietal cortical regions, are shown on a digital reconstruction of the skull and endocasts of KNM-ER3733 (Homo ergaster).

FIGURE 7.2 Left: Drawing of the endocranial cavity of a modern human skull (after Bruner, 2017), showing the parietal bone (blue) and traces of the middle meningeal artery. Right: Approximate spatial relationships between occipital, parietal, and frontal lobes (OL, PL, FL) and bones (OB, PB, FB) (after Bruner et al., 2015a).

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Parietal endocasts

Traditionally, endocasts were made by using resins and other kinds of plastic materials, filling the endocranial cavity with proper methods and according to complicate coordination between anatomical knowledge and technical expertise (Holloway, 2018). At present, most of the time the endocasts are prepared virtually, by using the toolkits of digital anatomy, after scanning the skulls with computed tomography and reconstructing the internal volumes with the methods supplied by pixel-based techniques in biomedical imaging (Gunz et al., 2009). Of course, an endocast is not a brain, and much information is lost during these morphogenetic (natural or artificial) steps. Nonetheless, the gross correspondence is good enough to make robust anatomical inferences on many general features (Kobayashi et al., 2014; Dumoncel et al., 2021). The fact that we use a mold of the cranial cavity to make inferences on brain anatomy leads to the question regarding what the relationship is, precisely, between the two anatomical elements. This intimate relationship is due to the reciprocal influences between the brain and braincase during growth and development, which is the foundation of what we can call functional craniology (Moss and Young, 1960; Bruner, 2015). Generally, the brain shapes the bones on the vault while growing, separating the bones at the sutures and inducing modeling of the bone surface and dimensions through the activation of osteoblasts and osteoclasts (Enlow, 1990; Richtsmeier and Flaherty, 2013). Conversely, on the cranial base, the spatial interaction with the facial and basal structures constraints brain development, forcing the final cerebral phenotype according to a complex topological environment (Lieberman et al., 2000; Bastir et al., 2006; Bruner and Ripani, 2008). To this complicated spatial frame, we must add the networks of polygenic and pleiotropic effects between biological traits that, when dealing with evolution, make it difficult to

disentangle causes from consequences, adaptations from random features, and selection from chance. In the past, almost any change in the endocranial morphology was interpreted as a possible indication of a cognitive change, supposing a one-to-one relationship between brain anatomy and cognition, or between brain form and behavior. However, the complex scenario associated with functional craniology suggests that it is not always so. Some gross changes in the brain morphology can be indeed associated with changes in the brain organization and proportions. However, in some cases, morphological changes can be due to spatial constraints exerted by the topological relationships between the skull and brain, and by the biomechanical and morphogenetic balance between soft and hard tissues. If we are interested in the former (primary brain morphological changes), we must be able to exclude the latter (secondary brain morphological changes).

Parietal endocasts The parietal bones (Fig. 7.2) have always provided valuable information in paleoanthropology, for three main reasons. The first is that this bone is particularly thick and evolved as armor to protect the brain. Because of their thickness and physical resistance, parietal bones and parietal fragments are an important part of the fossil record and are useful to provide phylogenetic as well as biological information (e.g., Bruner et al., 2017a; Hershkovitz et al., 2021). In H. sapiens, the parietal bone is less thick than in extinct human species, although both intra- and interspecific variations of this feature are probably due to systemic (i.e., hormonal and environmental) factors, and not to specific evolutionary pressure or biomechanical responses (Lieberman, 1996). The second reason concerns the fact that the parietal bone covers a large part of the brain,

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7. The evolution of the parietal lobes in the genus Homo: the fossil evidence

including all the parietal lobes and parts of the frontal, temporal, and occipital lobes (Ribas et al., 2006). The parietal bones, because of their size and location, can hence supply a great deal of anatomical information. The spatial relationships between the margins of the parietal bone and the cortical elements (i.e., sulci and gyri) vary between species and during ontogeny, and it also displays pronounced individual variation (Bruner et al., 2015a). Nonetheless, the shape of the bone (namely, its surface and curvature) matches the morphology of the underlying cortical structures, with two limitations. First, the bone and the brain are separated by the meninges and by the cerebrospinal fluid. This separation does not substantially alter the general correspondence between brain form and bone form, but it can obscure many details. The main exception is the midsagittal region, in which the brain and bone surfaces are totally separated by the superior sagittal sinus. Also, the midsagittal cortical region is depressed because of the interhemispheric fissure and the biomechanical action of the falx cerebri, a connective invagination of the meninges between the hemispheres that anchors the brain to internal ridges and bosses (the crista galli anteriorly, and the internal occipital protuberance posteriorly) and provides the route for large venous sinuses (the superior and inferior sagittal sinuses). In this sense, the falx cerebri is a complex connective tensor that generates an important structural interface that connects the skull, the brain, and the vascular system (Bruner, 2015). All these features make the midsagittal plane crucial for cranial and neurocranial morphogenesis, but less informative regarding brain details. Most of the information concerning the longitudinal extension of sulcal traits should be examined on the adjacent parasagittal planes, where the contact between bone and brain is tighter. A second limitation concerns the differences between superior and inferior endocranial regions. On the vault internal surface, the imprints of the cortical morphology are less marked than

in the basal regions (Dumoncel et al., 2021). Apart from the presence of the sinuses mentioned above, this is probably because gravity partially contributes to pushing the brain toward the base and away from the vault. Furthermore, in the basal regions, there is a strong spatial competition between the cerebral elements and other anatomical components (like the orbits and the masticatory apparatus, in the anterior and middle fossa, respectively), and the interposed bone reveals, with its curves and bosses, such close contact (Bruner, 2018a). Nonetheless, the parietal endocranial morphology can still reveal some major features that can help in making inferences about the gross proportions of cortical structures. On the endocasts, the elements that can be tentatively observed on the parietal surface are the central and postcentral sulcus, the perpendicular fissure, the intraparietal sulcus, the angular gyrus, and the supramarginal gyrus (see Fig. 7.1). Of course, these cortical elements are not really visible per se, and their position and extension is an inference made on the basis of two kinds of evidence. First, the sulcal pattern can be inferred by looking at the grooves and depression of the endocranial surface. Bosses are traditionally associated with gyri, and depressions can be due to sulci, when considering that endocranial pressure triggers osteoclast activation (surface bossing), and tension triggers osteoblast bone deposition (surface depression) (Enlow, 1990; Lieberman et al., 2000). Naturally, such a relationship is not always so straight, and other factors (like the thickness of the meninges) may influence this morphological correspondence. As a rule of thumb, smaller brains leave more imprints than larger brains, and endocranial traces become fainter with aging (Zollikofer and Ponce de Le on, 2013; Van Minh and Hamada, 2017). In general, sulcal traces are less visible in the dorsal region (frontal and parietal surface) and more marked on the basal ones (orbital and temporal surface), probably, as mentioned, because of the effect of gravity and

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Parietal endocasts

the spatial conflict between cerebral and other cranial elements (eyes, mandible, etc.). The second approach to characterize the sulcal patterns on endocasts does not consider the anatomical trait itself, but rather the global morphological arrangement. The position of sulci and gyri can be in fact inferred indirectly from the position of neighboring elements. For example, if the intraparietal sulcus is visible, then the postcentral sulcus must necessarily be in a more anterior position. Namely, the inferences on local sulcal features can be provided on the basis of the overall sulcal arrangement, and not only on the presence/absence of specific sulcal imprints. In these cases, the position of a sulcal element can be cautiously estimated even if the trait is not visible. However, I strongly suggest avoiding the brain terminology for these features, to circumvent confusion and to take into consideration the important uncertainty. The term “brain” should not be used when working with endocasts, and terms like “gyri” and “sulci” should be changed for “boss,” “groove,” or “region,” to highlight that observations refer to the endocranial imprints and not to the real cortical elements. With these limitations in mind (and a good dose of experience), we can tentatively localize some grooves representing the impressions of major sulcal elements on the endocranial surface. The paracentral lobule (central sulcus, precentral, and postcentral gyri) is usually represented by a very blurred and smooth region, in which the precise detection of the sulcal boundaries is hard to establish. Similarly, the perpendicular fissure (the parieto-occipital sulcus) is difficult to localize. For these major references of the parietal lobe longitudinal extension (central and parieto-occipital sulci), more information might be available on the parasagittal regions, and the midsagittal convergence of the sulci and gyri (which is nonetheless asymmetric) must be extrapolated. It is worth noting that sometimes the bone references (bregma and lambda) on the endocasts are used instead of the cortical

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ones (central and parieto-occipital sulci). In fact, the traces of the sutures are generally well marked on the endocranial surface (Holloway et al., 2004). However, the use of the cranial landmarks instead of the brain landmarks is useful only to analyze the bone morphology and not the morphology and proportions of the brain. It can be useful to consider that the central sulcus typically lies well behind the bregma, and that the parieto-occipital sulcus is generally a little anterior to the lambda (Ribas et al., 2006; Bruner et al., 2015a). However, there is an important individual variation, and such references are hence not consistent for all individuals. Interestingly, a recent analysis suggested that early humans underwent a posterior shift of the frontal lobes behind the coronal suture (that is, below the parietal bones), and it has been hypothesized that such spatial change can be due to an increase in the frontal lobe volume (Ponce de Le on et al., 2021). However, apes and humans display similar gross proportions of the frontal lobes (Semendeferi et al., 2002), and it is hence likely that this spatial shift was instead associated with a different position between the facial (orbital) and neural blocks (Pereira-Pedro et al., 2017a; see below). Actually, the correspondence between the brain and braincase is a matter of geometry and form, not of boundaries between bones and cortical element (Bruner et al., 2015; Alatorre-Warren et al., 2019). This partial independence in the position of the cranial and cerebral elements should be carefully considered when endocranial casts are used to make inferences in neuroanatomy. The postcentral gyrus can be tentatively localized as posterior to the precentral gyrus (which continues, in its inferior region, with the wellvisible Broca’s cap) and anterior to the intraparietal sulcus. The intraparietal sulcus can be recognized as a longitudinal depression or groove. It is generally very smooth, but it can be roughly interpreted as a zone that separates the superior and inferior parietal regions, which generally appear as distinct bulging surfaces

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(approximately corresponding to the superior and inferior parietal lobules). In terms of cytoarchitecture, the superior parietal lobule is probably separated into three areas (anterior, middle, and posterior) that cannot be distinguished in terms of gross morphological features, and it can be interpreted as the external (superficial and visible) part of the precuneus (Scheperjans et al., 2008). Some authors have described a consistent superior parietal sulcus, separating the anterior and posterior precuneal regions, and possibly remarking on the boundary between the anterior (7A) and posterior (7P) areas (Drudik et al., 2023). This sulcus may be part of the deeper precuneal sulcus, a branch of the subparietal sulcus that is sometimes associated with larger precuneal size (Bruner et al., 2017b). This macroanatomical feature will be really interesting to consider in a paleoneurological context if its relationship with the cytoarchitectonic areas of the precuneus will be confirmed. The supramarginal gyrus is localizable as a prominent boss behind the postcentral gyrus and above the posterior end of the lateral sulcus, although its precise morphology is difficult to assess. The angular gyrus can be localized as a minor boss, behind the supramarginal gyrus and anterior to the occipital lobe. The depression between these two bosses of the inferior parietal lobule is the region of the Jensen sulcus (Zlatkina and Petrides, 2014). The parietal region behind the postcentral gyrus is called the posterior parietal cortex, and it is particularly specialized, large, and differentiated in primates (Goldring and Krubitzer, 2017). It is important to consider that the term “parietal lobe” is used to define, as often in neuroanatomy, an arbitrary and conventional division of the brain. There is no reason to think that “a lobe” represents a single functional, embryological, or evolutionary unit, and it must be hence intended as a general label, aimed at improving communication but without real biological meaning. In general, it is assumed that the dimensions of the parietal bones are

influenced, at least in part, by the dimensions of the parietal lobe. Although this is somehow intuitive, there is at present no comprehensive quantitative evidence in this sense, particularly when dealing with different species or through ontogeny. In adult humans, the correlation between parietal bone and parietal lobe length is significant but modest (R ¼ 0.30) (Bruner et al., 2015a). This suggests that these two features are associated (presumably, the parietal cortical expansion induces bone expansion), but also that the parietal bone dimensions are influenced by many other morphogenetic factors. Also, the degree of morphological variation of the precuneus is much larger than the variation of the parietal bone, which further suggests that the parietal bone growth and development integrate the effects of distinct anatomical elements. It has been hence hypothesized that the influence of the parietal lobe on the parietal bone might be more associated with the earliest stages of cranial growth (Bruner et al., 2015a), when the globular brain form typical of modern humans is achieved (Neubauer et al., 2009). Successively, the position and extension of the parietal bone are possibly influenced by the volumetric and topological adjustments between neurocranial and splanchnocranial blocks, which are more determinant in successive ontogenetic stages. In other words, a topological correspondence between parietal bone and lobe is possibly stronger in the earlier years of life (when neurocranial growth is prevailing) and decreases later (when facial growth is dominant). It is worth noting that the parietal bone and lobe are spatially integrated with the morphology of the occipital bone and lobe, respectively (Gunz and Harvati, 2007; Bruner et al., 2018a). In general, the posterior cortical elements display integrated spatial relationships, and show, at the same time, an increase in folding complexity (Zilles et al., 1988, 1989, 2013). However, topological models based on the spatial contact between major cortical regions suggest that the elements of the posterior

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Parietal endocasts

parietal cortex are not particularly constrained by the neighboring brain areas, except for a minor influence of the temporal cortex on the inferior lobule and, probably, for the deeper part of the precuneus, which fades into the retrosplenial and cingulate cortex (Bruner et al., 2019; Bruner, 2022; see below). Taking into account that in the parietal region, the brain molds the bone, we can conclude that gross changes of the parietal sulcal or volumetric organization, as visible on the brain or on the endocranial traces, can be interpreted as genuine (primary) changes of the cortical anatomy, and not as (secondary) consequences of morphological changes of the adjacent anatomical components. The internal surface of the parietal bone is also rich in vascular imprints, in particular when considering the noticeable extension of the parietal branches of the middle meningeal artery in

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modern humans (Bruner and Sherkat, 2008; Eisova et al., 2016, 2019). Similarly, the diploic channels are particularly developed within the parietal bone (Rangel de Lazaro et al., 2016, 2018). The precise functions of these vessels are still unknown, although it has been hypothesized they can act like a “radiator,” using blood to regulate the temperature of the cortical surface (Bruner et al., 2011). Sometimes, the meningeal vessels can be of help to detect the sulcal patterns because they often run into the sulcal routes, growing through paths of least resistance. For example, in some cases, the central sulcus can be tentatively localized according to the middle ramus of the middle meningeal artery. The third reason of interest regarding the parietal bones deals with the outstanding morphological changes we can detect throughout human phylogeny (Fig. 7.3), in particular associated

FIGURE 7.3 Schematic phylogeny of the human genus (Homo), showing the main species and the approximate chronology. Redrawn from Bruner, 2017. II. Visuospatial behavior and cognitive archaeology

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7. The evolution of the parietal lobes in the genus Homo: the fossil evidence

with our species, Homo sapiens, and at least in part due to the evolution of the underlying parietal cortex (Bruner, 2018b; Bruner et al., 2018b, 2023a).

The fossil evidence on parietal lobe evolution in the human genus In the 1930s, Franz Weidenreich, studying the endocranial casts of Homo erectus from China and Indonesia, referred to the outstanding morphological differences of the parietal lobes, suggesting that this was the brain region showing the most striking evolutionary changes in our genus (Weidenreich, 1936). More recently, in the 1980s, Ralph Holloway published an innovative shape analysis of hominid endocranial variation by using a stereoplotting technique, and the results supported the early observation of Weidenreich: the parietal surface was the most variable region (Holloway, 1981). Additionally, Holloway proposed, in agreement with the early description by Raymond Dart, that an expansion of the parietal lobes is also a distinctive brain feature of the genus Australopithecus, at least when compared with living apes (Dart, 1925; Holloway, 1995). However, scarce attention was paid to these observations, and the evolution of the parietal lobe was rather neglected in evolutionary anthropology. The first reason is probably due to the fact that for one century or more the attention of neuroscience was largely captured by the frontal lobes, because of their specialized functional features in living humans associated with executive functions, decision-making, moral judgments, and language. Curiously, paleoanthropology assimilated rather passively (and probably with a scarce critical attitude) the statements on the importance of the frontal lobes and went on repeating the importance of the frontal lobe in human evolution also for the fossil species, although evidence was clearly lacking in this sense. Nowadays, we know that frontal lobes

are specialized in H. sapiens, but such specialization probably does not influence the macroscopic morphology of the frontal cortex, and this change is hence largely silent in the fossil record (Semendeferi et al., 2002; Parks and Smaers, 2018). The second reason responsible for this lack of attention to parietal evolution is probably associated with the fact that the pronounced bulging and globularity of the braincase in Homo sapiens, even though it has long been recognized as one of the most obvious morphological features of our neurocranium, was thought to be simply a question of spherical bending of skull shape, and not of brain changes. Modern humans have evolved a pronounced flexion of the cranial base, probably because of spatial relationships between the face and the braincase (Liebermann et al., 2000, 2002), and this could be sufficient to explain the globularity of our brain as a simple consequence of a flexion of the cerebral space, as if it was a sort of “balloon” attached to the base of the skull (see below). The third reason associated with the limited attention devoted to parietal evolution is that, until a few decades ago, there was scarce information on parietal lobe anatomy, even for our own species (Zilles and Palomero-Gallagher, 2001). The intraspecific diversity, in terms of sulcal patterns, metrics, or gross morphology, was not really known, and there were no data on the homology and ontogenetic changes within primates. This situation has changed recently, although many of these topics remain largely unexplored. However, at present, both neontological and paleontological disciplines suggest that the evolution of the parietal cortex in our genus definitely merits more consideration (Bruner et al., 2023a).

Early and archaic humans The term “humans” is often employed only when dealing with H. sapiens, although, literally,

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The fossil evidence on parietal lobe evolution in the human genus

it refers to the human genus, and hence, it should be used for any individual (and species) of the genus Homo. In this sense, early humans are those individuals and species that are associated with the origin of the genus, approximately 2 million years ago. Similarly, we can use the general term “archaic humans” for those species without pronounced specializations (for example, H. ergaster, H. erectus, and H. heidelbergensis), in contrast to those species that have evolved a larger set of derived features (that is, H. neanderthalensis and H. sapiens). The fossil record associated with early humans is scanty in terms of specimens and unclear in terms of phylogeny (Villmoare, 2018; Bobe and Wood, 2021; Faith et al., 2021). The few specimens, their uncertain taxonomy, and the pronounced intraspecific variability make any conclusion on the origin of our genus speculative. Furthermore, many endocranial features are largely influenced by allometry and size-related effect, which is a common source of diversity at both intra- and interspecific level (Bruner et al., 2023b). Human species display large variation in their brain size ranges that, furthermore, largely overlap. This may generate considerable confusion between anatomical differences due to phylogenetic reasons and allometric effects. Similarly, it may be difficult to detect significant parietal lobe differences between the early members of the genus Homo and other early hominids (e.g., Australopithecus spp.). Nonetheless, Phillip Tobias (1987) suggested that the parietal lobes and meningeal vessels in a few specimens attributed to the species Homo habilis were more developed than in fossils associated with australopithecines. He proposed that the frontoparietal cortex was more expanded in terms of volumes and sulcal convolution, most notably at the inferior parietal lobule. However, taking into account the lack of quantitative evidence and the many uncertainties associated with this taxon, a proper characterization of parietal evolution in early humans is still missing (Bruner and Beaudet, 2023).

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As for the archaic human species that followed (i.e., H. ergaster, H. erectus, and H. heidelbergensis), they display a wide size variation but a common parietal morphology, with flat and short parietal lobes, and a characteristic lateral (parasagittal) depression, corresponding to the superior parietal lobule (Bruner et al., 2015b, Fig. 7.4). As is the norm in paleoanthropology, the absence of the evidence is not the evidence of the absence. In other words, we cannot state that these species share a similar organization of the parietal lobe, but just that the current fossil record is not able to evidence significant differences in their parietal anatomy. This can be due to an actual lack of differences, to the presence of differences that cannot be evidenced in terms of external macroanatomy, or to scarce statistical power because of the small number of samples available. Interestingly, all these species are associated with similar Lower Paleolithic tools, mostly represented by large stone tools handled with the whole hand, like choppers or handaxes (see Chapter 11). Some individual or geographical differences can be mentioned, although they must be intended as general observations that, at present, are difficult to test. In particular, the pronounced expression of these parietal features can be observed in the Zhoukoudian specimens from China, typically used to represent the H. erectus morphotype. In earlier specimens associated with the African record, and often assigned to H. ergaster, the endocranial form is less platycephalic and, consequently, the parietal region is less flat and more rounded (Bruner et al., 2023b). Nevertheless, differences are not so pronounced to the point consistent dissimilarities between groups can be distinguished. An extreme case may be the skull from Buia, found in East Africa and dated to 1 million years, which displays a globular parietal lobe (Bruner et al., 2016). Also in this case, however, the rounded shape is not associated with patent changes of the relative dimensions, and the peculiar globular form of the parietal region has been interpreted as a secondary spatial

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FIGURE 7.4 The endocasts of archaic human species (such as H. ergaster, H. erectus and H. heildelbergensis) show short parietal length (PL) and a parasagittal depression of the dorsal parietal region (PD), here shown on a digital endocast of Zhoukoudian XII (H. erectus). Below, further examples from Africa and Asia. Redrawn from Bruner et al., 2015b.

modification due to a constraint exerted by the narrow cranial base. It is interesting to mention the existence of fossils that show intermediate situations and admixture of characters. For example, the skulls from Maba (China, 100e300 ka), Sima de los Huesos (Spain, 400e600 ka), and Nesher Ramla (Israel, 120e140 ka) display derived Neandertal features (especially in the facial morphology), but archaic parietal features (Bruner et al., 2003; Wu and Bruner, 2016; Hershkovitz et al., 2021). The skull from Florisbad (South Africa, 250 ka) displays an apparent admixture of derived Neandertal and modern traits, but a plesiomorph parietal morphology (Bruner and Lombard, 2020). The archaic parietal morphology in these specimens could be due

to random variation, because brain morphological differences between species are generally a matter of average values, with overlapping ranges. Therefore, single individuals of a species can present a morphology “more similar to” other taxa simply because of idiosyncratic (within-species) differences, with no functional or phylogenetic meaning. In this sense, the fossil record is so scanty that this possibility cannot be, at present, ruled out, at least for this feature. Nonetheless, if these “intermediate stages” are confirmed as a proper phylogenetic entity, it means that the last modifications in the parietal regions did occur afterdand not beforedthe cladogenetic divergence of large-brain human species, namely, H. sapiens and H. neanderthalensis. In this case, it can be

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The fossil evidence on parietal lobe evolution in the human genus

concluded that these two lineages split, approximately 300e500 ka ago, and, after their origin, they underwent changes that influenced the morphology of their parietal lobes.

Neanderthals When compared to archaic humans, Neandertals display larger brains, but most of the general proportions are scaled versions of that archaic cortical organization, at least when considering the gross endocranial metrics (Bruner et al., 2003). However, they display some minor but consistent differences in their brain form. This typical “Neandertal brain morphology” is well established in fossils dated to 100e200 ka, such as Saccopastore or Ganovce (Bruner and Manzi, 2008; Eisov a et al., 2019). Neandertal endocasts are extremely wide (the widest human brain ever). The frontal width is, on average, particularly large, a feature shared with H. sapiens, probably because in these two species the frontal cortex lies on the orbits and, during

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development, it is necessarily constrained to grow laterally (Bruner and Holloway, 2010; Pereira-Pedro et al., 2017a). Indeed, there is a consistent change in the parietal surface: the parietal lobe is still short and flat, and with scarce vascular ramifications, but the longitudinal depression of the dorsal parietal region is substituted by a convex bulging of the surface (Bruner et al., 2003, Fig. 7.5). This feature was early recognized as a typical Neandertal trait, and described, in the skull, as en bombe rear profile, in contrast to the “tent-like” rear profile observed in archaic humans. As mentioned, in this region, there do not appear to be important cranial constraints during morphogenesis, and therefore we may conclude that such bulging might represent a proportional expansion of the underlying cortical volume. In this case, it roughly corresponds to the superior parietal lobule or to the intraparietal sulcus. It is worth noting that the intraparietal sulcus is particularly complex in humans when compared to other primates (Choi et al., 2006; Grefkes and Fink, 2005;

FIGURE 7.5 Neandertals (here the digital replicas of the skull and endocast of Saccopastore 1) display short parietal length (PL) but lateral bulging of the dorsal parietal surface (LB). At the same time, they show many supernumerary (Wormian) bones at the parieto-occipital junction (WB), suggesting morphogenetic instability (i.e., ontogenetic stress) at the rear vault. Redrawn after Bruner and Manzi, 2008 and Bruner, 2014.

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Zlatkina and Petrides, 2014). We are not sure about when and in which species this increase in complexity occurred, but we can speculate that this expansion should be associated, at least in part, with a certain outfolding of the corresponding cortex. That is, at a given point during human evolution, part of the cortex (and cytoarchitectonic areas) of the primate intraparietal sulcus may have expanded and grown out of the fold, namely, on the external cortical region. This has probably contributed to the (still current) difficulties when dealing with the homology in this region between human and nonhuman primates (Zilles and PalomeroGallagher, 2001). The superior parietal lobule, including the intraparietal sulcus and the precuneus, is largely involved in body-vision integration and eye-hand coordination, functions that are crucial to behaviors associated with tool use and visual imaging (see Chapters 10 and 11). It is therefore interesting to detect such changes in a species which, at the same time, underwent important technological changes, moving from tools that are grasped with the whole hand to smaller and finer tools that are grasped (and sensed) with the fingers. Interestingly, Neandertals also show, with an outstanding frequency, supernumerary ossicles at the lambda and along the lambdatic bone, which are hyperostotic traits interpreted as a consequence of ontogenetic stress and morphogenetic imbalance (Manzi et al., 1996, Fig. 7.5). Indeed, despite the large brain volume, Neandertals display short and flat parietal lobes, and it has been suggested that this plesiomorph condition might have generated some allometric constraints when reaching some size limits (Bruner, 2004, 2014). The parietal cortex is in fact topologically forced between the frontal and occipital volumes, and the spatial variation of the three regions is mechanically channeled by the falx cerebri (Moss and Young, 1960). Also, the parietal and occipital bones are spatially integrated, and the flattening of one bone is associated with the bulging of the other (Gunz and Harvati, 2007).

Therefore, it can be hypothesized that the large brain size may have generated some spatial conflict in this complex morphogenetic system, forcing the parietal region beyond a proper growing balance because of its spatial position. The supernumerary ossicles (also called Wormian bones) might therefore be the remnant consequences of such an imbalance between size and shape ontogenetic changes, in a braincase of large size but with a plesiomorph architecture.

Modern humans The study of the evolution of the parietal region in H. sapiens, as the study of most features associated with the evolution of our own species, is problematic because of the paucity of the fossil record associated with the early stages (and origin) of our lineage. The fossil skull from Jebel Irhoud, found in Morocco and dated to 200e300 ka, is currently interpreted as part of the phyletic line associated with modern humans, but it shows an endocast with a typical Neandertallike appearance (Bruner and Pearson, 2013; Hublin et al., 2017). The fossils from Skhul and Qafzeh found in the Near East and dated to about 100 ka, show an “intermediate” parietal development between earlier and later H. sapiens (Fig. 7.6), although there are some taphonomic problems related to the reconstruction of their internal vault (Bruner et al., 2018b). In general, it can be proposed that the globularization of the vault in H. sapiens displays a gradual increase from the early specimens of the lineages (i.e., Jebel Ihroud) to modern populations (Neubauer et al., 2018). The current fossil record does not support discontinuity in this chronological trend and, as such, the hypothesis of a gradual expression of this feature cannot be rejected. Although a precise chronology of anatomical and cultural changes in modern human evolution cannot be accurately evaluated, it is apparent that there is a parallel tendency: when we find a large and bulging parietal

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FIGURE 7.6 Modern humans show longer parietal lobe length (PL) and a noticeable development of the middle meningeal artery (MMA), especially on the parietal surface. Such features can be roughly recognized, after 100 ka, in early H. sapiens specimens like Qafzeh 1 and Skhul V (left), but not in the earliest fossils attributed to the modern human lineage, before 200 ka, like in Jebel Irhoud. Images after Bruner et al., 2018b.

surface, we also find a comprehensive array of modern behaviors (art, ornaments, fine tools, complex societies, etc.). Such evidence, albeit provisional, cannot be overlooked. Of course, it is not a matter of origin or appearance of specific technological resources, for which the boundaries between species and chronology are much more blurred (see McBrearty and Brooks, 2000; Hoffmann et al., 2018). Instead, it is a matter of the degree of expression (and, furthermore, of combination) of complex behaviors, which, in H. sapiens, display a manifestation that is not comparable with any other human species (Wynn et al., 2016). In this sense, it is worth noting that the Neandertal and modern lineages have had a roughly similar duration, and hence,

the lack of modern-like cultural complexity in the former cannot be attributed to temporal limitations. In sum, after 100e50 ka, modern humans display large and bulging parietal bones, large parietal lobes, and complex parietal vasculature (Bruner, 2018b). These features are not generally based on discrete all-or-nothing differences. Instead, they show a large degree of intraspecific variation, which overlap to some extent with the morphology of extinct human forms (PereiraPedro et al., 2020). Nevertheless, as also mentioned for cultural traits, their degree of expression and their combination is pronounced only in our species, suggesting a consistent evolutionary and phylogenetic shift.

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Parietal lobes and brain globularity Regarding the expansion of the parietal lobes in modern humans, it is worth stressing further the nature of the fossil evidence associated with this anatomical change. Defining cortical boundaries on endocasts is clearly a hard task, and it necessarily involves uncertainties. On the one hand, the localization of such boundaries is based on subjective interpretations of anatomists, based on bosses and depressions of the endocranial surface that are thought to be associated with corresponding folding elements (i.e., gyri and sulci). Such correspondence is fairly good (Kobayashi et al., 2014; Dumoncel et al., 2021), but not always certain. On the other hand, even when those boundaries can be properly identified, they are smooth and fuzzy, and they cannot provide precise quantitative metrics. Nonetheless, the bulging parietal morphology of modern humans is so distinctive that it has always been interpreted as an autoapomorphic feature of our head (Lieberman et al., 2002). Of course, one can speculate on whether such a large and globular parietal region is due to a matter of “form” (namely, a change of shape associated with a change of size) or only a change of “shape” (namely, a change of shape without a change of size). In the latter case, the parietal bulging in modern humans could be due to a change in the vault geometry, and not to a real change in the proportions of the parietal cortex. The skull of modern humans underwent, for example, a noticeable increase in the cranial base angle (e.g., McCarthy, 2001), and this might have curved the vault profile. Also the change in the facial proportions is expected to have a role in the curvature of the braincase (Zollikofer et al., 2022). In this sense, as already mentioned, the globularity of the modern cranial vault would be just a secondary spatial consequence of a rounder cerebral shape. However, some of the evidence suggests that it is not only a matter of endocranial shape (i.e., roundedness), but of size and proportions of the

parietal lobes as well (Bruner et al., 2003; Bruner, 2004; Balzeau et al., 2012; Kochiyama et al., 2018; Pereira-Pedro et al., 2020). Despite the difficulties already mentioned regarding the possibility of localizing cortical boundaries in fossils, these results, to a different degree, have been replicated by different observers and on different samples, and such consistency must be taken into consideration. A general globularity of the parietal region only due to a secondary spatial effect of the skull flexion would mean, in this case, considering the brain as “a balloon,” growing and developing according to its biomechanical anchorages. The biomechanical environment is surely a crucial component in the brain versus braincase ontogenetic and evolutionary relationships (Moss and Young, 1960) but, nevertheless, the fact that we specifically found a genuine expansion of the parietal cortex in modern humans (e.g., Bruner et al., 2017c) suggests that the “balloon” effect is just a part of the story. An expansion of a cortical region must be associated with an increase in some of its components (white or gray matter, at least), and cannot be only explained as a general “stretching” of its volume. In modern humans, specifically, differences in the size of the superior parietal lobule (precuneus) are associated with differences in the cortical surface area (Bruner et al., 2015c), which means more neurons. Connections might have changed as well. Indeed, the parietal lobes display an intricate connective network of long and short fibers, between the superior and inferior lobule, with the somatosensory and occipital areas, with the temporal and frontal cortex, and with the thalamus (e.g., Parvizi et al., 2006; Maldonado et al., 2012; Wu et al., 2016; Catani et al., 2017; Cunningham et al., 2017; Cheng et al., 2021). The fronto-parietal system is particularly integrated in functional terms (Caminiti et al., 2015), and the parietal connections with the cingulate gyrus are, in this sense, of major evolutionary interest (Hecht et al., 2013; Goulas et al., 2014; Ardesch et al., 2019). It remains hence to be investigated how much changes in the

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The fossil evidence on parietal lobe evolution in the human genus

connectivity patterns can influence overall brain form, and specifically brain globularity. In any case, it is unlikely that the dimensional expansion of the parietal cortex can be explained simply by physical stretching associated with the flexion of the cranial base, particularly considering the outstanding differences in the parietal lobes of humans compared to nonhuman primates. Of course, these two alternatives (a passive change of shape vs. an evolutionary change of cortical proportions) are not mutually exclusive, mostly when considering the possible effects of allometric variation. Indeed, a recent comprehensive review suggested that a steeper allometric scaling relationship of the parietal lobe (and in general of the fronto-parietal cortex) characterizes catarrhine primates compared to other mammals, and humans would represent the largest allometric variant of the catarrhine pattern (Garin et al., 2022). Because of positive allometry, humans display the largest parietal lobes in terms of both absolute and relative values, being a scaled-up version of other primates. Such larger parietal size and proportions are expected to have behavioral consequences (a more complex integration of the corresponding sensorial signals), but it would be due to the general brain size increase, and not to specific evolutionary changes of the parietal cortex. However, it must be considered that the study is based on “lobes” volume, which misses the contribution of distinct areas and regions, and issues other than size. The contribution of each cortical region is a key aspect, because lobes are conventional units, and there is no reason to think that all their parts evolve together. Brain allometric analyses on hominids also suffer a major limitation due to the fact that humans are outliers, with an outstanding gap separating our species from the rest of primates. As outliers, residuals are particularly sensitive even to minor operational decisions (mostly when, like in this case, regressions are based on a large set of numerical adjustments to cope with issues in phylogeny and

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homology). At least for the precuneus, there are apparently no allometric differences among nonhuman primates, and Homo sapiens is the only species with larger precuneal proportions (Pereira-Pedro et al., 2017b). However, if parietal volume would be confirmed to be largely a matter of allometry, the observed differences between modern humans and Neandertals should be reconsidered further. In sum, brain globularity in modern humans can be in part explained because of its peculiar cranial arrangement (flexed cranial base and short face), and in part because of an expansion of the parietal cortex, particularly evident after 100e50 ka. Such parietal bulging and expansion are associated, in terms of gross morphology, with a flatter occipital region and larger cerebellar fossa (Gunz and Harvati, 2007; Bruner, 2008; Neubauer et al., 2018; Gunz et al., 2019). Apart from the anatomical changes associated with neurocranial globularity, it is interesting to note an additional consequence on overall body organization: the enlargement of the parietal region has a major influence on the anteroposterior functional alignment of the head, tilting the cerebral orientation from a fronto-occipital to a fronto-parietal axis (Bruner et al., 2017d). We may wonder whether this reorientation and redistribution of the head weights may have influenced general body balance and postural management.

Deep parietal A further note on parietal morphology and evolution concerns the deeper regions of the parietal cortex, namely, the inferior part of the precuneus (see Cavanna and Trimble 2006; Margulies et al., 2009). While the dorsal region is clearly separated by the subparietal sulcus and specifically involved in the integration of somatic and visual inputs, the inferior part fades into the posterior cingulate and retrosplenial cortex and is involved in a larger array of functions

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(including memory retrieval and the default mode network). Although the superior and inferior parts work together (memory retrieval and mind wandering associated with the default mode network largely rely on visual imaging and body references), they should be treated separately in terms of structure and function (Bruner, 2018b). Nonetheless, the two parts are contiguous and continuous (apart from the presence of the subparietal sulcus, which can be more or less enlarged and present a pronounced variability; Pereira-Pedro and Bruner, 2016), and probably share many morphogenetic influences. The superior region is thought to have few spatial or structural constraints (Bruner et al., 2019). Instead, the inferior region is embedded within a very complicated structural context (Fig. 7.7). This region is topologically influenced by many neighboring cortical areas, it is a spatial bridge between distant regions, it is relatively close to other parts of the brain, and is therefore associated with consistent anatomical and evolutionary “burden” (Bruner, 2022). Although it is part of the parietal cortex, it actually works as a structural bridge between the frontal and occipital lobes. Also, its position is particularly sensitive to heat accumulation (Bruner et al., 2012). Finally, apart from the constraints due to its relative location in cortical organization, it probably experiences allometric biomechanical influences due to the insertion of the tentorium cerebelli, a main connective tensor of the endocranial space (Bruner et al., 2010). This deep region is difficult to take into consideration in paleoneurology because it is not directly observable on endocranial casts. It is also scarcely considered in many perspectives regarding brain architecture. Nevertheless, its particular structural context should be taken into account when making inferences on the morphological evolution of the brain, and in particular of the parietal complex.

More on parietal vascularization The parietal bone displays many more vascular traces than the rest of the vault bones, and for this reason is particularly informative concerning possible vascular changes that occurred in different human lineages (Písova et al., 2017). On its endocranial surface, we can find the imprints of the middle meningeal artery and, within the bone itself, there are the channels of the diploic veins (Bruner and Sherkat, 2008; Rangel de Lazaro et al., 2016). In modern human parietal bones, we can easily recognize at least five dichotomic orders of branches for the middle meningeal artery (with a peak of distribution around 1 mm) and three for the diploic channels (with a peak distribution around 1.3 mm; Fig. 7.8) (Eisova et al., 2016). There is significant individual variation in these features (Eisova et al., 2019), in both the degree of vascular complexity (the number of branches) and topology (the distribution of the vessels), and hence these traits cannot be used, alone, for firm taxonomic assessments. Nonetheless, there are general differences between human species, differences that are consistent and well documented (Grimaud-Herve, 1997; Bruner et al., 2005). In general, in modern humans, there is a more complex vascular pattern with many anastomosis, particularly in the parietal region. In other human species, the network is definitely poorer, and without connecting channels. In many archaic human species, furthermore, the posterior branches are equally or more developed than the anterior ones, while in modern humans and Neandertals the anterior networks are generally dominant. In paleoneurology, we must necessarily rely on the traces of these vessels to make inferences about the vessels themselves. In the case of the middle meningeal artery, some vessels leave no

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FIGURE 7.7 There is a morphological integration between the posterior subcortical region and the midsagittal parietal cortex (a). The retrosplenial district also displays a remarkable individual variation and experiences a posterior stretching while the brain gets larger, probably because of an allometric and biomechanical effect of the tentorium cerebelli (b). This region is included in the thermal core (red) of the brain volume (c). In terms of spatial topology, the retrosplenial region (including Brodmann area 30) displays a high anatomical centrality, by virtue of its many connections (d), bridging position (e), and general proximity to the rest of the areas (f). In sum, the morphology of the region formed by the inferior precuneus, posterior cingulate cortex and retrosplenial areas, is influenced by independent functional and structural factors (g). All these factors introduce multiple constraints and complex patterns of integration in the phenotypic expression and evolution of this region. Data and images after Bruner et al., 2010, 2012, and Bruner, 2022.

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FIGURE 7.8 Traces of the middle meningeal artery (left) and diploic veins (right) in the parietal bone, with the median values of lumen size for the five branch orders in the middle meningeal and diploic vessels. Data after Eisova et al., 2016.

traces, while in the case of the diploe we can only detect the large vessels that generate channels, and not the fine vascular networks passing through the diploic spongy tissue. Nevertheless, the general experience in human anatomy and surgery suggests that the traces are generally informative regarding the actual vascular morphology, although with exceptions. If changes in the vascular traces in modern humans reflect real changes in the vasculature (in particular on the parietal region), we should wonder what is the functional reason behind such an important increase in the vascular system. Tissues like the meninges or the diploe do

not have noticeable metabolic requirements, so oxygenation alone is not a likely option. Instead, considering the exceptional energetic consumption of the brain in humans (Leonard et al., 2007), it has been proposed that these vascular networks can serve to enhance the thermal regulation mechanisms of the endocranial space (Bruner et al., 2011). The experimental information in this sense is still lacking, so other hypotheses (for example those associated with protection and hydrostatic support) cannot be, at present, discarded. Interestingly, the flat parietal surface in extinct human species is, by virtue of general endocranial geometry, a likely surface for heat

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Anatomy, cognition, and behavior

exchange, while the large and bulging parietal lobes of modern humans may generate heat accumulation in their deeper regions (Bruner et al., 2012). It is worth noticing that the precuneus is vascularized by two systems, namely, the anterior and posterior cerebral arteries (see Mavridis et al., 2016; Kalamantianos et al., 2019). The main deep blood supply comes from the former, but actually only through its most posterior and peripheral branches. Also, differently from most cortical regions, the precuneus display peculiar interhemispheric vascular anastomoses. From one side, such multiple sources suggest that it might be protected from vascular and metabolic damages (Parvizi et al., 2021). However, this marginal position between two vascular territories (a sort of nobody’s land, in terms of vascular supply) can also generate downsides and constraints. The existence of rare interhemispheric branches suggests indeed a peculiar vascular arrangement. These deep parietal regions include, as mentioned before, the inferior part of the precuneus, which is associated with metabolic impairment in the early stages of Alzheimer’s disease, a pathology which is, in terms of prevalence and combination of traits, specifically associated with modern humans (Bruner and Jacobs, 2013).

Anatomy, cognition, and behavior Fossils are the only direct evidence we have regarding the evolution of brain anatomy in hominids and, in particular, in our own lineage. An endocranial cast can only supply general and (literally) superficial information on brain morphology, but that little information is, indeed, crucial. On the one hand, we must take into account that an endocast is not a brain, and caution is required when extrapolating brain morphology from endocranial morphology. Experience is, of course, of great help. It is important to bear in mind that paleoneurology is an

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anatomical field, and connections with cognition and behavior are speculative and indirect. There are certain correlations between brain morphology and cognitive functions, but these, when any, range from scarce to modest (see Chapter 13). These correlations are useful to detect the factors influencing general cognitive domains, but only to a very gross level, and have limited efficiency when dealing with precise predictions or detailed behavioral considerations. At the same time, we must acknowledge that the phenotypic expression of the brain is due not only to structural cranial adjustments or random effects, but also to actual neurobiological functions and adaptations. Even minor modifications in brain biology can, in fact, trigger significant changes in behavior. It is unlikely that the anatomical changes observed in the parietal lobes of modern humans (both superior and inferior lobules; Reyes et al., 2023) were not accompanied by cognitive or behavioral variations. We can wonder whether such changes were due to genetic or environmental factors, adaptations or exaptations, standard reproductive selection, or retroactive feedback (like the Baldwin effect and genetic assimilation; Bruner and Iriki, 2016). The relationships between brain and behavior are to be expected in both directions and, in terms of evolution, it is hard to say when brain changes support behavioral changes, or alternatively when behavioral changes trigger selective or physiological responses. Neural plasticity, most of all for humans, is an issue (Sherwood and G omezRobles, 2017). With all this in mind, we must recognize the possibility that such parietal changes have been accompanied by functional modifications. For what concerns the superior parietal cortex, visuospatial integration, and body cognition might have experienced a profound specialization, possibly associated with technological and social complexity, as part of a larger system of networking tools, group dynamics, and consciousness (Bruner and Gleeson,

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2019). The evidence provided by the archaeological record does support this theory suggesting, for H. sapiens, increasing body-tool integration, throwing capacity, graphic skills, social organization, and a set of blurred and badly defined but crucial aspects that range from language to symbolism (Bruner and Lozano, 2014). With this in mind, we should further investigate the anatomical nature of such changes associated with the dorsal parietal regions. As described before, the precuneus is histologically divided into three main areas, with the anterior one more implicated in somatic perception, and the posterior more involved in visual perception (Scheperjans et al., 2008). As already evidenced, this subdivision concerns the medial internal region (generally labeled as precuneus) and the external dorsal region (generally labeled as superior parietal lobule), suggesting that these two districts are indeed one single cortical element (i.e., the precuneus). Comparing modern humans with chimpanzees, a spatial analysis suggests that the main expansion in the former occurs anteriorly (in the somatic area; Bruner et al., 2017c), and the same region is a main source of variation when considering the differences among adult humans (Bruner et al., 2017b). In contrast, the comparison between modern humans and Neandertals points to the posterior (visual) area (Pereira-Pedro et al., 2020). These spatial extrapolations are based on mathematical models and not on histological evidence, so must be taken as preliminary. They nonetheless highlight a major issue, which remains to be investigated. In this sense, it is important to consider that alternatives to the traditional mapping criteria may further stimulate the debate. According to classical neuroanatomy, the brain is formed by distinct specialized areas (e.g., Glasser et al., 2016). In this case, brain cortical evolution can be due (apart from changes in connections or biochemical and molecular factors) to

expansion/reduction of some areas, as well as their appearance or disappearance as evolutionary novelties. It is nevertheless possible that the patchy appearance of the brain cortex may be due to crossing developmental gradients between the primary (sensory) regions, generating a spotty distribution of features because of their different combination (Huntenburg et al., 2017). Accordingly, a morphological change of the cortex might be due to a change in the quality or quantity of such gradients. In the case of the precuneus, this is a likely option, because it is precisely the meeting zone between somatic (body) and visual (eye) information. Therefore, an enlargement could be interpreted as a larger amount of somatic and visual fibers, namely, a more powerful visuospatial sketchpad, deeply rooted within a body-based spatial, chronological, and social reference (Bruner, 2021). Certainly, although the precuneus is the most dorsal brain element of the parietal cortex and is much larger in humans than in other primates, we cannot discard the additional effect of more inferior parietal elements on the morphogenetic influences shaping the parietal bones. As mentioned, both the intraparietal sulcus and the inferior parietal lobules are specialized in humans and may contribute to the expansion and curvature of the parietal bones as well. Apart from these tentative interpretations, however, we must admit that there is a profound lack of information concerning the anatomy of the elements involved. The biological diversity, both within- and between-species, is largely unknown for most of the traits described in this paper, as well as for their ontogenetic background, functional roles, or evolutionary homology. While this information remains unavailable for modern humans (a living sample of millions of individuals), inferences on fossil species (a few bones of a few specimens) will continue to be speculative and preliminary, although suggestive.

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References

Acknowledgments I am grateful to the many friends and colleagues that have collaborated in my studies on parietal morphology and evolution, especially Sofia Pereira-Pedro, Stana Eisova, James Rilling, Roberto Colom, Naomichi Ogihara, Roberto Caminiti, Alexandra Battaglia-Mayer, and Giorgio Manzi. This article has been particularly improved thanks to the many comments and suggestions by Chet Sherwood. This article is supported by the Ministerio de Ciencia e Innovaci on, Spain (Proyect PID2021-122355NB-C33 funded by MCIN/AEI/ 10.13039/501100011033/FEDER, UE) and by the Italian Institute of Anthropology.

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Bruner, E., Mantini, S., Perna, A., Maffei, C., Manzi, G., 2005. Fractal dimension of the middle meningeal vessels: variation and evolution in Homo erectus, Neanderthals, and modern humans. Eur. J. Morphol. 42, 217e224. Bruner, E., Martin-Loeches, M., Colom, R., 2010. Human midsagittal brain shape variation: patterns, allometry and integration. J. Anat. 216, 589e599. Bruner, E., Mantini, S., Musso, F., et al., 2011. The evolution of the meningeal vascular system in the human genus: from brain shape to thermoregulation. Am. J. Hum. Biol. 23, 35e43. Bruner, E., De La Cuetara, J.M., Musso, F., 2012. Quantifying patterns of endocranial heat distribution: brain geometry and thermoregulation. Am. J. Hum. Biol. 24, 753e762. Bruner, E., Amano, H., de la Cuetara, J.M., Ogihara, N., 2015a. The brain and the braincase: a spatial analysis on the midsagittal profile in adult humans. J. Anat. 227, 268e276. Bruner, E., Grimaud-Herve, D., Wu, X., De la Cuetara, J.M., Holloway, R., 2015b. A paleoneurological survey of Homo erectus endocranial metrics. Quat. Int. 368, 80e87. Bruner, E., Rom an, F.J., de la Cuetara, J.M., MartínLoeches, M., Col om, R., 2015c. Cortical surface area and cortical thickness in the precuneus of adult humans. Neuroscience 286, 345e352. Bruner, E., Beaudet, A., 2023. The brain of Homo habilis: three decades of paleoneurology. J. Hum. Evol. 174, 103281. Bruner, E., Bondioli, L., Coppa, A., et al., 2016. The endocast of the one-million-year-old human cranium from Buia (UA 31), Danakil Eritrea. Am. J. Phys. Anthropol. 160, 458e468. Bruner, E., Písov a, H., Martín-Frances, et al., 2017a. A human parietal fragment from the late Early Pleistocene Gran Dolina-TD6 cave site, Sierra de Atapuerca, Spain. Comptes Rendus Palevol 16, 71e81. Bruner, E., Pereira-Pedro, A.S., Chen, X., Rilling, J.K., 2017b. Precuneus proportions and cortical folding: a morphometric evaluation on a racially diverse human sample. Ann. Anat. 211, 120e128. Bruner, E., Preuss, T., Chen, X., Rilling, J., 2017c. Evidence for expansion of the precuneus in human evolution. Brain Struct. Funct. 222, 1053e1060. Bruner, E., Pereira-Pedro, A.S., Bastir, M., 2017d. Patterns of morphological integration between parietal and temporal areas in the human skull. J. Morphol. 278, 1312e1320. Bruner, E., Esteve-Altava, B., Rasskin-Gutman, D., 2018a. Networking brains: modeling spatial relationships of the cerebral cortex. In: Bruner, E., Ogihara, N., Tanabe, H.C. (Eds.), Digital Endocasts. From Skulls to Brains. Springer, Tokyo, pp. 191e204. Bruner, E., Amano, H., Pereira-Pedro, A.S., Ogihara, N., 2018b. The evolution of the parietal lobes in the genus Homo. In: Bruner, E., Ogihara, N., Tanabe, H. (Eds.), Digital Endocasts. From Skulls to Brain. Springer, Tokyo, pp. 219e258.

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C H A P T E R

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Parietal lobe expansion, its consequences for working memory, and the evolution of modern thinking Frederick L. Coolidge Psychology Department, University of Colorado-Colorado Springs, Colorado Springs, CO, United States

The earliest prototype for human brains can be traced to the inchoate nervous systems of simple flatworms that first appeared in the fossil record about 545 million years ago. Although they are not thought to be the original progenitors of the animal kingdom (sponges and comb jellies vie for that position), they were the first of the animal kingdom to make intentionally directed movements. Their rudimentary nervous systems coordinated sensory cell information with appropriate motor cell movements. Thus, it may be argued that the brains’ original function was to coordinate movements. Therefore, if brains are for moving, it would have been critically important for organisms to move appropriately, i.e., toward things in their environment that enhanced fitness and away from things that did not. It is not surprising that a part or region of early brains was specifically dedicated to the location of body parts and to orientation in space. Thus, the original adaptation of the parietal lobes was the gathering and integration of somatosensory information to aid appropriate motor movements in physical space. This

Cognitive Archaeology, Body Cognition, and the Evolution of Visuospatial Perception https://doi.org/10.1016/B978-0-323-99193-3.00002-7

argument is strengthened by the fact that the premotor and motor cortices in the posterior portion of the frontal lobes in modern brains are directly adjacent to the somatosensory cortex in the anterior portion of the parietal lobes. Further, the latter regions are directly adjacent to an area of the parietal lobes (the precuneus) that handles visuospatial imaging (among other cognitive functions). An often-reproduced drawing of a cortical sensory homunculus by neurosurgeon Wilder Penfield demonstrates the critical importance of sensory information as his distorted body image shows that the hands, lips, tongue, and face have more devoted cortical regions than the trunk or toes (Fig. 8.1). It also reflects the importance of human evolution, in terms of reproductive fitness, of the hands and face rather than the trunks or toes. It is also well established that damage to parietal areas profoundly affects the perception of one’s body. In clinical neuropsychology, finger agnosia is the inability to distinguish among one’s fingers, and it results from parietal lobe damage. The failure to be able to

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FIGURE 8.1

A cortical sensory homunculus. Source: Holly R. Fischer, MFA.

recognize one’s body parts, autotopagnosia, is also a sequela of parietal lobe damage. A more detailed description of the parietal lobes appears later in this paper.

Working memory There is some confusion in the cognitive literature about the notion of working memory. There is a generic version, which defines working memory as the ability to hold information

in conscious attention while inhibiting irrelevant external (and internal) stimuli. The other is the far more popular, complex, and empirically investigated version: the multicomponent working memory model (Fig. 8.2) first proposed by British psychologist Alan Baddeley and his postdoctoral fellow Graham Hitch (Baddeley and Hitch, 1974) and subsequently revised by Baddeley (2001, 2012). Apparently, they recognized in the early 1970s the limited delineation of shortand long-term memory with a major focus on verbal memories, and thus they expanded

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Working memory

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FIGURE 8.2 Baddeley’s working memory model. Courtesy of James M. Hicks.

greatly upon it. It is important at the outset to note that the individual components of Baddeley’s working memory model were not entirely original but built upon earlier proposed preexisting cognitive processes. However, the major contribution of his model was to combine many well-established cognitive functions into a coherent whole, which arguably made the greatest impact on research in cognitive psychology in recent history. A Google Scholar search on the term “Baddeley’s working memory model” yields tens of thousands of citations since 1974. Baddeley proposed the metaphorical existence of a central executive that attended to relevant stimuli consonant with short- and long-term goals, processed information, made decisions, shifted attention appropriately, and inhibited interference. This central executive

was previously heralded by the concept of “executive functions of the frontal lobes” that had been earlier hypothesized by Russian neuropsychologist Alexander Luria (1966). Luria had proposed that the frontal lobes were responsible for the abilities to plan and organize tasks, sequence events, and inhibit behaviors (including the delay of gratification). Baddeley’s central executive relied upon two subsystems: A phonological loop consisted of a phonological store, which could briefly maintain sounds (2 s or so). During this maintenance, their meaning was determined by matching to longterm declarative/semantic memories. Baddeley’s phonological loop was similar to Crowder and Morton’s (1969) concept of precategorical acoustic storage (PAS). The precategorical aspect meant that sounds could be temporarily stored

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without regard to their meaning, that is, regardless of whether they might be meaningful (yet not recognized as such), nonsense syllables, or foreign language words. Crowder and Morton also hypothesized that PAS could only hold sounds for a few seconds, yet long enough to determine their meaning (e.g., familiar or novel). Thus conceptually, PAS presaged Baddeley’s phonological loop (although he objects to the comparison [personal communication]). Baddeley also hypothesized that the phonological loop also contained vocal and subvocal articulatory processors that acted as rehearsal mechanisms. In it, sounds or words could be repeated in order to store them in long-term memory. Again, subvocal rehearsal mechanisms have been proposed as early as 1965 (Hintzman, 1965); however, Baddeley’s multicomponent working memory model was unique in viewing all of these cognitive functions as a functioning, comprehensive, and highly interactive entity. Baddeley’s executive function’s second subsystem, the visuospatial sketchpad, temporarily holds visual and spatial information, and it can recall and even simulate story-like memories (also known as episodic memory). In addition to the two subsystems, Baddeley proposed a memory component serving at the behest of the central executive. He labeled it the episodic buffer, a kind of short-term memory that integrated multimodal sensory information from the phonological loop and the visuospatial sketchpad. Again, Tulving’s (1972, 2002) concept of episodic memory presaged Baddeley’s episodic buffer. Tulving established a firm delineation between memorizing facts or details (i.e., declarative/semantic memory) and recalling scenes from one’s past, as if viewing a movie clip (i.e., an episodic memory). Episodic memories are typically recalled visually and usually have an important emotional valence attached, either positive or negative, like the recall of joyous or traumatic events.

Baddeley (2001, 2012) did expand beyond Tulving’s (1972, 2002) concept of episodic memory, when he hypothesized that the episodic buffer used a multimodal or panmodal code to integrate information from the phonological loop and the visuospatial sketchpad. He also proposed that the episodic buffer served as the central executive’s temporary memory system, which would also integrate ideas and meanings from long-term declarative/semantic memory. One important evolutionary fitness advantage of the central executive’s use of the episodic buffer is that it provided the ability to create alternative models of the environment (Land, 2014), which could be mentally manipulated to solve old and new problems, plan for future actions, and debate various courses of actions. Baddeley also noted that if an initial plan failed, another could be generated and executed. Baddeley also hypothesized that the use of the episodic buffer to solve problems did not merely consist of calling up old solutions with the highest probability of success, but that its value was inherent in the ability to generate new solutions to solve novel problems. Shepard (2008) proposed the idea of “thought experiments,” which were akin to Baddeley’s notion of a novel problem-solving central executive with the aid of the episodic buffer. Shepard proposed that mental manipulations of novel problems preceded actual attempts to solve a problem. These thought experiments would draw upon the numerous results of real experiences, yet these prior experiences could be recombined to produce genuinely novel solutions. Shepard proposed that this unique recombinant ability was the critical evolutionary advantage of recollections. Importantly, he noted that these internalized thought experiments would, more often than not, avoid trial and possibly fatal error. Tulving (2002) had also proposed that the ability to manipulate episodic memories created a unique form of

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Regions of the parietal lobes

consciousness autonoesis. He defined this term as where humans become aware of the subjective nature of time because they can recall things from their past, and they can imagine future scenarios. Thus, they realize that time can be manipulated, and it is not necessarily an irreversible continuum. Further, he proposed that autonoetic Homo sapiens could therefore reflect, worry, and plan for the future in a heretofore unheralded manner than other species, thus transforming their natural world into one of culture and civilization. Baddeley, similar to Tulving and Shepard’s ideas, proposed that greater working memory capacity would benefit episodic memories by allowing individuals to choose future actions or create alternative actions, rather than simply choosing the highest path of probable success. One important question that arises is whether these models could be imagined without inner speech, i.e., language. It is obvious that most organisms can make decisions without language, including humans, as people often use intuitive or “gut feelings” to make some of their decisions. Additionally, episodic memories can be recalled and imagined without declarative memories (words), but could alternative solutions be debated without inner speech? I suggest not or perhaps not as effectively.

Regions of the parietal lobes The parietal lobes are not uniform in either cytoarchitecture or their neuropsychological functions. Due to more sophisticated neurological imaging techniques over the past decades, the parietal regions have become the epicenter of the search for regions that distinguish between modern Homo sapiens and Neandertal cultures. The following is a current summary of parietal regions and their purported functions. Superior parietal lobule (SPL) The SPL resides in the lateral superior portion of the parietal lobe (generally associated with

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BA 7). It is defined inferiorly by the intraparietal sulcus (IPS), which runs horizontally from the anterior SPL and then travels obliquely to the posterior portion of the SPL. Intraparietal sulcus (IPS) Approximately 40%e50% of the cortex is contained within its sulci, and the sulci of the parietal lobes are particularly convoluted, which may be an important evolutionary marker in terms of higher-cognitive processing. The IPS is a homologous brain region shared by humans, great apes, and monkeys, although it is clearly expanded in humans beyond that expected from body size. The anterior portion of the IPS (horizontal IPS or hIPS) has been shown to respond to numbers. Number appreciation is called numerosity, a capability shared by humans and many species of animals. It has been hypothesized that there are at least two core processes in numerosity: (1) subitization, the ability to differentiate between one, two, and three things, and (2) the ability to distinguish between smaller and larger sets of things. Coolidge and Overmann (2012) theorized that numerosity may have served as a feral cognitive basis for abstraction, as the basic concepts of one, two, or three things can be applied to any “things” in the world, like sticks, stones, bones, apples, and even mythical creatures like unicorns. Further, since human infants (as young as 8 months old) and monkeys can demonstrate numerosity, then it is a property independent of language. As noted earlier, it may have been useful evolutionarily to have a region of the brain (parietals) to be immediately able to distinguish between one, two, and three things, be they predators or fruits, and to pass that information on to other regions (frontal lobes) for a decision. In that same light, it may have been imminently useful (helping to ensure reproductive success) to know a tree with 50 fruits is less than a tree with 100 fruits, although the exact number of fruits in each set would not be necessary. It is also interesting to speculate whether this small and large set comparative ability was an ability of the earliest forms of life 3e4 billion years ago. Prokaryotes, single-walled-cell organisms without a nucleus (like the earliest forms of bacteria), could

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detect varying concentrations of metabolic enhancing molecules and could move from low concentrations to higher concentrations. Perhaps, it was this ability that thus serves as a protoform of small and large set judgments in the second core process in numerosity. The IPS also appears to be responsible for determining serial order in numbers and letters. For example, when humans are asked to determine whether “3” comes before “7” or if “F” comes before “C,” the left and right hemisphere IPS become active. Also different specific sets of neurons in the IPS become active as a function of whether the number or letter discriminations are required (e.g., Zorzi and Testolin, 2018). I have also argued that this “sense” of numbers in many ways resembles one of the primary senses, such as vision, audition, touch, taste, and smell, as numerosity is also inherited, has a distinct neural basis, and is not learned (Coolidge, 2018), as has also been earlier suggested by Harvey et al. (2013). Precuneus The superior portions of the parietal lobe (BA 7) have received much attention recently, especially the medial portion known as the precuneus. Because of its location, tucked into the middle and sides of the superior portion of the parietal lobes, it was relatively difficult to study until the advent of more sophisticated neurophysiological studies. Fletcher et al. (1995) conducted one of the first neuroimaging studies to implicate the precuneus in the recall of visual memories but not declarative/semantic memories. As noted earlier, the recall of visual memories is known as episodic memory and gives rise to his concept of autonoesis, i.e., the awareness that time is relative (Tulving, 1972, 2002). According to Tulving, the evolutionary value of autonoetic thinking is that the recall of personal memories allows them to be manipulated and blended to simulate novel solutions to a novel problem. Again, like Shepard’s thought experiments, one could imagine future

options and choose among them without the dangers inherent in trial and error. Nonhuman primates and other animals also have connectivity-based subdivisions within the precuneus (just like humans) with anterior, medial, and posterior regions of the precuneus connected to various other brain regions (e.g., Margulies et al., 2009). Cavanna and Trimble (2006) demonstrated that various functions are a product of different regions in the precuneus in healthy adult humans. However, it is important to note that the precuneus in humans is vastly expanded in form and function from chimpanzees, particularly in its anterior regions (e.g., Bruner et al., 2017). These regions and functions include the anterior precuneus subserving self-centered mental imagery tasks and the posterior portion more involved in episodic memory retrieval. From the results of neuroimaging studies, Cavanna and Trimble established the role of the precuneus in visuospatial imaging (critical to and synonymous with Baddeley’s visuospatial sketchpad), episodic memory retrieval, and tasks where one takes an egocentric perception of one’s self (self-consciousness), others and the world, and it perceives agency in those relationships (i.e., what causes what). Interestingly, Lou et al. (2004) demonstrated through neuroimaging that medial portions of the superior parietal lobe (i.e., the precuneus) along with medial portions of the prefrontal cortices are critical to self-representation and other-representation. In an interesting experimental paradigm, healthy human subjects (from Denmark) were asked to think of themselves, their best friends, and the Queen of Denmark. They found differential activation across the thinking tasks in varying regions of the precuneus, and they found some interhemispheric differences as well with a greater role for right precuneal activation when thinking of one’s physical self and self-representation overall. Lou et al. (2005) subsequently showed the precuneus was absolutely critical to self-representations more so

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Regions of the parietal lobes

than other representations, and they established the precuneus’ important role in meditation. Inferior Parietal Lobule (IPL) Inferior to the IPS is the IPL. Its two major regions are the supramarginal gyrus and the angular gyrus. Supramarginal gyrus (SMG) Neuroimaging studies of the SMG (BA 40) have shown its critical role in the temporary storage of sounds and their linkage to long-term declarative memories. The SMG is most closely aligned with Baddeley’s phonological loop component of working memory. The sounds are held in storage long enough (about 2 s) to match them to a meaning or to discover there was no match (like a nonsense syllable or novel word). Baddeley’s articulatory processor (as noted previously) is a component of the subsystem phonological storage in his working memory model. It allows sounds to be repeated, either vocally or subvocally, in order to be memorized. The SMG is a critical part of the neural network involved in inner speech, along with the superior temporal lobes, and other brain regions. The SMG is also involved in verbal working memory tasks, where acoustic or written verbal information is temporarily stored and attended to despite interference (e.g., Deschamps et al., 2014). The posterior portion of the SMG (SMGp) helps to detect the spatial orientation of another person. For example, based on the position of one’s head and eye orientation, it can be determined whether another person is standing upright or not. In humans, Kheramand et al. (2015) determined that the SMGp integrates different sensory inputs creating a “common spatial reference” (p. 6). Silani et al. (2013) found that the right SMG (rSMG) is involved in some cognitive functions of the processing of emotions. Interestingly, the rSMG’s unique role in emotional processing might be to avoid emotional biases that may be involved in social judgments. They hypothesized that one’s empathic ability is a large function of

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having experienced those emotions, although this self-projection may lead to an egocentric bias. They argued that when someone is aware that a self-projection may be biased, it is the rSMG that detects these biases and corrects them (to be discussed later). It has also been established that the left anterior SMG (aSMG) becomes active when people observe others manipulating tools; however, the aSMG is not similarly active when macaques view conspecifics using tools (Orban and Caruana, 2014). These authors hypothesized that the aSMG is part of an object-manipulation network that receives dorsal and ventral streams of visual information, and nonsemantic information for to-bemanipulated objects from the posterior parietal lobes and the anterior intraparietal sulcus. They further observed that modern Homo sapiens may have evolved two different parietal systems: (1) an older biological brain network for grasping and manipulating objects and (2) an emergent parietal system, with converging information including semantic information that is specifically devoted to tool manipulation. They speculated that it was this emergent system that aided humans in understanding causal relationships between tool manipulation in the interest of particular goals. Thus, they proposed that tool use may “.have triggered the development of technical reasoning in the left IPL [inferior parietal lobule], which in turn may have favored a development of language in the left hemisphere” (p. 9). Angular gyrus (AG) The AG (BA 39) is adjacent to the posterior SMG, and it is superior to the posterior portion of the superior temporal gyrus. French Neurologist Joseph Jules D
ejerine (1891) was the first to discover that one of its functions was an inability to write and read (agraphie and alexie in French) because of lesions to the left AG. Ramachandran and his colleagues (Oberman and Ramachandran, 2008; Ramachandran and Hubbard, 2001, 2003) have found that lesions of the AG can lead to disturbances in the production of metaphors and the appreciation

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of metaphors and proverbs, synesthesia, and attribution issues with the Bouba/Kiki effect. They also found lesions to the AG produced out-of-body experiences. The AG also plays a prominent role in mathematics, and damage to the AG impairs arithmetic and mathematical operations (called acalculia or dyscalculia). Seghier (2013) reviewed numerous neuroimaging AG studies and found it to be involved in at least 15 major cognitive functions and interacting with at least 15 other brain regions. Damasio (1989) labeled the AG (and related areas) convergence zones, that is, a multimodal hub that integrates different kinds of sensory information (e.g., visual, auditory, tactile), appropriates meanings to events, attends to salient aspects of the environment and manipulates their visualspatial representations, and solves mathematical problems and others (reasoning, social-cognitive, etc.) Further, the AG creates abstractions of events into concepts that allows for further processing. Finally, the AG has been implicated as part of the “default mode network”(DMN) of the brain that becomes active when humans are resting, not goal-oriented, and involved in their own thoughts, like daydreaming. Retrosplenial cortex (RSC) The RSC is considered part of the inferior and medial parietal lobe region and part of a network with critical connections to the hippocampus, parahippocampus, frontal lobes, and thalamus. The posterior portion of the cingulate cortex (which covers the corpus callosum) is called the RSC. It is called the RSC because it is posteriorly adjacent to the splenium, which is the most posterior portion of the corpus callosum. The splenium is the thickest part of the corpus callosum. The RSC’s chief function is the creation and storage of spatial information for spatial navigation (e.g., Mitchell et al., 2018). Damage to the RSC results in verbal and visual memory problems, including the retrieval of recent

episodic and autobiographical memories (but not old autobiographical memories). Its evolutionary importance appears to be its translational responsibility for processing egocentric spatial viewpoints (a view from one’s own perspective, which engages the precuneus as well) and allocentric (a view from other perspectives) spatial viewpoints (e.g., Mitchell et al., 2018; Vann et al., 2009; Zaehle et al., 2007). Zaehle et al. (2007) also proposed that both egocentric and allocentric frames of reference are processed by a hierarchical brain network involving the hippocampus, thalamus, precuneus, and frontal and parietal cortices, but the egocentric frame of reference only activates the part of the larger allocentric network. Auger et al. (2015) have found that the RSC is responsible for creating, storing, and navigating new environments. As the RSC is common to humans, the other great apes, monkeys, and even rodents, it appears that forming a dependable mental map of a new environment was important of effective spatial orienteering throughout evolution. This translational RSC function in humans between egocentric and allocentric frames of reference reinforces the notion that the exaptation of this multicomponent brain network was especially important as Homo erectus repeatedly left Africa beginning as early as about 1.8 million years ago. Again, the evolutionary value of being able to transform an egocentric perspective of the environment into an allocentric one, where other viewpoints can be explored mentally helps to avoid the dangers of actual trial and error. There is obvious additional value in recalling locations from episodic memories and being able to translate them into egocentric viewpoints so that one can perform actions from one’s own perspective. Vann et al. (2009) concluded that this is precisely the chief function of the RSC, “. acting as a short-term buffer for the representations as they are being translated” (p. 797).

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The parietal lobes and the default mode network of the brain

The SMG, phonological storage, and the evolution of language Aboitiz et al. (2010) proposed that the SMG (in conjunction with temporal, other parietal, and frontal areas) formed a unique auditoryvocal circuit that was a “key innovation” in the evolution of language. They reasoned thusly: the earliest primates 60 million years ago had to compete with other animals for nutritious fruits in order to fuel metabolically expensive brain tissue. They were successful because in spite of their small size, they used intentionally directed vocalizations with their conspecifics. The ability to store these sounds and apply “meaning” became critical in their success, allowing the expansion of their brains relative to their body size. As simple flatworms with only rudimentary “brains” and nervous systems can be classically conditioned, early primates were undoubtedly capable of all forms of associative learning: classical conditioning, operant conditioning (reinforcement and punishment), and observational imitation. Making phonological storage visible to natural selection may have expanded its storage capacity. The latter may have allowed increasingly longer utterances coupled with expanded meaning. Given that extant vervet and putt-nosed monkeys teach specific sounds to their young that elicit distinctly different behaviors, it is highly likely that later and much bigger-brained hominins, such as the australopithecines, habilines, and Homo erectus, had a much larger array of sounds and meanings. Aboitiz et al. further hypothesized that this continued use and expansion of the phonological loop promoted social bonding and allowed the coordination of various kinds of activities, ultimately resulting in increasingly complex communications. Further, increasing phonological storage capacity may have allowed more complex relationships within utterances, thus eventually providing a protobasis for recursion, i.e., thoughts within

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thoughts or thoughts modifying other thoughts (e.g., Coolidge et al., 2010).

The parietal lobes and the default mode network of the brain The DMN of the brain appears to be active when a person is resting and not engaged in goal-directed thoughts or external activities. The mental activities that activate the DMN include daydreaming, mind wandering, meditating, imagining personal future events, moral judgments, Theory of Mind, and spontaneous thinking. When a task requires external goaldirected attention, activity in the DMN is suppressed so these two states appear to have an internally competitive relationship, that is, activity in one suppresses activity in the other to a large extent (e.g., Spreng and Grady, 2010). Overall, neuroimaging studies strongly suggest that the basic neural substrate for the DMN is largely a frontal (medial prefrontal cortex) and parietal (posterior cingulate cortex, precuneus, RSC) network, but also includes the left and right medial temporal lobes. Interestingly, monkeys when not actively involved in their own environments, also have a DMN in similar brain regions as humans’ DMN (Mantini et al., 2011). Schacter (2012) has questioned whether the DMN is necessarily antagonistic to goal-directed cognition. As noted earlier, the precuneus becomes activated during episodic memories including remembering the past and imagining the future. It has also been observed that future simulations are also associated with activations of the DMN. Numerous fMRI studies do support the hypothesis that the default network is involved in autobiographical planning and in the adaptive value of mind-wandering, planning, problem-solving, and creativity (e.g., Ellamil et al., 2012; Gerlach et al., 2011; Mak et al., 2017; Raichle, 2015; Spreng and Grady, 2010; Vincent et al., 2008).

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Schacter (2012) and Schacter and Addis (2007, 2020) have also noted that episodic memories (especially autobiographical memories) are not essentially reproductive, that is, they do not contain exactly what happened in the past. They attributed this hypothesis originally to Bartlett (1932) who much earlier noted that the process of remembering is an imaginative reconstruction that is invoked when an automatic response is not immediately elicited. Schacter and his colleague argued that episodic memories are fundamentally constructive by recalling pertinent elements of past events and reconstructing them. Of course, such representations are fraught with errors, as what one wished would have happened provides that memory with errors and illusions. Yet again, given that parietal expansion in recent Homo sapiens likely included important changes in its inferior regions also (like the retrosplenial cortex), the ability to imagine events from an allocentric perspective may have ameliorated personal biases in these perceptions (to be further discussed shortly). Schacter and colleagues also proposed that such an essentially nonreproductive and fragile system would be useless unless it could also be used to construct future scenarios based on past experiences. They labeled the process constructive episodic simulation, and they noted that the neural substrates are similar for recalling the past and imagining the future (primarily parietal regions, frontal areas, and the hippocampus). One of Schacter and Addis’ (2007, 2020) most provocative aspects of this hypothesis is that the episodic memory system did not evolve to simply reminisce but to simulate the future. To do so, a system is required that can “flexibly extracts and re-combines elements of previous experiences” (2007, p. 1375). Further, they argued that the memory distortions and illusions that cooccur with reminiscences are a byproduct of this flexible and combinatory process.

Egocentric and allocentric frames of reference and emotional regulation Interestingly, egocentric and allocentric frames of reference may have played a more important role in the evolution of modern minds than simply spatial navigation. Webb et al. (2012) have empirically substantiated the relationship between emotional control goals (e.g., I will not get angry) and emotional outcomes (e.g., anger and aggression) and their subsequent regulation. They found traditional techniques that helped regulate behavioral goals were highly successful in the regulation of emotional outcomes. These techniques included forming future implementation intentions, i.e., creating “if-then” contingency plans. However, there are a number of mediating variables in if-then planning, and one of the most important of them resides in egocentric versus allocentric viewpoints. For example, King et al. (2022) found empirical evidence that autobiographical (sense of self) memories contain both episodic and declarative/semantic-specific information. More importantly, autobiographical memories can be recalled from an egocentric or allocentric view. King et al. found that when autobiographical memories were recalled from an allocentric perspective, there was a reduction in the emotional valence of the memory, which theoretically would reduce personal bias and distortions, as emotional states themselves may give rise to bias and distortions in the future simulations. Further, King et al. found there were no significant differences in the objective content in the recall of egocentric versus allocentric autobiographical memories. As has been previously noted (Coolidge and Wynn, 2009, 2012), diplomatic or indirect speech requires adequate phonological storage and working memory capacity, recursive inner speech (if-then or whatif contingencies), and higher levels of Theory of Mind (reading the attitudes and intentions of

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Do future simulations enhance prospective memory?

others). Thus, the regulation of emotion in future-oriented diplomatic simulations would have been enhanced by the ability to engage in allocentric perceptions of those negotiations. Therefore, expansion of the parietal areas (e.g., Bruner, 2018; Bruner and Iriki, 2016) may not only have been important consequentially for the cognitive processes earlier noted for the visuospatial sketchpad, but also for the great variety of cognitive functions of the inferior regions of the parietal lobes (e.g., SMG, AG, IPS), particularly the RSC.

The episodic buffer and the evolution of modern thinking Perhaps, one of the major exaptations of the human brain has been the neural reorganization of the parietal lobes to reflect upon one’s self and others, which allows for a kind of critical self-awareness, that is, an ability to take note of one’s own behavior and correct it. It has been argued that the essence of the disease schizophrenia, regardless of its specific emotional and cognitive difficulties, is the loss of critical selfawareness. This self-reflective process includes the ability to be aware of the prior experiences, both mistakes and successes, and to use them to construct and guide future behaviors. As noted previously, this is the essence of the evolutionary success afforded by an episodic memory system. Many scientists have pondered the prominent role of episodic memory in human evolution. Tulving (2002) noted the sole exception to time’s irreversibility appears to be one’s episodic memory, the ability to travel back in time in one’s mind, and even alter previous outcomes. He further proposed that this kind of mental time travel was phenomenologically different from human’s actual experiences, and their awareness of this difference is also an aspect of his concept of autonoesis. He also emphasized that episodic memory is not only an ability to call up the past but to imagine future scenarios.

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Do future simulations enhance prospective memory? I have emphasized throughout that the cognitive consequences of parietal lobe expansion (not only superiorly [precuneus and IPS] but inferiorly [SMG, AG, and RSC]) may have primarily resided in the ability to create and manipulate future simulations in order to solve novel problems. Is there evidence, however, that future simulations may also aid memories, such as prospective memory (i.e., increasing the probability of acting out intended actions)? As noted previously, “if-then” reasoning (implementation intentions) may have been a critical aspect of the success of future endeavors. Klein (2013; Klein et al., 2010, 2011) and his colleagues have demonstrated that the ability to remember what one’s planned actions were increases the efficiency and success of future actions. Further, by encoding information that contains survival value, it often results in better subsequent recall (Schacter, 2012). Interestingly, Schacter has also noted that future simulations can also enhance one’s sense of well-being. Simulated outcomes that contain a positivity bias (aka Pollyanna principle) have been shown to be associated with greater success in social bonding, increased productivity, and coping with the stresses of daily living. Further, simulations of encounters with unknown others have been shown to reduce negative stereotyping and reducing anxiety associated with such interactions. It is also important to reemphasize not only the enhancements of the functioning of visuospatial sketchpad associated with parietal lobe enlargement but also the concomitant emotional regulation that may have aided diplomatic negotiations (e.g., mating, trade, etc.). As noted previously, emotional states and their outcomes may be more efficiently and successfully regulated as a function of being able to easily translate between egocentric and allocentric frames of reference in the RSC (and its related brain regions). The success of these memorial implementations of

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intentions (if-then contingencies) may at least partially account for the archaeological evidence that Homo sapiens traded over much greater distances than penecontemporaneous Neandertals (e.g., Wynn and Coolidge, 2012). Further, Neandertals went extinct within 10,000 years or less after the entry of Homo sapiens into Asia and Europe. It was not mere fate, but some small but significant cognitive abilities that Homo sapiens carried in their DNA. Fully modern thinking is obviously a result of multiple correlated factors as is true of all complex human behavior. However, parietal lobe enlargement and its concomitant positive cognitive sequelae undoubtedly played a more critical role than heretofore imagined in the ultimate successes of Homo sapiens. Finally, as Bruner et al. (2017, 2018) have duly noted, the visuospatial system (specifically working memory’s visuospatial sketchpad) plays a critical role in integrating spatial, temporal, and social interactions between a person and their environment. These sensorimotor networks were important in the evolution of primates, particularly because of their great dependence upon haptics and vision in their social and environmental interactions. Further, it has been argued throughout this chapter that the parietal regions are also intimately involved with episodic memories, autonoetic thinking, and future simulations. Thus, visuospatial sketchpad integration undoubtedly represents a critical bridge that builds upon the hand-eye circuitry in nonhuman primates and the extended cognition, self-awareness, autonoetic thinking, and complex social perceptions of modern humans.

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Wynn, T., Coolidge, F.L., 2012. How to Think like a Neandertal. Oxford University Press. Zaehle, T., Jordan, K., W€ ustenberg, T., Baudewig, J., Dechent, P., Mast, F.W., 2007. The neural basis of the egocentric and allocentric spatial frame of reference. Brain Res. 1137, 92e103. Zorzi, M., Testolin, A., 2018. An emergentist perspective on the origin of number sense. Phil. Trans. Biol. Sci. 373 (1740). Article 20170043.

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C H A P T E R

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Experimental neuroarchaeology of visuospatial behavior Dietrich Stout Department of Anthropology, Emory University, Atlanta, GA, United States

Introduction Neuroarchaeology is a variety of cognitive archaeology that aims to use theory and methods from neuroscience to infer past cognitive and evolutionary processes from the material remains of behavior. It may be loosely divided into analytical neuroarchaeology approaches that apply neuroscience theory to interpret archaeological evidence (e.g., Wynn et al., 2009; Malafouris, 2009) and experimental neuroarchaeology approaches that apply neuroscience methods to study the cognitive implications of experimentally reconstructed behaviors (e.g., Stout et al., 2000; Putt et al., 2019). Neuroarchaeology is especially useful for addressing evolutionary questions because it explicitly grounds cognitive mechanisms in biological substrates, making theoretical, and empirical links between biological, cultural, and cognitive evolution relatively straightforward (Stout and Hecht, 2017). Like all archaeology, however, neuroarchaeology is constrained by the availability of material evidence. As the name implies, the Paleolithic archaeological record is dominated by stone artifacts and experimental neuroarchaeology has

Cognitive Archaeology, Body Cognition, and the Evolution of Visuospatial Perception https://doi.org/10.1016/B978-0-323-99193-3.00008-8

focused almost exclusively on stone-tool making, although this may be changing (e.g., Tyl
en et al., 2020). However, the neuroarchaeological focus on stone-tool making is not only a matter of convenience. The same properties that make stone exceptionally durable in the archaeological record make stone tools a particularly interesting object of study. As the hardest raw material worked in the Paleolithic, stone presented unique technical challenges (Nonaka et al., 2010) and provided the sharp and durable cutting edges critical to hominin subsistence strategies as well as the production of nonlithic technologies (Schick and Toth, 1993; Shea, 2017b; Rezek et al., 2018). Controlled fracture is the fastest and most efficient way to produce a stone cutting edge and, prior to the advent of more durable but labor-intensive ground stone tools associated with sedentism and early agriculture (Yerkes et al., 2012), such stone “knapping” was the dominant stone-tool making method and likely influenced hominin brain evolution (Bruner et al., 2023). Knapping stone involves the sequential detachment of flakes from a stone core using precise strikes with a

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handheld hammer (Fig. 9.1). Each such removal not only leaves traces that can be used to reconstruct discrete actions and sequential strategies (Fig. 9.2), but also demands exceptional perceptual-motor skill to deliver carefully calibrated force with sufficient accuracy (Nonaka et al., 2010; Pargeter et al., 2020) using an asymmetric bimanual strategy of sensitive core positioning and support coupled with high-velocity ballistic strikes (Faisal et al., 2010; Rein et al., 2013; see Chapter 12). Action sequences involved in Paleolithic knapping ranging in complexity from simple iteration to the multilevel goal hierarchies needed to produce later Paleolithic forms (Stout et al., 2021). Developing and executing such contingent strategies requires reliable control over individual flake removals (Roux et al., 1995; Pargeter et al., 2020), including sensitive adaptation of kinematics to variable core morphology, composition, and positioning. These perceptual-motor demands present a bottleneck for knapping skill acquisition, which can take hundreds of hours for even relatively simple Paleolithic technologies under supportive

FIGURE 9.1

teaching conditions (Pargeter et al., 2019). Learning to knap requires attention to subtle interactions between bodily kinematics, object properties, action outcomes, and technological goals, implicating evolved human capacities ranging from enhanced action and object perception (Hecht et al., 2013b; Mangalam et al., 2022), body awareness (Haggard, 2017; Hecht et al., 2017), and motor control (Heldstab et al., 2020; Goldring and Krubitzer, 2020) to executive attention and working memory (Engle, 2018; Carruthers, 2013). It is thus important to appreciate that stone knapping is a multisensory skill, integrating not only visual but also kinesthetic, somatosensory (Stout and Chaminade, 2007; Fedato et al., 2019), and auditory (DeForest and Lyman, 2022) perception. This review will focus on visuospatial processing while recognizing that it represents only one component of a broader perceptual-motor system. Once again, however, this focus on visuospatial aspects of behavior is not merely an arbitrary matter of emphasis. Primates in general experience a world or “Umwelt” (Bueno-Guerra, 2018) dominated by visual perception and

Stone knapping. The author making a Late Acheulean style handaxe.

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of stone tool making can thus shed light on many key issues in human cognitive evolution.

Neuroarchaeology as evolutionary neuroscience

FIGURE 9.2 Action traces reflect planning and perceptual-motor control. Thinning the cross-section of a handaxe requires removing flakes that travel at least halfway across the surface. This is achieved through careful preparation of surfaces and accurate delivery of sensitively calibrated force.

manual manipulation. Although the remarkable flexibility, plasticity, and learning capacity of our species also allow for remarkable feats of perception and action in other modalities (e.g., Thaler et al., 2011), visual specialization appears to have been a driving force in primate brain evolution (Barton, 1998), and visuospatial processing is now ubiquitous across diverse domains of human cognition (Groen et al., 2022), helping to support everything from tool making and technological evolution (Stout, 2021; Osiurak and Reynaud, 2020) to social cognition (Parkinson and Wheatley, 2013; Barrett et al., 2022), science and mathematics (Wang et al., 2021), and even memory (Reggente et al., 2020). While clearly only part of the story, experimental neuroarchaeology research on the visuospatial demands

Neuroarchaeology is part of a broader research domain that Stout and Hecht (2017) refer to as “evolutionary neuroscience.” In brief, evolutionary neuroscience seeks to understand neurocognitive evolution by evaluating comparative evidence of modern brain and behavioral variation with respect to (1) known evolutionary and developmental processes, (2) primary archaeological and paleontological evidence of evolutionary timing and context that cannot be resolved through comparative methods with extant species, and (3) the ethnographic, ethological, and experimental analogies needed to interpret this primary evidence. The role of neuroarchaeology in this program is to develop empirical links between neurocognitive processes and concrete archaeological evidence of past behavior. This is critical for the investigation of the pivotal evolutionary period between our last common ancestor with chimpanzees and the emergence of modern Homo sapiens, which is largely inaccessible to comparative approaches. At the same time, neuroarchaeology relies critically on the broader comparative and evolutionary framework in order to make sense of its results. Neuroarchaeology faces the challenge of attempting to base inferences about past neurocognitive mechanisms on neuroscientific experiments with modern human participants. As in any analogy-building exercise (GiffordGonzalez, 1991), neuroarchaeology thus needs to further consider both uniformity and disuniformity of neural structure and function between modern research participants and extinct hominids. This is assessed primarily by using classic comparative methods with extant species to infer shared and divergent neural

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characteristics of extinct ancestors, although important evidence about past brain size and some aspects of the organization also comes from fossil cranial evidence (Chapter 7). By grounding the interpretation of experimental results in this framework, it is possible to draw more secure inferences about past neurocognitive and evolutionary processes. More specifically, neuroarchaeological evidence can be used to identify long-term trends in the relative neurocognitive demands of archaeologically observed behaviors (Stout, 2011). As it is generally expected that neural adaptations should occur after the emergence of the behaviors that render them selectively beneficial (Laland et al., 2015), the most common application of experimental neuroarchaeology is to identify the neurocognitive demands of a particular behavior, and thus the most likely targets of selection acting on aptitude/performance of that behavior (Stout and Chaminade, 2009; Stout and Hecht, 2017; Hecht et al., 2015a; Morgan et al., 2015; Putt et al., 2019). However, theoretical approaches to human evolution (Ant
on and Kuzawa, 2017; Fuentes, 2015; Stout and Hecht, 2017) increasingly recognize the importance of evolutionary processes other than natural selection, including extended (Laland et al., 2015) interactions between plasticity, development, and nongenetic (e.g., cultural, ecological) inheritance. This provides the context for the integration of neuroarchaeological evidence into broader hypotheses regarding the evolution of human brains, behavior, and culture (Stout, 2021). The rest of this chapter will thus proceed to review the comparative foundations, experimental evidence, and evolutionary interpretation of the neuroarchaeology of visuospatial behavior.

Comparative evidence Visual processing in primate brains is thought to occur in distinct dorsal and ventral streams (Milner and Goodale, 1995; Goodale, 2014) that

FIGURE 9.3 Ventral (bottom), ventral-dorsal (middle), and dorsal-dorsal (top) pathways discussed in the text. ATL, anterior temporal lobe; IFC, inferior frontal cortex; IPL, inferior parietal lobe; PTL, posterior temporal lobe; SPL, superior temporal lobe.

converge in the frontal cortex to guide action (Arbib, 2010; Orban and Caruana, 2014) (Fig. 9.3). A ventral, occipital-temporal stream is commonly termed the “what” pathway and is associated with the perception of objects, individuals, and body parts. As with many cortical processing streams (Margulies et al., 2016), the ventral stream displays a gradient of increasing abstraction with increasing distance from the primary sensory cortex, culminating in generalizable object and action semantic representations in the anterior temporal lobe. These temporal lobe association cortices are greatly expanded in humans, as is the case with cortical association areas generally (Buckner and Krienen, 2013), but in many other respects appear to represent a fairly straightforward elaboration of functional anatomy observed in other primates (Braunsdorf et al., 2021). The white matter tracts comprising the ventral action semantic pathway in particular are well developed across macaques, chimpanzees, and humans (Hecht et al., 2013a), suggesting a deep phylogenetic history and qualitatively similar functionality.

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What appears to be more novel in human evolution is the integration of this ventral stream with an expanded dorsal stream supporting action kinematics, object affordances, and mechanical inference (Orban and Caruana, 2014; Hecht et al., 2013a; Osiurak and Reynaud, 2020) that effectively constitutes a “physics engine” in the human brain (Fischer et al., 2016). The initial conception of a dorsal stream was based on work with macaque monkeys, and specifically referred to connections from motion-sensitive fields (e.g., V5/MT) of visual cortex to posterior parietal cortex representations of spatial location (Ungerleider and Mishkin, 1982). It was thus characterized as a “where” pathway in contrast to the ventral “what” pathway. However, this has largely been superseded by the characterization of Milner and Goodale (1995) who drew heavily on work with the visually agnosic patient D.F. to reframed the dorsal stream as a “vision-for-action” system which they contrasted with a ventral “vision-for-perception” stream. This conception of a dorsal stream for action control has since been extended and is now commonly seen involving multiple regions/ pathways within parietal cortex extension as well as their extensions to the frontal cortex (Rauschecker, 2018). For example, Rizzolatti and Matelli (2003) proposed dividing the macaque dorsal stream in two: a dorsal-dorsal stream guiding movement execution in space and a ventral-dorsal stream representing objectbody relations (commonly termed “affordances”; Osiurak et al., 2017) useful for action planning and observational understanding. Macuga and Frey (2014) identify a similar gradient in the posterior parietal cortex of humans, with a superior parietal lobe (SPL) pathway supporting the “closed-loop” use of visual feedback to guide ongoing action and inferior parietal lobe (IPL) contributing to “open-loop” or feed-forward action planning and imagery not guided by visual feedback. These posterior parietal pathways converge in anterior IPL regions which then provide

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integrated sensory and motor information to frontal lobe action organization and execution regions (Orban and Caruana, 2014) allowing for the production of adaptive, goal-directed behavior. Comparative studies of the parietal lobe structure and function suggest evolutionary reorganization above and beyond a generalized increase in the size of association areas (Bruner, 2018). Interestingly, visual processing in the dorsaldorsal stream appears relatively conserved across macaques and humans, although portions of the intraparietal sulcus do show greater sensitivity to 3D-structure-from-motion stimuli (Orban, 2016). In contrast, human IPL appears to have undergone substantial reorganization in humans compared with chimpanzees and macaques (Reyes et al., 2022). This includes the emergence of a region in anterior IPL that has been posited as a novel human “tool-use region” based on its response to observed tool actions in humans but not macaques (Orban, 2016) as well as a new subdivision of human middle IPL identified by distinct neurotransmitter receptor patterns and potentially contributing to novel cognitive (Niu et al., 2021) and/or tool-using (Reyes et al., 2022) capacities. Overall, these findings suggest that ventral-dorsal stream affordance processing has been a locus of more substantial evolutionary changes to gray matter structure and function over human evolution, as compared to dorsal-dorsal spatial guidance and ventral stream action/object semantics. However, the most important evolutionary changes may pertain to enhanced white matter connectivity within and between these three pathways. Hecht et al. (2013a) showed that there is a gradient of increasing temporal-parietal and parietal-frontal connectivity across macaques, chimpanzees, and humans. This included an expansion of connections between ventral stream action (middle temporal cortex) and object (inferior temporal cortex) representations and the IPL affordance processing stream via

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the middle (MLF) and inferior (ILF) longitudinal fasciculi, respectively. Subsequent work found ILF can be subdivided into medial and lateral branches, and the appearance of a lateral branch is a novel specialization in the great apes. This would be expected to enhance the integration of kinematic representations of object/body relationships with semantic representations of object properties, associations, and functions (Lesourd et al., 2021). Orban and Caruana (2014) suggest that the human tool-use region in IPL represents just such a point of convergence. Hecht et al. (2013a) further found that, in human alone, middle temporal cortex also included substantial connectivity with SPL, potentially indicating a similar integration of action semantic representations (e.g., actions typically associated with an object (Lesourd et al., 2021)) with dorsal-dorsal stream visually guided action control. The most prominent pattern observed by Hecht et al. (2013a), however, was the progressive expansion of parietal-frontal connectivity via the superior longitudinal fasciculus (SLF) across macaques, chimpanzees, and humans. This represents a substantial augmentation of dorsal stream inputs to frontal action planning and execution regions in the inferior frontal cortex, where they converge with ventral stream inputs that are well established across all three species. Further dissecting the SLF, Hecht et al. (2015b) found that the superior branch (SLFI) connecting dorsal parietal and frontal cortex was quite similar between humans and chimpanzees but that the middle (SLFII) and especially inferior (SLFIII) branches showed evidence of reorganization in humans. SLFII originates in posterior IPL, a multisensory integration region involved, among other things, in space perception and oculomotor control (Niu and Palomero-Gallagher, 2023). In humans, SLFII exhibits relatively weaker connectivity with the inferior frontal cortex and stronger connectivity with the dorsal frontal cortex, perhaps reflecting displacement by an expanded SLFIII. Human SLFIII, which connects anterior IPL

(the site of other grey- and white-matter specializations reviewed above) with inferior frontal cortex, is relatively larger and extends further anteriorly, especially in the right hemisphere. These increasingly anterior portions of inferior frontal cortex are involved in increasingly abstract levels of action organization (Koechlin and Jubault, 2006) and representation (e.g., overall intention) and are well connected with ventral stream action semantics across macaques, apes, and humans (Hecht et al., 2013a; Kilner, 2011; Weiller et al., 2021). The inferior frontal expansion of human SLFIII provides further evidence for the integration of evolutionarily enhanced ventral-dorsal stream information processing into human action planning and understanding. It is echoed by functional evidence that whereas both humans and chimpanzees activate dorsal prefrontal cortex during object-directed action observation, humans show significantly more activation in the inferior parietal and frontal cortices (Hecht et al., 2013b). The preponderance of comparative evidence thus indicates that the evolution of human visual processing has involved substantial changes to the ventral-dorsal stream of affordance processing and what has been termed mechanical (Orban and Caruana, 2014) or technical (Osiurak and Reynaud, 2020) reasoning, rather than superior and medial parietal lobe structures classically associated with spatial representation and attention. However, it should be stressed that these evolutionary modifications to the ventraldorsal pathway largely reflect enhanced integration of spatial and semantic information into what might be characterized as a kind of “final common pathway” for perceptual inputs to frontal lobe action planning and understanding. This shift may enable enhanced sensitivity to and control over fine perceptual-motor details in human action (Hecht et al., 2013b), which is critical to the acquisition and execution of uniquely human technical skills (Arbib et al., 2009) including stone-tool making (Nonaka et al., 2010; Pargeter et al., 2020). More generally, it is increasingly

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Experimental evidence

apparent that visuospatial coding is incorporated into a wide range of cognitive operations and brain regions, as reflected, for example, in the presence of spatial maps in prefrontal cortical regions involved in executive attention and working memory (Groen et al., 2022). It is not clear how distinctive this is to humans, but it would be consistent with stronger dorsolateral prefrontal inputs from posterior IPL via a reorganized SLFII (Hecht et al., 2015b). Thus, while dorsal stream visuospatial processing per se appears to be relatively conserved across macaques and humans (Orban, 2016), its contribution to larger functional networks and distinctively human behaviors may nevertheless have undergone important reorganization. For example, comparative evidence indicates that the precuneus (medial parietal cortex) underwent volumetric expansion during human evolution (Bruner et al., 2017), perhaps coinciding with paleoneurological evidence of parietal expansion and reorganization in Homo sapiens (Bruner, 2018). This expansion likely includes portions of association cortex which, in both macaques and humans (Mantini et al., 2011; Margulies et al., 2009), display functional connectivity with posterior IPL visuospatial cortex as well as distributed Default Mode Network (DMN) characterized by its tendency to de-activate during externally directed tasks as compared to passive “mind-wandering.” In humans, the DMN is involved in aspects of internally focused cognition (e.g., remembering the past, imagining the future, introspection about the self, reflection on the thoughts of others) that are thought to be uniquely developed in our species (Laland and Seed, 2021). One unifying feature of these tasks is that they involve internal simulation, and it is possible the visuospatial coding (Groen et al., 2022) and more extensive and wellintegrated multisensory inputs from expanded human posterior IPL (Niu and PalomeroGallagher, 2023) have helped to shape the evolution of DMN in modern humans (Bruner and Colom, 2022). Of course, it is difficult to assess

many of the proposed human-unique functions of the DMN in nonlinguistic animals, who cannot report on or be instructed to engage in internally focus reflection, and so the comparative picture remains contentious. It may even be that language is required for the cultural evolution and developmental construction of distinctive human DMN functions (Heyes, 2018), raising interesting bio-cultural coevolutionary possibilities. However, neuroarchaeological research to date has largely focused on the externally directed behavior of Lower Paleolithic stonetool making, which substantially predates paleoneurological evidence of precuneus expansion. This research tells a story that primarily implicates lateral visual processing streams reviewed above and especially the evolutionarily derived features of the human ventral-dorsal stream and inferior frontal cortex. Archaeological behaviors that might hypothetically draw on evolved DMN functions, such as the imagination of future rewards to motivate current investments in tool-making skill acquisition (Pargeter et al., 2019; Suddendorf et al., 2016), the development and use of delayed return traps and other complex technologies requiring extended planning (Wynn and Coolidge, 2009), or increased rates of artistic and technological creativity (Fogarty et al., 2015), occur on spatiotemporal scales that are difficult to study with conventional experimental neuroscience methods. Developing new methods to study such complex, real-world phenomena is an important goal for neuroarchaeology and cognitive science generally (Stout, 2021).

Experimental evidence Over the past quarter century, neuroarchaeological studies have used using 18F-fluorodeoxyglucose positron emission tomography (FDG-PET), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS)

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to mapped neural responses to the execution and observation of Oldowan and Acheulean stone-tool making. The Oldowan is the oldest well-known stone technology (but see Harmand et al., 2015), dating to just over 2.5 million years ago (mya) (Semaw et al., 2003; Braun et al., 2019). It is characterized by the simple but efficient production of sharp stone flakes (Kuhn, 2021) by striking one stone (the “core”) with another (the “hammer”). Controlling stone fracture in this way is a challenging perceptual-motor skill (Nonaka et al., 2010; Stout and Chaminade, 2007) but efficient Oldowan flaking can be achieved using only simple procedural rules (Moore, 2020) and repetitive action sequences (Stout et al., 2021) with little need for advance planning (Stout et al., 2015). In an early FDG-PET study, Stout and Chaminade (2007) found that, compared with simply striking rock together without attempting to initiate controlled fracture, Oldowan toolmaking produces activations in each of the three visual processing streams reviewed above. These include (1) inferior frontal (premotor) cortex, (2) dorsal-dorsal stream cortex (middle part of the intraparietal sulcus) known to exhibit increased sensitivity to 3D-structure-frommotion stimuli in humans versus macaques (Orban, 2016), (3) an anterior intraparietal sulcus field associated with control of grasping and prehension in macaques (Orban and Caruana, 2014), and (4) early (occipital lobe) elements of both dorsal and ventral visual streams. Notably, these activations are relatively proximate to primary sensorimotor cortices, which are generally expected to indicate relatively direct connections to input/output and lower levels of representational abstraction (Margulies et al., 2016). Using fNIRS, Putt et al. (2019) similarly reported that participants learning to make Oldowan tools rapidly transitioned to automatic processing not requiring executive control and that more skilled individuals exhibited increased sensorimotor activation (postcentral gyrus somatosensory cortex). These findings are consistent with

archaeological understandings of the Oldowan as a cognitively simple but motorically demanding (see Chapter 12) and suggest that the selection of Oldowan tool-making aptitude could have contributed to the evolution of relatively peripheral elements of human dorsal and ventral visual processing streams, including regions involved in spatial attention, object recognition, 3D-structure-from-motion perception, and object manipulation. Acheulean tool-making dates to at least 1.7 mya (Lepre et al., 2011; Beyene et al., 2013) and represents the first major lithic technological innovation following the appearance of the Oldowan. The Acheulean’s most characteristic artifact is the teardrop-shaped Acheulean “handaxe,” which is believed to have functioned as a large, hand-held, cutting tool. Unlike the Oldowan, handaxe production involves the intentional shaping of the stone core into the desired form. This requires more complex action sequences (Stout et al., 2021) exhibiting a nested structure of goals and subgoals (Stout et al., 2008; Muller et al., 2017). Developing and realizing these more complex action plans requires greater perceptual-motor precision to reliably achieve more difficult (larger and on steeper core angles) flake removals (Pargeter et al., 2020). The imposition of the intended artifact form that appears with the Oldowan-Acheulean transition is widely regarded as a watershed in human cognitive and cultural evolution (Kuhn, 2021; Oakley, 1949; Lepre et al., 2011; Beyene et al., 2013). Although the actual degree and nature of imposed form evident in Acheulean artifacts are debated (Moore and Perston, 2016; Shipton et al., 2019), there is broad consensus that at least some examples resulted from procedurally elaborate, skill-intensive, socially learned, intentional production strategies (Moore, 2020; Stout et al., 2014; Shipton et al., 2019; García-Medrano et al., 2018; Sharon, 2009; Caruana, 2020). Comparisons between the Oldowan and Acheulean tool-making methods have thus been the most common focus

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Experimental evidence

for neuroarchaeological experiments. FDG-PET (Stout et al., 2008) and fNIRS (Putt et al., 2017, 2019) studies of action execution, as well as fMRI studies of action observation (Stout et al., 2011, 2021) and of technological judgments on stone tool stimuli (Stout et al., 2015) consistently find that Acheulean technology elicits greater frontal lobe activation. The single most consistently observed difference is greater Acheulean recruitment of the right inferior frontal gyrus (rIFG) (Stout et al., 2008, 2011, 2021; Putt et al., 2017), which is consistent with the apparent role of rIFG in complex action control and task switching (Dippel and Beste, 2015; Hartwigsen et al., 2018). However, Putt et al. (2017) proposed that rIFG activation instead reflects the use of inner speech to regulate behavior, based on the finding that rIFG was activated in learners who watched handaxe-making instructional videos at full volume (“verbal condition”) but not those with the sound off (“nonverbal condition”). In fact, inner speech can also help to support task-switching (Emerson and Miyake, 2003), but this interpretation is complicated by: (1) the fact that rIFG is not among the regions known to support inner speech (e.g., Geva et al., 2011), and (2) the likelihood that actual tool-making performance (the overt behaviors and action goals learned) of the two groups also differed as a result of different instruction, as suggested by a previous behavioral study (Putt et al., 2014) and its interpretation (Whiten, 2015). In support of a more direct role of rIFG in action sequencing, Stout et al. (2021) found that posterior rIFG response to tool-making specifically correlated with the structural complexity of observed action sequences quantified using hidden Markov Modeling and Context Free Grammar extraction methods. This interpretation is also supported by evidence that stone tool-making training induces structural remodeling in SLFIII white matter underlying rIFG, which is consistent with participation in the ventral-dorsal “vision for action” stream

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but would be difficult to explain as an effect of inner speech. Hecht et al. (2014) trained participants to make Paleolithic stone tools over a period of 2 years and used Diffusion Tensor Imaging (DTI) and Voxel-Based Morphometry (VBM) to measure white and gray matter structure before, during, and after training. They found changes in gray matter volume in IPL and in the structure of white matter leading into left IPL, bilateral premotor cortex in inferior frontal cortex, and rIFG. Results thus point specifically to structures associated with the ventral-dorsal processing stream. Strikingly, the observed effects of training parallel species differences between chimpanzees and humans (Hecht et al., 2015b), especially the overall expansion and increasing anterior extension of SLFIII. This suggests that similar plastic effects of tool-making experience in Paleolithic hominins would have provided a mechanism of phenotypic accommodation to behavioral innovations that could have then facilitated subsequent genetic adaptations (Laland et al., 2015). Such plasticity-led evolution, commonly referred to as the “Baldwin effect,” (Weber and Depew, 2003; Bruner and Iriki, 2016) is theorized to occur when novel, environmentally induced phenotypes generate selective pressure on formerly neutral genetic variants that increase the reliability and/or decrease the cost of developing the trait. However, the Baldwin effect can also be inhibited if behavioral/anatomical plasticity is sufficiently effective that the is little pressure or opportunity for genetic variants to improve upon it. The observation of stone tool-making training effect on SLFIII even in fully modern adult human brains raises the possibility that genetic accommodation of the phenotype remains incomplete, and that important aspects of human perceptualmotor processing and action organization, including neuroanatomical differences from chimpanzees, remain experience-dependent. This would be consistent with the relatively

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great plasticity and interindividual variability (Sherwood and G
omez-Robles, 2017) of human brains, as well as evidence that culturally transmitted behaviors can affect brain structure and function (Dehaene and Cohen, 2007; Zatorre et al., 2012).

Evolutionary interpretation Experimental neuroarchaeology evidence shows that stone tool-making exerts exceptional demands on visual processing streams (see also recent eye-tracking studies by Bayani et al., 2021; Silva-Gago et al., 2021; Silva-Gago et al., 2022). Comparative evidence in turn indicates that these streams have undergone substantial structural and functional change over the course of human evolution. This supports the argument that the selection of tool-making ability could have contributed to the evolution of distinctive human capacities for refined visuomotor control, complex skill learning, and observational action understanding that became central to further human bio-cultural evolution (Heyes, 2018; Muthukrishna et al., 2018). Thinking more broadly about potential evolutionary interactions between visuomotor behavior, developmental plasticity, and ecological/cultural inheritance, it suggests a perceptual motor hypothesis (PMH) for the evolutionarydevelopmental construction of the human brain and cognition (Stout and Hecht, 2017; Stout, 2021). Briefly, the PMH proposes that reliance on increasingly complex and skill-intensive paleolithic technologies generated selective pressure for enhanced perceptual motor processing as well as providing an intergenerationally recurring behavioral context (i.e., cultural/ ecological inheritance) driving plastic phenotypic accommodation in the same system. Such enhancements would have enabled the experiential construction of more sophisticated internal models of action (McNamee and Wolpert, 2019), including the “prosthetic” incorporation

of external objects (Arbib et al., 2009; Bruner, 2021) and well-developed intuitive physics (Fischer et al., 2016; Osiurak and Reynaud, 2020), that support both the acquisition of both object-directed technological skills and the development of social cognitive capacities for interactive alignment, affiliation, and mentalizing (Hasson and Frith, 2016; Shamay-Tsoory et al., 2019; Alcal
a-L
opez et al., 2019). These capacities would in turn have facilitated technological learning, collaboration, and innovation, potentially leading to further bio-cultural evolutionary feedback. As we have seen, Oldowan tool-making exerts demands especially on relatively concrete sensorimotor processing. It is commonly assumed that such “low-level” processes are highly similar across apes and humans and have not been an important locus of change compared to “higher-order” cognition supported by expanded cortical association areas. However, the relative perceptual-motor acuity and control of different primate species are surprisingly poorly known. Attempts to elicit stone tool making in captive apes suggest that Oldowan-like levels of proficiency are very difficult if not impossible for them to achieve (Toth et al., 2006; Rodrigo and Tennie, 2022, but see Eren et al., 2020). Further research is needed to confirm this and understand the specific learning challenges faced by apes and humans (Nonaka et al., 2010; Pargeter et al., 2020, 2021); however, Putt et al. (2019) reported an association between sensorimotor activations and Oldowan skill achievement in human novices and chimpanzee (nonlithic) tool-use performance has been found to correlate with heritable variation in ventral (superior temporal sulcus) and dorsal (inferior and superior parietal) stream gray matter volume (Mulholland et al., 2022). This is again consistent with the idea that the emergence of increased visuomotor dexterity, witnessed by but not limited to stone tool making, contributed to and/or was enabled by the evolution of enhanced visuomotor processing.

II. Visuospatial behavior and cognitive archaeology

Evolutionary interpretation

Critically for the PMH, these brain regions supporting these concrete perceptual and motor functions are closely associated, both topologically and in terms of connectivity (Margulies et al., 2016), with the primary sensorimotor cortices that connect the brain with the world. This makes them a key nexus for interaction between externalizing processes of niche construction (Flynn et al., 2013) and internalizing processes of neurocognitive development (Byrge et al., 2014). They are also early developing (Baum et al., 2020) and appear to be under relatively tight genetic control (Kennedy et al., 2017; Buckner and Krienen, 2013) compared with the association cortex. Sensorimotor systems are thus expected to be relatively labile to adaptation through genetic selection and also to exert important influences on the later developing, more highly plastic association cortices (Sherwood and G
omez-Robles, 2017) by structuring the inputs they receive. This is consistent with the view that many distinctive higher order human cognitive capacities, such as mentalizing (aka “Theory of Mind”), metacognition, and language, are developmentally constructed (Tomasello, 1999) and culturally evolved (Heyes, 2018) rather than genetically specified, as well as with evidence of cultural influences on modern human brain structure and function (Dehaene and Cohen, 2007). Indeed, Heyes et al. (2020) recently developed a similar argument that genetic adaptation is more likely to occur in peripheral input mechanisms rather than central cognitive processing. The evolution of enhanced visuospatial perception and action by Oldowan times would have changed both the quality of inputs to central cognitive processes and the kinds and frequencies of behaviors generating these inputs. The potential impacts of such a shift should not be underestimated. Developmental, psychopathological, and experimental evidence all indicate that perceptual-motor experience plays a critical role in cognitive development, with sensorimotor simulation in particular being important

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for social cognitive development (Adolph and Hoch, 2019; Alcal
a-L
opez et al., 2019; Byrge et al., 2014). On this account, predictive internal models of action supported by the ventraldorsal stream are thought to provide a unifying computational basis for motor control and social interaction (Wolpert et al., 2003), including the perception of self/other agency (Haggard, 2017) and the development of imitation, perspective-taking, empathy, and mentalizing capacities (Guzman et al., 2016; Heyes, 2018). Thus, enhanced perceptual motor processing would potentially have favored not only individual learning of new technical skills, but also observational action understanding, social learning, and the development of more sophisticated models of the self and other. The motor synergies required for effective stone knapping (Rein et al., 2013) are not perceptually available from observation and the development of skill is heavily reliant on individual trial-and-error practice (Pargeter et al., 2019). Nevertheless, there is experimental evidence that Oldowan-style knapping skill acquisition is facilitated by social opportunities for observation and instruction (Morgan et al., 2015; Pargeter et al., 2021) as well as direct archaeological evidence for the social reproduction of arbitrary knapping habits in the early Oldowan (Stout et al., 2019). The value of social “scaffolding” in the form of structured learning situations, demonstration, feedback, encouragement, and explicit instruction (Stout and Hecht, 2017) would only have increased with more demanding technologies (G€ardenfors and Högberg, 2017). To the extent that individual learning, observational learning, and social cognition might all have shared evolutionarydevelopmental foundations in ventral-dorsal stream internal models for action, this creates the possibility for powerful bio-cultural evolutionary feedback. It is thus notable that, in addition to difficulties with stone knapping, chimpanzees appear less able to discriminate agency through

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sensory prediction when trying to determine if a computer cursor is under their control or not (Kaneko and Tomonaga, 2012) and also show lower rates of mirror self-recognition (MSR) (Povinelli et al., 1993). MSR is a classic test of self/other awareness that likely requires comparing internal motor commands and predicted outcomes (forward models) with observed sensory feedback (Haggard, 2017; Suddendorf and Butler, 2013). Strikingly, Hecht et al. (2017) found that individual chimpanzees with more human-like SLFIII connectivity to anterior rIFG were more likely to succeed at MSR. This is the same region that displays functional activation and anatomical remodeling in response to stone tool making in humans (Hecht et al., 2015a). Indeed, human rIFG is involved in functions ranging from action execution, inhibition, and spatial attention, to mental reasoning and social cognition (Hartwigsen et al., 2018). Predictive processing (Clark, 2013) using ventraldorsal stream internal models for action may be a common computational denominator helping to explain the close anatomical association of these diverse functions in rIFG. If so, this anatomical/computation overlap would provide a key nexus for coevolutionary interaction, exaptive (Gould and Vrba, 1982) repurposing, and bio-cultural feedback as hominin subsistence and life-history strategies became increasingly technology-dependent (Kaplan et al., 2000; Shea, 2017a). Although not the focus here, this ventraldorsal stream visuomotor processing may also have been relevant to the evolution of the human capacity for language (Stout, 2018). Language clearly requires intentional communication based on self/other awareness and mentalizing capacities (Tomasello, 1999) discussed above, but it is also important to remember that it requires concrete physical media and actions (speech, sign, writing). Many authors have posited that spoken language may have been preceded by manual gesture, with the Mirror System Hypothesis (MSH) of Michael Arbib

(e.g., Arbib, 2012) providing the most fully developed scenario. According to the MSH, enhanced action recognition capacities allowed the emergence of intentional pantomimic communication, which was then conventionalized proto-sign and decomposed into fully compositional sign systems through cultural evolutionary processes prior to the shift to a vocal modality. Critically, it is the actual use of signs in a community that allows processes of iterated learning (Kirby et al., 2014; Christiansen and Chater, 2016) to result in such abstraction and grammaticalization. The visuomotor capacity to produce and distinguish increasing numbers of subtly different signs would thus have been critical, with these externalized physical tokens serving both as anchors for discrete, combinatorial semantics (i.e., true “symbols”) (Clark, 2006; Deacon, 1997), and a medium for the cultural evolution of syntax enabled by the hierarchical action sequencing capacities of the dorsal stream (Greenfield, 1991; Stout et al., 2021).

Conclusion Archaeology faces the challenge of reconstructing past behavior from fragmentary physical remains and imprecise modern analogies (Schiffer, 1988), whereas cognitive neuroscience faces its own challenges in making inferences about neurocognitive mechanisms from behavioral and physiological data (Poeppel et al., 2020). Experimental neuroarchaeology multiples these challenges in its attempt to infer the neurocognitive mechanisms supporting the reconstructed behaviors of ancient hominins using modern human research participants. Nevertheless, it can be a valuable research method when properly contextualized with comparative and fossil evidence and carefully interpreted in the light of contemporary evolutionary theory. This review focused on the neuroarchaeology of visuospatial behavior and, even more narrowly,

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References

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C H A P T E R

10

Cognitive archaeology, attention, and visual behavior María Silva-Gago, Emiliano Bruner Programa de Paleobiología, Centro Nacional de Investigaci
on sobre la Evoluci
on Humana, Burgos, Spain

Vision, attention, and human evolution Mammals are, in general, nocturnal animals, and their sensorial landscape is largely shaped by olfactory and acoustic signals. In contrast, primates have regressed to a diurnal life style, through a large array of adaptations including color vision, binocular vision, and a complex visual cortex (Barton, 1998, 2004; Heesy and Ross, 2001; Surridge et al., 2003). These comprehensive changes involved frontation of the orbits, expansion of the occipital lobe, and several adjustments in ecological and social factors. As a result, primates largely rely on visual cues to generate a world made up of shapes and colors, signals which became crucial to cope with feeding, protection, and reproduction. Due to tens of millions of years of evolution in this direction, primate cognition is therefore strongly visual-based and visual-biased, giving the visual inputs a determinant role in channeling and determining our behavioral and emotional responses to the external environment. Attention is a key cognitive capacity, which represents a common root for any behavior based on perception, decision-making, learning,

Cognitive Archaeology, Body Cognition, and the Evolution of Visuospatial Perception https://doi.org/10.1016/B978-0-323-99193-3.00013-1

and general intelligence (Knudsen, 2007; Petersen and Posner, 2012; Rueda, 2018; Bruner and Colom, 2022), and visual attention is, for a primate, a major interface between behavior and environment (Hodgson, 2018). The cortical route of visual attention, in terms of neural activation, moves frontwards from the occipital regions (reception and decoding), to the parieto-temporal regions (association and integration), to the frontal regions (executive functioning and control) (Itti and Koch, 2001; Kastner and Pinsk, 2004). Despite the fact all these cortical districts are involved in the attentional flow, there is no doubt that the parietal lobes represent a crucial node of visuospatial coordination that integrates external (environment) and internal (body) frames (Rushworth et al., 2001; Tunik et al., 2007; Xu, 2018). Taking into consideration the importance of visual attention in the development and execution of most cognitive capacities, and the noticeable evolution of the parietal cortex in our species (Bruner, 2018; see Chapter 7), it is reasonable to speculate that visuospatial ability has undergone profound changes during the course of human evolution. In this context, it is important to

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consider that attention is based on different factors that, although working in an integrated way, are probably independent in terms of neural pathways (Petersen and Posner, 2012). Therefore, it may be expected that visual attention has possibly evolved differently in distinct human lineages. For example, Neandertals have been hypothesized to have a larger occipital cortex when compared to modern humans, and this has led to speculation regarding whether or not their visual system could have been based on a distinctdand specializeddneural mechanisms (Pearce et al., 2013). The cerebral proportions of Neandertals are, anyway, more similar to other extinct human species, and their occipital dimensions, if confirmed, could be hence interpreted as a plesiomorph retention shared with the rest of the human genus. Instead, Homo sapiens display several traits which suggest a larger and more complex parietal cortex and, in this case, changes in the visuospatial capacities and behaviors are therefore to be expected (Bruner et al., 2018a). However, the study of the visuospatial functions in evolutionary anthropology is not an easy task, taking into consideration the limitations when working with extinct species. Information on living taxa (like nonhuman primates) is interesting but not conclusive because living species do not represent ancestral conditions, but specialized and independent lineages with their own evolutionary adaptations (Bruner, 2019). So, an alternative is to use the archaeological evidence to make inferences on visuospatial behaviors or, at least, on the traces they have left on the ecological landscape and technological remains associated with those cognitive processes. Evidence from throwing capacity (Kim et al., 2011; Williams et al., 2014; Coolidge et al., 2016; Lombard, 2021), handling and haptic capacity (Fedato et al., 2019, 2020, see also Chapter 11) or graphic capacity (Hodgson, 2008) are directly involved in visuospatial skills. Visual attention can be specifically examined and quantified also by directly following the eye movements when a subject is visually

scanning the environment through both conscious and spontaneous patterns. Conscious patterns are related to voluntary changes in attention, while spontaneous patterns are influenced by exogenous factors (Connor et al., 2004). Because of the importance of vision in primates, visual exploration is generally the first stage in subjecteenvironment interaction (Goodale and Humphrey, 1998) and, accordingly, the process of visual scanning would be expected to be strongly influenced by evolutionary factors. In this sense, there is a reciprocal relationship between the observer and the focus of attention. On the one hand, the subject explores the environment according to sensorial, perceptual, and neural principles. At the same time, the environment triggers some specific responses, by virtue of salient features associated with its geometry or spatial organization. It is reasonable to speculate that evolution and selection have operated in both directions, calibrating the visual attentional mechanisms according to this feedback. In this case, the archaeological record can contain the remnants of such a connection, and the archaeological evidence (in particular its technological aspects) can therefore reveal major changes in the subjectetool visual relationship.

Eye tracking technology Eye tracking is a technology that consists in measuring eye positions and eye movements, based on the fixation-saccade system (Xiao et al., 2018). Vision is an active process that involves different kinds of eye movements, although the main events are the fast, exploratory shifts called saccades, which ensure that the central fovea of the retina is focused on objects and regions of interest (Kowler, 2011). In fact, details can only be perceived correctly in the fovea. Hence, the location at which the fovea is fixed must be able to be changed quickly in order to maintain an accurate representation of the

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Eye tracking technology

environment and to perceive new stimuli appearing in the visual field (Proctor and Proctor, 2021). Fixations correspond to the intention to maintain gaze on a point of interest, while saccadic movements are manifestations of shifting the focus of attention. Fixations are mainly associated with the cognitive processing of information regarding the focused object, with a duration ranging from 200 to 400 ms. Longer or more frequent fixations on a specific region indicate an increase in cognitive response (Duchowski, 2017). In contrast, saccadic movements, with a duration between 30 and 120 ms, are fast movements between two fixation points and accordingly they are an efficient way of exploring a scene (Kowler, 2011). Despite its speed, saccades are intended as voluntary eye movements, which allow optimizing the position of the eyes and correcting fixation events (Proctor and Proctor, 2021; Willeke et al., 2019). Eye movements are essential to cognitive processes as they carry visual attention to the specific parts of some stimuli that are processed by the brain (Sharafi et al., 2015). The recording of eye movements provides a useful tool to understand the observer’s attention while performing an action (Xiao et al., 2018) and, in this sense, fixations and saccades can be seen as a proxy for the schemes and patterns underlying attention (Carrasco, 2011). Fixation patterns deal with overt attention, defined as the act of selecting an object or location over others by moving the eyes so that they point in that direction (Posner, 1980). However, information can also be extracted from peripheral vision via covert attention, namely, the ability to pay attention to a particular point even when not directly observing it (Rosenholtz, 2016; Wolf and Whitney, 2015). In this sense, eye tracking provides information concerning the position where primary and selective attention is directed (Findlay and Gilchrist, 2003; Kowler, 2011). An eye tracker is a device most often used for measuring eye movements. In general, there are two types of eye movement monitoring

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techniques: those that measure the position of the eye relative to the head and those that measure the orientation of the eye in space, also called “point of regard" (POR) (Duchowski, 2017). The first eye trackers were designed at the end of the 18th century and focused on the observation of eye movements during reading. Those devices showed that reading does not consist of a smooth sweep of the eyes along the text, but involves a series of fast movements combined between pauses (Javal, 1879). The first equipment that achieved high accuracy was based on the placement of contact lenses over the eye, employing a wire coil that measured the movements through an electromagnetic field (Young and Sheena, 1975). It was one of the most precise eye movement techniques but it required a mechanical or optical reference object mounted on a contact lens, which is then worn directly on the eye (Duchowski, 2017). The contact lens had to cover the cornea and sclera by employing a wire coil, causing discomfort. This method also measured eye position relative to the head. Later, eye movements were recorded by projecting a light source onto the eye due to the development of cinematographic photography. Another method used at the beginning of the past century was electro-oculography, which measures electric potential differences of the skin through electrodes placed around the eye. This technique measures eye movements relative to head position, and it was the most widely applied eye movement method during the 1970s. All these early eye-recording systems were scarcely accessible and invasive, although they did permit real-time data processing and allowed the first scientific investigation regarding this topic. From the 70s, eye trackers became less intrusive and began to use video recording to locate the position of the gaze on a computer screen by detecting the corneal reflexion and the center of the pupil (Duchowski, 2017). This equipment was the first real gaze point recording method. In these devices, the head must be fixed so that

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the position of the eye relative to the head and the point of regard coincide, or different ocular features must be measured in order to disambiguate head movement from eye rotation (Proctor and Proctor, 2021). Among the characteristics to be quantified are the corneal reflexion and pupil center. The corneal reflexion is measured through a light source, usually infrared light. The gaze is tracked through the calculation of the position of the center of the pupil and the corneal reflexion in a digitized image of an eye camera (Jacob and Karn, 2003). At present, this method is probably the most common one employed with current devices. Current eye trackers that use the latter method are mainly divided into remote or screen-based eye trackers (static and dependent on a computer screen), and portable eye trackers, with greater freedom, thanks to the use of mobile devices (Fig. 10.1A and B). With the former, visual responses to digital stimuli can be analyzed, recording eye movement data at high frequencies, above 1000 Hz. Screen-based eye trackers are designed to carry out comprehensive, high-precision, behavioral research, from fixation-based studies to microsaccadic movements (Proctor and Proctor, 2021). They can be used for eye-tracking of images, videos, websites, video games, software interfaces, 3D environments, or mobile phones. On the other hand, portable eye trackers deal with real-world studies in a wide variety of environments, because digital stimuli are not needed. Portable devices are generally designed as glasses and allow eye tracking to be performed more freely, as they do not require the subject to hold their head still. Accordingly, these devices offer users greater freedom to move and interact with the environment and allow more natural behavioral responses. The use of this type of equipment is becoming more common, due to the advantages it offers and, in recent years, there has been an increase in the number of devices available on the market. However, improvements in freedom of movement usually lead to a decrease in accuracy

and pupil detection, when compared to screenbased eye trackers. Screen-based eye trackers are easier to handle because the process is more automatic, although they need a more complex (and time-consuming) preparation of the stimuli displayed, like the generation of the different areas of interest through the items. Additionally, they can measure a wider number of variables and methods for data analysis. In contrast, portable eye trackers are more comfortable to apply but need more attention from the experimenter during the recording, to make the sampling procedure successful. For example, it is common that pupil detection is lost during the experiment due to some sudden movement of the participant, and the corresponding data has to be deleted. Furthermore, in the case of portable devices, data processing is more difficult and slower, compared to screen-based eye trackers. Depending on the aims of the study, one or another kind of device will be chosen. Inevitably, real-world studies need portable eye trackers. Nevertheless, in some cases, similar visual behavior can be detected when observing images or real stimuli (Silva-Gago et al., 2021, 2022a). Additionally, the budget must be taken into account. Although there are economical screen-based eye trackers, they do not usually include the necessary resolution or requirements to develop a research work. Another example of low-cost digital eye tracker is the webcam-based algorithm (Papoutsaki et al., 2016; Semmelmann and Weigelt, 2018). This method provides reasonable accuracy, but it is not advisable in studies that require detailed spatial resolution of the eye movements (Semmelmann and Weigelt, 2018). The price of appropriate remote devices is generally expensive due to the high efficiency and data quality characteristics. On the other hand, portable eye tracker costs range from a 1000 to more than 20000 euros. The price depends on the hardware specifications and software access. For instance, one of the most accessible devices involves open-source software and

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FIGURE 10.1 Eye trackers used in the experimental procedures (a: Screen-based eye tracker, b: Portable eye tracker), Different conditions analyzed (c: Stone tool interaction, d: Tool-making) and examples of videos analyzed (e: Visual-only exploration in the real world, f: Visual exploration during manipulation). Modified from: Silva-Gago et al. 2021, 2022b, 2023.

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open platform for the development of new approaches, not originally included. Recently, new portable devices have been developed in order to improve pupil detection, accuracy and cover the entire field of view with no need for calibration. As mentioned, traditional portable eye trackers sometimes fail to detect the pupil in experimental procedures that involve tasks where participants have to look down and up regularly, or if the subject covers a large area within the user’s field of view. Another reason for nondetection might involve the morphology of the participant’s eye. In this regard, a general recommendation is to perform the task frontally to avoid loss of pupil detection, as well as to warn participants in case they lower their gaze. An interesting theoretical enhancement of these new devices involves the adaptation to eye and face morphology. Make-up can also causes the pupil to be detected incorrectly. Therefore, it is advisable to request that participants do not wear make-up during the experiment. Eye-tracking studies on low-size items may not be possible using most portable eye trackers. From a methodological point of view, with small stimuli, it is not possible to assign the different fixations to the regions of interest described, because the point of fixation occupies an area that is similar or even larger than the object itself. Taking into account all these limitations, in general, some subjects must be excluded from the sample during a first data screening because their measurements are invalidated due to failures of detection or registration of the signal. Traditionally, cognitive psychology researchers have used eye trackers to study information-processing tasks. Since eye trackers have become more accessible, their use has increased as well as their application in different disciplines, such as neuromarketing, industrial design, computer sciences, and medical research. For instance, eye tracking is used to understand customer behavior and improve sales (Li et al., 2014; Mou and Shin, 2018; Scott et al., 2019), to develop gaze-controlled robots, or enhance

safety in driving (Fujii et al., 2018; Gr€ uner et al., 2017; Wijayasinghe et al., 2019). Other applications deal with gaming and virtual reality (Rappa et al., 2019; Saldana et al., 2020; Souchet et al., 2021) or the diagnosis of specific conditions like Parkinson’s Disease and autism (Bekele et al., 2014; Hodgson et al., 1999, 2013; Pambakian et al., 2004).

Visual attention in cognitive archaeology Visual perception and prehistory One of the main aims of archaeology is to understand the behavior of past human populations, and their interaction with the environment. We can therefore wonder whether it is possible to investigate how humans viewed and interacted with objects through the analysis of visual perception (Pettitt et al., 2021). Apart from any theoretical speculation, this topic may be approached through experimental methods in cognitive science, which can support quantitative data in the exploration of perceptual patterns and the testing of specific hypotheses. The interest in visual culture is not new from an archaeological perspective (Opitz, 2017). Some scholars have focused on the visual response to prehistoric art (Janik, 2021; Meyering et al., 2020; Romankiewicz, 2021), although following other psychological methodologies rather than eye-tracking technology. These studies have analyzed the relationship between the artistic styles or topics and the underlying mechanisms of the visual system. For instance, the neurophysiological responses were addressed according to different types of art (Janik, 2021; Romankiewicz, 2021). On the other hand, a computer-based program developed for facial expressions provided information about the saliency of some anatomical parts of animals in rock art (Meyering et al., 2020). Specifically, the anatomical parts most frequently depicted in rock engravings are the same as those considered most salient in photographs of present-day animals (Meyering et al., 2020).

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The application of eye tracking systems to archaeological artifacts is recent (Bayani et al., 2021; Criado-Boado et al., 2019; Silva-Gago et al., 2021, 2022a). For instance, Criado-Boado and colleagues (2019) published the first experimental study on images of archaeological pottery recipients from different prehistoric chronologies. According to these authors, pottery decoration imposes a strong bias on visual behavior, suggesting a firm relationship between the changes in the shape and decoration of pottery with the social complexity of the societies to which they belonged. This application of eye tracking to archaeological remains further develops the hypothesis that the cognitive system includes cultural artifacts, instead of considering the objects as products of the brain (Clark and Chalmers, 1998; Malafouris, 2010, 2013). Nonetheless, all these studies considered vision when dealing with artistic or esthetic components of past cultures, and were therefore concerned with recent stages of human evolution. If we want to investigate perceptual patterns associated with the evolution of the human genus, we must necessarily rely on lithic technology. Accordingly, in recent years, eye-tracking has been applied to the visual exploration of Lower Palaeolithic stone tools, such as choppers and handaxes (Silva-Gago et al., 2021, 2022). Despite the never-ending debates on the use and functions of these tool types, they represent the most archetypal tools associated with the early and most archaic human species (Baena Preysler et al., 2018; Peretto et al., 1998; Shea, 2020; Venditti et al., 2021). There is an abundant archaeological record and a vast literature on their morphology, on the archaeological contexts, or on their use according to experimental procedure (tool making and grasping). Importantly, these tools are handled with the whole hand and have a considerable size and weight, when compared to flakes and smaller technological traditions. These features make these tools more useful and critical to study when sensing and perception are involved.

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Saliency and affordances A number of factors can influence how attention and fixation are directed toward a visual scene. There are mainly two types of mechanism, called “bottom-up” and “top-down.” The former process concerns the visual features of an image that clearly stand out from the visual background due to their properties (i.e., local discontinuities in color, brightness, texture, or orientation of the visual elements). In this sense, vision is driven by the characteristics of the stimuli. It is a pure sensory input, which involuntarily draws attention to certain features because it may be important for any nonspecific reason (Duchowski, 2017). The bottom-up factors are directly related to the salient regions of an image, which are the areas that attract more attention because of their prominence relative to the context, to the background, or to the landscape. Saliency effects based on simple visual properties are implemented at early stages of visual processing (Connor et al., 2004). In this sense, salient object detection makes the scene processing more efficient as it extracts the most important and informative part of a scene without any previous knowledge (Xiao et al., 2018). A highly influential perspective has been the development of computational models of visual salience, which can quantify the extent to which parts of an image are conspicuous (Itti and Koch, 2001). These models create saliency maps from images, generated using algorithms that deconstruct the original image into different visual feature channels (color, brightness, angle, orientation, etc.). Then, contrast values are calculated at each position in the image extracted for each of the feature channels (Harel et al., 2007). The result is an individual feature map combined with a unique saliency map, which could be viewed as a “heat map” and overlaid over the original image to identify which areas have maximal salience based on image properties. Therefore, saliency maps are strongly related to “bottom-up” factors. Regarding the

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measurements of visual patterns, fixations usually correlate well with calculated visual salience in natural scenes (Fousham and Underwood, 2008; Tatler et al., 2011). However, a specific region or part of an object can be said to be salient if it captures the subject’s visual attention as defined in terms of frequency or duration of fixation (Ma et al., 2016) . In this sense, attention can be directed to other features of the visual scene apart from the visual saliency. For instance, attention can be influenced by aims and actions. These “top down” factors deal with the importance of an object or feature in a (conscious) task goal (Connor et al., 2004). Topdown processes are considered to be mediating components in the frontal activation of executive functions, thus constituting a mechanism underlying selective attention and producing a sequential activation of the frontoparietal region (Proctor and Proctor, 2021). In addition, topdown cognitive control requires subsequent operations on sensory input by a higher-level cortex (Connor et al., 2004). These characteristics, which draw attention despite not being salient, can be related to affordances (Gibson, 1979). Originally, the term affordance referred to all action possibilities of the environment (Gibson, 1979), but currently it is described as a characteristic of an object that provides information and allows a subject to perform an action (Vingerhoets et al., 2009; Makris et al., 2011; Turvey and Carello, 2011; Borghi, 2007). Regarding tool use, affordances have been described as “an animal-relative, biomechanical property specifying an action possibility within a body/handcentered frame of reference” (Osiurak et al., 2017). From an archaeological point of view, affordances have been defined as opportunities, resources, and constraints detected by the subject (in this case, an extinct hominid) in the materials and the environment through active exploration (Pargeter et al., 2020; Wynn, 2020). Such affordances might drive the eyes and visuospatial attention toward the regions of the tool that are most relevant to its action (Myachykov et al.,

2013; Roberts and Humphreys, 2011). Affordances can be processed by observing both pictures of objects and real objects, because they activate reaching and grasping actions (Ambrosini et al., 2011). Although these “bottom-up” and “topdown” mechanisms can act independently, they usually work together under natural conditions (Kowler, 2011; Krauzlis et al., 2021). With these premises in mind, visual salience and visual attention in stone tools were studied to explore whether the former influences the exploration or whether there are other mechanisms that affect the visuospatial pattern (SilvaGago et al., 2022a). Interestingly, there is no correspondence between the saliency maps and fixation maps for Lower Palaeolithic stone tools (Fig. 10.2). In general, visual attention was not spontaneously directed toward the most salient areas. Fixation maps generally highlighted the center and the upper region of stone tools, while salient areas were located on the stone tools’ edges (Silva-Gago et al., 2022a). Overall, these findings could be due to the action-specific perception account, according to which people perceive the surrounding environment in terms of their visual behavior (Ambrosini and Costantini, 2016). Consequently, the absence of correspondence between saliency and visual exploration has been interpreted as the processing of action affordances in Palaeolithic artifacts, because stone tools trigger the same visual response as any common tool (Silva-Gago et al., 2022a). Humans instinctively look for potential actions while passively exploring images, and they can identify stone tools for what they are, namely, tools, and not simply stones.

Visual exploration of stone tools Tools are a singular kind of object. They are mainly defined by their intrinsic properties that afford manipulability and, hence, their interaction with the environment (R€ uther et al., 2014), and activate both manipulation and functional

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FIGURE 10.2 Comparison between average saliency maps and average fixation maps overlaid over the average chopper (above) and handaxes (below) shape. Redrawn after Silva-Gago et al. 2022a.

information (Costantini et al., 2011). Indeed, tools have been described as an object attached to the body, which amplifies and enhances the user’s abilities (Federico and Brandimonte, 2019; Wagman and Carello, 2001). Viewing tools activates brain regions involved in action execution even during passive viewing, and when there is no specific task instruction (JohnsonFrey, 2004; Makris et al., 2011; Natraj et al., 2015; Tipper et al., 2006). It has been proposed that a “tool” must be intended as an extension of the neural and body system, essential to the cognitive process as part of a social and technological network (Bruner and Gleeson, 2019). Therefore, we should distinguish between tooluse and object-use. The latter case has been often described in a number of vertebrates, such as primates and birds and, although at different levels of behavioral complexity, it does not involve the intimate cognitive response and connection described for the human “prosthetic capacity”

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underlying our specialized brain-body-tool system (Bruner, 2021). The notion of tool is based on two different meanings, namely, the information concerning targets and the information associated with handling procedures. Therefore, the perception of these two kinds of references is mandatory to successfully use a tool (Osiurak et al., 2017). A tool can be divided into functional and manipulative areas and, accordingly, these properties are characteristic of each standard tool (Pilacinski et al., 2021). The handle is crucial in the grasping of a tool, while the opposite region provides information about th function. The mean duration of fixation in different areas of interest is commonly used as an indirect measure of the amount of visuospatial attention assigned by participants to different regions of the visual scene (Federico and Brandimonte, 2019). Consequently, fixations directed toward grasping areas should be more associated with handling, while fixations focused on functional regions would indicate the processing of potential uses. Regions of interest involving the theoretical functional and grip areas can also be described in stone tools (i.e., upper region and knapped surface; base and cortical surface, respectively). Studies developed in present-day utensils show that the functional part of a tool captures more visuospatial attention than the grasping area (Ambrosini and Costantini, 2016; Land, 2006; Natraj et al., 2015). However, humans also show a tendency to look at the center of objects or scenes in response to a visual field refinement strategy (Doran et al., 2009; Ioannidou et al., 2016; Le Meur and Liu, 2015; Tatler, 2007; Tseng et al., 2009; Xiao et al., 2018). All visual attention studies concerning tools have been carried out on current and common everyday artifacts, but recently these methods have also been applied to Palaeolithic artifacts in order to explore the spontaneous visual response elicited by stone tools (Silva-Gago et al., 2021, 2022a). This analysis of visual attention revealed that the stone tool’s center and the

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knapped or worked surface are the most observed areas. In general, directing the gaze to the middle region could be explained due to the above-mentioned “center bias” (Doran et al., 2009; Ioannidou et al., 2016; Le Meur and Liu, 2015; Tatler, 2007; Xiao et al., 2018). This behavior is associated with a general spatial processing strategy to optimize the visual field (Ioannidou et al., 2016; Tatler, 2007; Tseng et al., 2009). By looking at the center of an object, it is possible to observe the complete surface due to peripheral vision. On the other hand, focusing on the center of an object may be related to the visual location of the center of gravity (Brouwer et al., 2009) or to an easier way to attend to and act on an object (Fehd and Seiffert, 2010). Apart from the center bias, gaze was generally directed toward the active areas in common tools (Federico and Brandimonte, 2019). Studies on current tools have shown that the top region was usually more observed than the base (Federico et al., 2022; Juravle et al., 2015; Park et al., 2013). Indeed, the functional areas were frequently located at the distal or upper ends, while the base was associated with the grasping surface (Myachykov et al., 2013). Accordingly, also in stone tools, upper regions and knapped surfaces triggered more visuospatial attention than the tool base or cortical surface. As the upper region can be associated with the functional area of a tool, the knapped surface is also closely related to the working area of the tool. On the other hand, the cortex and the base of the stone tools mainly correspond to the areas where they would be gripped. Therefore, functional areas of stone tools trigger more visuospatial attention than grasping regions, as also observed in modern technological artifacts. Usually, the visual exploration of the standard tools starts with functional processing, directing more fixations to functional areas (Ambrosini and Costantini, 2016). As the exploration progresses, the eyes scan the rest of the

object, and the gaze is more directed toward the handle areas too (Federico and Brandimonte, 2019). In this sense, functional affordances initially guide the gaze to select the first useful information about its purpose and, then, toward information that deals with how to use or grasp the tool (Ni et al., 2019). Namely, the first information to be visually processed is the functional information (what), followed by the practical one (how) (Federico and Brandimonte, 2019). Therefore, it has been proposed that the reasoning process begins with semantic information, moving on to mechanical, and finally sensorimotor information. This mechanism is known as the “semantic-mechanical-motor cascade system” (Federico et al., 2021; Osiurak et al., 2020). Indeed, regarding the sequence of fixation in stone tools (Fig. 10.3), the middle region was the first area observed, maybe due to the center bias (Ioannidou et al., 2016; Tatler, 2007; Tseng et al., 2009). Subsequently, the next examined area was the upper region of the stone tools. In contrast, the tool base received no initial fixation. The knapped surface was also observed before the cortex. The second observed area was usually the upper region, where most of the knapped surface is also located. In this case, the middle region was barely observed compared to the other two regions. The location of the last fixation was more distributed across the different areas of interest defined in the tool. The last fixations were also mostly directed toward the center, although the upper region and the tool base also slightly attracted attention at the end of the visual exploration. The knapped surface also triggered more attention than the cortex as the visual scanning was finishing. At the beginning of the observation, more attention was paid to the functional area and, gradually, the areas associated with manipulation became more significant. In sum, Palaeolithic stone tools triggered the common behavior elicited by modern standard tools.

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FIGURE 10.3 Above: Fixation sequence observed in the analysis. Exploration starts in the center, moves upwards, then looks at the base and finally ends in the center. Below: Percentages of fixation in the first, second, and last observed area. Gaze is first directed to the middle and the knapped regions and then moves to the upper and the knapped surface. The exploration ends again at the middle and knapped surface. Please note that the cortical surface is shown in the center for graphical reasons.

Visual attention during stone tool manipulation Humans are specialized in eye-hand coordination and bodyetool interaction through a complex visuospatial system (Vaesen, 2012; Bruner et al., 2018a, Bruner et al., 2023). Eye-hand coordination is based on sensory mechanisms that control eye and hand motions as a single unit. It includes the visual control of both eyes and hands while using eye movements to optimize vision at the same time (K} ov
ari et al., 2020). Coordination of eye and hand movements requires an appropriate spatiotemporal activation of the subcortical

structures which control the eyes and hand (Battaglia-Mayer and Caminiti, 2018). Vision generally guides the movement of the hand to bring the observed target to the fovea, where visual acuity is higher (Battaglia-Mayer and Caminiti, 2018). Hence, fixations map the environment to determine the objects’ position and evaluate the human body’s coordinate system relative to the world’s coordinate system (K} ov
ari, et al., 2020). The area in which people tend to interact with objects using their hands and to which individuals direct more visual attention is called peripersonal space (Park et al., 2013; see Chapter 3).

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Peripersonal space is usually defined as the space that is within reach of the hand (Rizzolatti et al., 1997). It deals with the representation of the space near the body (Brozzoli et al., 2012) and can be modified by tools through visual and haptic perception (Malacuso and Maravita, 2010). In addition, peripersonal space is where the perceptual system integrates information from the visual and haptic senses (Park and Reed, 2015). The neuronal activation of the frontoparietal system is different according to the location of an object in relation to one’s own body (Cl
ery et al., 2015). Peripersonal space also plays an important role in the recognition of tool affordances because the effective processing of affordances depends on its spatial position. In fact, action perception only takes place when the object is located within the peripersonal space (Costantini et al., 2010, 2011). In this sense, tool features can be interpreted as affordances only when the tool is within the observer’s reach, suggesting that the functional aspect of peripersonal space is relevant for affordance processing (Costantini et al., 2010). Visual attention is naturally directed to the tool’s tip, although it can be redistributed due to functional information when positioned in peripersonal space (Park et al., 2013). The presence of visual stimuli near the body can cause a shift of attention, prioritizing space, and precise recognition (Park et al., 2013). In the peripersonal space, an object is perceived as a potential tool but, when handled, it is integrated into the body scheme (Cl
ery et al., 2015; Maravita and Iriki, 2004; Quallo et al., 2009). Regarding the role of vision during haptic manipulation, grasping is usually influenced by visual perception (Brouwer et al., 2009; Gonzalez and Niechwiej-Szwedo, 2016; Stone and Gonzalez, 2014). Although haptic and visual responses are complementary, vision guides the hand movement when an object is first observed

(Eloka and Franz, 2011; Hodgson et al., 1999; Juravle et al., 2015). It is possible to direct gaze at objects without moving the hand, but the opposite is more infrequent. All information on the characteristics of the object is obtained through previous eye movement toward the target (Gonzalez and Niechwiej-Szwedo, 2016). Additionally, grip modality generally depends on visual feedback regarding the body and the object during the movement (Land and Hayhoe, 2001; Schettino et al., 2003). Following these assumptions, visual behavior was also investigated while handling stone tools replicas (Silva-Gago et al., 2021). It was found that manipulation does not apparently influence the pattern of visual scanning (Silva-Gago et al., 2022a). Hence, stone tools triggered the same visual response when exploring two-dimensional images, real threedimensional items in a peripersonal space or during their handling. This evidence suggested that the haptic information only supports visual exploration because the visual behavior was identical regardless of whether the stone tool is touched or viewed in peripersonal space. The predominance of visual information over touch during object manipulation has been demonstrated in different experiments (Atkinson, 2008; Kassuba et al., 2013; Land and Hayhoe, 2001; Gonzalez and Niechwiej-Szwedo, 2016; Schettino et al., 2003; Stone and Gonzalez, 2014). Therefore, the visual response was considered to guide manual movement, which does not influence the visual exploration scheme (Eloka and Franz, 2011; Hodgson et al., 1999; Juravle et al., 2015, 2018). Since the visual behavior was the same regardless of the kind of exploration used, the amount of visuospatial attention directed to each tool region can be once more interpreted as the recognition of different affordances through visual perception (Cosentino, 2021).

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Sex differences in visual perception Concerning the comparison between females and males, the existence of sex differences in cognitive skills is supported by different sources of evidence (Halpern, 2000). In particular, males usually display better performances in activities requiring visuospatial abilities, pointing skills, tracking moving objects, or mentally rotating figures (Collins and Kimura, 1997; McGivern et al., 2019). On the other hand, females stand out in memory and verbal fluency tasks, object and location recognition, as well as episodic and autobiographical memory (Herlitz et al., 1997). In addition, in tasks concerning movement and orienting within the environment, males perform better when following a strategy based on cardinal points, while females are better when landmarks are available (McGivern et al., 2019). Differences concern of course the group average values, with a consistent overlap of the distribution ranges. These cognitive differences are based on the different processing of visual information from the dorsal and ventral streams (Handa and McGivern, 2015) and have been hypothesized to be the result of evolutionary selection (Geary, 2022). In particular, visuospatial performances have been hypothesized to be involved in sexual differences associated with social roles in prehistory (Estalrrich and Rosas, 2015; Villotte et al., 2010; Burke, 2012). In fact, females and males follow distinct visual strategies (Bock and Kolakowski, 1973; Robinson and Kertzman, 1990; Merritt et al., 2007). Males usually rely more on the general geometry of objects, following a bottom-up strategy, while females focus more on details and characteristics, following a top-down approach (Piber et al., 2018; McGivern et al., 2019). Furthermore, eyetracking studies confirmed sex differences in search strategies, with males consistently exploring more space in the early stages of visualization while females show initially longer fixation durations (Piber et al., 2018). Nevertheless, no differences have been found between males

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and females during the observation of stone tools or during the visual exploration aided by touch (Silva-Gago et al., 2021, 2022a).

Differences between technologies Generally, the visual exploration pattern of stone tools is influenced by the functional areas of the tool. However, a number of differences were found between the visual attention directed to distinct typologies of Lower Palaeolithic stone tools, as when comparing, for instance, choppers and handaxes (Fig. 10.4). Specifically, choppers triggered more visual attention on the upper and middle region, coinciding with the knapped surface. The reduced attention on the chopper’s base could be related to the lesser difficulty in understanding how they could be grasped, which allowed attention to be directed to the functional area of the tool (Silva-Gago et al., 2021, 2022a, 2022b). In other words, the simpler morphology of the chopper could drive the gaze toward its functional regions because a complex manipulation strategy is not needed. Choppers did not require exhaustive reasoning on how to be grasped, which caused the low attention dedicated to the base of the stone tool. Hence, the affordances associated with functional aspects were considered more relevant when visually scanning this kind of stone tool. Concerning the influence of physical variables of the tools, weight has been described as the main characteristic which affects the visual exploration of a chopper (Silva-Gago et al., 2022b). Although weight is generally perceived through touch (Klatzky et al., 1987), weight estimations are generated visually before physical contact with an object (Zahariev and McKenzie, 2007; Vicovaro et al., 2019). In experimental studies with stone tools, heavier choppers triggered less attention toward the tool base when the tool was observed in the peripersonal space. Hence, the weight estimation of choppers and,

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FIGURE 10.4 Comparison between chopper and handaxes in terms of percentages of dwell time (DT) according to each tool area of interest during visual exploration. Knapped and cortex surfaces are shown on the left of the tool, while upper, middle, and base regions are shown on the right.

consequently, handling management, was evaluated prior to their manipulation. Taking into account that choppers were used in activities based on percussion, weight is considered a relevant stone tool characteristic (Pante and de la Torre, 2018; Merritt and Peters, 2019; Toth and Schick, 2015; Venditti et al., 2021). One possible explanation could be that heavier choppers trigger more attention to their potential use, while lighter ones on the possible interaction with the hand in other kinds of activities. Instead, handaxes were more observed across the midline of the tool, including the tip and base (Silva-Gago et al., 2022a). Specifically, the base of handaxes was more explored when compared with choppers. It has been suggested that the amount of visuospatial attention directed toward this region may be related to manipulation strategies. The existence of sharp edges on almost the entire outline of the tool, as well as the absence of a cortical surface to facilitate

handling, can cause difficulties in estimating a comfortable grip. Accordingly, more time was needed to consider an ergonomic position of the hand. Handaxes could have been used in a wide range of activities (Domínguez-Rodrigo et al., 2001; Panera et al., 2019), although they would have generally been handled on their base (García-Medrano et al., 2014; Gowlett, 2006; Key et al., 2016; Weiss, 2020; Wynn and Gowlett, 2018). Hence, it is likely that the need to obtain information regarding proper handling can require a longer exploration time of the handaxe base. Attention directed to the handaxe base may also be related to its possible use in some activities of percussion (that is, using the base as a functional region), although this was a less common practice. In addition, the tendency to look at the bottom of an object is also related to the visual calculation of mass and weight (Samuel and Kerzel, 2011). Following these assumptions, the longer observation of

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the handaxe base has been suggested to be an assessment of handling management due to its complex cognitive behaviors and diverse action characteristics (Gowlett, 2006; Herzlinger et al., 2017; Toth and Schick, 2015; Stout et al., 2015; Weiss, 2020; Wynn, 2020). All of the abovementioned interpretations are not mutually exclusive. In handaxes, attention is correlated with some physical features of the tool. Specifically, handaxe size and shape have a moderate but significant influence on the visual scanning of the tool (Silva-Gago et al., 2022b). In regard to general dimensions (size), psychological studies show that visual attention can be influenced by object size because of a necessary coordination between grasp kinematics and hand aperture (Collegio et al., 2019; Hesse et al., 2016; Hesse and Deubel, 2011). Indeed, this observation can be applied to handaxes, because smaller handaxes are easier to manipulate than larger ones (Gal
an and Domínguez-Rodrigo, 2014; Key et al., 2018). Concerning geometry, the main morphological feature of these tools is elongation, which is an influential factor for the optimal handling of an object (Almeida et al., 2014). This characteristic encompasses the overall proportions of the object and is primarily perceived by vision (Krishna, 2006; Kahrimanovic et al., 2010). In stone tools, elongation is generally measured by an index dividing length and width, but it can be also quantified through shape analysis, and it represents the main factor determining the morphological variation of these tool types (Bruner et al., 2018b; Silva-Gago et al., 2022b). It is therefore not surprising that the degree of elongation of a handaxe influenced the distribution of visual attention, albeit only when manipulated. In particular, the more elongated the handaxe, the less attention was devoted to its base. In this case, the visual scanning pattern could be due to the assessment of grip comfort. According to this approach, more elongated handaxes allowed an easier handling of the tool and consequently, less attention toward the base

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morphology was needed due to their inherent manipulability (Pilacinski et al., 2021). Apart from those features mentioned above, other physical characteristics did not apparently influence the pattern of visuospatial attention in handaxes. For instance, symmetry, the percentage of cortex, or the length of the knapped perimeter showed no correlation with the visual exploration time. Symmetry is a key feature in Acheulean handaxes (Hodgson, 2009, 2015; Lycett, 2008; Wynn and Gowlett, 2018), and it has a certain influence on visual exploration patterns of the environment (Apthorp and Bell, 2014; Makin et al., 2018; Norcia et al., 2005; Turvey et al., 1999). The degree of asymmetry can be calculated using the method proposed by Hardaker and Dunn (2005) called the Flip Test. This test evaluates the deviation from perfect symmetry by flipping the tool around its vertical axis and measuring the difference between the two overlapping contours (Hardaker and Dunn 2005). However, the analysis of the visual exploration of handaxes was not able to detect any association between the pattern of visual attention and the symmetry index (Silva-Gago et al., 2022b). In sum, we can hypothesize that the differences in the visual exploration scheme for choppers and handaxes could be related to the different affordance processing. In particular, heavier choppers facilitate the identification of function-related affordances, which are the first and easiest to recognize by providing key information about the tools’ purposes (Cosentino, 2021; Sakreida et al., 2016). On the other hand, the influence of handaxe morphology on visual attention can be associated with affordances that provide information about how a tool is manipulated (Cosentino, 2021; Sakreida et al., 2016). In other words, handaxe interaction could be more influenced than choppers to manipulation assessment. Therefore, tools such as choppers might induce primary exploration regarding the function of the tools, while handaxes might involve more cognitive processing

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by evaluating multiple grip positions in terms of the potential activities in which they can be used. In this sense, Herzlinger et al. (2017) propose that “tool-oriented technology,” characteristic of modern humans, already exists in the Acheulean technology. These authors define a “taskoriented technology,” present, for example, in chimpanzees, as the modification of an object for a certain activity, discarding the result afterward. In other words, the intrinsic characteristics of the stone tool, including its range of potential uses, are primarily assessed. In contrast, “tooloriented technology” involves the manufacture or selection of a suitable tool, which has not been designed for a specific activity, but for a set of potential tasks (Herzlinger et al., 2017). We can speculate whether such a tool-oriented sensing approach may be a part of a human “prosthetic capacity,” namely, an enhanced ability to integrate the tools within the structure of the body and in the cognitive function (Bruner, 2021). In this sense, eye-tracking studies on stone tools propose that functional affordances are more rooted in the first stone tools such as choppers, while affordances associated with manipulation progressively reach a more relevant position as the tool and its uses become more complex. The different visual processing in worked pebbles and handaxes could reveal an attentional shift from function-focused thinking to tool-focused reasoning. And consequently, the different patterns associated with the two technologies might suggest differences in the respective visuospatial processes triggered by their visual exploration.

The role of archaeological knowledge in visual attention The identification of affordances depends on the first visual processing of an object (Xu and Heike, 2017; Makris et al., 2011; Vingerhoets et al., 2009; Proverbio et al., 2011), although previous experience can generally influence the

pattern of visual behavior (Noorman et al., 2018; R€ uther et al., 2014). As mentioned, there are two types of tool-related action knowledge (Binkofski and Buxbaum, 2013). Structural action knowledge deals with the information about how to grasp a tool, and it is aimed at processing tool properties (Ni et al., 2019). On the other hand, functional action knowledge involves information about the tool purpose and includes previous experience (Ni et al., 2019). According to these two processes, different categories of affordances have been proposed (Cosentino, 2021). Stable or standard affordances are associated with the activation of the parietal and frontal cortex, and deal with tool use and functions. Functionrelated affordances provide information about tool purposes (Sakreida et al., 2016). They are mainly based on previous experience or acquired knowledge, including information related to the culturally considered proper function of that artifact (Cosentino, 2021). On the other hand, variables or ad hoc affordances provide information about how an object is manipulated (Cosentino, 2021). The latter process is related to the dorsal stream and regards temporal characteristics, such as the shape that the hand must adopt for grasping (Sakreida et al., 2016). Other perspectives suggest that structural features are connected with sensorimotor processing and embodied theories of cognition, whereas functional knowledge is associated with semantic and abstract information (Osiurak and Federico 2021). Consequently, functional knowledge is a kind of semantic knowledge that enables mechanical actions and joins semantic and sensorimotor information (Federico et al., 2021).Numerous studies in archaeology highlight behavioral differences between experts in prehistorical archaeology and inexperienced individuals (Geribas et al., 2010; Pargeter et al., 2019; Rivero and Garate, 2020; Stout et al., 2011; Williams-Hatala et al., 2020). It has been proposed that inexperienced individuals mainly follow a bottom-up strategy in archaeological tasks, while expert individuals are more affected

II. Visuospatial behavior and cognitive archaeology

Visual attention in cognitive archaeology

by top-down mechanisms (Stout et al., 2011), taking into account that experience enhances the identification of functionally relevant relationships in stone tools (Bril et al., 2010). With these premises in mind, the effect of previous experience on the visual perception of stone tools has also been evaluated (Silva-Gago et al., 2022c). Differences have been found between experts and naive people in the proportion of visual attention time directed to the different stone tool regions (Fig. 10.5). Although both groups devoted, in general, more attention to functional regions instead of handling areas (Ambrosini and Costantini, 2016; Federico and Brandimonte, 2019; Foester & Goslin, 2021; Xu and Heike, 2017), na€ive individuals were more affected by the center bias (Ioannidou et al., 2016; Tatler, 2007; Le Meur and Liu, 2015). In contrast, experts spent more time observing the functional areas of stone tools, while no differences were found in the regions associated with

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manipulation (base and cortex). Experts, who are familiar with prehistoric tools and have experience in tool handling and their possible uses, can explore the stone tool more quickly and need less effort to resolve the issues associated with potential manipulation strategies. Hence, they directed fewer fixations toward the center and manipulative areas, and the gaze remained more fixed on the functional regions of the artifacts. In other words, knowledge about grasping or potential use channeled the attention toward the functional areas. In present-day tools, it has also been hypothesized that manipulative regions are more quickly eye-scanned because they represent an easier task to process (Cosentino, 2021; Natraj et al., 2015). We can hence speculate that experience may influence the attention directed to the functional areas, but not to the manipulation areas. In this case, experienced participants, who have prior experience and semantic knowledge of stone tools compared to naive

FIGURE 10.5 Comparison between archaeologists and na€ive individuals in terms of percentages of dwell time (DT) according to the area of interest of each tool during visual exploration. Knapped and cortex surfaces are shown on the left of the tool, while upper, middle, and base regions are shown on the right. Redrawn after Silva-Gago et al. 2022c.

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participants, can process the tool more quickly. They need less effort to deal with the motor control (manipulation), directing fewer fixations to the center and manipulative areas (Federico et al., 2021). Therefore, the gaze remains more fixed on the functional regions of the stone tools. Overall, we can state that experience may influence the visual perception of lithic tools, increasing observation times in function-related areas. Accordingly, functional aspects of tools can be more significant for experienced people.

Visual attention during tool-making Tool sensing, tool use, and tool making rely on different cognitive resources and should hence be analyzed as separate behavioral components. Tool-making has traditionally been the main interest of many studies in experimental archaeology, focusing on different cognitive approaches (e.g., Dibble and Whittaker, 1981; Lombao et al., 2017; Nonaka et al., 2010; Pargeter et al., 2019; Stout et al., 2000; Toth et al., 1993). Indeed, the increase in technological development and the evolution of the human cognitive system are likely to be interrelated (Muller et al., 2017). Studies in functional imaging reveal that Oldowan tool-making largely relies on attention and motor control, without necessarily requiring complex predictive inferences (Stout et al., 2011, 2015). In particular, Oldowan tool-making is associated with the activation of parietal and frontal brain regions involved in sensorimotor coordination, grip selection, and assessment of shape perception (Putt et al., 2017). On the other hand, the production of Acheulean handaxes further requires the integration of sensorimotor planning, working memory, and auditory feedback mechanisms (Putt et al., 2017). Hence, knapping a handaxe increases the activity of the prefrontal cortex (Stout et al., 2015). From another point of view, tool knapping requires specific visuospatial abilities to

discriminate the most observable variables of the core and identify its salient features in order to control the shape of the flakes (Geribas et al., 2010; Nonaka et al., 2010). In this sense, vision serves as an interface between the subject and the raw material (Atkinson, 2008; Kassuba et al., 2013; Stone and Gonzalez, 2015). Accordingly, the study of visuospatial attention can provide further information about these specific aspects of tool-making cognitive behavior. Recently, the role of vision in stone toolmaking has been considered in experimental archaeology though the use of different eyetracking techniques. For example, a first study applied screen-based eye tracking to na€ive participants while they were looking at videos of an expert knapper that was producing tools, and measured how visual attention changes through 90 h of such visual training (Bayani et al., 2021). The analysis of eye movements was carried out during the training period. In the beginning, the eyes scan the scene randomly but, through the training period, the gaze gradually shifts more to the elements of the knapping process. Namely, the participants learned to focus on the main technologically informative features while observing tool making (Bayani et al., 2021). Another study explored the visual behavior using a portable eye-tracking device in a real stone tool-making scenario (Silva-Gago et al., 2023). Specifically, visual attention was considered while an expert tool knapper produced Lower Palaeolithic tools. In this case, differences were found in the visual pattern of the knapper, when making choppers and handaxes. While knapping choppers, every tool triggered a distinct visual behavior, suggesting that visual attention was influenced by idiosyncratic features of the stone. In contrast, handaxes triggered a more homogeneous visual scanning. This was probably because they require a more defined standardization of the process (Beyene et al., 2013; Muller et al., 2017; S
anchez-Yustos et al., 2017).

II. Visuospatial behavior and cognitive archaeology

Vision and cognition in prehistory

However, Oldowan and Acheulean knapping procedures also displayed some shared visual aspects (Silva-Gago et al., 2023). The position of the next point of impact attracts significant attention, and it is likely to be related to the planning of the next strike rather than assessing the current one. Indeed, the area of percussion is a key factor in the knapper’s strategy and has been considered as evidence for the increasing complexity of lithic technology (Carbonell et al., 2009; Gerib as et al., 2010; de la Torre et al., 2003). Looking toward the next action objective is a common pattern in complex cognitive tasks, which requires the integration of visual and manual information, as well as visuospatial coordination (Hodgson et al., 2019; Land and Furneaux, 1997; Land, 2009; Mennie et al., 2007). Accordingly, gaze was generally directed toward the next target before the hand movement is completed. In this sense, during tool-making, the decisions about where to produce the next stroke and how much force to use were often made while the action was in progress (Chakrabarty, 2018). Overall, the distribution of visual attention, during tool-making, can be associated with the identification of the most salient features and, accordingly, the processing of action affordances, which is essential in stone tool production (Chakrabarty, 2018; Pargeter et al., 2020). Therefore, different parameters or affordances need to be identified depending on the knapping strategy and the stone tool production. The divergences found in tool knapping procedures may suggest there are different visuospatial abilities required to elaborate each stone tool technology (Silva-Gago et al., 2023).

Vision and cognition in prehistory Attention is a key component of the general cognitive capacity and, in this sense, it is expected that it has undergone major changes during the evolution of the genus Homo. Interestingly, the

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parietal lobes, particularly large and complex in humans when compared to other primates, are crucial to both visuospatial imagery and attention, two functions that may collide, when not properly balanced (Bruner and Colom, 2022). In fact, our outstanding ability in mental imaging is the very root of mind wandering, which is in turn the main factor disrupting the attention system. The attentional capacity can be interpreted as a limiting factor for any specific cognitive domain and, hence, understanding its evolution is mandatory in evolutionary anthropology. Vision is a fundamental sense in primates, and visual attention has probably played a major role during the evolution of complex technological behavior. In fact, vision is probably the first remote interface for a “prosthetic capacity” able to coordinate and integrate the body (touch and manipulation) and brain (sensing and perception) with external peripheral devices (“tools”). According to the available evidence, visual exploration of Lower Paleolithic stone tools is more influenced by top-down mechanisms than by saliency. As in presentday objects, visual scanning is mainly focused on the functional aspects (tool tip), and less on the grasping surface (tool base). This pattern shows a minor but possibly relevant change when moving from simpler tools (Oldowan) to more complex technology (Acheulean), that is when the handetool interface becomes more important (tool-based technology). Weight and elongation influence these visual scanning patterns, and this is interesting when considering a certain tendency, during technological evolution, from rounder and larger tools to more stretched and smaller ones. Also experience has a major influence on visual scanning, shifting the attention to the functional parts. A personal milestone in infant development is the ability to fingerepoint to objects and people, and to follow the finger-pointing of someone else with the gaze (Matthews et al., 2012). This behavior is timely associated with language development and suggests an intimate mental

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relationship between vision (gaze) and body (hand). The analysis of visual behavior associated with the prehistoric material culture may evidence, in this sense, automatic sensing responses (either top-down or bottom-up) due to the cognitive matching between a subject and its environment. A first approach to employ such methods can be heuristic, namely, aimed at exploring the visual interaction between subjects and tools, identifying and describing the underlying patterns of visual scanning. A second approach deals with hypotheses testing, namely, collecting data to support or discard a specific prediction. In both cases, experiments and quantification methods are necessary to move the field of cognitive archaeology from a pure theoretical frame to a proper scientific discussion. This goal is not easy because, apart from the standard difficulties in investigating cognition and mind, in this case we are interested in hypotheses that deal with extinct species, and we can only provide experimental paradigms counting on the only living one, that is Homo sapiens (Bruner, 2022; see also Chapter 13). This limitation should not be used anyway to reject an experimental approach in cognitive archaeology, instead preferring theoretical alternatives that, although necessary, reasonable, and appealing, will be never verified. After all, we cannot perform direct cognitive analyses on Neanderthals, and modern humans are good models when considering the phylogenetic proximity with the other human lineages. More importantly, research in body and environmental sensing is relevant for the understanding of our own evolution and cognition, and this is by no means trivial when taking into account the scarce information we have on the importance of perception in mind and consciousness (Tang et al., 2015). During the transition from occasional to habitual and then obligatory tool-use (Shea, 2017), the perceptual relationship between self and technology probably underwent a profound reorganization. What we can look for,

therefore, is the possibility to detect some biological or cultural remnants of such cognitive change.

Acknowledgments We are grateful to Timothy Hodgson for his key support with eye-tracking analysis, and to Annapaola Fedato, Marcos Terradillos Bernal and Rodrigo Alonso Alcalde for their collaboration in most of the studies reviewed in this article. We thank Giovanni Federico and Justine Cl
ery for providing comments and suggestions on an early version of this chapter. MSG is funded by the Junta de Castilla y Le
on, cofinanced by European Social Funds (EDU/574/2018). EB is funded by the Spanish Government (PID 2021-122355NBC33) and by the Italian Institute of Anthropology (ISITA).

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C H A P T E R

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Handling prehistory: tools, electrophysiology, and haptics Annapaola Fedato, Emiliano Bruner Programa de Paleobiología, Centro Nacional de Investigaci
on Sobre La Evoluci
on Humana, Burgos, Spain

A brain at hand: from haptics to cognition Tools and technology have been always regarded as crucial elements in human evolution, and this has led to a longstanding interest in their associated grasping and handling behaviors (Stout and Chaminade, 2012). Nonetheless, such interest has been generally oriented to biomechanical issues (hand-tool spatial coordination and general motor abilities) or to the cerebral regions involved in tool use and tool making. Things become more complex when following theories in cognitive extension, which interpret cognition as a process generated by an interaction between the brain, body, and environment (Clark and Chalmers, 1998). In this case, the body is hypothesized to be an active interface between the brain and technology, and technology itself becomes an extrasomatic peripheral element of the cognitive system (Bruner and Iriki, 2016; Malafouris, 2010a). Grasping is hence not only a mechanical issue, but it also has a functional connection, associated with an embodied circuity rooted in perception and sensing (Bruner et al., 2018a,b; Malafouris, 2010b). In other words, through a cybernetic

Cognitive Archaeology, Body Cognition, and the Evolution of Visuospatial Perception https://doi.org/10.1016/B978-0-323-99193-3.00012-X

analogy, the hand is a port to connect a central processor with external devices, and grasping is the compatibility adapter. Action and sensing are intimately associated and reciprocally dependent (Ackerley and Kavounoudias, 2015), tools are incorporated within the body architecture when sensed (Turvey and Carello, 2011), and the brain extends the tool properties to include them in the body scheme during such interaction (Miller et al., 2019b). It has been hence proposed that humans evolved a sort of “prosthetic capacity,” namely, the enhanced ability to integrate external elements within the cognitive machinery, outsourcing specific cognitive functions to peripheral components (Bruner, 2021; Bruner and Gleeson, 2019). This is particularly relevant when taking into account that, in the last 300,000 years, tool use became obligatory for humans (Shea, 2017), which means that their ecology, culture, and cognition necessarily rely on such technological extension. A major issue is therefore how can we test how this prosthetic capacity has evolved within the human genus? Eye-hand coordination, hand-tool integration, body perception, and a long set of abstract and practical skills associated with tool use, making,

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and sensing are deeply regulated by the activity of the parietal cortex, which is expected to be somehow involved in these evolutionary changes (Bruner et al., 2023). An expansion of the parietal lobe and a consistent explosion of cultural and technological complexity are specifically associated with Homo sapiens (Bruner, 2018; Bruner et al., 2023), and it is hence reasonable to think that these factors have been relevant in the evolution of our own species. When considering the importance of tool-use and the intimate relationships between human evolution and technology, we may wonder whether some specializations concerned haptic abilities. Haptics is traditionally intended as a perceptual response based on cutaneous and kinesthetic feedback during manual exploration (largely based on mechanoreceptors and thermoreceptors) aimed at identifying material and spatial properties of objects and surfaces (Kappers and Bergmann Tiest, 2013). As with most perceptual inputs, also haptic ability can be separated in a what (physical properties and recognition) and where (space perception) components, and interacts with other sensorial processes (mainly vision) as well as with affective and emotional aspects (Lederman and Klatzky, 2009). The changes in the haptic capacity to handle tools and to integrate body and technology should be investigated in terms of evolutionary behavior and sensing responses, introducing the topic into the field of cognitive archaeology, namely, bridging the archaeological evidence with the theoretical and methodological frame of psychology. Brain functional scanning is helping a lot in this sense, as it shows the neural correlates of tool use and tool making (e.g., Stout et al., 2015). Nonetheless, it is important to design experimental approaches that are able to test the psychological and cognitive correlates of such interaction, in order to localize factors and skills influenced, selected, or trained by our peculiar prosthetic behavior. According to the current theories on the structure of cognitive abilities (Schneider and McGrew, 2018), our

specialized technological extension and haptic skills should at least involve visuospatial (Gv), tactile (Gh), kinesthetic (Gk), and psychomotor (Gp) sensorial, perceptual and mental processes. A good starting point, therefore, can deal with research aimed at investigating if and how changes in these components might be associated with the Lower Paleolithic tools, for two main reasons. The first is because these tools are remnants of the earliest human cultures, having represented key extensions of the human body for more than one million years (Semaw, 2000). According to the hypothesis on extended cognition, they are, strictly speaking, remnants of those “minds” they were part of. The second reason is because, although with a certain diversity in size and handling procedures (Key et al., 2018a; Williams-Hatala et al., 2018), choppers and handaxes were generally large and required the whole hand to be manipulated. Therefore, in this case, the sensorial effect is indeed pronounced, in terms of dynamic touch and perceptual feedback. There is still a longstanding debate on the actual function of these large early artifacts (Domínguez-Rodrigo, 2002; Nowell and Chang, 2009; Semaw et al., 2009), but there is little doubt about the fact that they were, at some time, handled by the earliest human species. Furthermore, because they have been systematically investigated in the last century, these two tool types can supply basic information on the early evolution of human technological sensing.

Minds, hands, and stone tools Lower Paleolithic stone tools Although there is evidence of earlier tools (Harmand et al., 2015), the first technology for which we have a robust and consistent archaeological record is called Mode 1 or Oldowan technology, which appeared around 2.6 million years ago (Semaw et al., 2003; Stout et al., 2010). It includes worked or shaped pebbles, chopper-cores,

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FIGURE 11.1 Shape differences between choppers (A) and handaxes (B) are due, in the latter, to elongation and stretching of the tip (C), and both tool types are further characterized by the longitudinal variation of the point of maximum thickness (D). Figure modified after Silva-Gago et al., 2022.

unretouched and retouched flakes, and battered stones (Toth, 1985). One of the most iconic elements of Mode 1 is the worked pebble (Fig. 11.1A). This tool is characterized by being knapped on one (chopper) or two faces (chopping tool) with few extractions to generate a short, convex, abrupt, or semiabrupt and usually sinuous edge. They are usually made from round and thick pebbles through direct percussion with a hard hammer (Trigger and Clark, 1969). Choppers have been long debated because there is no agreement as to whether or not they can be considered tools. However, several studies have shown use-wear evidence on these worked pebbles, and, at present, they can be interpreted as both cores to produce flakes or pebble tools (Shea, 2007; Venditti et al., 2021). Flake-tools are another abundant component of Oldowan technology, and probably they were as important as the core-tools for processing meat (Toth, 1985). Experimental studies and use-wear analysis showed that flakes and fragments may have been used by the early tool users, especially for butchering animal carcasses, since a sharp edge is essential for the initial slitting of the skin (Toth, 1985; Venditti et al., 2019a,b). Handaxes were representative of Mode two or Acheulean technology (Fig. 11.1B), and their

earliest record dates to around 1.7 million years ago (Beyene et al., 2013; Diez-Martín et al., 2016; Lepre et al., 2011). These stone tools are large, symmetrical, and tear-drop shaped with a more qualified selection of raw materials (Harmand, 2009; McHenry and de la Torre, 2018; Shipton et al., 2018; Wynn, 2002). The manufacture of these tools represents the incorporation of bifacially shaped artifacts into the human technological repertoire (Shea, 2007). Compared with other more basic flake cutting tools, handaxes are more efficient when tasked with cutting relatively large, resistant portions of material (Key and Lycett, 2017a). Although the two tool types coexisted together for a very long period, it has been proposed that the technological transition from Oldowan to Acheulean technology is related to improvements in the ergonomic design of stone tools (Wynn and Gowlett, 2018). The hypothesis concerning ergonomics has yet to be directly tested, but the main idea is that new stone technologies may have become dominate over previous alternatives because of their increased ease of use when held by the hand (Key et al., 2020). Shape analysis of these two tool types (see Silva-Gago et al., 2022) shows that the main difference deals, as expected, with the degree of

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tool elongation and narrowing of the tip (Fig. 11.1C). This pattern separates handaxes from choppers, although with a degree of morphological overlap. It must therefore be considered how and to what extent this shape change (namely, elongation) can influence the haptic feedback in hominids that were moving from occasional to habitual tool users. Another important shape factor is the longitudinal position of the maximum thickness of the tool (Fig. 11.1D). Although this feature does not seem to be different in choppers and handaxes, it can have an influence on tool grasping and sensing and merits further attention. Apart from ergonomics, functional advantages are a primary cause underpinning the production of handaxes. In fact, handaxes are known to be more effective and temporally efficient compared with flakes during heavy-duty cutting tasks (Gal
an and Domínguez-Rodrigo, 2014; Key and Lycett, 2017a; Toth and Schick, 2009). We should also consider the potential influence of other factors in promoting handaxe technological “stagnation” including (besides cognitive issues) information transmission, social learning, and demographic variables such as group size, composition, and distribution (Finkel and Barkai, 2018).

Perceiving tools: biomechanical aspects of tool manipulation Relying on the information evinced from the material culture, cognitive archaeology aims at making sound inferences about the behaviors and the cognitive features of extinct human species (Lombard and G€ ardenfors, 2021). It is reasonable to think that many behaviors leave no material traces and, therefore, archaeologists can assess only the minimal set of abilities needed to produce an artifact (Wynn and McGrew, 1989). This concept is particularly useful when it comes to comparing different technological modes. A key piece of information, in this

context, comes from the possibility to associate tool use with a specific neural activation (see Chapter 9). In this case, through biomedical imaging, we can observe which brain regions undergo metabolic increase during a specific behavior, task, or exercise. Stout et al. (2008) designed an experiment to test the neural correlates of Oldowan and Acheulean stone tool production, using positron emission tomography (PET) and employing modern human expert stone tool makers. Results suggest that Oldowan toolmaking largely relies on spatial information and the activation of the parietal cortex, while, during Acheulean tool making, knapping also involves more resources of the prefrontal cortex, probably due to increasing requirements on action planning and execution. Is it worth noting that the difference between the two types of tool-making does not seem to be influenced by the level of manipulative ability, and brain activation should hence respond to functions other than those associated with biomechanics and movements (Faisal et al., 2010). However, tool-making is different from tool use in terms of cognitive mechanisms, and tool use is also something distinct from tool sensing. These three cognitive components (tool sensing, using, and making), although working together, should be considered separately, in both neural and psychological terms. A common factor in these three aspects is ergonomics, namely, the physical (spatial and structural) optimization of the human-tool system, which enhances comfort, reduces risks and injury, and increases productivity. Ergonomics, as a discipline, employs information about human behavior to design effective tools (Chapanis, 1995). Tools are planned to minimize muscular effort and maximize grip effectiveness in order to increase efficiency (Pheasant and O’Neill, 1975), reduce fatigue (Rohmert, 1973), and prevent mechanical traumas (Silverstein et al., 1986; Tichauer and Gage, 1977). Among the factors that could influence grasping effectiveness, we can include (i) handgrip strength, (ii) the contact area with

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Minds, hands, and stone tools

the object, and (iii) the hand and the object properties (shape, size, and weight). The strength of the grip is influenced by the size of the object grasped (Ayoub and Presti, 1971; Hertzberg, 1955). The relationship between the handle size and the hand dimensions has therefore a major effect on hand posture and grip strength, so the larger the hand length, hand width, and palm length, the stronger the total grip strength is (Kong and Lowe, 2005). Also, more contact area between hand and tool during grasping reduces mechanical stress (Pheasant and O’Neill, 1975). Finally, the dimension of the object grasped is also a crucial factor. A small, light object is typically grasped with the index finger and thumb only. An increase in object size and weight requires the use of more digits and a greater palmar surface area (Castiello et al., 1993). Another pivotal aspect of perception concerns object affordance, namely, the properties of the object channeling the action response (Yamanobe et al., 2017). An effective (functional, ergonomic, targeted) manipulation largely depends on the individual’s ability to perceive object affordance, and tool grasping (selection of fingertip placement) is influenced by the individuals’ intentions toward that object (end-goal of the action) (Sartori et al., 2011). At a biomechanical level, affordances play a major role in determining the structure of reach-to-grasp actions (Sartori et al., 2011) by influencing the aperture between the thumb and fingers (Mon-Williams and Bingham, 2011). The use of a tool requires the extraction of sensory information about object properties, which can then be translated into appropriate motor outputs (Osiurak et al., 2010). From an evolutionary point of view, indeed, the process of extracting information from tool properties (sensorimotor adaptation and affordance perception) was probably a central factor in the early stages of human technological evolution (Stout and Chaminade, 2007). Among the fields that investigate tool manipulation, robotics is particularly useful as, in this

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context, anthropomorphism (namely, the tendency to imitate human features) has always represented a great challenge. Recently, several works on robotic manipulation have paid attention to the concept of affordance, as it plays an important role when a robot performs a task, especially for the manipulation of a novel tool (Yamanobe et al., 2017). A key aim of cybernetics is to reproduce high dexterity in order to imitate the human hand’s kinesthetic and sensory abilities (Powell, 2016). To investigate the spatial patterns and force distribution during grasping, a data glove (or cyberglove) is often employed, namely, an interactive device which fits sensors on the fingers. It facilitates tactile sensing and fine-motion control in robotics and virtual reality. Data gloves are widely used in many applications, including virtual reality applications, robotics, and biomechanics (Tarchanidis and Lygouras, 2003). Cybergloves and other electromechanical devices can be used to investigate tactile sensing involving simulation of the human touch, including the ability to perceive pressure, linear force, torque, temperature, and surface texture. Fine-motion control involves the use of sensors to detect the movements of the user’s hand and fingers, and the translation of these motions into signals that can be applied to a virtual hand (for example, in gaming) or a robotic hand (for example, in remote-control surgery). A data glove allows normal interaction with objects, and the individual is able to feel the object as if it was in the hand (Tran et al., 2009). Other methods to study the biomechanical aspect of tool use and production include technologies such as high-speed motion capture devices, in vivo computer-based imaging and modeling, and real-time pressure sensing systems, to generate high-resolution biomechanical and functional data that document the rapid motions associated with stone tool behaviors. Thanks to these new methodologies employed in experimental archaeology, in the last 10 years, researchers have been able to quantify the

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biomechanics of those behaviors associated with stone tool use and production, and to test hypotheses about modern human hand anatomy and stone tool efficiency (Marzke and Shackley, 1986; Rolian et al., 2011; Marzke, 2013; Williams-Hatala et al., 2018; Key et al., 2018a,b; Key and Lycett, 2014, 2015, 2017b,c). Experimental data have demonstrated that individual biomechanical skills and biometric traits can influence the efficiency and effectiveness of stone tool use. For example, Key and Lycett (2018) have demonstrated how the strength and dimensions of a tool user’s hands are correlated with the cutting performance of flake tools and handaxes, with different biometric traits contributing to tool efficiency in variable ways depending on the type of tool used. Williams-Hatala et al. (2018) further analyze the magnitudes of stresses associated with stone tool behaviors, finding that the use of hammerstone during marrow acquisition and flake production

FIGURE 11.2

consistently required the greatest loads on the digits (see also Chapter 12). Apart from tool making and functional use, the handling patterns and haptic exploration of Lower Paleolithic stone tools can be also investigated in terms of comfortable grasping, namely, the tendency to achieve an optimal hand-tool integration in subjects with no previous information on the functional aspects of the tools (Fedato et al., 2020b, 2023) (Fig. 11.2). In this case, haptic exploration is aimed at finding the best sensorial feedback between the hand and the tool, namely, a sensing procedure based on perceptual stimulation and the physical properties of the two elements. A comfortable grasping is generally associated with an adequate and firm surface contact between skin and tool (Tichauer and Gage, 1977) and, in general, the wider the grip span, the weaker the grip than can be employed to hold the tool (Fransson and Winkel, 1991). When comfortable grasping is analyzed in

Common grasping patterns associated with Lower Paleolithic stone tools.

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terms of phalanx flexion, choppers and handaxes exert different hand responses, with the former requiring less flexion than the latter (see Fedato et al., 2020b). This difference is more evident in the middle, ring, and little fingers than in the index finger. For choppers, the variability of the pattern of phalanx flexion is mainly related to the flexion of the middle finger. In handaxes, the variability of the pattern of phalanx flexion is mainly associated with the little and ring fingers. The maximum dimensions of the tools are largely responsible for the grasping patterns. For choppers, anyway, only the maximum length is a suitable predictor of the grasping scheme, while for handaxes also the maximum thickness and maximum width are correlated with the pattern of finger flexion. Handaxes are often longer and thinner when compared to choppers. Therefore, handaxes require more phalanx flexion because they are generally thinner than the choppers, and with a more elongated shape (Gowlett, 2013). Increased phalanx flexion in handaxes, as mentioned before, suggests a firmer and more ergonomic grip, enhancing manipulative efficiency and sensorial feedback. We can also speculate that comfortable grasping is an important element in the evolution of a specialized prosthetic integration between body and tools (Bruner et al., 2018a), and we can wonder whether, besides their functions, differences in tool morphology can provide information undergoing the associated sensorial changes. Compared with choppers, handaxes present specific ergonomic properties which contributed to their wide distribution and long-lasting cultural endurance (Corbey et al., 2016). As proposed by Gowlett (2006), the elongation of handaxes provides stability and avoids twisting, offering support for working edges. The center of gravity is positioned toward the base, where the tools are usually thicker. The weighting of the distribution balances out the extension, by virtue of the proportion between the base and the tip of the tool. The weight of the handaxe could be managed by adjusting its thickness

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through the removal of mass from the upper part of the tool. The globular shape at the base is a feature shared with choppers and allows an easy grip. As suggested by Wynn and Gowlett (2018), thicker handaxes might have had different functional characteristics than thinner/lighter handaxes. Weight and dimensions are also likely to be a key factor in this sense, not only from a functional point of view (Key and Lycett, 2017a), but also because large tools are expected to exert a stronger somatosensorial response, and involve more comprehensive dynamic adjustments of the whole body, in terms of proprioceptive and exteroceptive reactions (Turvey and Carello, 2011). When humans moved from habitual to obligatory tool users (Shea, 2017), they included small flakes to their standard technology, and this probably represented a major change in the body-tool sensorial balance. Nonetheless, besides size, the shape can also influence the grasping pattern. In this case, handaxes, apart from their increase in functional complexity, also trigger a different pattern of finger flexion, mostly increasing the flexion of the last three fingers. We can hence wonder whether, and to what extent, this change may have been associated with a different sensorial feedback in terms of the somatic and cortical representation of the hybrid body-tool unit (Miller et al., 2019a, 2018; see also Chapter 6).

Perceiving tools: attention, activation, and emotional reaction As mentioned, anthropologists and archaeologists are generally interested in the different types of grips employed in making and using tools (Jones, 1980; Key et al., 2018b; Marzke, 2013; Marzke and Shackley, 1986; Rolian et al., 2011; Shepard and Metzler, 1971; see Chapter 12), as well as in the neurological aspects of stone tool use and production (e.g., Stout et al., 2015, 2008; Stout and Chaminade, 2007; see Chapter 9). Stone tool use efficiency is related to hand strength and hand size (Key et al., 2018b) and

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therefore individuals’ biometric traits have to be considered when studying Lower Paleolithic stone tool use and production. Indeed, in terms of technological evolution, the hand and tool should be analyzed as a single functional and structural system (Silva-Gago et al., 2019). However, when interacting with a tool, our cognitive balance is predisposed and oriented toward certain kinds of automatic behavioral responses, because of instinctual and subconscious emotional feedback to sensing, and influenced by additional and conscious attentional shifts. Perceptual processes deal with the sensorial relationships between body and tool, while executive processes deal with relational inferences based on goals and projections. In both cases, the body “reacts” to this interaction, with a large set of physiological adjustments and alterations. These reactions deal with arousal (namely, the explicit activation of the autonomic nervous system associated with wakefulness, alertness, and readiness to respond) and attention (the capacity to concentrate on one perceptual focus while ignoring other perceivable stimuli) (Boucsein, 2012). Actually, both components are parts of a widespread and heterogeneous attention system, with the former more associated with activation and emotional involvement, and the latter more influenced by orienting and executive functions (Petersen and Posner, 2012). It is hence interesting to investigate how arousal and attention are channeled during the interaction between body and technology, most of all when considering the morphological changes of the tools during human evolution. In this sense, theories on embodiment or extended cognition add one more dimension to grasping, which deals directly with the cognitive influence generated by haptic feedback (Wilson, 2002; Borghi and Cimatti, 2010). In other words, besides the ergonomic and neural factors, we should consider the psychological changes associated with the body-tool integration and stimulation. In humaneobject interactions, active touch has been described as the exploration of

the object’s properties (Klatzky and Lederman, 2002). These properties are explored by grasping the object, holding it, manipulating it, and following the contours with the fingers. During haptic exploration, we perceive shapes, temperature, weight, texture, or curvature (Kappers and Bergmann Tiest, 2013), through a sensing system that incorporates the object into the architecture of the body (Turvey and Carello, 2011; see Chapter 2). The properties of an object can hence directly influence and channel the hand-tool system (Fitzpatrick et al., 1994), and the physical interaction with the tool can alter the cognitive condition of a subject. After the perception of the tool, an attentional feedback does follow, triggering some kind of emotional reaction due to the contact with the external object. In fact, subjects react affectively to haptic stimuli, showing preferences toward specific surface properties of physical objects (Ekman et al., 1965; Hilsenrat and Reiner, 2011; Salminen et al., 2008). Such sudden and automatic reactions can be quantified by electrodermal activity (EDA), namely, the registration of the variation in the skin conductance associated with a specific stimulus or behavior (Boucsein, 2012; Lagopoulos, 2007). This approach can be employed to investigate the variations in arousal and attention when exploring haptically different stone tool types, in order to detect early psychological changes associated with the perception of body physical extension (Bruner et al., 2018a,b). This approach has two main scopes. First, taking into account the importance of technological extension in our cognitive capacity, we should expect that the sensing feedback is a functional part of the process of body-tool integration, and hence consider whether evolutionary changes in tool properties can influence the psychological response. In this case, EDA can supply information on the evolution of the prosthetic capacity in the human genus. Second, EDA can provide a practical and easy measure of attentional variation during tool handling. Attention is a core component of

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general intelligence, as a limiting factor in the selection and sustain of specific cognitive functions. As such, attention is a key factor in human evolution, as a crucial node for executive functions and behavioral inhibition (Bruner and Colom, 2022). Accordingly, the analysis of attentional variation during stone tool handling can provide cues on some evolutionary aspects of this critical psychological component.

Detecting emotions The skin is a selective barrier that maintains the balance of water and temperature through variations in the production of sweat (Gagge and Gonzalez, 1996). Humans have w2e4 million eccrine sweat glands which can be found on both glabrous (palms, soles) and nonglabrous (hairy) skin. Gland density is not uniform across the body surface area, and the highest gland densities are on the palms and soles (w250e550 glands/cm2) (Baker, 2019). Changes in body temperature activate the thermal sweating, thanks to thermoreceptors that provide feedback to the hypothalamus which, in turn, activates the eccrine sweat glands (Boulant et al., 1989). Even if their primary function is thermoregulation, the eccrine glands also respond to emotional stimuli (Baker, 2019). In fact, emotional states elicit precursor sweat production without the need for changes in body temperature (Homma et al., 2001). The term “psychological sweating” has been used to define the sweating that occurs in response to emotive stimuli like stress, anxiety, fear, and pain. This kind of sweating occurs over the whole body surface but is most evident on the palms (Harker, 2013). This emotional sweating is particularly important for psychophysiologists (Dawson et al., 2009), since it is responsive to psychological stimuli (Shields et al., 1987) and its variations describe individuals’ responses to specific environmental (including social) situations. In particular, the changes in the electrical properties of the skin

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generate electrodermal activity (Braithwaite et al., 2015), which hold a psychological significance. This electrodermal reaction is the consequence of attentional and affective processes integrated within the central nervous system, which trigger physiological changes in the body (Lagopoulos, 2007). Because sweat is mostly formed by water, a rise in sweating increases the electrical conductance of the skin. Thanks to the efficient conduction of the sweat, the changes in electrical properties of the skin can be recorded using a small constant current applied between two metal electrodes (Bartolom
e-Tom
as et al., 2020). This phenomenon is most evident in the hands and feet because the gland density is higher in those areas than in the rest of the body (Shields et al., 1987; Smith and Havenith, 2011), and lower on the thigh and legs (arms and trunk display intermediate densities) (Kuno, 1956). This distribution pattern is probably a consequence of differences in growth rates among body segments, as the number of sweat glands does not change throughout life (Kuno, 1956). It is important to mention here that there is no correlation between the density of the sweat glands in a specific region of the body and the sweat rate (amount of sweat) of that region. The highest sweat rates were observed on the middle and lower torso (back) and forehead while the lowest values were observed toward the extremities (Shields et al., 1987; Smith and Havenith, 2011, Fig. 11.3A). This is due to the fact that some regions of the body have higher sudomotor sensitivity and higher output per gland and, therefore, higher sweat rates. The human hands are enveloped by glabrous (ventral side) and nonglabrous (dorsal side) skin containing eccrine sweat glands, with the volar surface having a very high glandular density relative to the dorsal surface. The sweat secretion of the hand is not uniform, and greater sweat secretion is evident when moving from the proximal to the more distal sites of the volar surface of the hand, with the palm having the lowest sweat

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FIGURE 11.3 Absolute regional median sweat rates during low intensity exercise (A). Sweat rate is calculated in grams per meter square of body surface area per hour (g$m
2$h
1). Darker colors (red) are related to higher sweat rate. Data after Smith and Havenith (2011). Gland density in human hand (B). Volar side on the left, dorsal on the right. Data after Taylor and Machado-Moreira, 2013.

rate (Taylor and Machado-Moreira, 2013) (Fig. 11.3B). The arousal level of a person changes constantly and, during a calm or neutral state, the sweat production slows down to a minimal rate. When an emotionally arousing stimulus is experienced (e.g., stress), the sympathetic nervous system undergoes activation, and the skin responds by producing an increase in sweat

gland activity (Stern et al., 2000). This activation is part of the alerting attentional subsystem (Petersen and Posner, 2012). Accordingly, these properties of the skin have been used as biosignals, and fluctuations of the peripheral autonomic responses are markers of attention, decision-making, motor preparation, and other aspects of cognitive activity (Theodoros, 2014). The application of EDA measures to a wide variety of fieldsdincluding psychological assessments, police investigations, and neuromarketingdis due in large part to its relative ease of measurement and quantification, combined with its sensitivity to psychological states and individual reactions (Lagopoulos, 2007). The instruments used to measure the fluctuation of the electrical properties of the skin (called a “psychometer”) were first developed at the beginning of the nineteenth century. The original scheme of the electropsychometer, or bioelectronic instrument, is shown in Fig. 11.4. It allowed the recording of autonomic measures and became extremely popular as a tool to measure mind activity. Commonly known as a “polygraph,” it was invented by the cardiologist James Mackenzie in 1902. The polygraph measures several physiological parameters (i.e., blood pressure, pulse, respiration, and skin conductivity), while the subject answers a series of questions. The aim of this procedure was to detect physical fluctuations in the abovementioned parameters, in order to determine when a person is stressed, which is presumed to be an indication of lying. Mackenzie’s polygraph was first used in a criminal investigation in 1911 by the Berkeley California police department (and lately known in criminology as “lie detector”). This kind of instrument was also employed in psychotherapy. One of the first references to their use for psychological therapy is related to the Carl Gustav Jung. In his book, "Studies in Word Analysis” (1906), he measured the changes in the resistance of the subjects’ skin during the hearing of specific words. Words that evoked a larger than usual response were

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FIGURE 11.4

Left: Original scheme of the electropsychometer (US Patent 2684670 566 by Volney. G. Mathison). Right: The earliest polygraph machine was invented by the cardiologist James Mackenzie in 1902. Images available under Creative Commons Attribution.

assumed to be indicators of possible areas of conflict in the patient (emotionally charged), and these themes were then explored in more detail with the subject during the psychological session (Dawson et al., 2009). Nowadays, there are different devices that can record the individual variations of electrodermal activity (Martínez-Rodrigo et al., 2016). In general, these devices apply a constant current, recording the level of cognitive activation and attentional response while the individual performs a task or is exposed to a specific stimulus (Aiger et al., 2013). Some devices are simple and easy to employ in the field and consist of a wireless bracelet with two sensors placed on the index and middle fingers (Fig. 11.5).

Recognizing emotions Researchers often use two different models to interpret emotions. The “discrete emotion model” proposed by Ekman (1987) is a

categorical approach to emotion recognition, and it categorizes emotions into six basic emotion states surprise, anger, disgust, happiness, sadness, and fear. The model considers that these emotions are biologically and psychologically innate, universal, and associated with distinct affective states which might have

FIGURE 11.5 The electrodermal remote device (Sociograph technology) is wrapped around the left forearm, connecting two diodes at the second and third fingertips, and records both tonic and phasic activity (see Fedato et al., 2019). Image courtesy of Sociograph Technology.

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evolved to serve adaptive functions (Kowalska and Wr
obel, 2017). In contrast to this model based on discrete emotion, Lang (1995) proposed the “emotion dimensional model,” in which all the emotional states can be represented by two fundamental neurophysiological factors, namely, arousal and valence dimensions. The arousal dimension indicates the levels of emotional intensity experienced by an individual, while valence refers to the quality of the emotion, namely, the degree of pleasure (pleasant/unpleasant) associated with the experience. In other words, arousal indicates the degree of psychological activation (low to high), while valence ranges from negative to positive perception (Kim and Andr
e, 2008). EDA concerns mainly the level of arousal (activation) associated with a stimulus, while it is not informative on the valence of the emotional variation. Nonetheless, it can be successfully used to recognize distinct emotional states, as it is a direct measure of the response of the sympathetic nervous system (Boucsein, 2012; Caruelle et al., 2019; Ganapathy et al., 2021; Greco et al., 2017, 2019; Morrison et al., 2020; M€ uller and Fritz, 2015). It is indeed an efficient biosignal, among the alternatives, for measuring different emotions without interfering with individuals’ normal activity (Ooi et al., 2016).

Recording emotions Arousal is a state of activated attention to potentially important environmental affordances and threats (Moser and Uzzell, 2003). It can improve the performance of a specific task, or it can represent a general state of actionreadiness, and both conditions can be identified through autonomic outputs such as changes in electrodermal activity (Lagopoulos, 2007). Arousal can be formally separated into two components: phasic and tonic arousal (Boucsein, 2012; Boucsein et al., 2012; Dawson et al., 2009; Lagopoulos, 2007). Phasic arousal occurs over

short time periods (few seconds), and it is more dependent upon specific stimuli. It deals with a short emotional response rapidly increasing the attention level, and it is referred to as the skin conductance response (electrodermal responsed EDR). Tonic arousal deals with the subject’s general attentional (vigilance) state over a longer period, and it corresponds to the skin conductance level (electrodermal leveldEDL). Both kinds of responses are generally detected, with phasic (shorter and faster) changes overlapping tonic (longer and slower) patterns. Psychophysiological indices such as EDA are able to identify differences between low versus high task demand (Mehler et al., 2012) and, in general, challenging tasks are related to greater electrodermal activity (Andreassi, 2010; Klarkowski et al., 2018). Through the application of EDA in archaeology, we can measure the cognitive and ergonomic demands of different tasks associated with Paleolithic technology and make inferences concerning the quality of the interaction between the subject and the stone tool. Of course, in this case, experiments are performed with modern subjects, which raises the question on whether or not results are informative on cognitive processes associated with extinct species (see Chapter 13). This issue requires at least two kinds of considerations. First, we must take into account that science always works with models. Testing hypotheses means, in fact, contrasting a given scenario with the observed data, which leads to better models by discarding the ones that do not fit the expectations. Models have implicit limitations, and this is not specific to cognitive archaeology, but to science and research in general. Concerning reference biological models, for example, chimpanzees are often used as models for human evolution, macaques for human neurobiology, and mice for human medicine, although all these species are the result of independent evolutionary changes not related to humans, and adapted to environments that were totally different from those of early hominids. In the case of cognitive archaeology, the

II. Visuospatial behavior and cognitive archaeology

Electrodermal responses to Lower Paleolithic stone tool manipulation

phylogenetic and ecological distance between modern and extinct humans is definitely less pronounced than those cases, suggesting that any bias, in this sense, is even probably less influential. Second, we should consider that in this case, we are considering whether or not the morphological or physical properties of the tools may have some effect on the cognitive response of the subject. Therefore, what is really important, in this case, is to test whether tool changes may trigger distinct attentional reactions. Tool morphological differences that can generate different sensorial or attentional feedback in modern humans might have been important to shape the evolution of the human capacity to integrate technology, body, and brain.

Electrodermal responses to Lower Paleolithic stone tool manipulation Emotional engagement and haptic cognition are likely to be parts of a specialized prosthetic technological capacity of modern humans, and we can consider the possibility that they can provide indirect evidence of cognitive discontinuities through the information available in the archaeological record. Indeed, tools are

253

intimately tied to human cognitive processes, and the properties of the stone tools might have had an impact on the initial development of the cognitive machinery of the early stone tool users. We can hence speculate that, during the transition from occasional to habitual and then to obligatory tool use, such an increase in technological body-brain integration could have been associated with corresponding changes in the levels of arousal and attention (Bruner et al., 2018a,b). Therefore, the analysis of electrodermal activity during the manipulation of stone tools can detect differences related to the tool types and their morphological properties. Actually, it has been observed that electrodermal response and electrodermal levels show some differences when handling choppers and handaxes and are influenced by factors like tool size, tool morphology, or sex (see Fedato et al., 2019, 2020a; Silva-Gago et al., 2022). When choppers, handaxes, and small flakes are handled through haptic exploration, they trigger different mean levels of arousal (Fig. 11.6A and B). In particular, choppers stimulate more phasic reactions when compared to handaxes (Manne Whitney test, p ¼ .05), and flakes induce higher attentional level (p ¼ .04). EDA can also reveal differences among individuals, as haptic sensing

FIGURE 11.6

Averaged values for EDR, EDL, and cortex percentage in different tools. Choppers have a larger cortex surface (mean 81%) compared to handaxes (mean 10%) and flakes (mean 34%).

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11. Handling prehistory: tools, electrophysiology, and haptics

relies on an important idiosyncratic component, and the grasping strategies vary consistently among individuals (Silva-Gago et al., 2019). In general (as described for many EDA surveys; Kimmel and Kimmel, 1965; Kopacz and Smith, 1971; Purohit, 1966), females display higher variation and higher mean arousal than males. This difference does not appear to be associated with sexual differences in hand size (Fedato et al., 2020a), and it may be hence associated with different kinds of biological or cultural factors that influence the attentional reaction. The physical properties of stone tools (such as weight, dimensions, and proportions) influence their comfortability, and specific tool features are expected to have an influence on the electrodermal reaction. EDA is often associated with task engagement, stress, and cognitive demand (Kosch et al., 2019), and therefore, it is sensitive

to the specific characters of the tools that influence the sensing (proprioception) or reasoning (executive function) associated with its manipulation. Weight is surely a key-feature, showing a correlation with the electrodermal activity associated with tool exploration (Fedato et al., 2019; Silva-Gago et al., 2022) (Fig. 11.7A). The force required to control and handle the object is supposed to be proportional to the degree of body adjustments involved in the dynamic touching. Probably, choppers trigger more phasic fluctuations than other stone tools because of their weight. This factor probably played a crucial role in the earliest stages of prosthetic evolution, because adding a heavy element to the body prompts a stronger somatosensorial reaction. Weight is indeed a pivotal component during tool use, and previous studies already considered its impact on stone tool efficiency.

FIGURE 11.7

Correlation between EDR and tool (A) weight (p ¼ .01 r ¼ 0.70), (B) width (p ¼ .25 r ¼ 0.34), (C) thickness (p ¼ .05 r ¼ 0.54), and (D) length (p ¼ .39 r ¼ 0.26) in choppers (red dots) and handaxes (green dots). Data from Fedato et al. (2020b).

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Final considerations

Jones (1980) carried out a series of butchery experiments using various stone tools. He assessed that handaxes are more efficient than small plain flakes for most butchery tasks due to their weight, their long cutting edges, and the ease with which such tools can be held in the hand. Merritt and Peters (2019) also confirmed that bigger and heavier flakes were better for processing meat. Terradillos-Bernal and Rodríguez (2012) highlighted the importance of weight and cutting edge in cutting tasks and proposed a model to determine the relationship between these factors and tool efficiency. The importance of the weight for stone tool efficient use should be further explored, although that the later Middle Paleolithic technologies were probably handled through hafting should be taken into consideration (C^ arciumaru et al., 2012; Marquez and Preysler, 2002). Hafted tools (namely, artifacts attached to a handle to be grasped) reduce the stress related to the percussion (reducing the rebound and the mechanical trauma) and make the use more comfortable (Claud et al., 2015). The transition from hand-held to hafted tools has not only functional consequences, but also has an influence on the haptic perceptual system, as perception is sensed through the objects (Turvey and Carello, 2011). In this sense, it would be interesting to compare the emotional responses during the manipulation of stone tools when held directly and when hafted. Also the thickness of the stone tool seems proportional to the arousal associated with its handling, while length and width have no consistent influence (Fedato et al., 2019) (Fig. 11.7BeD). A feature which is yet to receive much attention in hand-tool sensing, in terms of cognitive archaeology, is object smoothness, namely, the property of having a surface free from irregularities, roughness, or projections. Our glabrous hands are specialized in both discriminating and recognizing the properties of the explored surfaces (Johansson and Vallbo, 1979; Lepre et al., 2011). The haptic esthetic judgments

(perceived pleasantness of a surface) are influenced by previous experiences with the materials and other cognitive factors that may affect people’s preferences (Gallace and Spence, 2014; Mccabe et al., 2008). There is a clear association between perceived pleasantness and smoothness of the surfaces, and smooth tactile surfaces are generally preferred over rough textures. Concerning Lower Paleolithic stone tools, choppers are normally less retouched (knapped) than handaxes and they have, therefore, a higher portion of smoother edges (Silva-Gago et al., 2022) (Fig. 11.6C). This means that although the raw surface of the pebble (named cortex) can be rougher that the knapped areas, the absence of edges makes it smoother to grasp and touch. Indeed, there is a correlation between the percentage of cortex and EDR (i.e., a phasic reaction), between different tool types and within the tool variation (Fig. 11.8), mostly for handaxes (r ¼ 0.94; p ¼ .005) and flakes (r ¼ 0.93; p ¼ .02). This means that smooth surfaces (the cortex) generate more phasic reactions than the knapped regions, which is apparently counterintuitive, when considering that smoother surfaces are generally more agreeable to touch (Etzi et al., 2014). Hence, we may speculate that arousal, in this case, is also due to the pleasantness associated with the comfortable touch, and not only with the activation associated with the haptic task. The percentage of the cortex does not correlate with changes in general attention (EDL).

Final considerations It is generally accepted that culture has a major influence on human evolutionary trajectories, and that artifacts, as socially learned and transmitted behaviors, form part of the ecosystem (Jones et al., 1994). Humans display advanced technological, symbolic, and social capacities, which undoubtedly stand out from other

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FIGURE 11.8 Correlation between EDR and % of cortex.

primates. Indeed, touch is a key factor in technology but also in social bonding (Dunbar, 2010), and it represents a bridge between proprioception (somatosensory feedback) and interoception (bodily well-being and associated emotional response) (Craig, 2002; see Chapter 1). Hence, the body is a physiological and evolutionary interface for social and technological reciprocal influences (Bruner and Gleeson, 2019). Following the material engagement approach, stone tools are not a mere output of a cognitive process, but they are part of the cognitive process itself, channeling the patterns of cultural evolution (Malafouris, 2019). In this case, stone tools may serve as remnants of the mental machinery in past human populations, and changes in the body-tool interaction may reveal corresponding changes in the sensorial aspects associated with a prosthetic capacity. Namely, tool morphological discontinuities in the archaeological record can be associated with a different sensing experience, evidencing underlying evolutionary changes in the cognitive relationships between the brain, body, and material culture. The modification of the ecological niche produced by the technologically extended organisms alters the conditions for future selective pressures, with major repercussions on the reproductive capacity of the species (Iriki and Taoka, 2012; Odling-Smee et al., 2013). Therefore, in this sense,

technology must be intended as an ecological factor associated with the human natural condition, and not as something external (“artificial”) to the ecological landscape. Ergonomics and electrodermal activity are quantitative and practical tools that can be used to investigate the evolution of the human sensing capacity, as a functional bridge between the body and technology. They concern only some aspects of a more comprehensive cognitive equipment, but have three major advantages. First, they provide experimental perspectives to go beyond pure theoretical approaches. Which is, indeed, a basic requirement in science. Second, they rely on cheap and portable devices, which is an important benefit when working with behaviors associated with field experiments. Third, they investigate features and processes (most of all in the case of EDA) which are still scarcely known for our own species, and that can reveal new levels of cognitive and psychological relationships. On the one hand, this information can provide cues on the evolution of the human specialized “prosthetic capacity,” which must include sensing within its general cognitive mechanisms (Bruner et al., 2023). At the same time, arousal, alert, and emotions are part of the attention system, which must be interpreted as a major bottleneck for the general cognitive integration

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References

(Bruner and Colom, 2022). Psychometrics is, in this sense, another field of interest, when dealing with the evolution of the human mind (see Chapter 13). Ergonomics and electrodermal analysis suggest that features like weight and smoothness may be relevant when haptics and sensing are considered within the frame of prehistory. Large and smooth tools (like choppers) trigger a strong somatosensory feedback, stimulating attention and arousal because of their weight (that requires attention during manipulation) and smoothness (that improves ergonomic grasp). Lately, large tools with knapped outlines (handaxes) keep on stimulating attentional resources by means of their size and, although the surface is less comfortable because of the cutting edges, ergonomic is improved by elongation, and attentional resources are dedicated not only to somatic integration but also to executive functions and task planning (Stout et al., 2015). These two general typologies roughly correspond to the stages of occasional and habitual tool use (sensu Shea, 2017). When humans become obligatory tool users, many tools became smaller (flakes) and hafted. Both aspects are expected to have major effects on the somatosensory and attentional systems. If humans (and, particularly, modern humans) have evolved a prosthetic specialization aimed at integrating tools with their body and brain schemes (Bruner, 2021), their stone tools may reveal part of such a “cyborg” adaptive process (Clark, 2004).

Acknowledgments We are grateful to María Silva-Gago, Marcos Terradillos Bernal, and Rodrigo Alonso Alcalde for their collaboration in the research presented in this review. We thank Jim Hicks for his stimulating comments on arousal and emotions. We thank Rochelle Ackerley and Erin Williams-Hatala for their comments on an early version of this article. The electrodermal analyses were performed thanks to the support of Sociograph and Elena Martín Guerra. This paper is funded by the Spanish Government (PID2021-122355NB-C33) and by the Italian Institute of Anthropology (ISITA).

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C H A P T E R

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A comparative approach to evaluating the biomechanical complexity of the freehand knapping swing Erin Marie Williams-Hatala1,2, Neil T. Roach3,4 1

Department of Biology, Chatham University, Pittsburgh, PA, United States; 2Center for the Advanced Study of Human Paleobiology, The George Washington University, Washington, DC, United States; 3 Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, United States; 4 American Museum of Natural History, New York, NY, United States

Introduction Paleolithic stone tools enabled our ancestors to engage with the world around them in a novel manner that provided them with substantial competitive advantages relative to other animals (Ambrose, 2001; Morgan et al., 2015; Wood and Collard, 1999). It is thought that these new abilities and advantages accelerated a series of adaptations that culminated in the emergence of our own genus, Homo (Aiello and Wheeler, 1995; Navarrete et al., 2011). Consequently, stone tool behaviors (e.g., tool production and use) are regarded as significant adaptations in the evolution of our species, shaped by and subject to the laws of natural selection. It then follows that the anatomical and cognitive capabilities underlying tool production strategies (e.g., raw

Cognitive Archaeology, Body Cognition, and the Evolution of Visuospatial Perception https://doi.org/10.1016/B978-0-323-99193-3.00015-5

material selection, knapping biomechanics) were also subject to refinement by natural and cultural selection and on average should represent the optimal response available within a specific context (Ferraro et al., 2006). Given the general agreement that the development of stone tool behaviors represents a significant turning point in human evolution, a complementary effort to tease out what specifically tool traditions and behaviors indicate about the makersdcognitively (e.g., Muller et al., 2017; Stout et al., 2015), culturally (e.g., Lycett et al., 2016; Tennie et al., 2017; Zwyns, 2021), materially (e.g., Eren et al., 2014; Goodman, 1944), biomechanically (e.g., Rolian et al., 2011; Williams-Hatala et al., 2018)dhas emerged based on the recognition that even contemporary stone tool behaviors are not

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monolithic. Each behavior contains specific and unique pieces of information on aspects of early human evolutionary development. Here, we build on this body of research by focusing specifically on the mechanics of the freehand knapping swing used to produce what we will refer to as “least-effort” Oldowan flakes, i.e., opportunistic sharp-edged flakes made without particular regard to maximization of the core as described by Toth (1985), or Mode A or C tools, those produced without a “stable hierarchy of fracture initiation and flake release surfaces” yielding flakes that are “relatively short and thick, with relatively large striking platforms,” as described by Shea (2013). We also ask how such tool production compares biomechanically to similar upper-limb dominated striking behaviors observed in other animal species (e.g., nut-cracking in capuchins or chimpanzees). This line of inquiry emerged following the hypothesis put forth by Tennie et al. (2017), positing that early stone tools are best regarded as “latent solutions rather than cultural material that derived fromdand depended upondmodern human-like high-fidelity cultural transmission” (652). The requisite implication of the hypothesis that the tools themselves are “latent solutions” is that the knapping swing used to produce these “least effort” tools was also a latent behavior, and hence the biomechanical underpinnings of the swing were already present, if not the swing itself. While it is impossible to generate direct evidence one way or the other, we can approach the issue obliquely by asking how the motor patterns used to produce “least-effort” Oldowan tools compare with other relevant upper limb behaviors. In making such comparisons, we rely first on nonhuman primate tool use behaviors, particularly information on percussive tasks such as nut-cracking, as the motor patterns required for these behaviors can help establish a baseline for the capacities of the earliest hominin toolmakers. While anatomical and behavioral reconstructions of the last common ancestor (LCA) of

humans and extant apes remains a controversial topic (e.g., Alm
ecija et al., 2021; Grabowski and Jungers, 2017; Prang et al., 2021; Young et al., 2015), the use of parsimony in attributing single behavioral capacities to extinct hominins is generally accepted. For example, the presence of simple tool use behaviors across many species of nonhuman primates (e.g., Bermejo and Illera, 1999; Canale et al., 2009; Fragaszy et al., 2004; Whiten et al., 2001) renders the parsimonious assumption that the LCA was also capable of such tool use rather uncontroversial. We extend this inference in examining the mechanics of percussive tasks by nonhuman primates not to assume early knapping behaviors used the same mechanics or cognitive pathways, but to establish a baseline for describing and quantifying what is unique to early tool manufacture and what is not. We ask, is the freehand knapping swing used in making “least effort” tools similarly or even less biomechanically complex relative to other upper limb behaviors practiced in other species? Or, are the biomechanics sufficiently distinct and complex such that it represents, as it has so often been described, a novel and complex motion pattern indicative of a newfound degree of precision and motor complexity among primates (Putt, 2015; Roche et al., 1999; Williams et al., 2010)? To answer this question, we focus on comparing the mechanics and goals of the freehand knapping swing to those of the nut-cracking swing performed by Sapajus libidinosus (bearded capuchin monkeys). Though Pan troglodytes is the obvious and preferable species for such a comparison, given our close phylogenetic relationship (Britten, 2002; Lockwood et al., 2004; Pearson et al., 2015; Uddin et al., 2004) and their welldocumented practice of nut-cracking behaviors (e.g., Boesch and Boesch, 1983; Carvalho et al., 2008; Inoue-Nakamura and Matsuzawa, 1997; Whiten et al., 2001), our focus on S. libidinosus reflects the reality of the wealth of kinematic, kinetic, and even optimization data on the latter (e.g., Fragaszy et al., 2004; Liu et al., 2009, 2016;

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Introduction

Mangalam and Fragaszy, 2015; Mangalam et al., 2018a,b, 2020) coupled with the dearth of such data on the former. And in truth, it is useful to consider the mechanics of the knapping swing relative to any similar nonhuman primate behavior given the tendency to assume the primacy of the former. Secondarily, throughout the discussion, we provide ad hoc comparisons of the mechanics of freehand “least effort” flake production to the mechanics of other evolutionary relevant, forelimb-dominated behaviors performed by modern humans (e.g., high-speed throwing, stick digging, fist fighting). While each of these behaviors is in some way unique to modern humans and our direct relatives, and therefore cannot inform inferences of baseline capacity for Oldowan tool manufacture, understanding the mechanics of these tasks can help illuminate what if anything is a particular challenge of knapping. With these comparisons, we ask, how mechanical and perhaps cognitively demanding is the knapping motion? We readily acknowledge that despite our goals, there are significant hurdles to conducting such comparisons. Foremost, many of the important comparative behaviors needed have been understudied and lack quantitative data for more substantive examination. Further, there is no simple way to compare two or more motor patterns, even those with similar intent or seemingly similar motions. Among other variables, one could consider joint coordination and degrees of freedom at each joint (e.g., Bernstein, 1967), the velocity required for successful completion of the task, the degree to which the speed-accuracy tradeoff is negated in carrying out the task (e.g., Fitts, 1954), the number of goals simultaneously sought (Monerto-Odasso et al., 2012), and myriad other variables. However, even with limited quantitative data, we argue that it is possible to compare and evaluate interspecific upper limb percussive behaviors based primarily on gross motor patterns (e.g., joint range-of-motion [ROM], degrees of freedom at

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each joint, the coordination of joints and limb segments), joint kinetics, and the amount of flexibility around successful completion of the task. In making our argument, we adapt Bernstein’s (1967) well-known theory regarding skillacquisition (the “degrees of freedom problem”) to allow us to compare the motor complexity of the freehand knapping swing used to make “least effort” tools and the nut-cracking swing of humans and S. libidinosus, respectively. We provide quantitative comparisons where we can and highlight areas in need of future research where we cannot. The “degrees of freedom problem” (Bernstein, 1967) describes the process of skill acquisition in successfully performing a motor task as the transition from movement stability (i.e., lack of variation in the movement patterns used to carry out a given task) and restraint (i.e., reduced multiaxial movement at a given joint and/or reduction in the number of coordinated joints) toward movement variability (i.e., intercycle variations in motor patterns) and “freedom” (i.e., increased exploitation of joint degrees of freedom and/or increased joint recruitment and coordination). In particular, among unskilled performers, movement at the more distal joints (e.g., the wrist or the ankle) is restricted in order to reduce “coordinative complexity” (Verrel et al., 2013) for the sake of task completion, but potentially at the expense of efficiency. The association between skill acquisition and movement “freedom” is a reflection of the performer’s ability to effectively exploit multiple motor pathways in the successful (and generally also more efficient (Hirashima et al., 2007, 2008)) completion of the task at hand (Bernstein, 1967; Biryukova and Sirotkina, 2020; Verrel et al., 2013). This transition from stability and restraint to variability and “freedom” is also regarded as a transition from motor simplicity toward motor complexity, as it involves the coordination of a greater number of degrees of freedom across a greater number of joints. Here, we extend this logic to discuss the motor complexity of the

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freehand knapping swing and the nut-cracking swing: complex movement patterns are those that take advantage of the degrees of freedom available at each joint and that involves the coordination of a greater number of joints. Overall, we find that the upper limb motion patterns, the kinetics, and the goals associated with the production of “least effort” Oldowanlike tools using the freehand knapping technique are no more complex than the percussive task performed by S. libidinosus and are significantly less biomechanically demanding compared with other forelimb-dominated, evolutionarily relevant behaviors. We argue that given this relative simplicity, the invocation of complex cognitive shifts on the basis of the biomechanics of freehand knapping is unwarranted. We thus support the proposal set forth by Tennie et al. (2017) and suggest that the reset of the null hypothesis to regard the emergence of stone tools as latent solutions rather than products of cultural transmission be expanded to include the associated biomechanics as well.

Mechanics of the freehand Oldowan knapping swing The knapping swing used to produce “leasteffort” Oldowan-style flakes is primarily an upper limb motor task that does not involve significant trunk rotation or lower limb movement contra many present-day, higher-velocity behaviors such as throwing a baseball (Roach et al., 2013; Roach and Lieberman, 2014) or javelin (Thotawaththa and Chandana, 2021), or the tennis swing (Elliot et al., 1995; Reid and Elliot, 2002). Instead, movement takes place primarily at the shoulder, elbow, and wrist joints (Rein et al., 2013, 2014; Roach et al., 2015; Williams et al., 2010, 2014), with changes in hand joints and digit position used to adjust the hammerstone (Marzke and Shackley, 1986; Key et al., 2019; Williams-Hatala et al., 2018). Assuming that the seated posture consistently used in

modern replication experiments at least approximates that of early tool makers (i.e., the lower body was used minimally), the elimination of trunk and lower limb contributions simplifies the motor control demands of the knapping swing significantly, making it less biomechanically complex than many present-day (and Paleolithic) upper limb tasks. The motions performed by the dominant-arm consist of an upswing, which includes the wrist cocking action, and a downswing, which includes strike and the follow-through (Roach et al., 2015; Williams et al., 2010, 2014). Otherwise, the defining characteristic of the knapping swing is variability with regard to the timing and the magnitude of nearly all of the biomechanical variables of interest (e.g., peak linear and angular velocities, joint angles, joint contributions to power and work, muscle recruitment patterns, manual pressures (e.g., Biryukova and Bril, 2008; Bril et al., 2010; Marzke et al., 1998; Rein et al., 2014; Williams et al., 2014; Williams-Hatala et al., 2018). Such variance is consistent with what has been observed in other highly trained, forelimbdominated actions such as throwing a baseball (Roach and Lieberman, 2014) or shooting a basketball (Button et al., 2003). During upswing, the hammerstone is typically elevated to just below the height of the shoulder (i.e., slightly less than one-half of the full length of the average human body (Quanjer et al., 2014; Shah et al., 2015)) through a combination of shoulder horizontal extension, shoulder external rotation, elbow flexion, and wrist extension. The upswing phase is quite variable but consistently shows low angular and linear velocities relative to those obtained during the downswing. Joint torques, power, and work are also considerably lower compared with the faster downswing phase (Roach et al., 2015). The downswing phase is typified by more rapid movements and includes shoulder horizontal flexion, shoulder internal rotation, elbow extension, and wrist flexion. The wrist is cocked back to its peak extension ROM during early-to-mid downswing,

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Mechanics of the freehand Oldowan knapping swing

setting it up for rapid wrist flexion and the attainment of maximum linear and angular velocity immediately prior to strike (Williams et al., 2014). Reported peak hammerstone linear velocities range from 1.59 to 3.39 m/s (Williams et al., 2014 and Bril et al., 2010, respectively) while joint angular velocities increase proximally to distally from w200 degrees/s at the shoulder (internal rotation), w400 degrees/s at the elbow (extension), and w600 degrees/s at the wrist (flexion) (Roach et al., 2015). For comparison, peak wrist and hand/glove velocities for various types of boxing jabs range from 7.4 to 11.7 m/s (Piorkowski et al., 2011; Whiting et al., 1988) and during overhand throwing, peak angular velocity at the elbow (extension) is approximately 2,400 degrees/s and at the shoulder (internal rotation) is at or above 4200 degrees/s (Roach et al., 2013). Downswing ends with a followthrough phase, during which the wrist is forced back up into a relatively high degree of extension (Williams et al., 2014). As is the case for upswing and downswing, the follow-through phase is also quite variable with regard to ROM, velocities, and accelerations/decelerations at each joint. Inverse dynamics estimates of peak work (the amount of energy required to move an object a given distance) and power (the rate of energy transfer from one object to another) at each joint have been reported from only one study thus far (Roach et al., 2015). The data are again highly variable between subjects and do not match the proximal-to-distal pattern of angular velocity contribution reported for the same subjects. The highest work values come from elbow extension, shoulder extension, and internal rotation of the shoulder. Despite high wrist flexion angular velocity, wrist flexion work is negligible. Though we did not calculate force (mass  [change in velocity/change in time]) in our study, Bril et al. (2010) provide data on the kinetic energy (1/2 mass  velocity2) of the hammerstone at strike. They report that it varies by hammerstone mass and skill level, with experienced knappers producing substantially

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(though not statistically significantly) lower kinetic energy compared with intermediate and novice practitioners (Table 1, Bril et al., 2010). Hammerstone kinetic energy ranged from 5.28 to 12.47 J across skill levels and hammerstone masses, with subjects accommodating heavier hammerstone by either increasing their muscular output (intermediate and novice knappers) or by taking advantage of gravitational forces (experienced knappers) (Bril et al., 2010). By comparison, the kinetic energy of a gloved boxing punch is approximately 2000 J (Whiting et al., 1988) or 3500e4800 N (Atha et al., 1985; Smith et al., 2000; Walilko et al., 2005). Consistent with the substantially lower velocities, angular velocities, and kinetic energy produced during free hand knapping, energetic expenditures are also quite low: the average MET (metabolic equivalent task of intensity) value is 1.86 (0.95e2.34) (Mateos et al., 2018) and is thus classified as a “light-intensity” activity (Ainsworth et al., 2000). The lower joint velocities, angular velocities, torques, power, and work achieved during the upswing portion of the knapping swing compared with the downswing portion are to be expected given that strike, which requires relatively large linear velocities to initiate and propagate a crack through the brittle core material (Chai and Lawn, 2007; Chai, 2017), occurs near the end of the downswing phase. In some ways, the knapping upswing is akin to the early and late arm-cocking phases of overhead throwing. Both show lower joint angular velocities compared with their respective immediate subsequent phase (downswing and arm-acceleration, respectively). Perhaps more importantly, both function in part to enable the upper limb to achieve higher angular velocities during the subsequent phase (Erickson et al., 2016; Fleisig and Escamilla, 1996; Fleisig et al., 1999). This is achieved using beneficial interactive torques that are generated through substantial trunk rotation and the proximal-to-distal sequence during overhead throwing (e.g., Hirashima et al., 2007,

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12. A comparative approach to evaluating the biomechanical complexity of the freehand knapping swing

2008; Naito, 2021). However, the two behaviors differ significantly in joint velocities, angular velocities, and kinetic energy, as described above. Additionally, seated knapping by necessity must employ a different strategy to generate power at the distal segments because the major muscles in the trunk that would normally channel significant amounts of power to those distal segments during throwing (Naito, 2021; Roach et al., 2013; Roach and Lieberman, 2014) are rendered largely inactive. While some power is generated at the shoulder and likely transmitted distally, the power center appears to be the more distal elbow joint. Power values from the wrist are negligible, while the elbow work and wrist angular velocity are both high (Roach et al., 2015), suggesting proximal-distal energy transfer is still occurring at this distal joint. Thus, a kinetic chain of sorts appears to be in place, albeit a very short chain running between the elbow and wrist. Unfortunately, significantly less data are available on the kinematics and kinetics of the nondominant upper limb during Oldowan stone knapping. Regarding movement patterns, Marzke and Shackley (1986) briefly mentioned that while the dominant limb is used to carry out the swing, the nondominant limb and hand are used to position the core, a widely known division of labor. We do not address the mechanics of core repositioning or grip here as nothing has been reported on the kinematics or kinetics of the nondominant upper limb, other than loads experienced by the hand (Key and Dunmore, 2015, 2018; Key et al., 2019). The work by Key and colleagues has revealed a great deal about the ways that modern knappers use their nondominant hand during the production (and use Key et al., 2018) of stone tools, demonstrating that they employ a large suite of grips while repositioning of the core to make the platform available. However, there is no clear reason to assume that the same was true or even possible in the earliest tool makers and we lack data demonstrating any advantage (e.g., temporal,

metabolic) gained through the use of the diversity of grips recorded. Another consideration is that these manipulations are carried out before the flake-generating strike occurs, when the nondominant arm is essentially still. This separation of motor control demands reduces the complexity of integrating these two discrete tasks. Though we currently lack data to speak authoritatively here, our assumption is that the slow speeds and likely low power requirements of the finger motions used to achieve appropriate grips on the core represent less of a biomechanical challenge than generating and coordinating the much more rapid downswing of the dominant arm. More data are needed on both grip mechanics and the cognitive demands of acquiring fine motor control in the hands to address the complexity of this portion of Oldowan knapping more fully. Despite the deficit of information on the nondominant upper limb during Oldowan-like tool production, a series of studies investigating bimanual movement among professional bead makers in Khambhat, Gujarat, India indicates that in this particular percussive behavior, task success is in part a function of bimanual differentiation and yet integration (Bril et al., 2012; Nonaka and Bril, 2012). However, direct application of these results to understanding the evolution of the biomechanics of Paleolithic tool production is not appropriate given that the task, and consequently, the biomechanics of the beadmakers are far more complex than those leading to the production of simple, sharp-edged stone flakes of nonuniform morphology. As discussed above, it is quite possible that the nondominant hand was held stationary during freehand knapping as performed by our ancestors and that the need for bimanual coordination was negated or significantly reduced by the completion of core positioning prior to the commencement of the knapping swing. Complex integration between the two limbs was very likely necessary for the later industries, perhaps even as early as the Acheulean based on recent research highlighting

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Nut-cracking mechanics in bearded capuchins

the extensive teaching and learning processes involved in the removal of large, invasive flakes (Pargeter et al., 2020). But there is currently no evidence to suggest that this was a necessary component of the earlier production strategies. For ready comparison to the nut-cracking swings used by S.libidinosus, it is useful to note that seated freehand knapping involves coordination of three upper limb joints (wrist, elbow, and shoulder) in the dominant arm, two of which move primarily through two degrees of freedom (the wrist and the elbow moving through flexion and extension) and one which moves through three degrees of freedom (the shoulder complex moving through flexion and extension, internal and external rotation, and abduction and adduction). Potentially the same three upper limb joints in the nondominant arm are also recruited, though the range of movement and degrees of freedom used at the joints is currently unknown. There is also some minimal degree of trunk rotation in the axial skeleton. It is also worth noting that the dominant and nondominant upper limbs may be working synergistically but not necessarily symmetrically. While the dominant upper limb carries out the knapping swing, at least during Acheulean and later reduction sequences the nondominant upper limb is used to position, rotate, and stabilize the core from which flakes are removed (Marzke and Shackley, 1986).

Nut-cracking mechanics in bearded capuchins S. libidinosus are widely known for their use of anvils and hammerstones to crack open nuts (Visalberghi, 1990; Fragaszy et al., 2004). Similar to the freehand knapping swing described above, the generalized capuchin nut-cracking motion pattern consists of an upward phase, during which the hammerstone is lifted to its maximum height (on average 60% of the full body length of the individual monkey), and a

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downward phase, which includes the “stonenut contact” event (Liu et al., 2009). It also notably involves a prelifting event at the start of the upward phase, in preparation to lift the considerable relative mass of the hammerstone to the desired height. Hammerstone mass varies by location, with some populations using hammers that weigh 2.58 Ma from Ledi-Geraru, Ethiopia, highlight early technological diversity. Proc. Natl. Acad. Sci. USA 116 (24), 11712e11717. Button, C., MacLeod, M., Sanders, R., Coleman, S., 2003. Examining movement variation in the basketball freethrow action at different skill levels. Res. Q. Exerc. Sport 74 (3), 257e269. Canale, G.R., Guidorizzi, C.E., Kierulff, M.C.M., Gatto, C.A.F.R., 2009. First record of tool use by wild populations of the yellow-breasted capuchin monkey (Cebus xanthosternos) and new records for the bearded capuchin (Cebus libidinosus). Am. J. Primatol. Off. J. Am. Soc. Primatol. 71 (5), 366e372. Carvalho, S., Cunha, E., Sousa, C., Matsuzawa, T., 2008. Chaînes op
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Wrangham, R.W., Boesch, C., 2001. Charting cultural variation in chimpanzees. Behaviour 138 (11e12), 1481e1516. Whiting, W.C., Gregor, R.J., Finerman, G.A., 1988. Kinematic analysis of human upper extremity movements in boxing. Am. J. Sports Med. 16 (2), 130e136. Williams, E.M., Gordon, A.D., Richmond, B.G., 2010. Upper limb kinematics and the role of the wrist during stone tool production. Am. J. Phys. Anthropol. 143 (1), 134e145. Williams, E.M., Gordon, A.D., Richmond, B.G., 2014. Biomechanical strategies for accuracy and force generation during stone tool production. J. Hum. Evol. 72, 52e63. Williams-Hatala, E.M., Hatala, K.G., Gordon, M., Key, A., Kasper, M., Kivell, T.L., 2018. The manual pressures of stone tool behaviors and their implications for the evolution of the human hand. J. Hum. Evol. 119, 14e26. Williams-Hatala, E.M., Hatala, K.G., Key, A., Dunmore, C.J., Kasper, M., Gordon, M., Kivell, T.L., 2021. Kinetics of stone tool production among novice and expert tool makers. Am. J. Phys. Anthropol. 174 (4), 714e727.

Wood, B., Collard, M., 1999. The changing face of genus Homo. Evol. Anthropol. Issues News Rev. 8 (6), 195e207. Wright, R.V.S., 1972. Imitative learning of a flaked stone technology - the case of an orangutan. Mankind 8, 296e306. Wynn, T., Hernandez-Aguilar, R.A., Marchant, L.F., McGrew, W.C., 2011. An ape’s view of the Oldowan revisited. Evol. Anthropol. Issues News Rev. 20 (5), 181e197. Young, N.M., Capellini, T.D., Roach, N.T., Alemseged, Z., 2015. Fossil hominin shoulders support an African apelike last common ancestor of humans and chimpanzees. Proc. Natl. Acad. Sci. USA 112 (38), 11829e11834. Zhai, S., Kong, J., Ren, X., 2004. Speed-accuracy tradeoff in Fitts’ tasks - on the equivalency of actual and nominal pointing precision. Int. J. Hum. Comput. 61 (6), 823e856. Zwyns, N., 2021. The initial upper paleolithic in central and East Asia: blade technology, cultural transmission, and implications for human dispersals. J. Paleolit. Archaeol. 4 (3), 1e39.

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Psychometrics, visuospatial abilities, and cognitive archaeology Emiliano Bruner1, María Silva-Gago1, Annapaola Fedato1, Manuel Martín-Loeches2,3, Roberto Colom4 1

Programa de Paleobiología, Centro Nacional de Investigaci
on Sobre La Evoluci
on Humana, Burgos, on y Comportamiento Humanos, Madrid, Spain; 3Departamento de Spain; 2Centro UCM-ISCIII de Evoluci
Psicobiología y Metodología en Ciencias del Comportamiento, Universidad Complutense, Madrid, Spain; 4 Facultad de Psicología, Universidad Aut
onoma de Madrid, Madrid, Spain

Psychometrics and cognition From an evolutionary perspective, humans (the species and individuals belonging to the genus Homo) have excelled because of their remarkable cognitive abilities, a specialization and hallmark of all hominoids and, in particular, of our own species (Sherwood et al., 2008). Probably, the most patent macroscopic change within the human lineage is associated with the growth and development of the neorcortex, and the term encephalization refers to a disproportionate increase in all brain dimensions. Although the mechanisms, patterns, and processes involved in such brain enlargement are unclear, it represents an anatomical change that has been associated with an increase in cognitive resources. However, brain size is a rough variable, which had a certain success in evolutionary anthropology because of its unquestionable increase in the last 2 million years of human natural history,

Cognitive Archaeology, Body Cognition, and the Evolution of Visuospatial Perception https://doi.org/10.1016/B978-0-323-99193-3.00005-2

and because it is easy to handle in terms of statistics and dissemination. Nonetheless, it is, at the same time, a tricky morphological feature. Anytime we detect a difference in brain size during human evolution, we assume it is due to more neurons, hence more computational units, which is interpreted as greater computational power. Indeed, differences in brain size might be due to neuron number or density, and this is likely a key feature in mammal evolution that probably influences behavioral complexity (Herculano-Houzel, 2016). However, there is no reason why this must be the only factor involved, and not in all cases. A general increase in the size of the cranial cavity does not reveal which parts of the brain have increased or reduced, and this is relevant when taking into account that brain evolution is often associated with mosaic changes that might have been largely independent. Neither does it show if it was a matter of neuron size or number (gray

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matter), connectivity networks (white matter), supporting cells (glia), vascular channels (blood), or supplementary tissues (meninges and other structural elements). The relationships between all these features and cognitive ability are, furthermore, not obvious at all (Songthawornpong et al., 2021). Hence, although we associate encephalization with more complex cognition, we must acknowledge that such an association is, at least in part, due to rough evidence (species with a larger brain size seem to have more complex behaviors) and not to a specific and validated cognitive theory that can work for all taxa. Furthermore, something that has been probably underrated in paleoanthropology is the amazing degree of variation in brain size. Most of the noted differences among hominid species are a matter of average values, while their ranges and distributions show large overlap. Many modern humans have smaller brains than those of many Homo erectus, and many Homo heildelbergensis had larger brains than the brains of many modern humans. Also, modern humans and Neandertals had similar average brain size values, despite the arresting differences in the complexity of their cultures and behaviors (Wynn et al., 2016). The endocranial volume of current H. sapiens spans from 1000 to 2000 cc, with coefficients of variation approaching 50% (Holloway et al., 2004; De Sousa and Cunha, 2012), which demonstrates a remarkable intraspecific variability. If brain size has some importance for general cognitive ability, variability among living humans is certainly impressive. Therefore, a major question in human evolution is how and how much brain morphology can vary, and how and how much this is associated with differences in cognitive skills. Indeed, considering the within-species level of variation, some estimations suggest that general intelligence and brain size could display an approximate correlation of r ¼ 0.40, when proper measurement requirements are met (Gignac and Bates, 2017). This means that up to

16% of the overall general mental ability might be influenced by such a fluctuating dimensional factor. Findings from other meta-analyses may reduce this figure to 5%e10% (Pietschnig et al., 2015). Whatever the chosen value, these results suggest that brain size per se is influential, but far from enough to establish, assess, or predict individual cognitive abilities. Nevertheless, and mostly in terms of evolution, size does matter, although we don’t know if and to what extent the differences among species follow the same intraspecific rule for the relationship between brain size and cognitive capacity. If we give importance to brain size alone, we must acknowledge that two members of different species (say for example one H. sapiens and one H. erectus) with the same brain size should show the same cognitive ability. At first glance, however, we suspect that this is not the case, because the two species are likely to have structural and functional differences that go beyond overall brain size. Both conclusions are nonetheless speculative because, at present, we are the only extant species of our genus and any direct comparison is impracticable. Indeed, there is a general agreement in recognizing that brain size is just a part of the story, and that brain differences among hominids had concerned many physical and physiological features that go beyond the overall brain dimensions (Sherwood et al., 2008; Bruner, 2021).

Measuring minds Variation is not the same as variability. The former refers to the actual range of diversification in a sample for a given variable. The latter refers to the capacity to vary. Unfortunately, in paleontology, both variation and variability are difficult to assess, because of the paucity of the fossil record, which generally hampers robust statistical approaches. Variation and variability are instead central in differential psychology, a discipline particularly aware of the potential

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relevance of human diversity. Simply speaking, in our species, the outstanding degree of variation detected for brain size matches a similar noticeable diversity in behavior, mental abilities, performances, psychological resources, or personality (Chamorro-Premuzic et al., 2015; Barbey et al., 2021). Human cognitive diversity is noticeable, and the mechanisms and factors behind such variability are still under exploration (Colom and Martinez, 2022). Psychology and neuroscience have continued to study and quantify such differences (and similarities), along with the biological, social, and cultural factors influencing our mental processes. Within this framework, psychometrics is the field of psychology dedicated to the design and analysis of psychological measurements and tests aimed at quantifying cognitive abilities and behavioral performance. Its key goal is to develop reliable and valid standardized measures to be used in different contexts, from basic research to applied settings (Abad et al., 2017). Psychometric statistical models deal with finding latent dimensions (generally through multivariate approaches) that summarize the performance revealed by these assessments. This requires simplifying the amount of data comprised of batteries of several cognitive tests. These tests often rely on shared cognitive resources, and the underlying mental processes generally show some level of integration. Accordingly, there is a certain redundancy, collinearity, and correlation among the scores associated with the measured performances, and multivariate statistics is hence used to identify the underlying common patterns. These cognitive dimensions obtained by test batteries are usually called factors (Haier and Colom, 2022; Hunt, 2011). A century of psychometric research has shown that performance scores obtained for different cognitive tasks are positively correlated (Barbey et al., 2021; Colom and Martínez, 2022; Deary, 2020; Haier and Colom, 2022; Hunt, 2011; Sternberg, 2020; Warne, 2020). On average,

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people demonstrating high, medium, or low scores in, say, standardized tests and tasks based on the use of language, also show high, medium, or low scores in standardized tests based on numbers, the management of abstract rules, or visuospatial reasoning and relationships. This is one of the best-replicated findings in psychological research and, in fact, it is currently considered a universal phenomenon (Warne and Burningham, 2019). Analyses of hundreds of datasets also demonstrate that there are more than 80 distinguishable cognitive abilities (Caemmerer et al., 2020; Carroll, 1993; Schneider and McGrew, 2018) that are partially correlated and, therefore, can be organized within a systematic general picture based on multivariate analysis. According to current models in psychology, these abilities are usually hierarchically organized (Fig. 13.1a). At the lower level, we identify specific abilities that can be directly quantified through administered psychometric tests and tasks. Specific abilities can be grouped into narrow abilities which, in turn, can be grouped into broad abilities. While specific abilities are directly measured by tests, the superior levels of this hierarchical organization are inferred through theoretical and empirical approaches. Models based on clustering and grouping abilities in higher ranks are supported through statistics (structural evidence), ontogeny (patterns and rate of development), and neuropsychology (neural networks involved and effect of lesions). At the top of the hierarchical model, there is a general ability (g) identified as general intelligence, which can be referred as the ability to integrate and coordinate the whole subset of cognitive skills (Colom, 2020). This factor is probably associated with a complex frontoparietal network, able to cope with executive functions, language, and visuospatial integration (Bowren et al., 2020; Jung and Haier, 2007; Haier, 2017). Beyond the statistical construct, the precise nature of this latent general factor is still debated, being possibly associated with the

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FIGURE 13.1

(a) According to the hierarchical theories on cognitive abilities, specific abilities can be grouped into narrow abilities, which are parts of broad abilities. General intelligence (g) is the general capacity to coordinate and integrate broad cognitive domains. Psychometric tests can be used to quantify the specific skills, while the higher levels can be inferred through theoretical and empirical approaches. (b) Broad abilities can be grouped or classified according to different criteria (in this case, following Schneider and McGrew, 2018).

overlap and correlation of different abilities in the cognitive tests (domain-general processes) or else to specific cognitive resources (like for example the attention control; Burgoyne et al., 2022). Currently, the most accepted psychometric model for organizing the identified cognitive abilities is the Cattell-Horn-Carroll (CHC) model. The

latest version of this model groups these broad abilities within four general domains (psychomotor abilities, perceptual processing, controlled attention, and acquired knowledge), which can be further separated into their level and speed components (Schneider and McGrew, 2018, Fig. 13.1b). These abilities show, as expected, correlations and communalities, that make their

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identification, separation, and grouping susceptible to debates, disagreements, and alternative interpretations. The possibility to identify modules within the cognitive network does not mean that these modules are discrete or independent, and cognition must be anyway interpreted as a single functional system. The main aim of these models is to understand the structure of the cognitive process through quantitative and experimental methods, based on numerical and comparative approaches that are necessary to investigate the organization and mechanisms of psychological diversity.

A multivariate cognitive space Psychometric tests are employed to produce scores that quantify specific abilities (Fig. 13.2). In general, scores are obtained after the number of hits, the number of errors, the time required to complete the exercise, or a mathematical combination of these parameters. Tests can tap inductive or deductive reasoning, language comprehension, short-term or long-term memory, perception, attention, visualization, speed, or any other mental factor. Although single tests can be used to measure specific skills, in general, they are included in batteries of many and varied tests, as to obtain a comprehensive assessment. Every examinee will be hence characterized by their own combination of scores, and multivariate statistics is used to localize the underlying patterns that generate the observed variation of

FIGURE 13.2 figure on the left.

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the sample. In psychology, these studies aim to investigate the psychometric variables, in order to localize and quantify the underlying or latent “factors,” that are assumed to be the numerical representation of conceptual abilities such as general intelligence, fluid reasoning, crystallized ability, learning, memory, attention, and so on. Accordingly, multivariate techniques employed to detect those associations are generally based on the principles of factor analysis, an approach that maximizes the covariation between variables (Fabrigar et al., 1999). The identification of these factors is also necessary to improve further the design of the tasks, as to develop tests able to focus on specific cognitive domains, excluding additional cognitive components that can decrease the validity of the measure (Lohman, 1988). Instead, in anthropology, the main target of the surveys often regards the distribution of individuals, according to the variation of the sample (e.g., the total variance). In this case, the multivariate statistics employed are often associated with principal component analysis, which maximizes group variation based on individual differences. Namely, while in psychology, the scope is to analyze variables to reveal latent cognitive factors (factor analysis), in anthropology the scope is to analyze subjects and their variability (principal components analysis). The former method is derived from the latter, rotating the axes to maximize the association between variables instead of the variation of the sample. Factor analysis should hence be used

A classic rotation test, in which the examinee must identify which of the four figures on the right matches the

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when the aim is to study the structure of the psychological factors, while principal component analysis is more suited to investigate differences between individuals or groups. If sample variation is strongly channeled along the underlying latent factors, the two approaches are expected to converge on similar results. In previous studies, we analyzed a sample of 102 young adults (45 men, 57 women) with a comprehensive battery of 21 psychometric tests (see for example Bruner et al., 2011; Colom et al., 2013). The results from these tests were used to compute composite scores for Fluid ability (a broad reasoning capacity based on abstract rules and not depending on culture), Crystallized ability (reasoning based on culture and contents associated with language and numerical skills), Spatial ability (visuospatial reasoning, mental rotation, and visualization skills), Working Memory (verbal, numerical, and spatial short-term storage along with concurrent attentional and executive processing), Mental Speed (verbal, numerical, and spatial speed tasks), Updating (short term storage of items), and Controlled attention (verbal, numerical, and spatial conflict resolution tasks). Table 13.1 shows the correlations between these composite factors, including also a general factor (g) interpreted as general intelligence or general cognitive ability. Correlations are TABLE 13.1

negative for those factors measured in terms of time required to complete the tasks (in these cases, smaller values reflect shorter time and hence better performance). All factors display a positive association: on average, better performance in each factor is associated with better performance in the remaining factors (the positive manifold noted above), although to a different degree. The correlation values range from 0.13 to 0.81. The interpretation of this correlation coefficient is crucial in psychological research and, without a proper context, it can lead to misleading conclusions (Funder and Ozer, 2019). When taking into consideration the multifactorial nature of these variables, values around 0.10 can be considered small, around 0.20, they are moderate, around 0.30 large, and around 0.40 very large (Funder and Ozer, 2019). According to these correlation coefficients, the seven composite factors show a mean covariance (R2) of 11%  7%. This means that, on average, 11% of the variation of one factor is proportional to the variation of another. These values suggest that, although these cognitive indexes share common effects, their association is nonetheless moderate, and they are influenced by many other (idiosyncratic) features. Whether we use the original 21 test scores or the seven composite scores, a principal

Correlations between composite variables.

R/p

g

Gf

General intelligence (g)

0.000

Gc

Gv

WM

MS

0.000

0.000

0.002

0.000

0.010

0.000

0.000

0.000

0.089

0.000

0.102

0.007

0.000

0.004

0.000

0.003

0.010

0.009

0.000

0.191

0.024

0.000

0.023

0.022

0.000

0.81

Crystallized intelligence (Gc)

0.75

0.46

Spatial intelligence (Gv)

0.72

0.36

0.27

Working memory (WM)

0.45

0.40

0.36

0.25

0.31

0.17

0.28

0.26

0.22

0.53

0.48

0.37

0.36

0.48

0.23

0.25

0.16

0.29

0.13

0.23

0.50

Updating (UP) Attention (ATT)

ATT

0.000

Fluid intelligence (Gf)

Mental speed (MS)

UP

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0.002 0.30

Psychometrics and cognition

component analysis based on the correlation matrix reveals a blurred structure of the resulting multivariate space (Fig. 13.3). In fact, in both instances, the structure of the variance approaches a random model of distribution, which challenges the presence of substantial specific patterns when dealing with the overall sample variability. The only vector with a reliable consistency is the first principal component, which is associated with a generalized better performance across all tested abilities. Therefore, we can reach two general conclusions, at least when referring to the current sample. First, also when analyzing a set of psychometric variables according to the sample variation (that is, through principal component analysis), there is a general pattern of association between the measured abilities (namely, a general relationship between all values), a pattern that has been hypothesized to represent an underlying generalized cognitive effect (Jensen, 1998). From a statistical perspective, we can identify this factor with general intelligence (g), as the ability to integrate together different cognitive domains (Jung and Haier, 2007; Colom et al., 2009). As mentioned, it remains to be established whether this factor might be due to the overlap of general domains influencing distinct variables or else to specific cognitive resources, like attention control (Burgoyne et al., 2022). Second, this component explains between 24% and 41% of the variance and, therefore, the remaining variance must be attributed to idiosyncratic combinations of effects that are influenced by individual features, specific skills, and particular cognitive domains. The sample diversity is not apparently channeled along the latent psychological factors described by the current psychological models but shows a distribution that is transversal to those factors. The reason for this scarce concordance between sample variability and the latent factors will deserve further discussion, as to evidence the effects of biological versus methodological influences. Nonetheless, these results remark on the importance of using

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principal component analysis or else factor analysis depending on the aim of the study. If the goal is the study of sample diversity (as often the case in anthropology), principal component analysis is probably more suited to reveal the actual individual differences. Instead, when investigating the structure of the underlying cognitive components, factor analysis is necessary to reveal the pattern of association between variables, based on their modular organization. These two sources of variation are, indeed, not the same.

Limitations of psychometric tests Psychometric tests are designed to tap cognitive abilities in a quantitative way by using problems of varied complexity levels (generally presented as printed or screen exercises) to obtain scores that can be statistically analyzed. Therefore, it is important to understand that they quantify cognitive abilities through specific tasks or items. Composite variables obtained after the analysis of specific tests, generally through multivariate statistics or mathematical models, are labeled with names associated with the functional interpretation of these scores (e.g., mental speed, spatial ability, attention, and so on). However, such terms must be interpreted according to the tests administered in a specific survey. In this regard, although a factor can be labeled, for example, as “attention,” it must be properly interpreted as “attention as measured through that specific battery of tests.” This may seem obvious and implicit, but often it is not, and it can generate misunderstandings and unwarranted generalizations. This is even more important when taking into account the second characteristic of these tests: they necessarily measure an admixture of different abilities. Cognitive tests and tasks rarely tap isolated specific skills. On the contrary, these tests usually recruit multiple capacities (Colom et al., 2010; Tucker-Drob et al.,

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FIGURE 13.3

Principal component analysis (a) of seven composite cognitive factors (ATT, attention; Gc, crystallized ability; Gf, fluid ability; Gv, Spatial ability; MS, mental speed; UP, updating; WM, working memory). PC1 involves an increase in all performances (ATT and MS are measured with time, so a better performance is associated with shorter time). PC1, which explains here 51% of the total variance, is interpreted as a vector of general intelligence (g). However, the distribution of the variance along the PC axes (b) is hardly distinguishable from a random pattern (red line), suggesting that the level of integration of the specific components of these patterns, beyond their shared cognitive requirements, is not particularly strong. If we analyze all the 21 original variables or tests (that is, splitting each composite factor into its three components), PC1 explains a smaller part of the variance (24%) but it is still more stable than a random value (c). Data from Bruner et al., 2011.

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2022). For example, tests designed to measure visuospatial abilities may also require verbal abilities. Tests designed to measure abstract reasoning may also recruit visuospatial abilities. Working memory is also involved in many tasks that are not specifically designed to measure working memory capacity, like those used to quantify visuospatial ability or reasoning speed. These shared cognitive functions may explain why most cognitive tests do show significant correlations, albeit with a wide range of values. Further noise influencing the performance and scores can be due to personality (e.g., selfconfidence, anxiety, etc.) or to different strategies and experience associated with the typology of assessments employed (e.g., multiple choice test, the use of keyboard and mouse, etc.). A final limitation deals with the analyzed samples. Although many analyses are designed to consider the influence of culture, sex, age, or further factors, sample-specific characteristics can play a subtle but consistent role in the final multivariate assessment. An important bias, for example, is that many psychological and behavioral studies are performed by using samples and subjects from Western Countries (Henrich et al., 2010; Killin and Pain, 2022). Therefore, the generalization of the results beyond the sample employed in a study should be put forward with caution. Furthermore, considering the noticeable individual variation and the importance of idiosyncratic features, large samples may be necessary to obtain robust and reliable multivariate results, so as to reveal stable and meaningful associations and patterns. Of course, many of these limitations are defining features of most statistical approaches to the analysis of covariation patterns, and they might bias the final interpretation of results only if they are ignored, and not considered as an intrinsic part of the models.

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Psychometrics and visuospatial ability Visuospatial processing is a complex function presumably supported by distinct neural pathways. At least three distinct subsystems can be involved: spatial working memory (parietoprefrontal pathway), visually guided actions (parieto-premotor pathway), and navigation (parieto-middle temporal pathway) (Kravitz et al., 2011). Standardized intelligence or cognitive tests usually include visuospatial tasks involving perceptual and reasoning mental processes. These tasks demand identifying hidden patterns, deciding, for example, whether two patterns in different orientations are the same, and so forth. When dealing with visuospatial ability, we can identify at least three kinds of factors (Colom et al., 2001, 2003): visualization (the ability to mentally manipulate visual patterns), spatial relationships (the speed in manipulating visual patterns by mental rotation or transformation), and dynamic spatial tasks (the ability to perceive and extrapolate motion, predicting trajectories, and estimating the timing of movements). Interestingly, many visuospatial tests have been historically designed to provide a quantitative tool for job selection in those professional fields in which spatial ability may influence performance, including factories, mechanical engineering, or driving/piloting. Recently, haptic and visual feedback have become further investigated in perceptual psychology, taking into account the importance of these mechanisms in virtual reality (e.g., Kappers and Bergmann Tiest, 2013; Katzakis et al., 2020). Within the CHC model mentioned above, general visuospatial ability (Gv) is characterized by simulated mental imagery to solve problems (perceiving, discriminating, manipulating, and recalling nonlinguistic images in the “mind’s

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eye”). The narrow abilities comprised within this general cognitive domain are visualization, speeded rotation, imagery, flexibility of closure, closure speed, visual memory, spatial scanning, serial perceptual integration, length estimation, perceptual illusions, perceptual alternations, and perceptual speed. Nevertheless, other abilities may be relevant for visuospatial cognitive processing. First, psychomotor abilities involve performing physical body motor movements with precision, coordination, or strength. Narrow abilities, in this case, deal with manual dexterity, finger dexterity, static strength, gross body equilibrium, multilimb coordination, arm-hand steadiness, control precision, and aiming. Second, touch abilities are aimed at detecting and processing meaningful information through haptic sensations. This domain includes perceiving, discriminating, and manipulating touch stimuli. Finally, kinesthetic abilities require the detection and processing of meaningful information in proprioceptive sensations. Interestingly, visuospatial tests frequently show differences between men and women, with average better performances in the former groups that can be tentatively interpreted as evolutionary traits based on the different ecological roles of the two sexes (Geary, 2022). Although it is often difficult to discriminate between the biological and cultural factors behind these sexual differences, this recurrent outcome merits attention, when dealing with the cognitive and statistical interpretation of observed results (Halpern, 2011). It is important to take into account that proper psychometric measures must satisfy several criteria, especially reliability and validity. Reliability refers to the consistency of a measurement and the proportion of true variance (in contrast to variance attributable to measurement error) in a given set of measurements. It is not strictly a parameter of a test, but instead of its scores, which also depends on the experimental conditions (design and sample). The average reliability of cognitive ability tests is close to 0.90,

and the usual values of estimated scores of general cognitive ability are close to the maximum possible value of 1.0 (0.97). Low-reliability values increase the error range of the estimated scores. Validity refers, instead, to the degree of accuracy with which psychometric tests measure what they are intended to measure, fitting empirical or theoretical expectations. There are many different types of validity (content validity, construct validity, convergent validity, divergent validity, etc.), but the validity coefficient, estimated by the correlation coefficient between the scores obtained from a given test and some external criterion (such as school grades or job performance), is the more relevant for any general purpose. Predictive validity refers to the degree of accuracy with which a test score estimates an individual’s performance on some criterion such as those noted above. A single visuospatial test can take from a few minutes up to tens of minutes to be performed (some tests have no fixed time limit, and in some instances, the trial can be suspended if the examinee fails to cope with the task). Here, we provide a brief description of some common tests administered to obtain estimates of the examinees’ visuospatial ability, with their indicative reliability values: Motor-Free Visual Perception Test (MVPT). This is a nonmotor visual perceptual assessment. The objective of this test is to measure the overall visual perceptual ability. The examinee is shown a line drawing and then asked to choose the corresponding drawing from a set of four drawings. The test sheets are contained in an easy-to-use book with an easel backing. The stimuli are composed of black-and-white drawings and designs, with response options presented in a multiple-choice format. The MVPT is used to determine differences in visual perception across several different diagnostic groups, and it is often used by occupational therapists to screen subjects with stroke or

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head injuries (Colarusso and Hammill, 2003; McCane, 2006; Han et al., 2014). The test reliability is 0.92. Surface Development. This test measures visualization. The stimuli are 3D figures associated with transformations and mental folding. The subjects must mentally fold a sheet of paper to form a solid figure. Then, they must decide on the correspondence between various numbers and letters on the unfolded and folded pieces, respectively (Thurstone and Thurstone, 1949). The test reliability is 0.97. Printed Puzzles. This is also a visualization test. The stimuli consist of several black figures displayed on the left side of the page and a white figure displayed on the right. The black forms must be put together in order to reproduce the white form, and the subject must identify the only black form which must be left over (Yela, 1974). The test reliability is 0.72. Visual Spatial Learning Test (VSLT). This is a visuospatial memory measure requiring little fine motor dexterity. The stimuli consist of a board with 24 locations and 15 cards with a different meaningless designs. The aim is to match cards with the same location on the board (Malec et al., 1991). The test reliability is 0.94. Rey Complex Figure Test and Recognition Trial (RCFT). This is a test to measure visuospatial constructional ability and visuospatial memory using complex figures. It quantifies visuospatial memory recall, visuospatial memory recognition, response bias, processing speed, and visuospatial construction ability. The stimuli are geometric figures which must be memorized and redrawn (Meyers and Meyers, 1995). The test reliability is 0.89. Brief Visuospatial Memory Test-Revised BVMTR. This is a multiple test form assessment of visual memory. The stimuli are geometric figures and recognition items. The aim is to

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draw as many figures as possible in their correct location (Benedict, 1997). The test reliability is 0.96. Eliot-Donnelly BeF test. This test measures spatial relationships. It consists of showing a chair inside a room with five points located in five different places. The objective is to decide from which point it is possible to see the chair as indicated by a model of the chair presented outside the room (Eliot and Donnelly, 1978). The test reliability is 0.48. Rotation of Solid Figures. This is a test to measure mental rotation capacity. The stimuli are five different solid figures. Each figure displays a three-dimensional solid block. The subject must choose which figure matches a given model shown from another perspective (Yela, 1968). The test reliability is 0.87. Trajectories appreciation. This test measures spatial relationships. Four arrows represent the trajectories of the curves of four cars. Five points are proposed as waypoints of the curved trajectories that the cars should presumably follow. The examinee is asked to decide which point lies within the trajectories of the cars (Germain et al., 1969). The test reliability is 0.29. Vandenberg and Kuse Mental Rotation Test. This test regards mental rotation. The stimuli are a two-dimensional image of a threedimensional object drawn by a computer. The objective is to select the same twodimensional figure seen from another perspective (Vandenberg and Kuse, 1978). The test reliability is 0.83. Shepard and Metzler Test. This test measures the mental rotation of three-dimensional objects. Each examinee is presented with multiple pairs of three-dimensional, asymmetrical, aligned, or cubed objects. The test was designed to measure how long it takes to determine whether the pair of objects was actually the same object or two different objects (Shepard and Metzler, 1971). The test reliability is 0.92.

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This is but a short list of the many tests available for visuospatial abilities (see Eliot and Smith, 1983), and it is aimed at showing what kinds of tasks and exercises are generally used to quantify these skills. It is worth noting that the tests presented here belong to the “small scale” tasks, in which the subject can visualize the whole scenario. In contrast, “large-scale” tests are aimed at testing environmental spatial ability and involve tasks in which most of the spatial context is not visible (Hegarty et al., 2006). In the case of large-scale tests, however, spatial abilities are further integrated with mnemonic, orientation and imaging capacities, dealing with egocentric and allocentric maps, and relying on a wider cerebral network (Ekstrom and Isham, 2017). The “spatial brain” involved in orienting and in the management of landscape includes the parietal lobes, the retrosplenial and the parahippocampal regions, merging perception, memory and learning in coordinate-based and category-based representations that are employed in problem-solving and behavioral planning. This process does require a fine integration of the visual and verbal resources within the frame of working memory (Tommasi and Laeng, 2012). It is important to highlight once more that, although psychometric research has identified a distinguishable visuospatial domain, the measured performance is always related to the remaining cognitive abilities. One key implication of this finding is that when we measure, say, spatial orientation, we must also consider the general ability of the examinees to cope with different levels of cognitive complexity across distinct mental challenges (JuanEspinosa et al., 2000). A correlation study between general visuospatial ability (Gv) and other psychological tests, in fact, showed consistent associations between visuospatial ability and distinct cognitive factors (Colom et al., 2003). General visuospatial ability can be therefore interpreted as a dimension that integrates

several visuospatial measures of spatial relations (coordinates, trajectories, arrows, maps, ElliotDonnelly BF test, PMA-S), visualization (surface development), dynamic spatial processing (Cross-Trajectories Test or CTT, spatial orientation dynamic test-revised, spatial visualization dynamic test-revised), and fluid reasoning as well (Cattell’s Culture-Fair Intelligence Test). The correlation between spatial ability and fluid reasoning ability (Gf) is generally consistent. Therefore, obtaining pure estimates of the cognitive processes targeted by any given psychometric visuospatial measure might be harder than usually assumed. At the same time, the general visuospatial domain is constructed through specific visuospatial abilities and skills that are only partially correlated, and this pattern suggests the presence of different (and independent) cognitive components. In the database used above for the principal component analysis, for example, the three tests employed to compute the index of spatial ability were rotation of solid figures (SOD), rotation of alpha-numeric characters (PMA-S), and mental visualization of a folding procedure (DAT-SR). Although they are all based on visuospatial requirements, they show modest correlations, ranging between 0.36 and 0.42.

Body and perception Spatial tests are often associated with visuospatial ability. This is probably due to the fact that printed or computerized tasks are quick and easy to understand and organize in terms of administration, and this is a crucial advantage both in research, psychological assessment, clinical practice, and job selection. Consequently, general labels such as “spatial ability” and “spatial intelligence” are often used to describe a specific segment of spatial cognition, mainly based on visual processing. Probably, interesting spatial skills are left out of such a definition, including those involving the role of the body

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Psychometrics and visuospatial ability

and body perception. In fact, beyond visuospatial skills (Gv), haptic and proprioceptive processes also involve tactile (Gh), kinesthetic (Gk) and psychomotor (Gp) abilities (see Schneider and McGrew, 2018 for a detailed review), rely on both what (properties and recognition) and where (spatial relationships) neural pathways, and involve affective and emotional components (Lederman and Klatzky, 2009). All these potential broad abilities are integrated parts of what can be called “body cognition,” and they share some methodological issues. First, they deal with sensorial, perceptual and cognitive mechanisms, three complementary aspects that may be difficult to disentangle at theoretical and experimental levels. At present, most tests investigating these abilities generally deal with perceptual and mechanical features (e.g., sensory acuity, speed, dexterity, strength, precision, etc.). The cognitive counterpart (that is, how the sensorial information is integrated and used within the cognitive process) is more difficult to assess, in terms of quantitative or experimental approaches. A second difficulty is an absence of concluding evidence on their structural organization as distinct cognitive modules. On the one hand, this is partially due to the fact that these abilities have been less investigated, when compared to other kinds of skills. At the same time, they are probably finely integrated and contributing to wider cognitive levels (mostly Gv and Gf). For example, tactile, kinesthetic, and psychomotor skills converge in those mechanisms associated with exteroception and dynamic touch, two crucial aspects of bodytool integration and technological extension. Third, all these abilities are involved in a very recent wave of emerging research, after recognizing that body management is not only a biomechanical issue but is deeply involved in decisive cognitive mechanisms. In fact, all these abilities are essential when dealing with current theories in embodiment and embodied cognition, which consider the role of the body and perception as functional parts of the cognitive

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system (Borghi and Cimatti, 2010; Wilson, 2002). In this case, these abilities are supposed to have experienced a profound specialization during the evolution of the human genus (Bruner et al., 2018a,b). There is in fact a systematic feedback between somatosensory perception and action, which generates a system of interdependence between body and action planning (Ackerley and Kavounoudias, 2015; see Chapter 1). Body perception is iteratively adjusted according to physical and spatial interaction with tools and environmental features (Turvey and Carello, 2011; see Chapter 2), and such bodyenvironment integration is based on body range and body references (Cl
ery et al., 2015; see Chapter 3). Moreover, neural organization might be directly influenced by body perceptions (Maravita and Iriki, 2004; Miller et al., 2019a,b; see Chapters 4 and 6). This is even more intriguing when considering tool use and sensing in human evolution (see Chapter 11), and possible evolutionary changes in the precuneus and intraparietal sulcus, involved in body-vision and eye-hand integration (Bruner, 2018, 2021; Bruner et al., 2023). Although psychometric research regarding body cognition and spatial sensing is less developed when compared to visuospatial studies, there are nonetheless some commonly administered tests that quantify tactual abilities, which are often employed in clinical trials to assess sensorial impairments. Here, we present some examples: Bender Visual-motor Gestalt Test. This test assesses visual-motor functioning in children and adults. It includes nine index cards which pictures have different geometric designs. A card is presented to the examinees, and they are asked to copy the design before the next card is shown. Test results are scored based on the accuracy and organization of the reproductions. This test has been used to sample visual-motor proficiency and estimate nonverbal IQ, as a screening technique for

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neuropsychological dysfunction, and as a standard projective technique in the assessment of personality (Bender, 1938; Brannigan and Decker, 2006; Pascal and Suttell, 1951; Piotrowski, 1995). The test reliability is 0.92. ReyeOsterrieth Complex Figure. This test is a standard neuropsychological assessment tool. It is a visuospatial task that applies graphomotor impressions, which are presumed to be a product of complex cognition, perception, and motor skills. Examinees are asked to copy complex geometric shapes and then reproduce them from memory. Writing and drawing tests are widely used as psychometric tools for the diagnosis of a variety of neuropsychological disorders such as dyspraxia, visuo-spatial neglect, and Parkinson’s Disease. Neuropsychological dysfunction can be estimated by drawing performance, including attention and concentration, finemotor coordination, visuospatial perception, nonverbal memory, planning and organization, and spatial orientation (Meyers and Meyers, 1995; Nazar et al., 2017; Gallagher and Burke, 2007). The test reliability is 0.97. Purdue Pegboard Test. This test regards fingertip dexterity and gross movement of the fingers, hand, and arm. It consists of a rectangular wooden board with two parallel lines of 25 holes each spaced 1 cm apart running up the length of the board, with four holders along the top for pegs, washers, and collars. The aim is to place as many pegs into the holes in 30 s, first with each hand separately and then together, followed by the assembly of as many pegs, washers, and collars as possible in 1 min. This test is used in patients with impairments of the upper extremity resulting from neurological and musculoskeletal conditions or to assess loss of dexterity against age-related normative data

(Tiffin and Asher, 1948; Agnew et al., 1988; Reddon et al., 1988). The test reliability is 0.91. Tactual Performance Test. This test is a subtest of the HalsteadeReitan Neuropsychological Battery (Reitan and Wolfson, 2005). The test requires a blindfolded individual to place 10 wooden shapes into a form board placed at a 45 degrees angle to the vertical. The test measures motor skills, tactile perception, and manual dexterity. Subjects are asked to complete the task as fast as possible and, therefore, the faster the candidate is, the higher the score. The test reliability is 0.79. Haptic research is currently experiencing a renewed and fruitful development (Kappers, and Bergmann Tiest, 2013), for two main reasons. First, haptics and body management still represent a major difficulty in cybernetics (Seminara et al., 2019). Following the Moravec’s paradox, since the late 80s, it was clear that it is easier to build a machine that performs amazing calculations or stores a huge amount of data, than to make it walk, perceive, or handle objects properly. Indeed, body cognition still represents an ultimate frontier for robotics and engineering. Second, the advent of virtual reality and the human-computer interface adds new, stringent (and practical) objectives to the agenda of haptic biology (Katzakis et al., 2020).

Visuospatial integration, working memory, and brain development An additional issue in the measurement of visuospatial abilities concerns growth and development. The visuospatial sketchpad is part of the working memory system and, although its cognitive domains are partially independent in terms of neural pathways and functions, there are obvious synergies. This topic is particularly difficult to disentangle during growth and development, taking into account that visuospatial processing is a crucial node within the relation-

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Psychometrics and archaeology

ships between general intelligence, working memory, and attention (Gray et al., 2017). Working memory, in general, is limited in children because of difficulties in the balance between processing and storage capacity, particularly constrained by fast memory decay and low mental speed (Towse et al., 1998). Within this scenario, visuospatial skills develop rather gradually during childhood, although from 7 to 8 years of age they are prompted by the integration and coupling of the visuospatial elements with phonological references (Gathercole et al., 2004). Latelydand probably through enhanced connections within the fronto-parietal systemd visuospatial abilities are also reinforced by personal knowledge, processing strategies, increased processing speed, and increased attentional capacity (Pickering, 2001). The development of any cognitive domain is a complex admixture of genetic and environmental multifactorial effects, which generates a considerable variability and idiosyncratic differences. Nonetheless, the ontogenetic sequence associated with the progressive development of these abilities can reveal some important evolutionary cues. Although the old Haeckel’s lemma “ontogeny recapitulates phylogeny” should not be interpreted too strictly, we can wonder whether the cognitive development in modern humans may suggest similarities with the evolution of those integrated capacities. From one side, for example, we can infer that the evolution of language has probably boosted visuospatial integration through phonological association. At the same time, the importance of visuospatial sketchpad and posterior parietal cortex in working memory, executive functions, and fluid intelligence suggests that visuospatial capacity may have long represented a sort of limiting factor for many other cognitive domains. In this case, it is worth noting that the parietal cortex, particularly evolved and specialized in humans, has a key role in both visuospatial integration and attention, which represent a bottleneck bridge between general intelligence and specific

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cognitive domains (Bruner and Colom, 2022). The intimate relationships between fluid intelligence, attention, and visuospatial ability suggest that these three components may have important reciprocal influences not only in terms of ontogeny but also when considering their evolutionary interactions.

Psychometrics and archaeology Visuospatial functions and experimental archaeology In primates, behavioral complexity and intelligence, as well as their hierarchical cognitive components, are rooted in selective processes associated with ecological and social factors (Burkart et al., 2017). The unique and ultimate value in evolution is genetic fitness, namely, the capacity to procreate, and hence we must expect that our cognitive abilities and skills have been shaped according to this kind of selective pressure, as any other anatomical or physiological trait. In general, however, selection does not operate on single traits, but instead on “packages” of characters, namely, groups of features joined and constrained by pleiotropic and polygenic effects. The main aim of evolutionary anthropology is therefore to analyze how and why these cognitive packages evolved, through the analysis of the association between their different components (Bruner, 2022). In the past, visuospatial abilities have been somewhat neglected in theories on human evolution. The attention was mainly centered on linguistic or symbolic aspects, although these issues have never really found any empirical or consistent evidence when dealing with fossils and extinct human species. Nonetheless, it is patent that, when compared to other primates, humans display large and complex parietal lobes, and a specialized manipulative capacity that is the foundation of their technical skills (Goldring and Krubitzer, 2017; Bruner, 2018; Bruner et al.,

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2023, Chapter 1). The eye-hand system is associated with a complex fronto-parietal network, which implies fine integrated coordination between vision and body (Caminiti et al., 2015; Bruner et al., 2023). Also, visuospatial abilities are a core component of working memory and executive reasoning (Miyake et al., 2001), which have been hypothesized to be a key factor in the evolution of modern human behavior (Wynn and Coolidge, 2004; Coolidge and Wynn, 2005, Chapter 10). Indeed, visuospatial functions in humans can be part of a specialized prosthetic capacity that allows the inclusion of a tool within the body and neural schema, extending cognitive functions to peripheral extrasomatic elements (Bruner and Iriki, 2016; Bruner, 2021). At the same time, in fact, modern humans display large and bulging parietal lobes (including regions associated with the precuneus, the intraparietal sulcus, and the inferior parietal lobules), complex technologies, throwing abilities, and enhanced graphic skills (Bruner and Lozano, 2014; Bruner et al., 2016; Pati~ no et al., 2017). All these features suggest that visuospatial abilities may be of great interest for cognitive archaeology and, in particular, for the evolution of our own species (Bruner et al., 2018c; MartínLoeches, 2016). Psychometric tests can be introduced in experimental archaeology to explore the cognitive patterns behind a specific behavior (heuristic approach) or else to evaluate specific cognitive models (hypothesis testing). Such approaches can rely on two distinct paradigms. First, psychometric scores can be compared between different groups in order to evaluate the effect of variables such as sex, age, education, or behavioral specializations, when dealing with tasks associated with the archaeological evidence. For example, it is possible to test cognitive differences between na€ive subjects and expert tool knappers, to quantify possible relationships between visuospatial scores and such technical knowledge. Second, psychometric scores can be

correlated with other factors or variables, such as physical attributes (e.g., brain or body proportions) or parameters associated with behavioral performance (for example, the quality of a given technological product or the time required to accomplish a target). Here, we present two pilot case studies using two well-established visuospatial tests (Rotation of Solid Figures and Printed Puzzles Test [PPT]) and one haptic test (Tactual Performance Test) (Fig. 13.4) to explore possible influences and associations between visuospatial abilities and visual/tactual Paleolithic tool exploration.

Example 1: paleolithic tool grasping The process of prosthetic extension, in the sense of neural and haptic integration with tools, probably began (either gradually or more abruptly) when humans moved from occasional to habitual tool users, that is after 1.7 million years, finally to become established when we became obligatory technological primates, in the last 300.000 years (Shea, 2017). Indeed, it has been hypothesized that our genus may have evolved a tool-dependent culture (and, hence, a tool-dependent cognition) since the Lower Paleolithic (Plummer, 2004). It is therefore interesting to investigate hand-tool prosthetic relationships regarding the use of the early stone tools, such as choppers and handaxes (Fig. 13.5). Although there is a debate on whether or not the former were actually tools or cores, in both cases there is compelling evidence of handling, and therefore, we may test whether their morphology can supply evidence of some changes associated with haptic response or body cognitive integration. It is worth noting that choppers and handaxes must be grasped by the whole hand, while later and more derived tool types involve the use of fingers (flakes) or fingertips (microliths). Hence, large tools like choppers and handaxes are the remnant of the early stages of a process of haptic specialization,

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FIGURE 13.4 In the printed puzzles test (PPT), the examinee must identify the black form which has to be left over to complete the triangle (a). In the rotation of solid figures (RSFs), the examinee must identify which of the labeled figures corresponds to the rotation of the left one (b). The tactual performance test (TPT) requires the physical fit of solid figures into the corresponding empty space only by haptic exploration (blindfolded). The score regards the time elapsed to complete the task. In this test, based on the time necessary to complete the task, a shorter time means better performance (c).

FIGURE 13.5

An example of chopper (Oldowan tool; left) and handaxe (Acheulean; right).

whose interface later moves on from the gross haptic element (the hand) to its smaller, finer, and more sensitive extremities. Somehow, this

is reminiscent of the evolution of the running ungulates, which, during their specialization, gradually reduced their large feet to the point where

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they touched the ground with their middle toe only. Often, in evolution, specialization means increased sensitivity through a process of reduction, which concentrates information. Large tools such as choppers and handaxes are large solid figures, and we can expect that visuospatial abilities are involved during the sensing, making, and use of these tools. Although their proper use requires ergonomic grasping (Chapter 11), they can be held in different ways, and we may wonder whether visuospatial skills might influence haptic integration between hand and tool. In a previous study, we employed a digital glove to quantify the degree of finger flexion during the ergonomic exploration of these two Paleolithic tool types (Fedato et al., 2020). Here, we measured the correlation between the pattern of tool grasping, as estimated through the combination of phalanx flexion, and the scores of the three psychometric tests. The sample included 27 right-handed adult individuals (17 females) from 23 to 67 years old, who handled 20 choppers and 20 handaxes, while blindfolded. The subjects had no previous archaeological knowledge, so as to reduce functional inferences and thus limiting the experience to haptic sensing and exploration. In this sample, sex differences for the three tests were not significant. We extracted the first principal component of finger flexion in order to characterize the main

grasping pattern, and then looked for correlation with the psychometric scores. We found a significant correlation between the main grasping pattern and the two visuospatial tests (r ¼ 0.47, P ¼ .01, in both cases), but only for the choppers (Fig. 13.6). In particular, subjects with a higher visuospatial ability tend to grasp the tool by flexion of the distal phalanges and extension of the proximal ones. In contrast, subjects with lower visuospatial scores tend to grasp the tool by flexion of the finger base (proximal phalanx) and leaving the fingers more extended. Although preliminary, this pilot study shows how psychometric scores can be analyzed to consider whether hand-tool relationships can be related to visuospatial skills, in this case suggesting that subjects with different visuospatial abilities can ergonomically grasp a chopper in distinct ways. Although the correlation is moderate, it suggests that visuospatial ability can partially influence the grasping process. This information can be useful to develop hypotheses on the cognitive abilities and skills associated with early tool-use, and on the behavioral aspects associated with the tools themselves. Also, we may wonder whether different grasping patterns can influence the body-tool interface in terms of prosthetic ability, proprioceptive feedback, and dynamic touch (Turvey and Carello, 2011).

FIGURE 13.6 There is a correlation between the grasping pattern associated with ergonomic handling of choppers (PC1choppers) and the RSF scores. Flexion of the distal phalanxes (red color) and extension of the proximal phalanx (blue color) are associated with an increase in this visuospatial skill. Finger flexion analysis after Fedato et al. (2020).

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Psychometrics and archaeology

Example 2: visual attention and tool affordances A second pilot example deals with Paleolithic tool visual exploration. In primates, visual cues are the main source of environmental information, and attentional processes are largely based on visual inputs that depend on discontinuities of the visual scene (Wang et al., 2018), or on specific executive filters associated with tasks and active search (Noudoost et al., 2010; Baluch and Itti, 2011). The former are called bottom-up processes and are based on the passive shift of the attention triggered by exogenous sensorial stimulation. The latter are called top-down processes and are based on active and conscious redirection and maintenance of endogenous attention (Connor et al., 2004). In previous surveys, we used eye-tracking technology to investigate the pattern of visual attention during the exploration of choppers and handaxes, evidencing differences associated with the functional and structural parts of the tools and with the technological skills of the subjects (Silva-Gago et al., 2021, 2022, Chapter 10). To check whether

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these visual patterns are influenced by visuospatial skills, we applied the same three tests of the previous example to a sample of 43 na€ive subjects (22 females) and 33 expert archaeologists (16 females). In the pooled sample (N ¼ 76), the two visuospatial tests showed a significant correlation (r ¼ 0.49; P < .001), but they have no significant correlation with the haptic test. In this sample, there is a sex difference for the Rotation of Solid Figures test which is barely significant (P ¼ .03; effect size ¼ 0.52), with higher mean scores for males, as frequently reported in the literature (see Geary, 2022). Interestingly, all samples (pooled and not pooled) revealed, after a cluster analysis based on the three psychometric scores, the existence of two separate groups, namely, “better” and “worse” visuospatial performers (showed in Fig. 13.7a). The visual pattern of exploration, as measured through the dwell time spent on each tool region, is not different between these two groups, after a ManneWhitney test. However, for the handaxes, in the na€ive group, there is a correlation between RSF scores and the time spent

FIGURE 13.7

Left: In the sample used for eye-tracking, there is a strong correlation between RSF and PPT values, normalized as z-scores (triangle: Archaeologists; circles: Na€ive subjects; red: low performance group; green: High performance group). Right: for PPT, expert archaeologists display a significantly higher mean value when compared to na€ive subjects. Samples from Silva-Gago et al. 2021.

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visualizing the base of the tool (r ¼ 0.44, P < .005), while in the archaeologist group, there is a correlation between RSF scores and the time spent visualizing the cortex surface, namely the part which is not knapped (r ¼ 0.45, P < .05). Therefore, examinees with better mental rotation ability spent more time exploring the grasping site or the rough surface of the tool, depending on whether they are na€ive or expert Paleolithic tool users, suggesting that the affordance of the tool may depend on both the know-how of the subject and the examinee’s visuospatial skills. Minor but significant differences between archaeologists and nonarchaeologists are also reported in PPT scores, with a higher mean for the former group (ManneWhitney test P ¼ .01; effect size ¼ 0.49; Fig. 13.7b). Taking into consideration the small sample size, this result is promising. Technical specialization is often associated with improving task performance, and it would be interesting to evaluate and quantify the cognitive abilities associated with expert tool making, tool use, and tool sensing, when dealing with prehistoric technology. Of course, correlation does not implicate causality, and results in this regard always deal with the entrenched effects of nature and nurture. Cross-sectional samples reveal group differences only, without the possibility to detect the causes behind the divergence. Longitudinal samples in controlled experimental settings are required to cope with this limitation but are seldom available and difficult to organize. It is, however, also interesting to note that we found no differences between na€ive subjects and expert archaeologists for the Rotation of Solid Figures and Tactual tests. Despite the small sample size, the results suggest that the differences are, at least, negligible, and that those geometric and haptic skills are not stimulated by standard archaeological practice or knowledge. As in the preceding example, findings must be interpreted with caution, and they only show how psychometric scores can be employed to test cognitive differences associated with behaviors that can be of interest to interpret the

archaeological record. In particular, in some instances psychometrics can be used to compare (a priori) groups, while in some others it can be used to identify (a posteriori) groups in order to analyze the differences in their behaviors.

The issue of modern humans A further note must be taken into account when discussing a general limitation in cognitive archaeology: the use of modern humans to test hypotheses that implies inferences about extinct species. Often, harsh criticisms are raised on the employment of “modern minds” to investigate cognitive theories on fossil taxa. On this issue, three points should be considered. First, as often occurs in evolutionary anthropology, many features and factors involved in human evolution are not yet sufficiently clear even for our own species. In paleoanthropology and archaeology, we often see hypotheses dealing with processes or traits for which there is a general lack of information even for modern humans (like the degree of variation and variability of the considered features, their homology within primates, their pattern of growth and development, their biological functions, and so on). It is unfruitful to investigate a complex biological feature in a few fragmented fossils if the same information is still lacking for millions of living humans, which are a better sample to test hypotheses with reliable, robust, and quantitative experimental approaches. Therefore, information on modern human behavior is mandatory to promote further inference on aspects dealing with extinct hominids. Second, we ignore to what extent we can apply results on modern humans to extinct human species, but surely we can use that information to investigate our own biology and evolution, namely, to explore the cognitive factors, patterns, and limitations associated with Homo sapiens. Which is, indeed, a valuable target. Third, science works with models, not with reality itself. As wisely described by Karl

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Human evolution: the body and beyond

Popper, we provide interpretations of reality according to hypotheses that are supported or rejected on the basis of observationsdpreferably quantitative and experimental observationsd which can be numerically tested and reproduced. Theories and hypotheses are models which we submit to empirical scrutiny. Similarly, we use models to simulate situations that we cannot reproduce in detail. Chimpanzees are often used as models to investigate human evolution, although their lineage diverged from our own more than 5 million years ago and evolved in an environment totally distinct from that of early humans. Macaques are often used in neurobiology as models to explore primate brain organization, although they are only a few species of an evolutionary radiation that includes some 300 diverse and specialized taxa. Mice, worms, and sea urchins are often used as models in medicine, to make experiments relevant to human health, although they belong to distinct zoological orders. Such approximations are necessary when considering that we cannot make social observations on a group of australopiths, that we cannot analyze 300 species of primates in every survey, and that we do not want to perform cruel and painful experiments on our conspecifics. Similarly, we cannot perform a biomedical scanning or a psychometric test on a Neandertal. It is therefore surprising that all the above-mentioned examples using animal models are largely (and often uncritically) accepted, while the use of modern humans to investigate features of other humans often represents a major concern in the field. Historically, anthropology and archaeology have long relied on theoretical approaches. Cognitive archaeology, in particular, has largely been developed through theoretical and logic assumptions. A theoretical foundation is necessary to generate a proper and consistent rationale behind a new field, or to promote the formation of new theories. Nonetheless, after this early stage, experimental and quantitative approaches are mandatory to develop hypotheses that go

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beyond opinions and speculations, and which bring the discipline into a formal and testable scientific framework. Criticisms on the use of modern humans in cognitive archaeology (or, in general, in experimental archaeology as a whole) usually fail to consider these limitations, defending instead an imprudent perspective in which theoretical speculation is better than a quantitative experimental approach. A position that, indeed, is rather in contrast with the basic foundation of science.

Human evolution: the body and beyond Psychometrics deals with the quantification and analysis of psychological and cognitive diversity. Although tests are based on very specific tasks which require the integration of different cognitive abilities and skills, their reliability has been demonstrated at different population levels, and validated through their correlations with several social and demographic indexes, as well as with biological parameters (Barbey et al., 2021; Colom et al., 2009, 2013; Martinez and Colom, 2021). Because of the multifactorial nature of cognition, based on the complex integration of many anatomical, physiological, and environmental influences, such quantification of specific cognitive abilities and skills is not necessarily determinant or conclusive at the individual level, but it allows the identification of influences, trends, and patterns, which is the ultimate aim of evolutionary and comparative biology. The application of psychometric analysis in prehistoric studies is an arduous assignment because, besides the usual difficulties when dealing with cognition, we must also consider the limitation of working with extinct species and cultures. The archaeological record can only supply a partial and fragmented view of the behavioral repertoire associated with extinct hominids, and experiments can be performed with living humans only. Despite these limitations, experimental research is still the

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ultimate approach to support and test hypotheses, which is the principle of science. Theoretical perspectives, in this regard, are fundamental and revealing but must be seen as a first step, necessarily followed by empirical and quantitative evidence. In particular, visuospatial abilities and skills should be carefully taken into account, when considering the changes in the parietal cortex through human evolution, and the corresponding specialization in brain-body-tool relationships. It is worth noting that taking into consideration that visuospatial processing is based on multiple pathways (Kravitz et al., 2011), and that human evolutionary radiation includes parallel and independent lineages (Wood and Baker, 2011), we must expect that different hominids may have evolved diverse combinations of visuospatial abilities and skills. Although modern humans (Homo sapiens) have an outstanding specialization in their visuospatial ability, we should consider the possibility that other human species, although less specialized in this regard, may have evolved a distinct perception and processing of the visuospatial information. This means that extinct human species may have had not only a poorer visuospatial ability, but also a different way to perceive and process visuospatial inputs. The same is obviously true for any other cognitive ability, and we must acknowledge that extinct human groups may have had cognitive specializations that we have never evolved. Lacking the possibility to analyze the visuospatial functions in paleoanthropology directly, we should focus our attention on those visuospatial behaviors that reveal underlying cognitive variations. Considering the evolution of the parietal cortex and our specialization as obligatory tool users (Shea, 2017), further research should be devoted to visuospatial cognition involving body sensing and technological extension. This direction is particularly stimulating also when considering that conscious awareness of the own body might be the fundamental component of meta-cognition, a bridge between the body

and the environment in space and time that, ultimately, might be the foundation of the self (Varela et al., 2017).

Acknowledgments We are grateful to Marcos Terradillos-Bernal and Rodrigo Alonso-Alcalde for their collaboration in experimental archaeology, and to Timothy Hodgson for his support in the eye-tracking analyses. We thank Gerardo Prieto for his comments on an early version of this article. EB is supported by the Ministerio de Ciencia e Innovaci
on, Spain (Project PID 2021-122355NB-C33 funded by MCIN AEI/10.13039/ 501100011033 FEDER, UE) and by the Italian Institute of Anthropology (ISITA). MSG is funded by the Junta de Castilla y Le
on, cofinanced by the European Social Funds (EDU/574/ 2018). MML is funded by the Spanish Government (PSI 2017-82357-P).

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II. Visuospatial behavior and cognitive archaeology

Index ‘Note: Page numbers followed by “f” indicate figures and “t” indicate tables.’

A

B

Acheulean, 56e57, 85, 87, 100e101, 201e203, 227e228, 230e231, 243e244, 268e269 Action, 15, 17, 55, 59e66, 69e70, 95e100, 109e112, 114, 116e117, 119, 121e123, 133, 145, 196e206, 197f, 215, 220e221, 223e224, 226e227, 231, 241e242, 244e245, 266, 270e271, 273, 290e291 Affordance, 28, 32, 34e37, 39, 46e47, 97, 199e201, 219e220, 222e225, 227e229, 231, 245, 252, 297e298 AG. See Angular gyrus (AG) Agnosia, 181e182 Alan Baddeley, 182e183 Allocentric, 188 frames of reference, 188, 190e192 maps, 290 perspective, 190 representations, 99e100 Angular gyrus (AG), 101e102, 158e160, 187e188 Arousal, 247e255 ATT. See Attention (ATT) Attention (ATT), 9e11, 16e17, 66e67, 92e93, 97, 132, 139, 143e144, 162, 183, 186, 196, 200e201, 213e215, 219e220, 222e227, 229e231, 245, 247e253, 255, 285, 288, 292e294, 297e298 Auditory, 196 auditory-vocal circuit, 189 feedback mechanisms, 230 modalities, 56 peripersonal space (PPS), 66e67 stimulations, 53 Australopithecus, 57e58, 162e163 Autonoesis, 184e186, 191

Bimodal neurons, 54, 57e58 Biomechanics, 244e246, 264e265, 272e273 former, 270 freehand knapping, 266 Paleolithic tool production, 268e269 Biotensegrity, 28, 44e47, 46f Bipedalism, 88e89 Body illusion, 66 Body scheme/schemata, 10, 15, 26e27, 59, 62e64, 94, 96e97, 112e113, 132, 223e224, 241e242 Body-tool integration, 132e143, 173e174, 248e249, 290e291

C Cattell-Horn-Carroll model (CHC model), 282e283 Central executive, 183e185 Cerebellar/cerebellum, 13, 15e17 cortex, 16e17 fossa, 169 projection, 16e17 CHC model. See Cattell-Horn-Carroll model (CHC model) Chimpanzees, 56, 62e64, 88, 98e99, 101e102, 154, 174, 186, 197e200, 203e206, 227e228, 252e253, 269, 273, 298e299 Choppers, 163e164, 219, 225e228, 226f, 230, 242e244, 243f, 246e247, 253e255, 253f, 272, 294e298, 295f Cingulate, 16 cortex, 12, 16, 160e161, 169e170, 188 gyrus, 168e169 Climbing, 88, 270e271 Covert attention, 215 Crystallized ability, 283e285 CTC. See Cumulative technological culture (CTC)

305

Cumulative technological culture (CTC), 63, 109e110, 113e115, 118e119, 121e123, 125 Cyber cyberglove, 245 cybernetic, 241e242, 245, 292

D Decision-making, 14, 16, 97, 162, 213e214, 250 Default Mode Network (DMN), 169e170, 187e189, 201 Dermis, 5e6 Development, 6, 15e16, 32, 51, 57e61, 69e70, 87e90, 124e125, 132, 135e136, 144, 155, 157, 160, 187, 198, 201, 204e205, 213e214, 216e220, 230e232, 253e254, 279e280, 292e293, 298 Dexterity, 62, 88e89, 204, 245, 288e289, 292 Diffusion Tensor Imaging (DTI), 203e204 Distalization, 97, 113, 115, 123 DMN. See Default Mode Network (DMN) Dorsal stream, 199e201, 206, 228e229 Downswing, 266e268, 271 DTI. See Diffusion Tensor Imaging (DTI) Dualism, 26

E EDA. See Electrodermal activity (EDA) Effortful touch, 28, 37, 39 Egocentric distance, 42 frames of reference, 190e191 maps, 290 perception of one’s self, 186

306 Egocentric (Continued) representations, 99e100 Electrodermal activity (EDA), 60e61, 69, 248e257 Electropsychometer, 250e251, 251f Elongation, 93e94, 227, 231, 243e244 Embodied/embodiment, 3, 28, 63e66, 132, 248, 290e291 circuity, 241e242 theories of cognition, 228e229 Emotion, 11, 16e17, 59, 61e62, 66e68, 187, 251e253 Emulation, 101e102 Encephalization, 279e280 Endocasts, 56e57, 101e102, 124, 155e159, 164f, 166e168, 173 Epidermis, 5e6 Episodic buffer, 184e185, 191 memory, 184e186, 188e190, 225 Ergonomic(s), 243e245, 248, 256e257 demands, 252e253 design of stone tools, 243e244 grasping, 296 position of hand, 226e227 Executive attention, 196, 200e201 Extended, 57e58, 96 body parts, 143 cognition, 242, 248 stage of development in Homo lineage, 136 Exteroceptive/exteroception, 3e4, 11, 61, 246e247, 290e291 Extrapersonal space, 138 Eye-tracker, 215e218, 217f Eye-tracking, 60e61, 204, 214e219, 225, 227e228, 230, 297e298, 297f

F Flake, 195e196, 201e202, 219, 230, 242e247, 253e255, 268e269, 271e272 Fluid ability, 284e285 Frontal eye fields, 16 Frontal lobes, 13, 53, 56, 159, 162, 181, 183, 185e186, 188, 199e203 Fronto-parietal axis, 169 network, 101e102, 293e294 system, 168e169, 292e293 Functional craniology, 153e157

Index

G

I

Gaze, 57e58, 214e218, 221e222, 224e225, 229e232 Gibson, J. J., 26, 28, 32e37, 97, 113, 220 Grasp(ing), 43, 55e56, 58e61, 63e64, 109, 112, 137, 187, 221, 224, 241e242, 244e245 comfortable, 246e247 kinematics, 93e94, 227 location, 38e39, 41 movements, 55 objects, 95e96 paleolithic tool, 294e296 position, 39 two-handed, 42 Grooming, 15e16, 89e90 Group size, 102, 244

Imitation, 97, 101e102, 109e110, 125, 189, 205 Incorporation, 15, 63e66, 113e115, 132, 243 Inferior frontal gyrus, 16, 120e121 Inferior parietal lobule (IPL), 94e95, 101e102, 138, 159e160, 163, 187 Insula, 13e16, 61 Intelligence, 27e28, 213e214, 248e249 Intention, 87e88, 92e93, 97, 109, 136, 138, 214e215, 271e272 Interoceptive/interoception, 3e4, 10e11, 13, 61 Intraparietal sulcus, 91, 93e96, 101e102, 119e121, 124, 137e138, 158e160, 165e166, 174, 185, 187, 199, 290e291, 293e294 IPL. See Inferior parietal lobule (IPL)

H Hammerstone, 245e246, 266e267, 269e273 Hand, 6, 12, 16e17, 40e44, 54e56, 63e64, 69, 86e87, 93e94, 96e97, 112, 138, 140e142, 168, 221e224, 226e227, 268, 296 Hand size, 247e248, 253e254 Handaxes, 163e164, 202, 219, 225e228, 230, 242e247, 253e255, 294e298 Handedness, 62e63 Haptic, 139, 143, 192, 214, 223e224, 241e242, 246e249, 253e255, 288, 292, 294e298 Hippocampus, 188, 190 Holloway, R.L., 162 Hominid, 85e88, 100e102, 118, 162, 169, 173, 197e198, 243e244, 280, 299e300 Homo, 57e58 H. erectus, 85, 162, 188e189, 280 H. ergaster, 57e58, 100e101 H. habilis, 85, 163 H. heidelbergensis, 85, 280 H. neanderthalensis, 164e165 H. sapiens, 57e58, 60e61, 90e91, 161e162, 164e165, 169, 184e185, 187, 190e192, 197, 201, 213e214, 231e232, 242, 298e299 Homunculus, 54e55, 181e182, 182f Human genus, 161f, 162e173, 213e214, 219, 241e242, 248e249, 290e291

J Joints, 3e4, 10e12, 39, 43e44, 133, 144, 265, 267, 269e272

K Kinematic(s), 268 free-hand movements, 133 grasping, 93e94 signature, 136e137 Knapper, 230e231, 267e268, 270e271, 273, 294 Knapping, 62e63, 195e196, 205, 230e231, 244, 264e265, 267e270, 273

L Language, 51, 90e91, 97e98, 100, 102, 162, 173e174, 185, 189, 231e232, 281, 283e285, 293 Learning, 11, 16e17, 59e61, 63, 85, 96, 98e100, 109e110, 145, 205, 290 Left hemisphere, 187 Limb, 40e41, 54, 59e60, 96, 132e133, 141e142 Locomotion, 87e88

M Macaque, 53, 56e57, 62e64, 69, 88e89, 92e95, 97, 100, 154, 202, 252e253, 298e299 Magnetic resonance imaging, 91

307

Index

Manipulation, 6, 13e14, 62, 85, 89e90, 187, 220e221, 245, 268 Mechanoreceptive/ mechanoreception/ mechanoreceptors, 3e4, 6e9, 11e13, 93e94 afferents, 6, 11, 13e14 low threshold, 10 Mental imagery, 115, 117e118 Mental simulation, 115, 119e120 Microneurography, 4, 6, 11e14 Middle meningeal artery, 161, 170e172 Motion, 60, 115, 117, 264e265 Motor action, 111e112, 116e117 areas, 16 control, 64, 88e90, 96, 111e117, 123, 132, 135e136, 205, 230, 271, 273 Multisensory integration, 15, 137

N Neandertal, 164e166, 185, 298e299 Neuroarchaeology, 195, 197e198 Nociceptive, 3e4, 6, 7te8t, 10e13 Numerosity, 185e186 Nut-cracking, 264e266, 269e272

O Occipital lobe, 157e160, 169e170, 213 Oldowan, 56e57, 85, 87, 100e101, 201e205, 230e231, 242e244, 264e270, 272e273 Orientation, 14, 16, 39e40, 87e88, 115, 120e121, 181, 215, 219e220, 287, 290 Overt attention, 215

P Paleolithic, 57, 100e101, 195e196, 203e204, 252e253, 263, 268e269, 294e296 Paleoneurology, 110, 124e125, 153e157, 170e173 Pan troglodytes, 264e265 Parietal lobe, 12e13, 57, 59, 87, 97, 101e102, 117e118, 131, 159e160, 162e174, 181, 185e190, 242 Parietofrontal networks, 54e55 Perceptual-motor demands, 196

experience, 205 precision, 202 skill, 195e196 Peripersonal space (PPS), 51e56, 59e61, 65f, 69e70, 96e97, 133 Personal space, 51e52, 66e68 Phasic arousal, 252 Phonological loop, 183e184, 187, 189 Plasticity, 57, 61, 63e64, 133e137, 198 Polygraph, 250e251 Postcentral gyrus, 159e160 Posture, 56e59, 116e117, 132 PPS. See Peripersonal space (PPS) Precentral gyrus, 159e160 Precuneus, 15, 101e102, 124e125, 138e139, 159e161, 165e166, 169, 173e174, 186, 188e189, 201, 290e291, 293e294 Prefrontal cortex, 16, 54, 230, 244 Premotor cortex, 13e16, 53, 57, 94, 112, 203e204 Proprioceptive/proprioception/ proprioceptors, 3e4, 11, 15, 56, 61, 136e137, 254e255 acuity, 136e137 afferents, 11 feedback, 296 signals, 17 Prosthetic capacity, 59, 132, 138e139, 220e221 Prosthetic/prostheses, 64e66, 131, 143e145, 296 Psychometric(s), 256e257, 279e293, 299e300 research, 291e292 scores, 294, 296, 298 test, 281e287, 298e299 Putamen, 53, 55e56

R Reliability, 203e204, 288, 299e300 Retrosplenial cortex (RSC), 169e170, 188 Right hemisphere, 123e124, 186, 200 Robotic(s), 245 arm, 144e145 body augmentation, 145e146 devices, 132 sensorimotor interfaces, 143 Rotation, 40, 111, 115, 290 RSC. See Retrosplenial cortex (RSC)

S Saccade, 214e215 Saliency, 218e220 Senses, 3e4, 15e17 Sensing, 10, 87, 114, 132, 139, 141, 143, 219, 241e242, 246e247, 256e257, 297e298 Sex, 225, 253e254, 287, 294, 297e298 Skin, 3e6, 9, 17, 36, 44, 144, 249e250 Skin conductance, 248e249 SLF. See Superior longitudinal fasciculus (SLF) SMG. See Supramarginal gyrus (SMG) Social cognition, 59 cues, 61e62 interactions, 15e16, 51 learning, 56, 125 PPS, 69 societies, 67e70 touch, 15e16 Somatosensory/somatosensation, 4e10 afferent system, 3 association cortex, 14 cortical activity, 13 perception and action, 290e291 signals, 12e17 Somatotopic(al) relations, 13 separation of fingers, 88e89 Spatial ability, 284e285, 287, 290e291 attention, 97 dimension, 119 judgment, 93e94 reasoning, 115 relationships, 68e69, 115, 117, 157e158, 162, 287 SPL. See Superior parietal lobule (SPL) STS. See Superior temporal sulcus (STS) Subcortical areas, 55e56 nuclei, 13 structures, 13, 15 Subparietal sulcus, 159e160, 169e170 Superior colliculus, 53

308 Superior longitudinal fasciculus (SLF), 200 Superior parietal lobule (SPL), 101e102, 137e138, 159e160, 163e166, 168e169, 174, 185 Superior temporal sulcus (STS), 15e16, 91e93 Supramarginal gyrus (SMG), 120e121, 124, 158e160, 187 Sweat, 249e250

T Tactile discrimination, 9e10 feature extraction, 13 interactions, 11 motion, 17 sensing, 245 stimulations, 54 Temporal lobe, 198

Index

Tensegrity, 44e45 Tonic arousal, 252 Tool use, 59, 62e67, 112e115 Tool-making, 56, 97e98, 202e204, 230e231 Touch, 15, 140e141

U Upswing, 266e268

V Valence, 61, 251e252 Validity, 283, 288 VBM. See Voxel-Based Morphometry (VBM) Ventral stream, 187, 198e200, 225 Vestibular processing, 14e15 Vision, 9e11, 14e15, 29e30, 36, 57e58, 89e90, 137, 186, 213e214, 223, 231e232

Visual areas, 16e17, 57 Visualization, 225, 283e285, 287e288, 290 Visuospatial ability, 213e214, 285e293, 296, 299e300 sketchpad, 174, 184, 190e192, 292e293 skills, 115e119 transformations, 115e118 Voxel-Based Morphometry (VBM), 91, 203e204

W Weidenreich, F., 162 WM. See Working memory (WM) Working memory (WM), 16, 97e98, 124e125, 182e185, 196, 284e287, 292e294