The Routledge International Handbook of Neuroaesthetics (Routledge International Handbooks) 0367442744, 9780367442743

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THE ROUTLEDGE INTERNATIONAL HANDBOOK OF NEUROAESTHETICS

The Routledge International Handbook of Neuroaesthetics is an authoritative reference work that provides the reader with a wide-ranging introduction to this exciting new scientific discipline. The book brings together leading international academics to offer a well-balanced overview of this burgeoning field while addressing two questions central to the field: how the brain computes aesthetic appreciation for sensory objects and how art is created and experienced. The editors, Martin Skov and Marcos Nadal, have compiled a neuroscientific, physiological, and psychological overview of the systems underlying the evaluation of sensory objects and aesthetic appreciation. Covering a variety of art forms mediated by vision, audition, movement, and language, the handbook puts forward a critical review of the current research to explain how and why perceptual and emotional processes are essential for art production. The work also unravels the interaction of art with expectations, experience and knowledge, and the modulation of artistic appreciation through social and contextual settings, eventually bringing to light the potential of art to influence mental states, health, and well-being. The concepts are presented through research on the neural processes enabling artistic creativity, artistic expertise, and the evolution of symbolic cognition. This handbook is a compelling read for anyone interested in making a first venture into this exciting new area of study and is best suited for students and researchers in the fields of neuroaesthetics, perceptual learning, and cognitive psychology. Martin Skov is Senior Researcher at Copenhagen Business School and the Danish Research Centre for Magnetic Resonance. His research focuses on understanding the neurobiological mechanisms of sensory liking. He has published extensively on neuroaesthetics, including the book Neuroaesthetics (2009) and an influential series of papers on the conceptual foundations of the field. Marcos Nadal is Associate Professor at the Department of Psychology of the University of the Balearic Islands, Spain. His research is devoted to characterizing the psychological, neural, and evolutionary foundations of aesthetic appreciation. His contributions earned him the Baumgarten Award from the International Association of Empirical Aesthetics and the Daniel Berlyne Award from the American Psychological Association Division 10.

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THE ROUTLEDGE INTERNATIONAL HANDBOOK OF NEUROAESTHETICS

Edited by Martin Skov and Marcos Nadal

Cover image: Based on a drawing by Santiago Ramón y Cajal First published 2023 by Routledge 4 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 605 Third Avenue, New York, NY 10158 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2023 selection and editorial matter, Martin Skov and Marcos Nadal; individual chapters, the contributors The right of Martin Skov and Marcos Nadal to be identified as the authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data Names: Skov, Martin, 1968– editor. | Nadal, Marcos, editor. Title: The Routledge international handbook of neuroaesthetics / edited by Martin Skov and Marcos Nadal. Description: Milton Park, Abingdon, Oxon ; New York, NY : Routledge, 2023. | Series: Routledge international handbooks | Includes bibliographical references and index. Identifiers: LCCN 2022015583 (print) | LCCN 2022015584 (ebook) | ISBN 9781032348803 (paperback) | ISBN 9780367442743 (hardback) | ISBN 9781003008675 (ebook) Subjects: LCSH: Aesthetics—Psychological aspects. | Neuropsychology. | Visual perception. Classification: LCC BH301.P45 R68 2023 (print) | LCC BH301.P45 (ebook) | DDC 111/.85—dc23/eng/20220630 LC record available at https://lccn.loc.gov/2022015583 LC ebook record available at https://lccn.loc.gov/2022015584 ISBN: 978-0-367-44274-3 (hbk) ISBN: 978-1-032-34880-3 (pbk) ISBN: 978-1-003-00867-5 (ebk) DOI: 10.4324/9781003008675 Typeset in Bembo by Apex CoVantage, LLC

CONTENTS

List of contributors List of figures List of tables Preface

viii xiv xvii xviii

1 Neuroaesthetics as a scientific discipline: an intellectual history Martin Skov PART I

Aesthetic liking

1

29

2 Sensory liking: how nervous systems assign hedonic value to sensory objects Martin Skov

31

3 The neurobiology of liking Eloise Stark, Kent C. Berridge and Morten L. Kringelbach

63

4 Disliking: from adaptive disgust to ugliness Christoph Klebl, Michael Donner and Indra Bishnoi

71

5 The influence of interoceptive signals on the processing of external sensory stimuli Alejandro Galvez-Pol, Enric Munar and James M. Kilner

89

6 Neural correlates of visual aesthetic appeal Edward A. Vessel, Tomohiro Ishizu and Giacomo Bignardi

v

103

Contents

  7 Auditory pleasure elicited by music Ernest Mas-Herrero

134

  8 Odour aesthetics: hedonic perception of olfactory stimuli Gulce Nazli Dikecligil and Jay A. Gottfried

148

  9 Movement appreciation Kohinoor M. Darda, Ionela Bara and Emily S. Cross

172

10 How architectural design influences emotions, physiology, and behavior Alex Coburn, Adam Weinberger and Anjan Chatterjee

194

11 Sexual selection, aesthetic appreciation and mate choice Michael J. Ryan

218

12 Aesthetic sensitivity: origin and development Ana Clemente

240

13 The evolution of sensory valuation systems Esther Ureña and Marcos Nadal

254

PART II

Art293 14 Perception and cognition in visual art experience Rebecca Chamberlain

295

15 The music system Amy M. Belfi and Psyche Loui

315

16 Watching and engaging in dance Beatriz Calvo-Merino

337

17 Making sense of space: the neuroaesthetics of architecture Zakaria Djebbara, Lars Brorson Fich and Giovanni Vecchiato

346

18 Literature and poetry Arthur M. Jacobs

366

19 Narrative Franziska Hartung

379

vi

Contents

20 Music-evoked emotions: their contribution to aesthetic experiences, health, and well-being Liila Taruffi and Stefan Koelsch

397

21 The health benefits of art experience Claire Howlin

410

22 Experiencing art in museums Aniko Illes and Pablo P. L. Tinio

423

23 Context and complexity of aesthetic experiences: a neuroscientific view Julia Crone and Helmut Leder

438

24 Experiencing art in social settings Haeeun Lee and Guido Orgs

448

25 Top-down processes in art experience Aenne A. Brielmann

461

26 Preferences need inferences: learning, valuation, and curiosity in aesthetic experience Sander Van de Cruys, Jo Bervoets and Agnes Moors

475

27 Neuroscience of artistic creativity Oshin Vartanian

507

28 Expertise and the brain of the performing artist Fredrik Ullén

526

29 The emergence of symbolic cognition Francesco d’Errico and Ivan Colagè

539

30 Neuropsychology of art and aesthetics Alejandro Dorado and Marcos Nadal

555

Index

571

vii

CONTRIBUTORS

Ionela Bara Department of Psychology Bangor University Bangor, UK Amy M. Belfi Department of Psychological Science Missouri University of Science and Technology Missouri, USA Kent C. Berridge Department of Psychology University of Michigan Ann Arbor, Michigan, USA Jo Bervoets Department of Philosophy University of Antwerp Antwerpen, Belgium Giacomo Bignardi Language and Genetics Department Max Planck Institute for Psycholinguistics Nijmegen, The Netherlands Indra Bishnoi Department of Neuroscience University of Western Ontario London, Canada

viii

Contributors

Aenne A. Brielmann Department of Computational Neuroscience Max Planck Institute for Biological Cybernetics Tübingen, Germany Beatriz Calvo-Merino Department of Psychology City, University of London London, UK Rebecca Chamberlain Department of Psychology Goldsmiths, University of London London, UK Anjan Chatterjee Penn Center for Neuroaesthetics University of Pennsylvania Philadelphia, Pennsylvania, USA Ana Clemente Institute of Neurosciences University of Barcelona Barcelona, Spain Alex Coburn Penn Center for Neuroaesthetics University of Pennsylvania Philadelphia, Pennsylvania, USA Ivan Colagè Pontifical University of the Holy Cross DISF Research Centre and Faculty of Philosophy Rome, Italy Julia Crone Vienna Cognitive Sciences Hub University of Vienna Vienna, Austria Emily S. Cross Institute of Neuroscience & Psychology University of Glasgow Glasgow, UK

ix

Contributors

Kohinoor M. Darda Penn Center for Neuroaesthetics University of Pennsylvania Philadelphia, Pennsylvania, USA Francesco d’Errico University of Bordeaux, PACEA, UMR 5199 Pessac, France Gulce Nazli Dikecligil Department of Neurology and Psychology University of Pennsylvania Philadelphia, Pennsylvania, USA Zakaria Djebbara Department of Architecture and Media Technology Aalborg University Aalborg, Denmark Michael Donner School of Psychological Sciences University of Melbourne Melbourne, Australia Alejandro Dorado Department of Psychology University of the Balearic Islands Palma, Spain Lars Brorson Fich Department of Architecture and Media Technology Aalborg University Aalborg, Denmark Alejandro Galvez-Pol Department of Psychology University of the Balearic Islands Palma, Spain Jay A. Gottfried Department of Neurology and Psychology University of Pennsylvania Philadelphia, Pennsylvania, USA Franziska Hartung Department of Psychology

x

Contributors

Newcastle University Newcastle, UK Claire Howlin Autism Research Centre, University of Cambridge Cambridge, UK Aniko Illes Moholy-Nagy University of Art and Design Budapest, Hungary Tomohiro Ishizu Department of Psychology Kansai University Osaka, Japan Arthur M. Jacobs Center for Cognitive Neuroscience Freie Universität Berlin Berlin, Germany James. M. Kilner Institute of Neurology University College London London, UK Christoph Klebl School of Psychology University of Queensland Brisbane, Australia Stefan Koelsch Department of Biological and Medical Psychology University of Bergen Bergen, Norway Morten L. Kringelbach Center for Music in the Brain Aarhus University Aarhus, Denmark Helmut Leder Faculty of Psychology University of Vienna Vienna, Austria

xi

Contributors

Haeeun Lee Department of Psychology Goldsmiths, University of London London, UK Psyche Loui Department of Music Northeastern University Boston, Massachusetts, USA Ernest Mas-Herrero Institute of Neurosciences University of Barcelona Barcelona, Spain Agnes Moors Research Group of Quantitative Psychology and Individual Differences KU Leuven Leuven, Belgium Enric Munar Department of Psychology University of the Balearic Islands Palma, Spain Marcos Nadal Department of Psychology University of the Balearic Islands Palma, Spain Guido Orgs Department of Psychology Goldsmiths, University of London London, UK Michael J. Ryan Department of Integrative Biology University of Texas at Austin Austin, Texas, USA Martin Skov Danish Research Centre for Magnetic Resonance Copenhagen University Hospital Hvidovre Copenhagen, Denmark Eloise Stark Department of Psychiatry xii

Contributors

Oxford University Oxford, UK Liila Taruffi Department of Music Durham University Durham, UK Pablo P. L. Tinio Educational Foundations Department Montclair State University Montclair, New Jersey, USA Fredrik Ullén Department of Cognitive Neuropsychology Max Plank Institute for Empirical Aesthetics Frankfurt, Germany Esther Ureña Department of Psychology University of the Balearic Islands Palma, Spain Sander Van de Cruys Laboratory of Experimental Psychology KU Leuven Leuven, Belgium Oshin Vartanian Department of Psychology University of Toronto Toronto, Canada Giovanni Vecchiato Institute of Neuroscience Italian National Research Council Rome, Italy Edward A. Vessel Max Plank Institute for Empirical Aesthetics Frankfurt, Germany Adam Weinberger Penn Center for Neuroaesthetics University of Pennsylvania Philadelphia, Pennsylvania, USA xiii

FIGURES

1.1A Data from a search of Google Ngrams’s corpus of publications in English. 1.1B Data from Pubmed showing the number of publications indexed as “neuroaesthetics” in the period between 1965 and 2020. 1.2 A timeline of the development of aesthetics as a conceptual category. 1.3 Data from a bibliometric analysis of the development in the way the word “aesthetic” was used together with other conceptual terms. 1.4 A figure adapted from Descartes’ book L’homme (Paris: Girard, 1677) depicting the physiological system underpinning vision. 1.5 Chatterjee’s (2003) and Leder et al.’s (2004) two models of the neural processes involved in aesthetic experience combined into one representation. 2.1 Sensory liking evaluations always occur in the context of behavioural tasks. 2.2 A schematic depiction of the functional components that constitute sensory liking systems in biological organisms. 2.3 Midsagittal representation of the human brain showing the approximate location of the neuroanatomical structures that make up the human evaluative system. 2.4 A schematic overview of the human evaluative system that shows how computational mechanisms map onto structures in the mesocorticolimbic reward circuitry. 2.5 Model of computational mechanisms known to be involved in human sensory liking evaluations, based on the empirical evidence reviewed in the chapter. 3.1 The Pleasure Cycle. 4.1 Illustration of the human brain with the bilateral insula highlighted in dark orange. 5.1 Interoceptive processes and their influence on stimulus processing. 5.2 Active sensing of stimuli in interoception. 6.1 Methodological “sources of variation.” 6.2 Amount of “shared taste” across observers varies by visual aesthetic domain. 6.3 The visual system and aesthetic appreciation. 6.4 Prefrontal and subcortical structures implicated in visual aesthetic appeal. 6.5 The default-mode network (DMN) is a network of highly interconnected brain regions that are thought to support self-referential and inwardly directed thought and are typically suppressed by tasks that require external focus. xiv

2 2 5 7 10 15 33 35 37 42 52 65 72 93 97 105 108 111 116

120

Figures



8.1 8.2 9.1 9.2 9.3

10.1 11.1 11.2 11.3

12.1 13.1 13.2

13.3 13.4 13.5 13.6

13.7 13.8 13.9 14.1 14.2 14.3 17.1 17.2 17.3 18.1 19.1 21.1

Illustration of two differing models of odour valence perception. 151 Neuroanatomy of the human olfactory regions. 153 The representation of motion in photography, paintings, and sculpture. 173 Typical motion cues used in visual art. 175 Brain networks implicated by previous research in movement appreciation include the perceptual/visual areas, sensorimotor network, reward network, and regions of the default-mode network. 184 The Aesthetic Triad. 200 Examples of sexually selected traits. 219 The túngara frog has an unusual larynx characterized by a large fibrous mass (FM) that protrudes from the vocal cords (VC). 225 A power spectrum of human speech normalized to the frequency with the greatest amplitude, and consonance rankings of musical dyads as a function of the normalized spectrum of speech sounds. 231 Frequency of books mentioning aesthetic sensitivity (blue), esthetic sensitivity (red), aesthetic sensitiveness (green), and esthetic sensitiveness (orange) from 1800 to 2019. 241 Simplified representation, in the form of a sequentially branching tree, of the evolutionary relations among the major groups of organisms mentioned in the text. 259 The figure shows a representation of how neurons evolved from individual choanoflagellates as they joined in colonies and became able to exchange the contents of vesicles in a regular fashion. 261 The photograph shows an exemplar of the ctenophore species Mertensia ovum swimming in the dark. 262 Schematic representation of the central nervous system of the vertebrate ancestor with differentiated diencephalon, mesencephalon, rhombencephalon, and nerve cord. 263 Representation of the telencephalon of an amphibian (salamander, top row), reptilian (turtle, middle row), and mammal (hedgehog, bottom row). 266 Phylogenetic tree showing the relations among major groups of mammals and illustrating the extension of primary and secondary visual, auditory, and somatosensory cortical areas. 269 Illustration of the evolutionary relations among hominoids species, with macaque monkeys as outgroup, with a comparison of adult brain sizes. 272 Distributed association zones are disproportionately expanded in humans. 273 Differences between humans and macaques in cortico-striatal resting-state functional connectivity of the left and right dorsal caudate. 276 Tinio’s (2013) Mirror model of art. 298 Leder et al.’s (2004) model of aesthetic appreciation and aesthetic judgements. 299 The Aesthetic Triad (Chatterjee & Vartanian, 2014). 302 The anatomy of a neuron. 347 Magnetic resonance imaging. 348 The proposed network relevant to the identification and interaction with the built environment is based on the reviewed studies. 358 Extension of the neurocognitive poetics model sketching the likely main neural correlates of subprocesses involved in implicit and explicit fiction processing. 373 Hierarchical processing memory for narratives by Hasson et al. 381 The cognitive mechanisms identified in the literature and how they fit together.414 xv

Figures

25.1 25.2 26.1 26.2 26.3 26.4 29.1 29.2 29.3 30.1 30.2 30.3 30.4 30.5

A simplified schema illustrating the interplay of top-down and bottom-up processes involved in perception and evaluation. 462 Schematic overview of the development of models of aesthetic experiences. 465 Art, a prime example of appreciated stimuli, often breaks order or simplicity. 477 Two-tone or so-called Mooney images are created by blurring and thresholding greyscale photographs. 483 A photograph of a frog that has been used to create Figure 26.2.484 A squiggly line that actually makes up the boundary of profiles of two different opposing faces. 491 Occurrence of cultural innovations in four regions of the world during the last 850 ky. 543 Frequency of cultural innovations in the last 850,000 years at the regional scale and (insert) at the global scale. 545 Schematic representation of the “top-down-also” view. 546 Two examples of Loring Hughes’ artistic work. 559 Four examples of Federico Fellini’s performances in line-bisection tasks, with characteristic personal cartoons. 560 William Utermohlen. Series of Self-Portraits.562 Change in painting style observed after the start of dopaminergic treatment in a patient with Parkinson’s disease. 564 Results of Bromberger and colleagues’ (2011) voxel lesion symptom analyses showing areas where damage was associated with significant deviations of art attribute judgments. 566

xvi

TABLES

17.1 A brief overview of the main disadvantages and advantages of fMRI and EEG that the reader must consider when further reading through the different studies. 17.2 An overview of the positively involved brain regions as reported by the reviewed studies.

xvii

349 356

PREFACE

When we first dipped our feet into neuroaesthetics, in the early 2000s, it was a new area of inquiry on the fringes of neuroscience and psychology. The people doing the research were few and scattered about, and it wasn’t difficult to read all the relevant literature in a single semester. The picture today is very different: papers, chapters, and talks on neuroaesthetics can be found in major psychology and neuroscience journals, in handbooks, and in mainstream neuroscience conferences such as Human Brain Mapping or Society for Neuroscience. We now count our colleagues in the hundreds, and it has become difficult to keep up the pace with everything that is being published. In only 20 years, neuroaesthetics has become a true scientific field that is attracting a new generation of scientists. This publication is the first handbook to provide a broad and comprehensive overview of neuroaesthetics. As editors, we have been confronted with the daunting task of deciding the best way to present the scientific accomplishments of a discipline that is still very much in its infancy. We decided that, rather than focusing on prominent theories or authors, it would be better to organize the book around a selection of the most important topics addressed by neuroaesthetics. We selected only topics that have generated a substantial body of experimental evidence. That we managed to assemble a list of 30 topics that warranted inclusion in the book shows the amount of progress neuroaesthetics has made in just two decades. For historical reasons explained in Chapter 1, aesthetics refers to two different issues: the experience of art, or how and why artworks are created and appreciated, and hedonic valuation, or how and why objects are liked or disliked. Accordingly, neuroaesthetics has sought to identify the neurobiological mechanisms underpinning both the experience of art and hedonic valuation. But it is important to realize that, although in some cases art and aesthetic liking overlap, the two problems are not identical. Engaging with music, dance, or narrative storytelling involves the activation of many neural processes that are not related to aesthetic evaluations. In contrast, the human brain assesses the hedonic value of many sensory objects other than artworks. We have therefore chosen to divide the handbook into two parts that deal with each problem separately. In the first part, on aesthetic liking, we have collected chapters that provide a solid introduction to what is known about the neurobiology of sensory liking. The first chapters in Part 1 present the general context for the study of neuroaesthetics and the general principles of sensory liking, reviewing findings that explain how liking and disliking occur as a function of perceptual computations occurring in different sensory modalities, as well as projections from cognitive and interoceptive systems. The next chapters present a detailed account of how the human brain uses this capacity for hedonic evaluation in different artforms: xviii

Preface

music, visual art, dance, architecture, and so on. We have also included a chapter that discusses the ongoing effort to understand why aesthetic evaluations vary not only across the human population but also in the same individual as a consequence of experience and context. Finally, we have included two chapters in Part 1 that specifically review examples of sensory liking in other animals, both in the context of mate choice and in contexts that are perhaps less obviously adaptive. While comparative work remains limited, we are convinced that developing a common theory of hedonic evaluation that can explain how sensory liking and disliking work in different functional contexts and in different nervous systems will be the next frontier in “aesthetic” neuroscience. Part 2, on art, provides a comprehensive overview of the existing research on the mechanisms underlying the experience of works of art. Each chapter presents what is known about the neural mechanisms involved in the perceptual, cognitive, and affective aspects of our engagement with music, visual art, dance, built environments, and literature. This part also includes chapters on the way art is experienced in social situations, such as museums or concerts, and how art experiences may serve to elicit physiological and emotional responses that can benefit health and well-being. Furthermore, we have included several chapters that discuss research showing that art experiences are profoundly shaped by contextual conditions, including knowledge, experience, and expectations. Finally, the last four chapters of the book survey work on different aspects of art production, including questions of why humans began creating art, what artistic creativity is, how the brains of musicians differ from non-musicians, and how art production can break down or change in patients with brain damage or neurodegenerative diseases. Our overarching aim in editing the book has been to provide a first entry into the field of neuroaesthetics with introductions that are comprehensive, reliable, and succinctly written. We especially hope that the handbook will find use as a textbook in some of many academic courses that have started to appear over the last few years around the world. But we also hope the book will enjoy wider use. We both believe that neuroaesthetics has reached a point where there is a need for taking stock of what is empirically known about perennial questions such as whether aesthetic liking is driven by stimulus properties or modulated by contextual conditions. Furthermore, the continuing progress of neuroaesthetics requires a collective enterprise with a common framework. This includes finding common ground with respect to how key concepts are conceived and employed in experiments. We also hope that the handbook can make a modest contribution in helping neuroaesthetics take these next steps in its development. It has not been easy putting together a volume with 30 contributions at a time when the lives of many people have been severely disrupted. We want to thank the contributors who, living through a global pandemic, persevered and wrote excellent chapters. We think the end result is much more than could have been expected under such circumstances and a true testament to the vitality of neuroaesthetics as a scientific discipline and to the motivation of the researchers that make it all happen. Martin Skov Copenhagen Business School & Copenhagen University Hospital Hvidovre, Copenhagen, Denmark Marcos Nadal University of the Balearic Islands, Palma, Spain January 2022

xix

1 NEUROAESTHETICS AS A SCIENTIFIC DISCIPLINE An intellectual history Martin Skov

A Google Ngram search reveals that use of the word neuroaesthetics was almost non-existent before the year 2000. It first became part of academic discourse between 2000 and 2005 and was only widely adopted after 2010 (Figure 1.1A). A similar trend can be observed if one searches for publications indexed with the keyword “neuroaesthetics” in databases such as PubMed or Web of Science: Before 2000, few such publications exist, if any. They then start to appear in increasingly frequent numbers during the 2000s, and multiply year by year throughout the last decade (Figure 1.1B). What do these numbers tell us about the history of neuroaesthetics? First, they tell us that there was no scientific enterprise that referred to itself as neuroaesthetics before 2000. The discipline that exists today under this name was born between 2000 and 2010. Second, they tell us that the first 20  years of neuroaesthetics can best be described as two historically different periods: an initial era of incubation, where neuroaesthetics was established as a viable idea (2000–2010), followed by a mature period much richer in scientific output (2010–2021). The new scientific discipline of neuroaesthetics did not, however, appear out of thin air. As I will show in this chapter, it is possible to trace an effort to furnish aesthetics with a neuroscientific basis back to the 18th century. We must therefore ask: what changed around 2000? Why did a new discipline dedicated to the exploration of the neurobiological basis of aesthetic experience emerge, and what was “new” about it? The answer, I will suggest, is that neuroaesthetics arose as a concerted effort to transform what had previously been almost exclusively a speculative and theoretical endeavour into an actual experimental science. What set neuroaesthetics apart from earlier attempts to craft a neuroscientific basis for aesthetics was the development, in the 1980s and 1990s, of non-invasive neuroimaging methods that allowed for the exploration of neural activity associated with complex human thought. What was new about the neuroaesthetics that emerged in the 2000s was an ability to probe the human brain as it engaged in aesthetic experiences. It is, however, important to note that this new scientific enterprise did not simply break with the historical tradition that preceded it. Rather, the way the new field of neuroaesthetics conceived of its own mission and the problems it wanted to pursue was highly influenced by ideas that had developed much earlier, starting with the conceptual invention of the category of aesthetics in the 18th and 19th centuries. In my view, this prehistory remains both unknown and unexplored. I will therefore try to show how, over the course of the 18th and 19th centuries, notions of beauty and taste were fused with that of fine art to form the idea

DOI: 10.4324/9781003008675-1

1

Martin Skov

Figure 1.1A Data from a search of Google Ngrams’s corpus of publications in English.

Figure 1.1B Data from Pubmed showing the number of publications indexed as “neuroaesthetics” in the period between 1965 and 2020.

that humans have distinctive experiences called aesthetic experiences and why this idea prompted a quest to understand the psychological and physiological nature of these experiences that endures to this day. In some detail I trace how generations of researchers tried to apply emerging insights into the neurobiology of the human brain to (speculative) explanations of how aesthetic experience arises. My aim is to give the reader an 2

History of neuroaesthetics as a discipline

idea of how these theories served as a direct inspiration for the establishment of neuroaesthetics, influencing both theoretical models and experimental work in the early years of the new discipline’s existence. I should perhaps stress that this chapter should not be thought of as a traditional introduction to neuroaesthetics. Its purpose is not to review findings or provide a representative overview of the work conducted over the last 20 years; the rest of the book covers that material. Instead, it is meant to be a historical study of the intellectual ideas and theories that prefaced neuroaesthetics. I hope it will give the reader an idea of where the impetus to study the neurobiological basis of aesthetics came from and why the scientific enterprise of neuroaesthetics took the form it did, especially in its first incarnation.

Inventing the concept of aesthetics Neuroaesthetics calls for a neuroscientific study of aesthetics, but what does this mean? Most introductions to neuroaesthetics take “aesthetics” to refer to the study of mental states that are associated with the experience of art or evaluating sensory objects for their aesthetic value. For example, Chatterjee (2011) defines aesthetics as a term “used broadly to encompass the perception, production, and response to art, as well as interactions with objects and scenes that evoke an intense feeling, often of pleasure” (p. 53). Upon this definition, neuroaesthetics can be viewed as a branch of cognitive neuroscience “focused on understanding the biological bases of aesthetic experiences” (Chatterjee & Vartanian, 2016, p. 172). Aesthetic experiences, more specifically, “are an emergent property of the interaction of the sensory-motor, emotion-valuation, and knowledge-meaning neural systems” (Chatterjee & Vartanian, 2016, p. 178). It is worth pondering where this idea comes from. Why do we believe that the human brain experiences a special category of mental states called aesthetic experiences that are related to the engagement with works of art and evaluative appraisals? There are many forms of behaviour and experience that seem specific to Homo sapiens that are not singled out for similar psychological and neuroscientific scrutiny. For instance, sport is an activity that is every bit as particular to human behaviour and experience as art and aesthetic appraisals, yet there is no existing field of neurosports. Most neuroscientists simply consider the experience of a football game the emergent property of neural systems that are common to any other experience that involves perception, emotion, cognition, and so on. Aesthetics is treated differently. Not only are aesthetic experiences considered mental traits that are critical to an understanding of the human mind, but they have been the subject of scientific inquiry for more than 150 years (Nadal & Ureña, 2022). While there is no concept in psychology and neuroscience of, say, specialized sports emotions, most psychologists and neuroscientists are convinced that distinctive aesthetic emotions exist (Menninghaus et al., 2019; Skov & Nadal, 2020b). Indeed, the ability to entertain aesthetic experiences has been routinely used in fields such as palaeontology or ethology to distinguish the psychology of Homo sapiens from that of other species, suggesting that modern humans have evolved a dedicated mental “faculty” for generating aesthetic experiences (Ayala, 2017; Dobzhansky, 1962; Klein, 2002; see also Chapter 13). What motivates this conviction? Since many of the assumptions associated with the idea of specialized aesthetic experiences are unsupported by empirical knowledge (Skov  & Nadal, 2020a, 2020b), we can surmise that it does not stem from psychological or neuroscientific findings. For example, while there is ample evidence that aesthetic evaluations rely causally on executive functions such as working memory (e.g., Cattaneo et al., 2014; Che et al., 2021; Sherman et al., 2015; see also Chapter 2), no theory of neuroaesthetics has developed a concept of “aesthetic working memory” akin to that of aesthetic emotions. Instead, the conception of aesthetics that continues to inform contemporary psychology and neuroscience was almost entirely invented by philosophers working before modern psychology and neuroscience. It is this complex of ideas, developed over the course of the 18th and 19th centuries, that not only accounts for the continued belief in aesthetic experiences but also, by and large, determines how we still conceive of these today (i.e., that aesthetic experiences entail aesthetic emotional states but not aesthetic working memory states). 3

Martin Skov

It goes beyond this chapter to trace all the historical roots of the ideas that constitute the conception of aesthetics (see, e.g., Dickie, 1996; Kivy, 2003; Shiner, 2001). However, several events are critical to the understanding of why Western philosophy became convinced that engagements with art and evaluative appraisals elicit specialized aesthetic experiences and why explaining these as a function of neurobiological mechanisms became an enduring preoccupation. First, in the 16th century, Italian philosophers began using the word taste to describe the mental power governing judgments of liking and disliking (Tonelli, 2003). Before, classical and medieval philosophers had considered judgments of beauty as responses to specific objective properties (Dieckmann, 1974; Tatarkiewicz, 1972). This view implied that objects could either be beautiful or not and that judgments of beauty therefore had to be universal. Greek philosophers did not develop anything like a psychological explanation of how humans detect the beauty of objects, but as a general principle, they believed that mental judgments involved the application, by a faculty of reason, of concepts to sense impressions. Under this assumption, beauty judgments could be thought of as mental acts involving the recognition that the perceived properties of an object adhered to the principle of beauty (specifically that they were organized in a particularly harmonious and orderly fashion; Tatarkiewicz, 1972). Greek philosophers agreed that successful beauty judgments were accompanied by feelings of pleasure (Dieckmann, 1974). An enduring problem for the classical conception of beauty was the fact that people often did not agree on whether an object was beautiful. This observation cast doubt on the idea that beauty is an inherent quality of sensory objects which the human mind recognizes and responds to. To many Renaissance philosophers, it seemed more reasonable to assume that beauty is an evaluative reaction to sensory impressions that varies from person to person. They introduced the concept of taste to capture this change in understanding of how beauty judgments worked: from an objectivist relation between object and evaluative power to a subjectivist conception. Rather than universal judgments, the philosophers of taste proposed that the evaluative power of the human mind produced different tastes that varied from one mind to another (Tonelli, 2003). The concept of taste had two significant consequences for the development of a notion of aesthetic experiences. Both of these consequences sprung from the subjectivist implication of the concept of taste: that liking and disliking are based on individual evaluations, not universal judgments. Accepting this theory invariably raised the problem of a “standard” of taste. If beauty is a creation of subjective taste, how can we know which tastes are “good” or “bad”? During the 17th and 18th centuries, European intellectuals became fascinated by this question. Every aspect of social life was subjected to debates over what constituted good and bad taste, with “good” manners, “eloquent” speech, “haute” cuisine, and so on, being codified in normative treatises that the nobility and emerging bourgeoisie devoured to fit in with “good” society (Shiner, 2001; Smith, 1997). Deciding what counted as good taste was seen as crucial to Bildung—the edification both of the person and the nation (Shiner, 2001; Smith, 1997): only objects and behaviours determined to embody good taste were considered “good” for you. The first consequence of this development was the creation of a category of “Fine Arts” (Kristeller, 1951, 1952). As the philosopher Paul Oskar Kristeller (1951) has shown, before the 18th century, there was no Western concept of art that comprised only “the five major arts of painting, sculpture, architecture, music and poetry” (p. 497), “all by themselves, clearly separated by common characteristics from the crafts, the sciences and other human activities” (p. 498). When the Greeks had spoken of techné and Latin philosophers of ars, they meant “all kinds of human activities which we would call crafts or sciences . . . something that can be taught and learned” (Kristeller, 1951, p. 498). Neither was art specifically associated with beauty or other forms of evaluative judgments. Only in the late 17th century did art emerge as a “modern system” (Kristeller, 1951, 1952) that invested certain objects with a value and prestige they had hitherto not been assumed to possess and set them apart from nonart objects. This development was very much interwoven with the contemporaneous societal quest for standards of “good” taste: The new concept of fine art posited

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that, in contrast to “mechanical arts” (Kristeller, 1952, p.  21) and other human endeavours, objects that qualified as art were thought to conjure the especially refined and elevated kind of pleasure that characterized “good” taste. The other consequence was a gradual reckoning with the psychological causes of taste. If beauty and other judgments of taste were subjective in nature, then the evaluative power they were the result of must operate by some discernible principle. The human mind had to contain some form of mechanism that determined the taste response to a given sensory input. In parallel to the invention, around 1700, of fine art as a sui generis category, philosophical attempts to provide a foundation for taste resulted in a psychological invention as well: the idea that taste is the result of its own “faculty” or “sense.” The Scottish philosopher Francis Hutcheson (1725/1973) was the first to argue the human mind comprises an “inner sense” specialized for beauty judgments. It is this sense of beauty, he argued, that gives rise to our individual taste experiences by acting upon the impressions we receive from the outer senses. Furthermore, Hutcheson distinguished the sense of beauty from a moral sense, introducing the notion—later to be much further developed—that beauty judgments only apply to certain sense impressions (Costelloe, 2018; Dickie, 1996; Kivy, 2003). His compatriot David Hume soon adopted Hutcheson’s idea that taste responses are grounded in a specialized faculty and extended Hutcheson’s account to the question of a standard of taste (Hume, 1757/1985). While taste is a subjective response to sense impressions, some tastes are better than others. How can this be possible? Because, Hume argued, the faculty of taste can be “improved by practice, perfected by comparison, and cleared of all prejudice” (1757/1985, p. 241). In other words, people can acquire “good” and “bad” taste by means of experience and learning, presumably through some sort of reshaping of the beauty sense.

Figure 1.2 A timeline of the development of aesthetics as a conceptual category. Between 1500 and 1700, European philosophers invented two new concepts: taste and fine art. When Baumgarten introduced the word aesthetics into the literature in 1735, it was adopted as the general name for the study of judgments of taste, especially through the influence of the work of Kant. In the 19th century, spearheaded by Hegel, aesthetic judgments, especially beauty, came to be seen as primarily connected to an engagement with fine art objects. This novel interpretation created the idea that the human mind enjoys specialized aesthetic experiences that are elicited by fine art objects.

Now, none of these intellectual events happened under the banner of “aesthetics.” That word did not exist until 1735, when it was invented by a German philosopher, Alexander Gottlieb Baumgarten (1735/1951; see Figure  1.2). Indeed, as originally conceived by Baumgarten, aesthetics did not even refer specifically to existing questions of taste, beauty, or art. Rather, Baumgarten (1735/1951) had argued that philosophy needed a general study of those sensory judgments that are a not accounted for by logic; that is to say, all forms of judgments that involved “lower-level epistemology” (Allesch, 2018). Aesthetics was the name of this proposed study. It only became applied to the debates regarding the nature of taste discussed previously

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several decades later (Reiss, 1994) and primarily through the influence of Kant’s third critique, Critique of Judgment (Kant, 1790/2001). Kant embraced Baumgarten’s nomenclature and used the term aesthetic judgment to distinguish judgments of taste from judgments of knowledge and moral judgments (the topics of his two other critiques). Furthermore, Kant (1790/2001) introduced a set of criteria for defining different kinds of judgments of taste, distinguishing between, amongst others, judgments of beauty, judgments of the sublime, and judgments of the agreeable. Most famously, he argued that pure judgments of beauty would be disinterested, universal, exhibit “purposiveness without an end,” and be necessary (Kant, 1790/2001). The influence of Kant was so pronounced that, by the beginning of the 19th century, aesthetics had taken over as the de facto name of philosophical inquiries into taste. At first this seemed to be nothing more than a rebranding of the ideas developed in the preceding decades. Instead of speaking of a sense of beauty, the norm became to speak of an aesthetic sense as the mechanism of evaluative taste. However, it soon was clear that under the name of aesthetic evaluations, the phenomenon of taste became invested with a more specific and restricted meaning. Evaluations rooted in the aesthetic sense were considered to involve specific mental states, first and foremost feelings of pleasure or pain. But aesthetic evaluations also seem to involve distinct perceptual and cognitive states (concepts that philosophers at the time would not have used). For example, Kant insisted that aesthetic judgments were characterized not only by evoking states of subjective feelings but also by aspiring to be correct. They were subjective but also “universally valid” (Stolnitz, 1961). Similarly, Joshua Reynolds (1798) claimed that aesthetic evaluations were shaped by learning and experience. They depend on “our skill in selecting, and our care in digesting, methodizing, and comparing our observations” (pp. 212–13). The idea took hold that aesthetic evaluations apply a special form of contemplation to the object being evaluated, a unique type of “gaze” characterized by a particular kind of attention and “sentiment.” At first, this emerging concept of aesthetic appreciation as a distinct kind of taste evaluation, produced by a special aesthetic sense, did not make any claims about being applicable only to certain aesthetic objects. However, this changed as the 19th century unfolded. In 1820, the German philosopher Hegel gave a series of lectures later collected in a book called Philosophy of Fine Art (1920). In the course of these lectures, he redefined the concept of aesthetics to mean the study of “schöne Künste”: Art, he claimed, is the principal means for “revealing” beauty (I, p. 125) because of its unique ability to embody “the Sensous Semblance [das sinnliche Scheinen] of the Idea” (I, p. 154). Hegel’s argument merged the idea of “aesthetic taste” with the new system of “Fine Arts.” In consequence, aesthetic experiences—a novel concept—came to be seen as a special class of experiences that human can have that involved the application of aesthetic appreciation to a limited set of Fine Art objects (Carroll, 2008; Shiner, 2001). By the end of the 19th century, aesthetics had ceased to be understood as a (broad) study of taste and was now exclusively known as the study of art and the aesthetic experiences art objects were believed to afford (Kranjec & Skov, 2021; Figure 1.3). Collectively, the events recounted here forged the concept of aesthetics we still employ. The assumptions and theories with which we continue to invest aesthetic concepts were inherited from the philosophic ideas described previously. This is especially true of the idea that the human mind is equipped with a distinct aesthetic sense, a mechanism for producing specific aesthetic evaluations of sensory objects. Even among contemporary neuroaesthetics researchers, it is common to see the notion of aesthetic evaluation characterized as a specific form of appreciative contemplation that is principally directed at fine art objects. Yet this idea was invented and formulated by philosophers who lived before the study of the human mind evolved into an empirical and experimental science (Reed, 1997). The assumptions underlying this idea continue to inform work in psychology and neuroscience on aesthetics. Indeed, this idea has guided empirical research unceasingly since experimental psychology and neuroscience first became possible: We focus our investigations on certain properties (e.g., aesthetic emotions) and disregard others (e.g., aesthetic working memory),

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Epoch

1800–1850

1851–1900

RANK 1 2 3 4 5 6 7 8 9 10

culture feeling education character criticism principles value pleasure judgment part

sense judgment value pleasure point culture taste feeling(s) nature

1901–1950 experience value(s) sense theory pleasure appreciation enjoyment judgment emotion

1951–2000 experience value(s) appreciation theory pleasure quality(ities) sense object

Figure 1.3 Data from a bibliometric analysis of the development in the way the word “aesthetic” was used together with other conceptual terms. The topmost panel shows the correlation of the frequency of “aesthetic” with questions of art and beauty over the period between 1800 and 2000, demonstrating that the notion of aesthetics went from being associated with notion of beauty to becoming increasingly associated with the notion of art. The lower panel shows the top-ranked nouns modified by “aesthetic” in the same period. This data demonstrates how aesthetics went from being used to describe concepts related to taste (“aesthetic feeling,” “aesthetic character”) to primarily describe a general experience (“aesthetic experience”). Figure adapted from Kranjec and Skov (2021).

because we still—explicitly or tacitly—accept the basic ideas of aesthetics set out in the course of the 18th and 19th centuries.1

A neuroscience of aesthetics before neuroaesthetics While the concept of aesthetics was motivated by pre-scientific ideas, it evidently made claims about human psychology and physiology. For example, every philosopher involved in the invention of aesthetics as a concept agreed that judgments of beauty involved the generation of pleasure ( Judgments of ugliness were thought to involve the generation of pain.) As we have seen, the central new intellectual invention inspiring theories of aesthetics was the claim that the human mind is equipped with a mechanism for producing aesthetic evaluations—an aesthetic sense. This claim implied that such an evaluative mechanism operated

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according to specific principles—that the aesthetic sense was characterized by what we today would call a computational function with a physiological implementation. Even though 18th- and 19th-century philosophers did not speak in such modern terms, they understood that there had to be “laws” of some sort that determined if a sensory input was experienced as liked or disliked and that the aesthetic sense, if it existed, had to occupy a specific place within the architecture of the human mind (linking, for example, sensation to emotions). In consequence, they tried to explain how the aesthetic sense worked. Naturally, these explanations were wholly uninformed by modern ideas about human brain structure and function. They were uninformed by modern knowledge of anatomy and molecular biology. They obviously were all speculative, based on analytic arguments rather than empirical findings. When, for instance, Hutcheson (1725/1973) argued that the “power of receiving” (p. 34) the idea of beauty consists of the mind forming “sensible ideas” (p. 36) that are characterized by “uniformity amidst Variety” (p. 40), he clearly did not advance a psychological theory that conforms to any contemporary models of human perception or cognition, nor did he bother to test if people do in fact experience beauty as a cause of the “uniformity amidst variety” principle his theory advanced. Still, it should be acknowledged, I  think, that Hutcheson (1725/1973) was trying to explain how the evaluative mechanism—the beauty sense—he claims to exist functions. He understood that there is a psychological mystery at the heart of the new mental faculty he had proposed, and he realized that if we want to understand taste as a subjective response to sensory stimuli, we need to flesh out the computational rules according to which these evaluations operate. The same can be said of Hume, Baumgarten, Kant, or any of the other 18th- and 19th-century philosophers who helped found the concept of aesthetics. In this sense, we can talk of a psychology of aesthetics avant la lettre. Indeed, from the very start, theories of aesthetic evaluation took their departure from existing models of the human mind. Thus, Hutcheson’s theory, to stick to this example, owed its notion of an “inner sense,” dedicated to beauty evaluations, almost completely to Locke’s theory of perception (Dickie, 1996; Kivy, 2003), which had radically upended the classical view of how sensations interface with reason (Reed, 1997). It has been argued that before Fechner, direct calls for a “psychological aesthetics” were limited to a few, largely ignored publications such as Johann Heinrich Zschokke’s 1793 book Ideen zur psychologischen Aesthetik (e.g., Allesch, 2018). This argument, however, misrepresents the degree to which all theories of aesthetic evaluation were conditioned by the existing (natural philosophical) understanding of human psychology.2 Of course, only with the advent of a scientific psychology in the 19th century (Reed, 1997) did theories of aesthetics begin to incorporate knowledge of psychological functions and processes that had their origin in empirical studies of the human mind (Allesch, 1987, 2018; Nadal & Ureña, 2022). Similarly, the development of aesthetic experience as a category gave rise to a neuroscience of aesthetics avant la lettre, first in the form of speculation about the physiological properties of the aesthetic sense and later in the form of what Chatterjee and Vartanian (2014) have dubbed a descriptive neuroaesthetics. The term descriptive neuroaesthetics, in Chatterjee and Vartanian’s definition, encompasses all attempts to account for aesthetic phenomena that make use of facts about the brain derived from mainstream neuroscience research. In contrast, Chatterjee and Vartanian (2014) label experiments that specifically examine neurobiological processes thought to play a causal role in the generation of aesthetic experiences experimental neuroaesthetics. If we accept this terminology, I think it is appropriate to call the numerous papers and books published before 2000 that tried to incorporate findings produced by the nascent field of experimental neuroscience into theories of aesthetics examples of descriptive neuroaesthetics. These writings only rarely relied on actual examples of experimental neuroaesthetics, but they did attempt to connect the hypothesized functions of aesthetic experience to revealed functions of the brain. To keep my discussion of this literature manageable, I will here only touch upon a few cases that illustrate this point. Common to all of them is the fact that they demonstrate how efforts to develop a neuroscience of aesthetics begin with a received conception of what aesthetics is. These publications all assumed the existence 8

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of an aesthetic sense that produces aesthetic experiences. What they wanted to understand were the neural mechanisms of this putative mental faculty, and they tried to do so by applying relevant findings from the emerging field of neuroscience to the problems they were interested in. As described previously, the concept of aesthetics presented theorists with several psychological problems that arose from the contention that humans experience distinct aesthetic experiences. These problems were copious. For example, the notion of aesthetic experience raised the question of how aesthetic experiences are to be distinguished from non-aesthetic experiences. It also posed the developmental question of why people have different aesthetic experiences for the same sensory objects. Finally, it presented theorists with the thorny question of how it is possible to define fine art as a psychologically or neuroscientifically relevant category. At one point or another, in the course of the 19th and 20th centuries, theories of aesthetics sought inspiration from discoveries in neuroscience and biology to provide possible explanations for these problems. Here, I will only deal with theories that tried to address the most central problem created by the concept of aesthetics: explaining the psychological mechanism that must underlie the process of aesthetic evaluation. As noted, aesthetic theory posited that this mechanism took the form of an “inner” sense—a “power” or “faculty,” and so on—that acted upon information it received from the body’s outer senses. This inner sense was thought to embody an evaluative function that reacted to individual sense impressions with either pain or pleasure. As early as the second half of the 18th century, philosophers began to seek inspiration from the emerging field of physiology to craft material accounts that would explain how this putative function worked. In doing so, they established a literature of physiological aesthetics that used models of nerve action to speculate on the way transfer of aesthetic information would happen and why different neural processes resulted in feelings of pleasures and pain. Physiology had become a distinct science in the 17th century with the work of René Descartes (Ochs, 2004). Descartes’ theory of human physiology built on his mechanical physics and described sense impressions as the movement of animal spirits through hollow nerve tubules (Ochs, 2004). Observing the general laws of mechanics, this action was thought to happen when external objects acted on the body’s sense organs and forced animal spirits to flow through the nerve fibres (in a way akin to a hydraulic system; see Figure 1.4). Famously, Descartes retained a dualistic view of the human mind, so he did not believe thinking could be explained by the actions of physiological processes. Instead, he proposed that sense impressions terminated in the pineal gland, where they were somehow transferred to the soul that did the actual thinking. This account began to break down in the 18th century, though. This change can be seen, for instance, in the publication, in 1767, of Albrech von Haller’s Elementa physiologia corpris humania. In this eight-volume tome, von Haller claimed—partly informed by his own observations—that the human body contains two different kinds of nerve actions: On the one hand, some parts of the human body, in line with Descartes, contracted automatically when stimulated. Von Haller called these parts irritable. But other, sensible, body parts appeared to respond according to their own power of movement. In contrast to the irritable parts of the body, the sensible parts were not simply caused to act by the external stimulation but seemed to respond to external sense impressions by obeying some intrinsic power. The reason for the difference between irritable and sensible reactions, von Haller conjectured, was to be found in their physiological make-up, especially the type and number of fibres that constituted the two different kinds of sensation (Ochs, 2004). The new ideas of von Haller and other 18th-century physiologists broke with Descartes by claiming that not all nerve actions occur as mechanistic bottom-up processes. Some have an inherent sensibility that makes them react to sense impressions according to their own intrinsic principles (Fastrup, 2007; Ochs, 2004). This “Vitalistic” account (Rey, 2002) greatly inspired physiological theories of aesthetic evaluation by suggesting that different parts of the sensory system can exhibit different sensibilities. To philosophers interested in aesthetics, the concept of sensibility suggested that “aesthetic” reactions to sensory objects might consist of specific forms of nerve movement. For example, in Philosophical Enquiry into the Origin of our Ideas of the Sublime and the Beautiful, Edmund Burke (1757) proposed that pain and pleasure arose from either a “stretching” or a 9

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Figure 1.4 A  figure adapted from Descartes’ book L’homme (Paris: Girard, 1677) depicting the physiological system underpinning vision. Descartes envisioned the outer senses as a system of hollow nerve tubes through which animal spirits travelled according to the rules of mechanical physics when stimuli engaged the sensory systems. In the later tradition of physiological aesthetics, scholars attempted to work out the way that aesthetic experiences could arise from this material basis. The majority of this work concentrated on identifying on one or more of three possible physiological causes of aesthetic experience: a specific sensitivity or movement characterizing the sensory organs by which aesthetic objects are perceived (“Outer sense”); a specific mechanism for apprehension of sense impressions (“Beauty sense”); or a specialized way in which we experience feelings of pleasure and pain as a result of the aesthetic experience.

“relaxation” of nerve fibres. These nerve actions, in turn, had their origin in particular responses of sensible nerves to external objects. For instance, Burke hypothesized that because large objects strike the retina all at once, the stretching of nerves leads to the elicitation of pain responses that further self-preservation. However, in cases where such objects are perceived from a safe distance, the nerves can become relaxed, prompting feelings of awe and astonishment, a form of pleasurable pain (Burke, 1757). The emotional state we feel forms the basis of our aesthetic judgments: feelings of pleasure yield the idea of beauty, while feelings of pain yield the idea of ugliness. As a kind of intermediate response, feelings of “pleasurable pain” were thought to yield the idea of the sublime. The latter part of the 18th century saw the publication of a slew of books arguing for different models of physiological aesthetics. In addition to Burke’s, important treatises were published by Théophile Bordeu, Denis Diderot, Henri Fouquet, Daniel Webb, and Uvedale Price, among others (Fastrup, 2007; Rey, 2002). All of these models used ideas of nerve sensibilities to root the psychology of aesthetic evaluations in a material physiology. Proponents of physiological aesthetics were convinced that mental states (“Ideas”) were generated by certain nerve acts, especially individual kinds of fibre movements. This, of course, amounted

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to a “psycho-physiology.” However, an empirical exploration of this relationship between physiology and psychology seemed doomed, since it was difficult to see how it was possible to differentiate between individual thoughts. Kant (1786/1970) had made this very argument in the Metaphysical Foundations of Natural Science, declaring any empirical science of psychology impossible. Then, in 1834, the German physiologist Ernst Weber discovered that for the sensations of two different weights to be perceptually noticeable, the difference in intensity of the larger weight to the smaller weight had to obey a specific ratio, ΔI/I = k (Ochs, 2004). Weber’s fortuitous observation demonstrated that it was in fact possible to relate different psychological states to physiological measurement and that this relationship could even be expressed in mathematical terms. Weber’s work inspired Gustav Fechner and Wilhelm Wundt to create psychology as an experimental science (Reed, 1997). By measuring responses to controlled sensory stimuli, Fechner and Wundt hoped to discover “psychophysical” laws akin to the one unearthed by Weber (Ochs, 2004; Reed, 1997). Such laws laid the foundations of psychology in the physiological substrate of the brain, explaining how “complex ideas” arise from the processes of perception. Both Fechner and Wundt considered aesthetic experiences amenable to this approach. For example, in his textbook Grundzüge der physiologischen Psychologie, Wundt (1873) explained how aesthetic feelings arise as a consequence of the psychological constitution of the human brain. In Wundt’s theory, sensations (Empfindungen) constituted the interface between the physical and the physic by forming the elemental basis of mental representations (Vorstellungen). Mental representations, in turn, comprised the content of consciousness (Kim, 2016; Ochs, 2004; Wundt, 1873). Wundt argued that, as physiological processes, sensations had three properties: quality, intensity, and a “feeling-tone” (Gefühlston), all determined by the “peculiar laws of excitation of the neural matter” (Wundt, 1873, I, p. 390). However, for individual sensations to come together as psychological ideas, they had to be subjected to a process of “psychological synthesis of sensations” (Wundt, 1873, II, p. 256). This process, termed apperception, used acts of separation and combination to compound individual associations into specific representations (Kim, 2016; Wundt, 1873). Some such representations produced aesthetic feelings, as explained by Wundt (1873) in chapter 17 of his book: These were feelings of liking and disliking (“Gefallens und Missfallens”; p. 691) that accompanied particular mental representations. Like representations, aesthetic feelings were not identical to sensations but psychological constructs built through the apperception of certain sense qualities. For example, Wundt (1873) claimed that rhythm is experienced as pleasurable because recurring sense impressions of equal intensity or quality are experienced as regularly occurring auditory representations (Gehörsvorstellungen). Fechner took a different approach to psychology to Wundt’s (Höge, 1995; Kim, 2016; Nadal & Ureña, 2022). For him, sensory activity and psychological states are interrelated, two manifestations of the same phenomenon (Nadal & Ureña, 2022). In other words, Fechner did not view psychological acts as independent of physiological activity. In his work on aesthetics, he argued that for a stimulus to be liked, it must elicit a certain level of pleasure (Fechner, 1876). Based on his empirical findings, Fechner argued that this “pleasingness” (Wohlgefälligkeit) could be caused by either direct factors, namely the physical properties of the object being sensed, or by associative factors, the knowledge and experience marshalled by the individual’s memory (Fechner, 1876). Thus, even though Fechner did not conceive of psychological acts as different from physiological processes in the way Wundt did, he did admit that aesthetic evaluations must involve some form of “aesthetic apprehension” that interacted with the “direct impressions of the matter” (Fechner, 1876, p. 94). How the mechanics of this interaction worked, Fechner had to admit he did not know. Describing its physiological nature was a job for future generations. Neither Wundt nor Fechner considered aesthetic evaluations a unique psychological mechanism. The mechanisms of perception, psychological apprehension, and pleasure associated with aesthetic experiences were all be understood as functions of general principles of sensation, cognition, and emotion. Other writers

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did, however, try to use physiology to explain how aesthetic liking and disliking might emerge as sui generis psychological states. For example, the Canadian Darwinist Grant Allen (1877) published a book, Physiological Aesthetics, in which he presented a theory of “Aesthetic Feelings” as “special cases” (p. 1) of emotions. Allen’s work, which was strongly informed by the Herbert Spencer’s “Æstho-Physiology,” contended that aesthetic feelings are those “pleasures or pain . . . which result from the contemplation of the beautiful or the ugly, in art or nature” (1877, p. 3). Since “the physical states that accompany each such Aesthetic Feeling are the same in kind as those which characterize all other pleasures or pains” (p. 2), Allen needed to explain how “the special class of pleasures and pains known as aesthetic differ from the remainder of their genus” (p. 2). The answer he provided was that aesthetic pleasures and pain are differentiated from ordinary pleasures and pain by “their remoteness from life-serving function and their having pleasure alone as their immediate end” (p. 33). This difference he attributed to a difference in physiological activity: Ordinary pleasure is “the concomitant of the healthy action of any or all of the organs or members supplied with afferent cerebro-spinal nerves, to an extent not exceeding the ordinary powers of reparation possessed by the system” (p. 21). In contrast, aesthetic pleasure was “the subjective concomitant of the normal amount of activity, not directly connected with life-serving function, in the peripheral end-organs of the cerebro-spinal nervous system” (Allen, 1877, p. 34). Humans were believed to “exercise” the latter kind of process by contemplating either “Art” or “nature generally” (p. 36). As a whole, 18th- and 19th-century works on physiological aesthetics represent an enduring dream of understanding the “aesthetic sense” in materialist terms (Morgan, 2017). As should be clear from the examples I  have discussed, the questions they sought to answer arose not from an investigation into the physiological constitution of the nervous system, but from pre-physiological assumptions about the human mind. Theories of physiological aesthetics consistently treated this mechanism as distinct, in the sense that it was thought to be characterized by a specialized set of properties: sensory, cognitive, or emotional acts that distinguished aesthetic experiences as psychological acts. What is also clear is that the success of these theories was rather limited. While the description of the nervous system improved considerably during the 19th century, a fundamental gap remained between what was understood at the level of nerve activity and what was understood at the level of psychological function (Ochs, 2004). Theories of aesthetics inspired by physiology were often greeted by initial enthusiasm but were then eventually found lacking. Perhaps no example illustrates this trajectory better than the notion of Einfühlung (empathy), launched by Robert Vischer in 1873 as a key psychological function meant to explain the existence of aesthetic experiences (Allesch, 2017). Vischer argued that aesthetic experiences were not the result of “mere” perception; rather “the contemplative eye . . . reconstructs the forces expressed by the motion of objects through ‘reproductive empathy’ [reproduktive Einfühlung]” (Allesch, 2017, p. 229). This reconstructive process, Vischer claimed, involved an object-matching activation of perceptual, emotional, and motor systems. The concept of Einfühlung was soon taken up by Theodor Lipps and proved very popular for several years, indeed well into the 20th century (Allesch, 2017). However, as it became clear that the emotional content of works of art rarely produced an identical correlate in the observer, it was abandoned again, only to seem, by the 1930s, never to have existed at all (Allesch, 2017). At the same time, the central ambition of physiological aesthetics, to explain aesthetic experiences as a function of physiological mechanism, came under attack by philosophers who questioned the ability of physiological aesthetics to reduce mental states to a physiological psychology. For example, reacting to Fechner’s aesthetics “from below,” the Austrian philosopher Franz Brentano argued that it was impossible to explain the mental acts involved in aesthetic experiences by recourse to physiology; Fechner’s approach was consequently doomed to failure (Höge, 1995). This was in fact a common criticism levelled at any attempt to reduce the human mind to psychological explanation that eventually, around 1900, erupted into the socalled Psychologismusstreit (Kush, 1995). Philosophers such as Gotlob Frege and Edmund Husserl claimed that higher-order thought, as a matter of principle, could not be reduced to psychology; any adequate description 12

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of, say, logic would require its own special science. This anti-psychologist argument was soon adopted by other scholars in the humanities, leading to a rapidly expanding rejection of psychology and neuroscience as means to explain human thought and behaviour (Smith, 1997). Together, the general failure of physiological theories of aesthetics and the mounting anti-psychologism conspired to make any neuroscience of aesthetics seem like a pipe dream. The complex constitution of the aesthetic experience appeared far removed from the explorations of cell structure, anatomy, and neural activity that were taking place in early 20th-century neuroscience. As a result, by the 1930s, attempts to do descriptive neuroaesthetics had dwindled to almost nothing and seemed to no longer play any role in the further advancement of aesthetics—even amongst psychologists engaged in empirical aesthetics research.3 This situation only started to change in the 1960s when the English psychologist Daniel Berlyne began to publish on what he would himself come to call a new aesthetics (1966, 1974). Berlyne proposed that aesthetic evaluation could be understood as a specific form of exploratory behaviour, with hedonic liking determined by degree of autonomic arousal (1971). Depending on how surprising, complex, or novel a sensory object was perceived to be, the brain would respond with different levels of arousal. Those objects able to elicit high levels—albeit not abnormally high levels—of arousal were experienced as pleasing (Berlyne, 1971). Berlyne’s theory of aesthetics was directly inspired by new neurophysiological work that had begun to connect a range of autonomous processes—skin conductance, pupil dilation, respiration, and muscular tension—to neural activity in localized parts of the brain, especially the reticular formation of the brain stem, the hypothalamus, and the thalamus (Fuller et al., 2011; Swanson, 2000). Building on this work, Berlyne was able to formulate a theory that actually attributed psychological function to a specific neurobiological mechanism. No more vague invocations of “nerve movements,” “normal amount of activity,” or “aesthetic apprehension.” Berlyne’s theory postulated a physiological cause of aesthetic liking that could be proven either right or wrong. However, while Berlyne’s work certainly had a galvanizing effect on experimental aesthetics research in the 1960s and 1970s, it failed to produce a neuroscientific effort to test its core hypothesis (Martindale, 2007; Machotka, 1980; Silvia, 2005). Examination of Berlyne’s theory remained purely behavioural (e.g., Berlyne, 1974; Martindale et al., 1990; Nadal et al., 2010), at least until experimental scrutiny of arousal and reward became possible in the context of experimental neuroaesthetics (see Chapter 2). A second finding from the neurosciences that came to greatly influence theories of aesthetics was the realization that the visual system is functionally specialized (Felleman  & van Essen, 1991; Livingstone  & Hubel, 1988; Schiller, 1997; Zeki, 2005). By mapping neuronal responses to a variety of stimulus features, vision neuroscientists had discovered that nuclei located in different parts of the visual system coded for different stimulus features. Moreover, the first (posterior) cortical areas to receive projections from the eyes appeared to break down and analyse the visual scene in terms of individual features such as edges, brightness, movement, or colour. Only as information was projected forward to later (anterior) parts of the occipital and temporal lobes were these elements synthesized into meaningful object recognition (Livingstone & Hubel, 1988; Schiller, 1997; Zeki, 2005). Collectively, these observations fostered two important principles that would inform aesthetics researchers’ understanding of both visual art in particular and aesthetic experiences more generally: First, the principle that individual functional properties of perception could be attributed to segregated anatomical regions and, second, the principle that perceptual-cognitive experiences seemed to be constructed by a sequence of processing steps, where information was projected from module to module. The first principle, finally, opened the door to an understanding of how the brain represents the content of aesthetic objects, especially works of art (Changeux, 1994; Gregory et al., 1995; Latto, 1995; Livingstone, 1988; Solso, 1994). With a detailed description of the way sensory stimuli were computed by the brain, it became possible to ask how particular objects such as representative or abstract paintings engaged these neural nodes. Such a “framework,” in the words of Chatterjee (2003, p. 55), offered the “promise of sorting out the perceptual and cognitive aspects of aesthetic viewing, as well as the emotional response to beauty” (p. 59). The second principle provided a key to solving the conundrum of how aesthetic experiences could arise 13

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from a combination of both “bottom-up” and “top-down” processes, thus possibly revealing which parts of brain underpinned Fechner’s direct and associate factors, as well as explaining how they came to interact. The first question was pursued by two comparable but slightly different approaches. Both utilized the advances in vision neuroscience to provide a description of the way the human brain constructed aesthetic experiences from the physical properties inherent to artworks but had different conceptions of the nature of these experiences. The first approach, spearheaded by Margaret Livingstone, Patrick Cavanagh, and others, considered art a visual object on par with other visual objects, subject to the same computational rules as other stimuli. What set art objects apart from ordinary visual objects was the way artists could create novel, sometimes unique, strategies of presenting visual information that were able to exploit these general rules. As such, the composition of art objects often contravened the physics of everyday perception and gave rise to forms of visual “illusions” (Cavanagh, 2005; Conway & Livingstone, 2007; Grossberg, 2005; Livingstone, 1988, 2002). By mining insights from vision neuroscience, the ambition was to explain how such “illusions” worked. The other approach, principally associated with Semir Zeki (1999a, 1999b), suggested that art objects were better understood as visual stimuli that served a specific perceptual and cognitive function. Rather than perpetually inventing novel ways to exploit the brain’s computational mechanisms, Zeki (1999a) hypothesized that artists created objects that were specifically designed to provoke the acquisition of knowledge about the world. The function of art, as he wrote in the book Inner Vision, was to represent the constant, lasting essential and enduring features of objects, surfaces, faces, situations, and so on, and thus allow us to acquire knowledge not only about the particular object, or face, or condition represented on the canvas but to generalize about a wide category of objects or faces. (Zeki, 1999b, pp. 9–10; italics in the original) In this way, the ambition of Zeki was not only to explain how individual artworks elicited particular experiences by engaging specific neural mechanisms but also to provide an overarching theory of what art is—an ambition not altogether dissimilar from the way theorists of evolutionary aesthetics have tried to identify the adaptive purpose of art (Davies, 2012). What united both approaches, though, was a preoccupation with individual stimulus features. Attempts to explain art experiences as emerging from an architecture of functionally specialized modules favoured bottom-up descriptions of how individual stimulus features come to engage different pathways of the visual system. At the same time, it was clear that top-down processes associated with knowledge and experience had to play an important role in mediating responses to perceived stimulus features. The first theorist to suggest a theory that attempted to account for the role played by top-down processes in aesthetic experiences was Robert Solso (1994), who attributed the “meaning, or semantic value, derived from . . . basic forms” to the “(internal) context in which art is viewed” (p. 102). These contextual components, he claimed, aroused expectations that influenced the way the perceptual properties of an artwork is computed. Solso (1994) applied the information-processing notion of schemata to argue that later (more anterior) parts of the visual system activate “art schemata” that impose subjective knowledge on the interpretation of what is seen by modulating the processing of bottom-up information (Solso, 2003). The work on crafting models to account for aesthetic experience based on a visual informationprocessing architecture came to its conclusion with the publication by two papers by Anjan Chatterjee (2003) and Helmut Leder and colleagues (2004). Both models were ambitious in their goals by explicitly aiming to suggest a framework with which it was possible to understand aesthetic experiences in their totality. They accomplished this goal by enumerating computational modules hypothesized to be specific to aesthetic responses (see Figure 1.5). Both models included modules involved in perceptual analysis, modules involved in cognitive “mastering” of the artwork, and modules subserving two forms of “output”: On the one hand aesthetic “decisions” (Chatterjee, 2003) or “judgments” (Leder et  al., 2004) and on the other 14

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Figure 1.5 Chatterjee’s (2003) and Leder et al.’s (2004) two models of the neural processes involved in aesthetic experience combined into one representation. Both models take their departure from the finding that the visual system is functionally specialized. Despite a slight disagreement—Chatterjee’s modules are depicted in white, Leder and colleagues’ in grey—with respect to the precise nature of the functional modules hypothesized to underpin aesthetic experience, the two models concur that aesthetic responses involve three hierarchically organized processing stages: an initial stage of perceptual processing, a later stage of cognitive “mastering,” and a decision stage leading to outputs of aesthetic judgments and emotions. Figure adapted from Vartanian and Nadal (2007).

“emotional responses” (Chatterjee, 2003) or “aesthetic emotions” (Leder et al., 2004). Together, the two models articulated earlier intuitions by codifying three “stages” of any aesthetic experience—perceptual analysis, meaning construction, and aesthetic judgment—and naming candidate processes that might make up the individual stages. In combination they set up a potential new research program outlining “a general framework for aesthetic experience at the psychological level” that could also “be tested experimentally using biological methods at a more micro level” (Vartanian & Nadal, 2007, p. 442).

Neuroaesthetics as an experimental science Even a brief survey of historical efforts to apply neuroscience to theories of aesthetic experience makes two things clear: (1) The quest to explain the mechanisms and functions of aesthetic experience in neurobiological terms originated with the philosophical invention of aesthetics as a concept. Indeed, all the main explananda pursued by any neuroscience of aesthetics derive from the philosophical formulation of aesthetics as a psychological category. (2) It is far from a novel enterprise. To claim, as is sometimes done in articles on neuroaesthetics, that inquiries into the neural bases of aesthetic experiences only began with advent of modern neuroaesthetics is historically misleading and dilutes the degree to which much contemporary research consists in a re-examination of problems already honed by previous generations. To name just three examples, all of the following problems first posed by physiological aesthetics are still considered central to contemporary neuroaesthetics research: 1

Are aesthetic responses determined by the physical properties of the aesthetic object or by the internal conditions of the neural processes that react to the information acquired from the stimulus? As we have seen, this question has recurred in different forms over the last two and a half centuries, first in the form of an interaction between sense impressions and nerve sensibilities, then as an interaction between Fechner’s direct and associative factors, and more recently as a transfer of information between “bottom-up” and 15

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2

3

“top-down” processing modules. It has gone on to form a cornerstone of contemporary neuroaesthetics, with the vast majority of studies of aesthetic evaluation dedicated to understanding the role played by neural processes associated with perceptual analysis, memory, conceptual knowledge, or framing, in determining aesthetic liking (see reviews of this body of work in the other chapters collected in Part 1 of the book). To what degree liking and disliking are primarily encoded by stimulus features or by the “eye of the beholder” remains an unresolved and contentious topic. Is it possible to distinguish aesthetic experiences from other psychological states? The idea that the human brain might contain neural mechanisms that code for distinct aesthetic responses to sensory objects emerges from the philosophical proposition that aesthetic evaluations spring from the application of a distinct evaluative mechanism to a specific set of sensory qualities. It immediately gave rise to an enduring quest to identify the neural processes that are specific to the “aesthetic” processing of sensory stimuli. As described previously, this endeavour has historically focused on modelling those perceptual-cognitive and emotional processes that are unique to aesthetic experiences, especially processes that are exclusive to the representation of art objects and aesthetic emotions. If anything, the field of neuroaesthetics is founded on the belief inherited from these predecessors that these questions merit an answer. Thus, the experimental work published under the moniker of neuroaesthetics can be viewed as being concerned with two overarching questions: First, how different categories of art—first music and visual art, later also dance, architecture, and literature—are represented by the perceptual, cognitive, and emotional systems of the brain (see the chapters collected in Part 2 of the book) and, second, how the brain is engaged when it evaluates these and other objects considered aesthetic for their perceived hedonic value (Part 1). Are aesthetic experiences intrinsically connected to works of art? The first theories of sensory evaluation to be called theories of aesthetics, after Baumgarten’s invention of the word, did not view aesthetics as specifically concerned with art objects. The establishment of the art object as the main elicitor of aesthetic experiences only happened as a consequence of the 19th-century events I  reviewed in the previous section. For this reason, there has since been a lasting tension in the psychological and neuroscientific research that seeks to explain the material bases of aesthetic experiences. Since the time of Fechner and Grant Allen, people have failed to come to an agreement on whether aesthetic evaluations only arise in response to art objects or if they apply to all categories of stimuli. If we accept that aesthetic experiences can be elicited by some non-art objects, which are these? Abstract visual forms, faces, everyday artefacts? There is no accepted answer to this question, even today. To experimental neuroscientists, especially those preoccupied with measuring neural responses to specific stimuli and tasks, this question continues to matter a lot. Is neuroaesthetics research defined by the use of artworks as stimuli in experiments? Are the “outcomes” of aesthetic responses to sensory objects—whether in the form of “judgments” or “aesthetic emotions”—unique or general? At the most fundamental level, individual researchers conceive of neuroaesthetics by harboring different answers to these questions. I will return to this issue below.

In sum, the scientific enterprise that starts to take form in the early 2000s under the name of “neuroaesthetics” is best understood as an extension of an existing aspiration to account for the phenomenon of aesthetic experience in neurobiological terms. It effectively takes on problems that have crystallized over the preceding two centuries. What is novel about neuroaesthetics is that it succeeds in transforming what had been, by necessity, a purely descriptive neuroscience of aesthetics into an experimental science. The key to this change was a revolution in experimental methods that took place over the course of the 1980s and 1990s. These inventions made it possible to measure neural activity in living humans as they engaged in tasks considered aesthetic in nature. More than anything, neuroaesthetics in this modern sense owes its existence to this historical development. Of greatest importance to the emergence of neuroaesthetics was the invention of a range of non-invasive neuroimaging methods, especially positron emission tomography (PET), functional magnetic resonance 16

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imaging (fMRI), and magnetoencephalography (MEG), as well as electroencephalography (EEG). But it is important to stress that neuroimaging methods were not the only advancements in methodology that helped make problems in aesthetics amenable to experimental scrutiny. In addition to methods that allowed for access to neural processes, methods that enabled the control and presentation of art stimuli also played a significant role. These included the availability of technical equipment such as colour scanners that facilitated the reproduction and handling of visual artworks (Helmut Leder, personal communication) and the development of computer programs such as MATLAB and Eprime that enabled a controlled presentation of art stimuli (Gerry Cupchik and Chris McManus, personal communication). Even as late as the 1980s, such tasks had been more than a little cumbersome. Another important development was the appearance of computational methods allowing for a characterization of the physical and perceptual constitution of works of art—infamous for their often inscrutable complexity. For example, using statistical methods such as Fourier analysis, backed up by ever-increasing computer power, it became possible to reveal regularities in paintings and to ask how these deviated from non-art visual scenes (e.g., Graham & Redies, 2010; Redies, Hasenstein et al., 2007; Redies, Hänisch et al., 2007). Similarly, music researchers demonstrated that computational models can be employed to describe the statistical relationship of tones in melodies and how these regularities become encoded as predictive models when people listen to them (e.g., Pearce, 2018; Pearce & Wiggins, 2005). Importantly, understanding the informational content being perceived in the context of an aesthetic experience can greatly aid the neuroscientist in exploring the specific neural correlates of individual art stimuli (Brachmann & Redies, 2017; Cheung et al., 2019; Gold et al., 2019; Pearce et al., 2009). Finally, developments in eye-tracking equipment facilitated important explorations into physiological processes associated with aesthetic experience before the advent of neuroimaging. Since eye movements reflect the aspects of the visual scene we fixate on, the ability to measure fixations and gaze patterns helps inform our understanding of what perceptual information the visual system is attending to and processing (Martinez-Conde et al., 2004). Attempts to build measurement systems able to measure eye movements go well back to the 19th century. One such early device was used by Stratton (1902) to test the hypothesis that people like curved contours because their smooth, uninterrupted lines afforded easier gaze patterns than sharp or angular lines. In violation of his own assumptions Stratton (1902) found both kinds of contour to elicit jerky and discontinuous microsaccades. Later, in the 1960s Yarbus (1967) invented an eye-tracking “cap” that could be attached to the eyeball by suction. Using this system, Yarbus (1967) described several perceptual experiments, including one demonstrating that different task instructions modulated the way the visual system scanned a painting for information. In the 1970s and 1980s, Yarbus’ research inspired several vision scientists, including Calvin Nodine and Paul Locher, to start using visual art objects to explore how visual attention and scan patterns are informed by symmetry (Locher & Nodine, 1977), artistic style (Nodine & McGinnis, 1983), art expertise (Nodine et al., 1993), or degree of abstractness (Zangemeister et al., 1995). With modern, laser-based eye-trackers, it has since become possible to also measure how dilated the pupil is at a microsecond interval, allowing for a coupling of visual fixation and autonomic arousal. Several recent experiments have integrated this advance into the field of neuroaesthetics. For example, Ramsøy and colleagues (2012) used the analysis of pupil dilation change to show that paintings experienced in unpredictable contexts are less liked than when experienced in predictable contexts, a variance in preference explained by a difference in the ability of predictable and unpredictable contexts to elicit physiological arousal. Still, these important predecessors notwithstanding, it was the introduction of neuroimaging to the wider study of human cognitive neuroscience that more than anything helped make neuroaesthetics possible as a viable experimental study. Cognitive neuroscience itself was a neologism first coined in the 1970s by George Miller and later popularized by Michael Gazzaniga (1988). It was initially thought of as a program for bridging the gap between neuroscience and the emerging field of cognitive science (Churchland & Sejnowski, 1988). However, the neural correlates of complex cognitive thought such as language or reasoning were 17

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difficult to study in humans, expect for the rare situations where neuropsychological cases permitted testing of cognitive states. Such studies were marred, though, by the fact that they rested on the problematic business of inferring function from brain damage that was not only arbitrary but also could be difficult to describe accurately.4 Without the development of methods for recording neural activity in a non-invasive manner, either in the form of electrical and magnetic field recordings (Hillyard, 1993) or deduced from changes in local blood flow and metabolism (Raichle, 1998), cognitive neuroscience might well have remained a pipe dream. With such methods starting to emerge in the 1980s, however, the possibility of measuring the location, functional character, and temporal unfolding of neural signals while human subjects engaged in cognitive tasks finally became possible. As they became more widely adopted in the 1990s, a virtual revolution in human neuroscience took place, ushering in a multitude of individual cognitive neuroscience fields dedicated to any conceivable aspect of higher-order human psychology: economics, morality, social behaviour, politics, and so on. Neuroaesthetics was borne out of this general transformation of the field of neuroscience. The first aesthetic topic to be systematically probed by use of neuroimaging techniques was music perception (Peretz & Zatorre, 2005; Tervaniemi, 2009). As a temporal art form, the perception of which relies on the processing information organized sequentially in time, music lends itself well to studies conducted with EEG. Since EEG can record electrical activity produced by cortical cells on a millisecond scale (Hillyard, 1993), it is well suited as a tool for tracing neural activity unfolding in responses to music stimuli. In the 1980s, music researchers increasingly began to use EEG—especially by measuring event-related potentials (ERPs)—to characterize the neural correlates of music perception (e.g., Besson & Macar, 1987; Klein et al., 1984; Paller et  al., 1988; Pantev et  al., 1989; Wieser  & Mazzola, 1986). These experiments sought to describe the neural mechanisms underpinning the key perceptual constituents of music such as pitch or the grouping of tones into chords and melody. The ultimate hope was to ascertain if the neural processes involved in music perception were distinct or shared with other forms of auditory perception, including language. Unfortunately, the ability of EEG to localize the anatomical source of neural is limited, so when access to PET and fMRI improved in the 1990s, these techniques, with their superior ability to localize neural activity, were soon taken up by music scientists as well (e.g., Griffiths et al., 1999; Hughdal et al., 1999; Sakai et al., 1999; Zatorre et al., 1992). Compared to music, neuroimaging studies of visual art perception were much slower to appear. A few EEG experiments reporting neural responses to complexity in visual images had been published in the 1960s and 1970s (e.g., Baker & Franken, 1967; Berlyne & McDonnell, 1965; Gale et al., 1971, 1975). But while these experiments were motivated by Berlyne’s theory of aesthetics, they did not employ traditional examples of visual art as stimuli, and it was also unclear how to infer function from their reported observations. One EEG experiment conducted by Nicki and Gale (1977) reported alpha abundance to increase with complexity in abstract paintings. This finding they interpreted, not quite convincingly, as evidence for a depression of “cortical arousal.” Perhaps disappointed by the obscurity of such results, interest in using neuroimaging to investigate visual art perception seems to have waned in the 1980s, only to be resurrected by Zeki and Marini’s (1998) fMRI study on how the visual system represents irregular colour-object combinations in Mondrian paintings. Even in the 2000s, though, neuroimaging of visual art perception remained spotty, possibly because it was not considered sufficiently dissimilar to ordinary vision to warrant its own study (though see, e.g., Fairhall & Ishai, 2007; Kirk, 2008). Only at the end of the 1990s did the focus on perception widen to encompass other topics of interest to aesthetics, especially emotional responses elicited by art and the neural correlates of aesthetic liking. Since most neuroimaging studies centred on these two issues concentrated on understanding what happens when music or visual art generate pleasure or displeasure, they often pursued both questions without quite distinguishing between what it means to experience pleasure as a consequence of different forms of (evaluative or not) engagements with art. Collectively, PET and fMRI experiments of revealed that aesthetic pleasure and displeasure correlate with activity in the mesolimbic reward circuit combined with modality-specific 18

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engagements of auditory (Blood et al., 1999; Blood & Zatorre, 2001; Brown et al., 2004; Koelsch et al., 2005; Menon & Levitin, 2005) or visual processes (Cela-Conde et al., 2004; Kawabata & Zeki, 2004; Vartanian & Goel, 2004a), depending on the stimulus employed. Most of the music experiments, though, eschewed subjective reports of hedonic experiences, relying instead on a pre-determined categorization of stimuli as either pleasurable or displeasurable, while the visual art experiments all based their analyses of neural correlates on locating correlates to liking ratings. Together they raised the question of how much pleasure and displeasure— as well as, possibly, other emotional states—are modulated by conditions under which artwork are appraised. A third, related topic was introduced by a series of experiments starting to appear in the early 2000s (Brattico et al., 2003; Jacobsen & Höfel, 2001, 2003). Using EEG, these studies compared neural activation elicited by aesthetic and descriptive judgments of graphic patterns and musical cadences. Thus, rather than asking how the brain computes liking and disliking in the form of hedonic responses, they sought to identify a general neural system for actively enacting aesthetic evaluations. Findings, supported by results reported later by fMRI experiments (e.g., Chatterjee et al., 2009; Ishizu & Zeki, 2013; Jacobsen et al., 2006), suggested that sensory and emotional systems are modulated differently when sensory objects are attended to with the explicit purpose of forming an aesthetic judgment than when they are not. It is not surprising that the gradual embrace of neuroimaging methods to pursue topics of interest to aesthetic theory followed a trajectory similar to other ventures into cognitive neuroscience. By concentrating its initial efforts on perception and memory, the budding cognitive neuroscience program could apply the risky, new techniques to aspects of human cognition that already were being studied in behavioural labs and of course were thought to share neural architecture with other animals already under neuroscientific scrutiny. Because it was possible to scan participants using tasks that were well established by psychological experiments, as well as stimuli that were believed to be well controlled, studies of perception in different functional contexts seemed the safest bet to get human cognitive neuroscience off the ground. It took most of the 1990s to edge neuroimaging research closer to the exploration of cognitive states that were considered subjective and less “rational.” An important force in driving this change was the growing realization, especially following the work on patients with ventromedial prefrontal lesions pioneered by Antonio Damasio, Antoine Bechara and Daniel Tranel, that emotional states contributed significant inputs to both cognitive reasoning and behavioural motivation (Dukes et al., 2021). The ability to employ neuroimaging techniques to explore the neural underpinnings of psychological states considered important to aesthetics benefitted from this general evolution, opening research slowly up to include complex subjective responses to works of art. Still, topics relevant to aesthetics were embraced only incrementally. Very few neuroimaging studies published in the 1990s were dedicated to the investigation of either art perception or emotional processing related to aesthetic liking. Moreover, it is doubtful if any of the researchers involved in neuroimaging experiments on such topics pre-2000 thought of their work as part of a common “neuroaesthetics” endeavour. Indeed, there are good reasons to believe that none of the music researchers who, in the 1980s and 1990s, took up EEG, PET, or fMRI to explore the neural mechanisms of music experience considered their research part of a broader inquiry into human aesthetics (Robert Zatorre, personal communication). As I began this chapter by showing, word usage data suggests that the word neuroaesthetics itself only became widely adopted after 2000 (Figure 1.1A). This begs the question of how a scattered and uncoordinated series of ventures into “neuroaesthetics” was transformed into a bona fide scientific discipline and why this process began to take place in the early 2000s. While more historical research is needed to provide a clear answer to this question, I will suggest that three events played an important role. First, the popularisation and general acceptance of the word neuroaesthetics was itself important to the fostering of a collective identity. Its invention has often been attributed to Semir Zeki, but it is doubtful he was the first to use it. The first reference to “neuroaesthetics” I have been able to find appears in the introduction to the 1988 book Art and the Brain. Here the word is used to identify an “emerging field” dedicated to research on the “biological foundations of aesthetics” (Rentschler 19

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et al., 1988, p. 10). Art and the Brain grew out of a series of meetings convened by the German Studiengruppe Biologische Grundlagen der Aesthetik in the early years of the 1980s. It is possible that someone involved in these meetings was the first to coin the word, although none of the participants I have spoken to remember who the originator was (Chris McManus and Ernst Pöppel, personal communication). It is true, though, that Zeki—who first seems to have referred to the neuroscientific study of art as “neuro-esthetics” (with a hyphen) in Inner Vision (Zeki, 1999b, p. 2)—would go on to play an outsized role in popularizing the use of the word in years between 2000 and 2005. In any case, regardless of who deserves the accolades for having invented it, I think it is evident that having one catchy name to rally around greatly helped foster the idea that the experimental investigation of the neurobiological basis of art and aesthetics should be considered a single scientific enterprise. The second factor I would point to is an emerging desire to establish neuroaesthetics as a scientific enterprise that happened to gain momentum between 2000 and 2005. It is possible that a growing number of researchers began to see an experimental neuroaesthetics as a possibility because the advances of the 1990s had demonstrated the feasibility, at least in principle, of studying the neurobiological basis of art and aesthetics. But it also true, I think, that the advocacy that this desire gave rise to (e.g., Chatterjee, 2003; Hagendoorn, 2003; Ramachandran & Hirstein, 1999; Skov, 2005; Zeki, 1999a, 1999b; Vartanian & Goel, 2004b) in many ways ended up functioning as a self-fulfilling prophecy. For example, two articles by Ramachandran and Hirstein (1999) and Zeki (1999a), published together in Journal of Consciousness Studies, had a huge impact on a broad audience of readers. Both articles argued that any theory of art and aesthetic liking that did not consider the neural mechanisms underlying aesthetic experience was doomed to fail. As emphatic calls to arms, they both provoked a lot of excitement and a large number of detractors. In retrospect, neither paper contained any theoretical idea—such as Zeki’s (1999a) claim that art serves the purpose of acquiring lasting knowledge or Ramachandran and Hirstein’s (1999) famous hypothesis that the peak shift principle is one of eight universal rules that account for aesthetic experience—that would go on to influence the course of experimental neuroaesthetics. As rallying cries, however, they undoubtedly played a crucial role in putting the idea of neuroaesthetics on the map. Finally, I will point to the process of working out a theoretical foundation for neuroaesthetics as a third factor that helped establishing and solidifying neuroaesthetics as an experimental discipline. To clamour for an experimental neuroscience of aesthetics was to invite several tough questions about the objectives and aims of such a science. What problems, exactly, should the intended field of neuroaesthetics pursue? On an abstract level, most evangelists of neuroaesthetics could probably agree that its main topics were to be the phenomena of art experience and aesthetic liking. But defining the precise scope of these concepts was a less self-evident endeavour. What should count as “art” for example? As noted, it was not clear to music researchers that music fell within the scope of neuroaesthetics. Did instances of graphic design and product packaging qualify? Similarly, it was unclear what was to be meant by “aesthetic liking.” Should the study of neuroaesthetics include experiments where hedonic pleasure was elicited by faces, artefacts, or even food and drink? Or was it to be restricted only to the study of pleasure responses elicited by fine art stimuli? Furthermore, the new field had to content with criticisms from philosophers who argued that to even consider using neuroscience to explain art and aesthetic liking was a category mistake (e.g., Hyman, 2010; Tallis, 2008). Looking back on the period between 2000 and 2010, it is striking how much energy was channelled into providing convincing answers to these troublesome questions. A flurry of theoretical papers that sought to not only justify the existence of neuroaesthetics as an experimental discipline but also to clarify what type of problems would fall within its purview saw the light of day (e.g., Brown & Dissanayake, 2009; Chatterjee, 2003; Jacobsen, 2006, 2009; Skov, 2009; Skov & Vartanian, 2009; Vartanian & Goel, 2004b; Vartanian & Nadal, 2007). As findings from neuroimaging experiments began to slowly appear, this debate was further augmented by discussions about how to make sense of experimental results and to what degree these supported this or that theoretical model of art experience and aesthetic liking (e.g., Di Dio & Gallese, 2009; 20

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Nadal et  al., 2008; Skov, 2007, 2010; Vartanian, 2009). In my view, this theoretical work played a very important role in transforming the budding idea of having a neuroscientific study of aesthetics into an actual scientific discipline concerned with a specific set of problems and a methodological framework for experimentally examining these problems.

Conclusion Neuroaesthetics is a very young discipline. The historical record informs us that it only began taking shape as a concerted, experimental science in the 2000s (Figure 1.1). Indeed, most of that decade served as a Gründerzeit, establishing the parameters of neuroaesthetics as an experimental science. Only in the last 10 years or so has neuroaesthetics expanded into a viable scientific discipline as a growing number of researchers and labs have become attracted to problems associated with art experience and aesthetic liking. Neuroaesthetics was not created ex nihilo, though. Before neuroaesthetics, there were 250 years of sustained attempts to explain the physiological bases of aesthetic experience by employing results from mainstream neuroscience. I have tried to show how these efforts were motivated by the belief that the human mind is endowed with a dedicated psychological mechanism for generating such experiences. This belief did not originate in any empirical observation of human psychology or neuroscience but was based on philosophical arguments. Of course, such a historical trajectory is not unique to neuroaesthetics. As pointed out by György Buzsáki (2020) and others, it is the rule more than the exception that the objective of neuroscientific research has been conceived of as providing neurobiological mechanisms for psychological and behavioural concepts inherited from philosophy and folk psychology. Casting light on the historical roots of neuroaesthetics is not only important because it reveals an intellectual history that is fascinating in itself but also because understanding these roots will help explain why neuroaesthetics emerged as an experimental science concerned with a specific set of hypotheses and problems. For example, the ambition to identify a neural system underpinning aesthetic evaluations, or the conviction that art objects such as visual art or music elicit unique perceptual, cognitive, and emotional responses, both had their origin in this prehistory. Indeed, any of the controversies witnessed by neuroaesthetics over the past two decades can conceivably be understood as attempts to reconcile emerging experimental findings with the conceptual roots of the field. This includes debates over whether neuroaesthetics is a science of art or a science of aesthetic liking (or perhaps a science of the aesthetic liking of art; Brattico & Pearce, 2013; Brown & Dissanayake, 2009; Pearce et al., 2016; Skov & Nadal, 2020a) or whether emotional responses such as pleasure which are encountered as part of aesthetic experiences can be distinguished from other emotional states (Chatterjee & Vartanian, 2014, 2016; Skov & Nadal, 2020a, 2020b). As the other 29 chapters in this book demonstrate, neuroaesthetics has evolved tremendously since its early years. Where the period between 2000 and 2010 in many ways was a struggle to get a new discipline off the ground, the last decade has produced a historically unprecedented amount of novel insights into the neural mechanisms associated with art experience and aesthetic liking. Many of the ancient problems that have preoccupied scholars for centuries are now finding empirical solutions or are being reformulated based on the rapid improvement in our understanding of human neurobiology. Some of the assumptions that motivated the intellectual history I have sketched in this chapter are becoming obsolete. But they are only ceasing to be relevant to theories of art experience and aesthetic liking because neuroaesthetics instigated a program for experimentally investigating their provenance. In this sense, neuroaesthetics has been a true success story (Skov, 2019). Perhaps the best indication of its success as a research program is the way neuroaesthetics has expanded in both the number of researchers and scientific output over the last 10 years. Especially in the last five years, the field has seen an unprecedented influx of new researchers and labs, many based outside Europe and North America. For instance, neuroaesthetics is currently expanding aggressively in Asia, especially in China, where 21

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many universities have begun allocating funding to the establishment of dedicated research centres. These groups, such as the Neuroaesthetics Center at Guangzhou University, often host a large number of both senior researchers and students who pursue many different topics (Xianyou He, personal communication). It is quite possible that, as a result of this development, neuroaesthetics will start to move in quite new directions, taking up questions that were unknown to its historical ancestors. Parts of this future we are already getting a glimpse of: Neuroaesthetics researchers are increasingly shifting their focus to the investigation of the possible health benefits of aesthetic experiences or the existence of aesthetic brain systems in other animals (see Chapters 2, 11, 13, 20, and 21 for descriptions of this new research effort). A new generation for whom neuroaesthetics is a given, not a mere possibility, is taking over the field and will get to define its purpose in new ways. The next 20 years will no doubt be even more interesting than the first 20.

Acknowledgements I want to thank the following people for generously agreeing to either talk to me, respond to my inquiries, or comment on an earlier draft of this chapter: Bevil Conway, Anjan Chatterjee, Gerry Cupchik, Xianyou He, Thomas Jacobsen, Stefan Koelsch, Helmut Leder, Paul Locher, Chris McManus, Gisèle Marty, Marcos Nadal, Ernst Pöppel, Chris Redies, Oshin Vartanian, and Robert Zatorre. Naturally, none of the historical interpretations presented in this chapter should be attributed to anyone but me.

Notes 1 It is worth noting that neuroaesthetics is far from the only branch of neuroscience that has inherited most of its central explananda from philosophy and folk psychology. See Buzsáki (2020) and Pessoa et al. (2021) for important discussions of how this state of affairs can be argued to have led neuroscience astray in important respects. 2 Unfortunately, to the best of my knowledge, no historical study of the intersection between philosophy of mind and the development of aesthetic evaluation as a category exists. 3 For example, reading Charles W. Valentine’s (1962) book The Experimental Psychology of Beauty, you will be hard pressed to find any mention of possible neuroscientific explanations for why we find sensory objects beautiful. Tellingly, this book was Valentine’s attempt to summarize work in empirical aesthetics that primarily spanned the period between 1920 and 1960. 4 Not surprisingly, this also applies to the use of neuropsychology in aesthetic theory. Over the years, many case studies of artists and other patients have been reported in the literature (reviewed by Zaidel, 2005). However, the impact of these studies on aesthetic theory has been minimal. Chatterjee (2009) attributes this lack of influence to the anecdotal nature of many case reports, their absence of measurement, and the endemic inaccessibility of most publications that have reported relevant neuropsychological case studies.

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History of neuroaesthetics as a discipline Sakai, K., Hikosaka, O., Miyauchi, S., Takino, R., Tamada, T., Iwata, N. K., & Nielsen, M. (1999). Neural representation of a rhythm depends on its interval ratio. Journal of Neuroscience, 19(22), 10074–10081. https://doi.org/10.1523/ JNEUROSCI.19-22-10074.1999 Schiller, P. H. (1997). Past and present ideas about how the visual scene is analyzed by the brain. Cerebral Cortex, 12, 59–90. https://doi.org/10.1007/978-1-4757-9625-4_2 Sherman, A., Grabowecky, M.,  & Suzuki, S. (2015). In the working memory of the beholder: Art appreciation is enhanced when visual complexity is compatible with working memory. Journal of Experimental Psychology. Human Perception and Performance, 41(4), 898–903. https://doi.org/10.1037/a0039314 Shiner, L. (2001). The invention of Art: A cultural history. University of Chicago Press. Silvia, P. J. (2005). Emotional responses to art: From collation and arousal to cognition and emotion. Review of General Psychology, 9(4), 342–357. https://doi.org/10.1037/1089-2680.9.4.342 Skov, M. (2005). Hvad er neuroæstetik? [What is neuroaesthetics?]. Kritik, 174, 1–10. Skov, M. (2007). Følelser og æstetik [Emotions and aesthetics]. In M. Skov & T. W. Jensen (Eds.), Følelser og Kognition (pp. 167–196). Museum Tusculanum. Skov, M. (2009). Neuroaesthetic problems. A framework for neuroaesthetic research. In M. Skov & O. Vartanian (Eds.), Neuroaesthetics (pp. 9–26). Baywood Publishing. Skov, M. (2010). The pleasure of art. In M. Kringelbach & K. Berridge (Eds.), Pleasures of the brain (pp. 270–283). Oxford University Press. Skov, M. (2019). Aesthetic appreciation: The view from neuroimaging. Empirical Studies of the Arts, 37(2), 220–248. https://doi.org/10.1177/0276237419839257 Skov, M., & Nadal, M. (2020a). A farewell to art: Aesthetics as a topic in psychology and neuroscience. Perspectives on Psychological Science, 15(3), 630–642. https://doi.org/10.1177/1745691619897963 Skov, M., & Nadal, M. (2020b). There are no aesthetic emotions: Comment on Menninghaus et al. (2019). Psychological Review, 127(4), 640–649. https://doi.org/10.1037/rev0000187 Skov, M., & Vartanian, O. (2009). Neuroaesthetics. Baywood Publishing. Smith, R. (1997). The human sciences. W.W. Norton & Company. Solso, R. L. (1994). Cognition and the visual arts. MIT Press. Solso, R. L. (2003). The psychology of art and the evolution of the conscious brain. MIT Press. Stolnitz, J. (1961). On the origins of “aesthetic disinterestedness”. The Journal of Aesthetics and Art Criticism, 20(2), 131–144. https://doi.org/10.1111/1540_6245.jaac20.2.0131 Stratton, G. M. (1902). Eye-movements and the aesthetics of visual form. Philosophical Studies, 20, 336–359. Swanson, L. W. (2000). Cerebral hemisphere regulation of motivated behavior. Brain Research, 886(1–2), 113–164. https://doi.org/10.1016/s0006-8993(00)02905-x Tallis, R. (2008). The limitations of a neurological approach to art. Lancet, 372(9632), 19–20. https://doi.org/10.1016/ S0140-6736(08)60975-7 Tatarkiewicz, W. (1972). The great theory of beauty and its decline. Journal of Aesthetics and Art Criticism, 31(2), 165–180. https://doi.org/10.1111/1540_6245.jaac31.2.0165 Tervaniemi, M. (2009). Musical sounds in the human brain. In M. Skov & O. Vartanian (Eds.), Neuroaesthetics (pp. 221– 231). Baywood Publishing. Tonelli, G. (2003). Taste in the history of aesthetics from the Renaissance to 1770. In P. P. Wiener (Ed.), Dictionary of the history of ideas, 4. Charles Scribner’s Sons. Valentine, C. W. (1962). The experimental psychology of beauty. Methuen, and Co. Vartanian, O. (2009). Conscious experience of pleasure in art. In M. Skov & O. Vartanian (Eds.), Neuroaesthetics (pp. 261– 273). Baywood Publishing. Vartanian, O., & Goel, V. (2004a). Neuroanatomical correlates of aesthetic preference for paintings. NeuroReport, 15(5), 893–897. https://doi.org/10.1097/00001756-200404090-00032 Vartanian, O., & Goel, V. (2004b). Emotion pathways in the brain mediate aesthetic preference. Bulletin of Psychology and the Arts, 5(1), 37–42. Vartanian, O., & Nadal, M. (2007). A biological approach to a model of aesthetic experience. In L. Dorfman, C. Martindale & V. Petrov (Eds.), Aesthetics and innovation (pp. 429–443). Cambridge Scholars Press. Wieser, H. G., & Mazzola, G. (1986). Musical consonances and dissonances: Are they distinguished independently by the right and left hippocampi? Neuropsychologia, 24(6), 805–812. https://doi.org/10.1016/0028-3932(86)90079-5 Wundt, W. (1873). Grundzüge der physiologischen Psychologie, 2 vols. Engelmann. Yarbus, A. L. (1967). Eye movements and vision. Plenum Press. Zaidel, D. W. (2005). Neuropsychology of art. Psychology Press.

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PART I

Aesthetic liking

2 SENSORY LIKING How nervous systems assign hedonic value to sensory objects Martin Skov

All biological organisms show preferences for sensory objects they encounter in their physical environment. Such preferences are manifested in the organism’s behavioural response to the object: some objects the organism will devote resources to approach, while others it will avoid at all costs. These behavioural preferences are rooted in some form of evaluative mechanism that tags sensory stimuli as either liked or disliked. The scientific study of this evaluative function is called the study of sensory liking. This chapter is intended as an introduction to what is known about the neurobiological processes that the human brain uses when it engages in such sensory liking evaluations. Historically, the study of sensory liking has pursued three main questions. The first concerns the relation of the stimulus to sensory liking: which stimulus properties are liked or disliked, and what is the cause of such relationships? Are liking and disliking responses to a given sensory percept governed by universal laws, or are they subject to subjective and contextual variation? These questions have intrigued Western philosophy for a very long time, at least since the Pythagoreans, 3000 years ago. This esoteric group of Greek mathematicians and philosophers originated the view that beauty responses are governed by objective relations and believed that all objects organized according to principles of proportion and harmony are experienced as pleasurable (Dieckmann, 1974). Later, Renaissance philosophers began to oppose this view, arguing that people like different things and that liking responses to stimulus properties therefore vary according to individual “tastes” (Tonelli, 2003; see also Chapter 1). When experimental psychology emerged in the second half of the 19th century, it also felt compelled to examine the relation between stimulus and liking response (Nadal & Ureña, 2022). To this day, most experimental paradigms used to investigate sensory liking consist of measuring reported liking ratings for stimuli whose features are systematically manipulated. The second question concerns the genesis of stimulus-liking relationships: why are certain stimulus properties liked while others are disliked? If humans or some other species like the colour red, where does this preference come from? The first to systematically collect observations to support a scientific answer to this question was Charles Darwin, who, in the book The Descent of Man, and Selection in Relation to Sex (1871), argued that biological organisms have evolved a “power of discrimination and taste” (p. 246) that serves adaptive needs. Specifically, Darwin (1871) hypothesized that, in sexual species, females evolve preferences for male sensory traits that signal fecundity, that is, a greater likelihood of reproductive success if the female chose to mate with males that embody the preferred trait. According to this evolutionary argument, a preference for stimulus features such as the colour red can arise because females who chose, say, males with red plumage as sexual partners have more viable progeny than females who do not. Since Darwin, this evolutionary DOI: 10.4324/9781003008675-3

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thesis has been adopted as a possible general explanation for the existence of stimulus-liking relationships: biological organisms evolve preferences for stimulus features that are statistically associated with behavioural choices that have benefitted survival (Adolphs & Andler, 2018; Damasio & Carvalho, 2013; Panksepp, 1998). This theory also explains why different species have different stimulus preferences: since individual species have different survival needs and inhabit different physical habitats, sensory liking will evolve to take different forms across biological taxa (Rosenthal, 2017). Finally, the third question that has dominated the study of sensory liking concerns the nature of the mechanism that implements stimulus-liking relationships—Darwin’s putative “power of taste.” The idea that the human mind must contain a dedicated psychological mechanism for assigning liking and disliking to sensory inputs was first introduced in the 18th century (see Chapter 1). Using an array of different methods, psychologists and neuroscientists have tried to identify the psychological and physiological processes involved in generating liking and disliking for stimulus properties. This has revealed a set of neurobiological processes that take place throughout a network of mesocorticolimbic structures that encode states of pleasure and displeasure in response to perceptual representations of sensory stimuli (Becker et al., 2019; Berridge & Kringelbach, 2015; Martínez-García & Lanuza, 2018; O’Connell & Hofmann, 2011; Panksepp, 1998; Skov, 2020). The character of these affective responses appears to determine if a stimulus is liked or disliked. A popular way to unify the work prompted by these questions is to think of sensory liking as a psychological and neurobiological reaction to sensory input. If a given stimulus property elicits states of pleasure, it is experienced as liked. If it elicits states of displeasure, it is experienced as disliked. This coupling of stimulus and hedonic response is thought to be determined by evolution: stimuli that prove beneficial to survival are linked to pleasure through innate mechanisms; stimuli that prove harmful to survival are linked to displeasure. However, even though this account of sensory liking contains a kernel of truth, it fails to explain many of the empirical findings produced by recent psychology and neuroscience. Specifically, it does not accord well with the ubiquitous observation that fixed liking and disliking responses to individual stimulus features are not the rule but rather the exception, especially in organisms with complex nervous systems (Coppin & Sander, 2012; Corradi et al., 2020; Hayes, 2020; Rosenthal, 2017; Skov, 2020). Instead, studies of sensory liking consistently find that liking and disliking vary with endogenous and contextual factors. A possible explanation for variation in stimulus-liking responses is learning. To proponents of the classical theory of sensory liking, learning has served as a possible modulating factor that explains why individuals differ in their reactions to a given stimulus feature: whereas pleasure and displeasure reactions to stimulus features are generally innately determined, they can be modulated through learning mechanisms. Studies of reinforcement, personality and knowledge differences, and cross-cultural variation certainly suggest that learning plays a central role in shaping liking and disliking (Che et al., 2018; Pessiglione & Lebreton, 2015; Rosenthal, 2017; Schultz, 2015). Yet admitting learning as a modulating factor does not explain why stimulus-liking reactions need to be modulated to fit individual and contextual conditions. In this chapter, I  present an alternative account of sensory liking that takes the fact that, for many biological organisms, sensory liking and disliking are flexible responses to sensory objects as its starting explanandum. Based on multiple findings, I show that sensory liking should be considered not a stereotypical reaction to stimuli but an evaluative event whereby biological organisms seek to establish the potential benefit or harm of a sensory object relative to their current physiological, behavioural, and environmental conditions. This alternative account suggests that, rather than being solely determined by the sensory nature of the stimulus, liking and disliking emerge from a conjunction of evaluative processes that are not only driven by stimulus information but also by a range of other factors. The functional purpose of this computational system is to generate liking and disliking constructs that service the organism’s current physiological and behavioural needs. In order to do so, sensory liking evaluations assess the hedonic value of a stimulus as it pertains to the organism’s immediate concerns: Can the sensory object being evaluated help restore the metabolic balance of the organism’s body? Is it possibly toxic? Has the organism encountered the object before? 32

Sensory liking

Figure 2.1

Sensor y liking evaluations always occur in the context of behavioural tasks. For this reason, how much an organism likes or dislikes a given stimulus can yield different survival benefits under different physiological and environmental circumstances. For example, when foraging for something to eat in an unknown terrain, decisions about whether to venture into an environment with low visibility hinge on several considerations: Does the organism believe, based on previous experiences, that the forest contains delicious fruit? Are there grounds to believe that the forest hides predators? What is the risk of searching for fruits in the forest (i.e., how certain is the organism that it will be rewarded by deciding to go into the unknown?). Is the organism very hungry? Only by factoring in these factors can the organism compute how valuable a stimulus is vis-àvis its actual physiological needs and current behavioural undertakings.

Is there a risk associated with working to obtain and ingest the object? These and other considerations are factored into the evaluation of the object’s hedonic value (Figure 2.1). It is this computational constitution that explains the flexible nature of sensory liking evaluations: under some circumstances, a stimulus can be highly liked; under other circumstances, it can be less liked; and when certain conditions come together, it can even be disliked. In what follows, I will review experimental findings that cast light on how the human brain implements such evaluative events. As we shall see, central to the human evaluative system’s ability to factor endogenous and contextual conditions into its computation of sensory liking and disliking is the existence of a number of different functional processes that are able to represent and integrate information about the organism’s interoceptive states, cognitive knowledge, and learned expectations. Mounting empirical evidence suggests that liking and disliking outcomes are determined by the specific coordination and integration of these different mechanisms that characterize individual evaluation events. A possible implication of this observation is that human evaluative events may have systematic characteristics that reflect the different purposes they serve. For example, some evaluative events might rely more on learned expectations, while others involve greater explicit attention to the stimulus being assessed. At the end of the chapter, I will briefly discuss how this idea lends itself to new neuroscientific investigations. But I first expand on how to understand sensory liking as a general biological phenomenon.

Sensory liking When we speak of “liking something” in everyday talk, we usually think of the conscious states of pleasure that eating a chocolate bar or watching a great Netflix show can produce. This folk psychological usage suggests that the word “liking” primarily refers to a specific experiential state that is unrelated to behaviour. 33

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In the context of biology, however, the term sensory liking describes a broader physiological event, the purpose of which is to assess the hedonic value of sensory objects and motivate behaviour that promotes survival (Adolphs & Andler, 2018; Berridge & Kringelbach, 2015; Damasio & Carvalho, 2013; Rosenthal, 2017). The function of these hedonic states is not to give rise to introspective experiences of feelings but to elicit action programs that allow biological organisms to detect and respond to adaptive opportunities and threats (Damasio  & Carvalho, 2013; LeDoux, 2012; Panksepp, 1998; Pessiglione  & Lebreton, 2015; Rosenthal, 2017). While evidence suggests that sensory objects become liked through the generation of positive affective emotions and disliked through the generation of negative affective emotions, these emotional processes can occur without conscious awareness (Berridge & Winkielman, 2003). Indeed, some researchers have claimed that only humans represent sensory liking and disliking in the form of conscious feelings (e.g., Adolphs & Andler, 2018; LeDoux, 2012, 2021). We can illustrate the relationship between sensory liking evaluations and behaviour with the example of sweet and bitter tastes. In many animals, the sensation of sweet and bitter compounds leads to liking and disliking responses that manifest themselves in a specific repertoire of behavioural acts (Hayes, 2020; Peciña, 2008, Steiner et al., 2001). In the case of liking responses, these typically take the form of upwards tongue protrusions, smiles, and the initiation of motor outputs associated with ingestion of foods or fluids (Hayes, 2020; Peciña, 2008; Steiner et al., 2001). Disliking responses, in contrast, elicit downwards tongue protrusions, mouth gaping, grimacing, and a rejection of food and fluid objects (Hayes, 2020; Steiner et al., 2001; Dinehart et al., 2006). Because the former set of responses leads to engagement with sweet compounds, we say that these objects are liked. Similarly, because the latter set of responses prompt the organism to avoid bitter compounds, we say that those are disliked. Importantly, sweet and bitter tastes derive their hedonic values from the way they signal different survival consequences of engagement with sweet and bitter objects. For example, organisms like sweet-tasting compounds such as sugar or sucrose because these contain molecules that are important to the restoration of metabolic homeostasis (Hayes, 2020; Zimmerman et al., 2017). Conversely, bitter compounds are disliked because they are usually encountered in objects that also contain toxic chemicals that are a danger to the organism (Hayes, 2020; Katz & Sadacca, 2011; Zimmerman et al., 2017). Liking and disliking evaluations tag sweet-tasting compounds as conducive to survival and bitter-tasting compounds as threatening and help elicit appropriate behavioural actions. Experiments with mice show that stimulating the different cortical fields in the insula that represent sweet and bitter tastes—even absent any sensory input—is sufficient to initiate appetitive and aversive behaviours (Peng et al., 2015). Because liking and disliking function as assessments of an object’s survival value, the hedonic value of a stimulus depends on the adaptive relevance it holds for a given organism. Organisms differ in terms of their physiological constitution and survival needs and therefore will exhibit different liking and disliking responses. For example, not all species like sweet-tasting compounds, and even those that do can develop disliking responses to stimuli such as glucose if environmental conditions change. Thus, Wada-Katsumata and colleagues (2013) found a population of German cockroaches that have been exposed to poisoned glucosecontaining baits to have evolved disliking responses for glucose through a reorganization of their gustatory system: where glucose previously stimulated sweet-taste receptors, it now stimulates bitter-taste receptors. Even in species with an innate preference for sweet compounds, liking is frequently modulated by the homeostatic state of the organism, yielding different degrees of liking depending on satiety signals that are transmitted from blood and gut receptors to the brain (Han et al., 2018; Führer et al., 2008; Thomas et al., 2015). In short, hedonic values communicate the survival value of the object to the individual organism and as such vary greatly across biological taxa and as a function of physiological and behavioural conditions. In terms of mechanisms, all sensory liking systems consist of three basic functional components (Figure 2.2): a sensory system that allows the organism to detect and represent physical objects that are of relevance to the organism, an evaluative system that computes a hedonic value based on the perceptual output of the 34

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Figure 2.2 A  schematic depiction of the functional components that constitute sensory liking systems in biological organisms. Regardless of their physiological organization, all organisms assign hedonic values to parts of their physical surroundings (A) when stimulation of sensory receptors (B) elicits projection of information to an evaluative mechanism (C). This evaluative system computes a hedonic value for the stimulus that forms the basis of decisions the organism makes with respect to behaviours that involve the sensory object (D). As a general rule, the way biological organisms engage with sensory objects reflects their preference for these objects. The three core functional components (B, C, D) are instantiated by different physiological processes across species, but in many species, including all vertebrates, the mechanisms of sensory representation, valuation, and choice are implemented by different neuroanatomical systems.

sensory system, and a decision-making system that initiates behaviour on basis of the hedonic value assigned to the stimulus (Damasio & Carvalho, 2013; LeDoux, 2012; Panksepp, 1998; Rosenthal, 2017). The way these systems are constituted varies across taxa as a function of the individual species’ evolutionary history. The computational mechanisms employed by a species can change as a consequence of evolutionary innovations affecting any of the three systems. For example, the make-up of a species’ sensory systems determines the physical information it is capable of detecting and responding to and thus conditions the range of sensory objects it is able to compute liking or disliking evaluations for. Mate choice research has shown that changes to a species’ sexual preferences can follow directly from evolutionary alterations to its sensory systems that are driven by changes in its ecological environment (Cummings & Endler, 2018; Rosenthal, 2017; Ryan & Cummings, 2013; see also Chapter 11). Similarly, comparative work suggests that the evaluative system is subject to evolutionary modification. As described by Esther Ureña and Marcos Nadal in Chapter 13, a series of evolutionary innovations have expanded the ability of vertebrates to compute hedonic evaluations that integrate information representing the organism’s physiological state, prior experiences, and ongoing task requirements (see also Cisek, 2021; Martínez-García & Lanuza, 2018; O’Connell & Hofmann, 2011; Swanson, 2000). The evolutionary benefit of these changes appears to have been an increased control over behavioural responses to a broader range of sensory objects, improving the odds of survival. An important consequence of this variation in physiological constitution is how fixed a species’ liking and disliking responses are. As a general rule, the simpler a sensory liking system, the more automatic and stereotypical the liking and disliking outcomes it produces. For example, most single-cell bacteria produce appetitive and aversive behaviours that are primarily determined by the way receptors on the surface of the cell are stimulated (van Duijn et al., 2006). Thus, in E. coli cells, a chemotaxis connects sensory receptors on the cell surface with propeller-like motor structures (Falke et al., 1997). Depending on the way these receptors are stimulated, chains of protein complexes are elicited, terminating in one of two movements of the cell’s flagella: “running” behaviour that moves the cell towards attractive stimuli, and “tumbling” behaviour that moves it away from repellent stimuli (Falke et al., 1997; van Duijn et al., 2006). Similarly, in male silkmoths, stimulation of olfactory receptors by the female pheromone bombykol is sufficient to elicit fixed appetitive behaviours related to courtship (Rosenthal, 2017; Sakurai et al., 2011). In contrast, more complex sensory liking systems allow for flexible hedonic evaluations that are less determined by sensory stimulation. Such systems often consist of a multitude of cell assemblies that are able to 35

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represent endogenous and contextual factors that are of relevance to the organism’s survival (Rosenthal, 2017; Skov, 2020). These processes contribute inputs to the neural mechanisms that compute hedonic evaluations. As a consequence, liking and disliking outcomes are modulated by factors other than the sensory stimulus. For instance, studies of vertebrate mate choice have revealed that irrespective of their “innate” attractiveness, courters can remain unattractive to choosers unless they are able to engage the chooser’s sensory system in a way that makes them stand out from conspecifics and other environmental stimuli (Rosenthal, 2017). Thus, examination of courtship behaviour in túngara frogs by Michael Ryan (1980) has shown that male túngara frogs combine two sounds—a “chuck” and a “whine”—to make themselves better heard by—and more attractive to—female frogs in their vicinity. Male túngara frogs that add chucks to whines are more effortlessly detected by female frogs and as a consequence become more liked (Griddi-Papp et al., 2006). Investigating the computational reasons for this outcome, Hoke and colleagues (2004) found that, compared to other sound combinations, listening to whine-chuck combinations elicited increased neural activation in the auditory nucleus of female túngara frogs and enhanced functional connectivity between auditory brain regions and the reward circuit. Findings such as these suggest that how liked a male túngara frog is experienced to be by a female chooser is partly dependent on the way her auditory system is able to attend to and represent calls amidst a cacophony of other sounds and that projections from her sensory system to her evaluative system are mediated by this auditory process. Similarly, processes involved in the computation of decision outputs can have profound effects on the outcome of sensory liking. For example, whereas as a general rule, decision-making involves the identification and choosing of sensory objects with the highest hedonic value (Pessiglione & Lebreton, 2015; Rangel et al., 2008), exceptions to this rule exist. Both when foraging for food and when choosing between different mate options, many species will occasionally, under certain contextual circumstances, choose a lessdesired stimulus (Kolling et al., 2016; Ryan et al., 2018). One example of this phenomenon is the so-called decoy effect where organisms exhibit different preferences for two options, A and B, depending on whether a third option, C, is present (Bateson et al., 2002; Lea & Ryan, 2015; Shafir et al., 2002). Another contextual circumstance that affects choice is time discounting. Many organisms will accept a less desired option over a more attractive option if the latter is expected only to occur at a later point or perhaps not at all (Calhoun & Hayden, 2015). For example, Lynch and colleagues (2005, 2006) found that female túngara frogs will ignore an unattractive synthetic mating call early in the evening but then begin to approach it as the night comes to an end, presumably because they become more permissive of less valued options when the opportunity to mate starts to dwindle. In such cases, liking outcomes are influenced by neural activity in the decisionmaking system that weighs not only the hedonic output of the evaluative system but also other factors when selecting which behavioural act to implement. In sum, sensory liking evaluations vary significantly with the physiological constitution of the sensory liking system. The computational complexity of the sensory liking system affects how flexible the relation of liking and disliking outcomes to a stimulus can be. Organisms with simple sensory liking systems produce more fixed liking and disliking outcomes characterized by stereotypical behavioural responses to a stimulus. In contrast, organisms with more complex sensory liking systems produce more flexible behavioural responses where liking and disliking outcomes are influenced by endogenous and contextual factors that go beyond the stimulus. It is worth stressing that this difference does not suggest that the latter form of sensory liking represents a qualitative improvement over the former form of sensory liking. All sensory liking systems are adapted to the species’ habitat and have their pros and cons. Thus, while the neuroanatomical innovations to vertebrate evaluative systems have expanded the factors sensory liking evaluations can take into account—and hence potentially make liking and disliking evaluations better fitted to fluctuating or uncertain circumstances—they have also increased the risk of producing non-adaptive or “irrational” outcomes (Camerer, 1995; Santos & Chen, 2009). In humans, for example, it is well known that many liking and disliking outcomes can be detrimental to survival. For instance, humans will decline free money if 36

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they deem such offers “unfair” (Henrich et al., 2001) or will willingly ingest disgusting (and thus potentially dangerous) food items (Herz, 2012). Human sensory liking evaluations are especially flexible. Over the last 30 years, experimental research has revealed that this flexibility can be explained by the ability of neural mechanisms to represent and integrate interoceptive information reflecting the physiological state of the body and the individual’s prior experience with the stimulus being evaluated, as well as cognitive knowledge that pertain to the evaluative context. These neural processes are distributed widely across the human brain in structures that are involved in perception, cognition, emotion, and motor control. During evaluative events, they are integrated in idiosyncratic ways that reflect the contextual conditions of the specific evaluative event: what the person knows about the object being evaluated, what her physiological needs are, what the demands are of the task she is presently engaged in, and so on. The result is liking and disliking outcomes that are tailored to these conditions. Although we do not yet know the computational details of how this integration of evaluative processes happens, we do know quite a bit about the neural mechanisms the brain uses to represent information that is of relevance to contextual sensory liking evaluations.

Hedonic evaluation In humans, for a stimulus to acquire a positive or a negative hedonic value, it must engage neural nuclei in a network of mesocorticolimbic structures (Figure 2.3). Specifically, whether the stimulus is tagged as liked or disliked depends on the nature of emotional responses generated by this network of neural processes: liking outcomes require the generation of states of pleasure, whereas disliking outcomes require the generation

Figure 2.3 Midsagittal representation of the human brain showing the approximate location of the neuroanatomical structures that make up the human evaluative system: anterior cingulate cortex (ACC), amygdala, insula, striatum (including nucleus accumbens and pallidum), and the ventromedial prefrontal cortex (vmPFC), including orbitofrontal cortex (OFC).

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of states of negative emotions such as disgust, fear or pain. Experimental evidence suggests that specific patches embedded in the striatum, amygdala, insula, and ventromedial prefrontal cortex (vmPFC) encode these hedonic states but that they probably do so through coordinated activity that involves the activation of processes in all structures. In addition to pleasure and displeasure, the human evaluative system also encodes a number of other functional mechanisms. These include processes that predict how rewarding a stimulus is expected to be, processes that calculate risk contingencies, and processes that compare hedonic values associated with competing behavioural options. Furthermore, all the mesocorticolimbic structures that make up the evaluative system receive projections from other neural systems, and these projections will often modulate processes involved in the computation of hedonic value.

Evidence that reward structures encode hedonic values The bulk of our understanding of the human evaluative system comes from neuroimaging studies where participants are presented with some stimulus and are asked to rate how much they like or dislike it (Becker et al., 2019; Kringelbach, 2005; Skov, 2020). Several meta-analyses have pinpointed regions of the human brain that are commonly activated across such studies (Bartra et al., 2013; Brown et al., 2011; Kühn & Gallinat, 2012; Sescousse et al., 2013). Collectively, they have found that subjective liking co-varies with neural activity that occurs in the striatum, the amygdala, the orbitofrontal cortex (OFC), the anterior cingulate cortex (ACC), and the insula (Figure 2.3). Thus, findings from human neuroimaging suggest that nuclei located in these structures are involved in the encoding of hedonic valuation. However, neuroimaging results cannot prove that a relation between observed behaviour and neural activity is causal. We therefore need additional information to determine if activation of nuclei in the mesocorticolimbic reward system is necessary and sufficient to paint a stimulus as pleasant or unpleasant. One line of research that helps cast light on the causal role of the evaluative system consists of examinations of neuropsychological patients with acquired anhedonia, the inability to experience pleasure from stimuli that used to elicit pleasure. Several psychiatric disorders, including depression, Parkinson’s disease, or schizophrenia, can cause patients to lose the ability to form pleasure responses (e.g., Gold et al., 2008; Holcomb & Rowland, 2007). Studies show that this damage to pleasure responses is associated with structural and functional alterations to precisely the mesocorticolimbic reward circuitry (e.g., Holcomb & Rowland, 2007; Keller et al., 2013; Schlaepfer et al., 2008), lending support to the notion that engagement of this system is in fact necessary for the successful generation of sensory liking. Especially compelling evidence comes from recent work examining the phenomenon of specific musical anhedonia (SMA). SMA is characterized by a loss of pleasure experienced for musical stimuli even as the patient’s capacity for generating positive hedonic responses to other stimuli remains intact (Belfi & Loui, 2020). For example, Mas-Herrero and colleagues (2014) demonstrated that a cohort of SMA participants evinced normal pleasure responses to monetary rewards but reduced pleasure ratings and a lack of autonomic responses to music. In a follow-up study, Martínez-Molina et al. (2016) found that this dissociation could be explained by a difference in the way music and money engaged nucleus accumbens (NAcc) in SMA subjects. Whereas monetary gains elicited normal NAcc responses, the ability of music to activate this structure was almost non-existent (Martínez-Molina et  al., 2016). Furthermore, modelling of functional connectivity between auditory cortex and ventral striatum, the anatomical location of NAcc, showed decreased functional connectivity, a finding subsequent diffusion tensor imaging (DTI) experiments have attributed to a significantly lower volume of axonal tracts connecting auditory cortex and NAcc, as well as OFC, in SMA patients compared to healthy controls (Loui et al., 2017; Sachs et al., 2016). Even further corroboration of the notion that hedonic evaluations are encoded by neural activity centred on mesocorticolimbic structures comes from the observation that people with SMA appear fully able to perceive and represent musical stimuli despite their inability to experience them as pleasurable. Thus, comparing 38

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SMA participants to controls Martínez-Molina and colleagues (2016) found no difference in activation of the superior temporal gyrus that could explain the former’s inability to generate normal hedonic responses for music. Similarly, neuropsychological testing has found music perception to be intact in SMA patients (Satoh et  al., 2011, 2016) and the ability to experience pleasure from music preserved in many patients with auditory agnosia (Matthews et al., 2009), suggesting that the ability to perceive music and the ability to evaluate its hedonic value are dissociated. Together, these findings strongly support the hypothesis that hedonic evaluations of sensory inputs require a transfer of activity from sensory systems to nuclei located in the mesocorticolimbic reward circuitry. In addition to neuropsychological evidence, direct manipulations of neurons in different parts of the mesocorticolimbic reward circuitry also support the notion that this network is responsible for generating hedonic values. Though such experiments are rare in human subjects, extensive work conducted in nonhuman animals has demonstrated that targeted excitation or inhibition of neurons in striatum, insula, amygdala, or OFC are sufficient to elicit liking and disliking responses. For example, in the study by Peng and colleagues (2015) mentioned previously, photostimulation of sweet and bitter cortical fields in the insula of transduced mice was sufficient to evoke appetitive and aversive behaviours, even in the absence of any sensation of actual bitter and sweet tastants. Additionally, Berridge and his group have published a multitude of experiments on rats that have found neurochemical stimulation of nuclei in the ventral striatum, ventral pallidum, the insula, and the OFC to enhance or diminish liking and disliking (e.g., Castro & Berridge, 2017; Smith & Berridge, 2005, 2007; Smith et al., 2010). Although it is important not to simply equate animal neurobiology with human neurobiology, evidence from studies where human subjects are administered naltrexone, an µ-opioid antagonist, suggests a similar pattern (Nummenmaa  & Tuominen, 2017). Ingestion of naltrexone blocks opioid receptors in the mesolimbic reward system, causing an attenuation of hedonic liking experienced for stimuli such as faces (Chelnokova et al., 2014), sweet tastants (Eikemo et al., 2016), money (Petrovic et al., 2008), music (Mallik et al., 2017), or erotic images (Buchel et al., 2018), as well as diminished neural activity in regions such as ventral striatum, amygdala, OFC, and ACC as measured by functional magnetic resonance imaging (fMRI) (Buchel et al., 2018; Petrovic et al., 2008).

The emotional basis of liking Based on the research reviewed previously, a general consensus holds that liking outcomes occur when the brain’s evaluative system applies a pleasure gloss onto a sensation (e.g., Becker et al., 2019; Berridge & Kringelbach, 2015; Kringelbach, 2005; Panksepp, 1998; Smith et  al., 2010). What “pleasure” is, from a neuroscientific point of view, remains unclear, though. Psychologists and neuroscientists speak of pleasure as an emotion or an affective process. However, there is disagreement over how to best conceive of emotions (e.g., Adolphs & Andler, 2018; Barrett, 2017; Berridge, 2018; Damasio & Carvalho, 2013; LeDoux, 2012; Panksepp, 1998). One way to characterize pleasure is to say that it is an emotional process that entails the generation of states of positive valence, but in some respects, this feels like exchanging one poorly defined concept for another. Berridge has called pleasure a “niceness” gloss (Kringelbach & Berridge, 2010, p. 10), a characterization that seems to tap into our folk psychological understanding of emotions as conscious phenomenological states. As noted previously, it is still unclear whether conscious representations are necessary for, or even a prevalent component of, emotional processes (see Adolphs & Andler, 2018; Barrett, 2017; LeDoux, 2012; Panksepp, 1998 for opposing positions on this issue). Certainly, pleasure processes appear to occur unconsciously, even in humans (Berridge & Winkielman, 2003). For example, Flexas and colleagues (2013) displayed faces exhibiting happiness—a stimulus known to elicit pleasure—for 20 ms before the presentation of an abstract art stimulus they had asked participants to rate. Even though the participants had no conscious knowledge of having seen the face, it still influenced their appreciation of the painting, with happy faces significantly enhancing subjective liking. 39

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It is quite likely that the folk psychological words we use to name emotional processes are too invested with pre-scientific associations to be of much help in describing neurobiological processes (Buzsáki, 2019; Pessoa et al., 2021). Indeed, rather than thinking of “pleasure” as one mental function that maps onto one specific neurobiological process or mechanism, it seems better to think of the word as shorthand for a range of different neurobiological events. First of all, states of pleasure appear to prompt liking outcomes. As mentioned, stimulation of nuclei believed to encode pleasure elicits motor outputs associated with appetitive behaviour (Castro & Berridge, 2017; Peng et al., 2015; Smith & Berridge, 2005, 2007). Thus, one function of pleasure computations seems to be to make sensory objects more liked: the more pleasure is generated following exposure to a stimulus, the more liked this stimulus becomes. In humans, this principle also appears to hold for reported liking ratings. For example, neuroimaging experiments that have investigated the relation between satiety and food liking have found neural activity in OFC to scale parametrically with reported pleasure and liking ratings (Kringelbach et al., 2003; Small et al., 2001; Thomas et al., 2015). Indeed, generation of a pleasure response seems to be a prerequisite for humans to experience sensory objects as likeable at all, as illustrated by the literature on anhedonia patients reviewed above. Together, these results suggest that humans probably use the magnitude of pleasure responses to a stimulus to decide how it rates on some liking scale. Another function of pleasure computations is the modulation of autonomic nervous system activity. Regulation of sympathetic arousal, heart rate, respiration, or body temperature is believed to be a central component in the generation of appetitive motivation (Bradley et  al., 2001; Lang  & Bradley, 2010). In short, these motive processes help mobilize the organism and play a crucial role in initiating approach and avoidance behaviour. In humans, reported pleasure for a stimulus is associated with physiological arousal (as measured by changes in skin conductance) and increased heart and respiration rates but decreased temperature and pulse amplitude (Bradley et al., 2001; Salimpoor et al., 2009). Interestingly, the different degrees of physiological activation also scale with how pleasurable and liked a stimulus is reported to be (Salimpoor et al., 2009), suggesting that the way we consciously experience pleasure depends, in part, on changes in body states. For example, musical “chills” that are experienced as intensely pleasurable exhibit a physiological profile that can be distinguished from that of musical pieces that are experienced as merely “highly” pleasurable (Salimpoor et al., 2009). Possibly, our phenomenological experience of pleasure computations reflects physiological changes to our body’s state rather than generation of positive hedonic value per se. Viewed together, current evidence suggests that we use pleasure to refer to several different neurobiological processes, the purpose of which is to enact decision-making and motor outputs associated with appetitive behaviour as well as to motivate the organism to engage in such acts by modulating autonomic physiological activity. For animals such as humans, who can consciously represent aspects of these processes, pleasure can also be accessed through introspection—which allows us to report how much we like an object—and phenomenological feelings. In both cases, the computational rule seems to be that enhanced pleasure predicts sensory liking: the more pleasure is generated for a sensory object, the more motivated the organism is to approach and engage appetitively with this object.

The emotional basis of disliking While the relation between pleasure and sensory liking is somewhat well described—albeit not fully understood—much less is known about the relation of sensory disliking to negative emotions: “while pleasure and positive valence of hedonics came into the focus of affective neuroscience in recent years, there is little work on the core processes of displeasure” (Becker and colleagues, 2019, p. 224). Of course, there is an abundant literature on negative emotions such as pain, fear, and disgust, but curiously little experimental effort has been expended to determine which of these emotions, if any, underwrite sensory disliking. In the same way that pleasure states appear to determine liking outcomes, it is to be expected that generation of 40

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negative emotion is necessary for disliking outcomes to occur. Yet the question of how this happens remains unanswered. Fear, pain, and disgust are all aversive emotions whose functional purpose is to aid organisms in detecting and avoiding harmful sensory objects (e.g., Cain, 2018; Corder et al., 2019; Fox & Shackman, 2019; Hart & Hart, 2018; Sharvit et al., 2015). In this sense, they can all be said to cause sensory disliking as a general principle. However, at least in humans there are many sensory objects for which it is unclear if states of fear, pain, or disgust code for disliking. For example, my dislike for Kanye West’s music does not seem to follow from any generation of acute fear or pain, at least as far as I am able to perceive consciously. Furthermore, humans are able to express liking for sensory objects that do elicit fear, pain, or disgust responses, as witnessed by our willingness to engage in recreational fear (Andersen et al., 2020), sexual sadomasochism (Georgiadis et al., 2012), or the eating of fermented food items (Herz, 2012). Thus, the computational role negative emotions play in encoding (human) sensory disliking is not straightforward. One possibility is that different disliking evaluations engage different negative emotions. Emerging evidence suggests that disliking for different categories of stimuli is associated with the activation of different negative emotions. Thus, stimuli that signal monetary losses appear to tap into the brain’s pain network (Delgado et al., 2006; Knutson et al., 2007; Seymour et al., 2007; Seymour, Singer et al., 2007). For example, the cost of buying retail goods co-varies with activity in a part of the insula (Knutson et al., 2007) that is also known to be involved in the encoding of nociceptive pain (Baliki & Apkarian, 2015). Similarly, neuroimaging studies have found common activity in the striatum to correlate with acute pain and financial loss (Delgado, 2007; Seymour, Singer et al., 2007; Tom et al., 2007). Though such results do not prove conclusively that disliking elicited by monetary losses is caused by the activation of the human brain’s pain system, they suggest aversive outcomes elicited by monetary losses share a neurocomputational architecture with the encoding of nociceptive pain (Delgado et al., 2006). In contrast, disliking for other categories of stimuli seems to occur as a consequence of the brain’s disgust network being engaged. Thus, in the experiment where Flexas and colleagues (2013) found 20-ms presentations of facial happiness to enhance liking ratings for abstract art, they also found similar brief displays of facial disgust to reduce participants’ reported liking. Other experiments have shown that experienced disgust for facial disfigurements predicts judgments of ugliness (Bull & Rumsey, 1988; Klebl et al., 2020). In a recent experiment, Klebl and colleagues (2020) extended this finding to also include the evaluation of buildings, demonstrating that buildings that were rated as ugly elicited greater disgust responses than buildings rated as beautiful. Building on these findings, Dorado and colleagues (2022) tested if liking evaluations of different types of sensory stimuli do in fact engage different negative emotions. First they assessed the participants’ individual sensitivity to the emotions sadness, fear, anger, and disgust. Then they asked the participants to evaluate stimuli displaying degrees of moral transgressions, more or less symmetrical geometrical patterns, and rooms varying in tidiness (Dorado et al., 2022). Results showed that individuals who were more sensitive to anger and fear disliked moral transgressions more than individuals with less sensitivity to anger and fear, while individuals who were more sensitive to disgust disliked asymmetrical patterns and untidy rooms more than individuals with less sensitivity to disgust (Dorado et al., 2022). Thus, some categories of sensory objects appear to become disliked through the engagement of the brain’s pain system, while others become disliked through the engagement of fear and anger network, and yet others become disliked through the engagement of the brain’s disgust system. Unfortunately, neuroscientific exploration of the emotional causes of sensory disliking remains limited. There is, for instance, an almost complete dearth of experiments that manipulate states of fear, pain, or disgust in order to test how excitation or inhibition of these systems affects disliking evaluations. Whereas, as reviewed, we have some evidence that magnitude of pleasure scales with liking outcomes, there is to the best of my knowledge no study that has investigated if degree of induced pain or disgust scales with disliking outcomes. Indeed, when it comes to evaluations of disliking for non-adaptive stimuli, it is far from clear that 41

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we have definitive evidence that these are caused by activation of core defensive emotions, even though this remains the best and the most parsimonious explanation.

Neural correlates of liking and disliking A central objective of the neuroscientific study of sensory liking is to describe the computational mechanisms used by the evaluative system. What neuroscientists especially hope to understand are the neurobiological causes of sensory liking. This aim entails identifying the neurobiological processes that determine whether a sensory stimulus is experienced as liked or disliked. Evidence from both human and nonhuman animal studies suggests that such neural processes are in fact widely distributed across more or less the whole evaluative system, with regions coding for liking and disliking found in most of the anatomical structures that constitute the mesocorticolimbic reward circuitry (Figure 2.4). Moreover, experiments have revealed that sensory liking evaluations also elicit neural processes that encode other functions than just the positive and negative affective states underwriting liking and disliking.

Figure 2.4 A schematic overview of the human evaluative system that shows how computational mechanisms map onto structures in the mesocorticolimbic reward circuitry. Coloured boxes indicate that experiments have found empirical evidence for the encoding of pleasure (red boxes), displeasure (blue boxes), motivational processes (grey boxes), or the integration of different source of information pertaining to value outcomes (green boxes). The overview makes clear that nuclei coding for pleasure and displeasure are distributed across all structures in the mesocorticolimbic reward circuit. In contrast, nuclei encoding motivational processes such as wanting or incentive salience appear to be primarily located in the striatum, amygdala, and insula, whereas processes involved in the integration and computation of value outcomes seem centred on structures in the ventromedial prefrontal cortex (vmPFC), including the orbitofrontal cortex and anterior cingulate cortex. Triangles indicate that processes in brainstem and hypothalamic structures project interoceptive information reflecting the body’s physiological state to mechanisms in the evaluative system.

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Much of our general understanding of the neurobiology involved in sensory evaluations comes from studies of nonhuman animals. Such studies allow for the systematic mapping of different functions by the stimulation of either individual neurons or small cell assemblies. Manipulating different parts of the evaluative system allows for observing the effect that activation or silencing of individual nuclei has on liking and disliking responses. As already mentioned, an influential example of this approach is the work conducted by Berridge and his group. A series of experiments from the Berridge lab has demonstrated that stimulation of small (1–6 mm3) regions of rat NAcc, ventral pallidum, insula, and OFC is sufficient to enhance liking for a stimulus, as indexed by orofacial reactions (for reviews, see Smith et al., 2010; Berridge, 2018; see also Chapter  3). Berridge himself has dubbed these regions hedonic hotspots. Stimulation of other surrounding regions (coldspots) appears to suppress liking responses to stimuli, while stimulation of yet other regions enhances disliking reactions to stimuli such as bitter tastants (Berridge, 2018; Berridge & Kringelbach, 2015; Castro et al., 2016; Smith et al., 2010). Hotspots that enhance disliking seem especially prominent in the amygdala and insula but are also found in parts of the striatum and OFC (Cain, 2018; Corder et al., 2019; Fox & Shackman, 2019; Hart & Hart, 2018; Sharvit et al., 2015). It remains unclear precisely how these putative hotspots and coldspots for liking and disliking work together in the course of an evaluative event. Findings from many studies suggest that the different functional patches interact and modulate the activity of each other (Berridge, 2018; Haber  & Knutson, 2010). For example, Wang and colleagues (2018) found projections from the gustatory insula cortex—thought to be involved in the encoding of sweet taste—to the basolateral nucleus of the amygdala to mediate taste liking but projections from the posterior insula—thought to be involved in the encoding of bitter taste—to the central nucleus of the amygdala to mediate taste disliking (Wang et al., 2018). Furthermore, it is still unclear whether evaluative events rely on the activation of single functional regions or the coordination of multiple hotspots and coldspots across the whole evaluative system, although the latter hypothesis seems more likely. Berridge (2018) has suggested that stimulating one hotspot recruits other hotspots as well. He has recently proposed that the “unanimous activation of multiple hotspots together appears required in order to amplify sensory pleasures” (Berridge, 2018, p. 10), pointing to the observation that simultaneously suppressing one hotspot while stimulating another fail to elicit liking (Berridge, 2018). While human neuroscience does not as readily allow for studies that manipulate neural activity, neuroimaging experiments have broadly confirmed that subjective ratings of liking and disliking correlate with a distributed network of neural activity occurring in the striatum, amygdala, insula, and OFC (Bartra et al., 2013; Brown et al., 2011; Delgado et al., 2006; Knutson et al., 2007; Kühn & Gallinat, 2012; Sescousse et al., 2013; Seymour et al., 2007; Seymour, Singer et al., 2007). It has been suggested that liking and disliking might map onto dissociable regions in the human brain as well, but this has proven quite difficult to show with the imaging methods we currently have at our disposal. For example, in an early meta-analysis conducted by Kringelbach and Rolls (2004), activity related to rewarding outcomes was primarily located in the medial part of the OFC, whereas activity related to punishing outcomes was mostly located in the lateral part of the OFC. However, not all neuroimaging findings have supported such a clean-cut dissociation. For instance, fMRI studies that have modelled neural activity as a continuous function of reported subjective liking have found blood oxygen level–dependent (BOLD) activity in the medial OFC to scale parametrically with both positive and negative ratings (e.g., Small et al., 2001; Ishizu & Zeki, 2011; Kringelbach et al., 2003; Lebreton et al., 2009). It is quite likely that the inability of neuroimaging to probe activity below large volumes impedes the adequate distinction between different computational functions associated with liking and disliking evaluations. Moreover, evidence suggests that neural activity occurring in the evaluative system is mediated by the specific task conditions of the individual evaluation event examined. For example, Mas-Herrero and colleagues (2021) conducted a meta-analysis of imaging studies that reported neural correlates for subjective liking of food and music. Comparisons of evaluations involving these two different categories of stimuli found some regions of the evaluative system to be commonly activated by both types of 43

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evaluations (NAcc, OFC, insula) but also found certain regions to be more activated for music evaluations than for food evaluations (ventral striatum) and some regions to be more activated by food evaluations than by music evaluations (insula, putamen, amygdala). I will return to this issue later. Another problem associated with identifying neural mechanisms involved in hedonic evaluation is that it is difficult to distinguish between computational functions that encode the affective gloss (pleasantness, displeasure) assigned to a stimulus and the motivational impact of such hedonic values. Evidence from animal studies suggests that dissociable neural processes likely implement different functions. Thus, in the work by Berridge and his group, stimulation of a different region of NAcc than the hotspot encoding pleasure triggered increased ingestion of food rather than modulation of orofacial reactions (Smith et al., 2010; Berridge, 2018). These two patches in the NAcc therefore appear to code for different mechanisms associated with sensory liking evaluations: the liking hotspot computes the positive hedonic impact of the stimulus, and the wanting hotspot computes the motivational impact of the stimulus. Activity in the former patch is responsible for assigning pleasantness to the stimulus, while activity in the latter is responsible for eliciting appetitive behaviour (Berridge et al., 2009). Similarly, in the amygdala, there seem to be distinct neural ensembles in the basolateral region that encode the unpleasantness of pain (e.g., Corder et al., 2019) or fear (LeDoux, 2012, 2021), while other nuclei in the extended amygdala complex code for different types of aversion behaviours (Cain, 2018). The motivational processes elicited by sensory liking evaluations condition how the organism reacts behaviourally to stimuli it encounters in the environment. Mammalian brains exhibit a diversity of motivational responses, running the gamut from withdrawal reflexes, freezing, flight, or fighting behaviours to foraging, ingestion, and courtship behaviours that can be initiated depending on character of the hedonic value assigned to a stimulus. How an organism reacts to a sensory object in a given situation seems to depend on the activation of individual motivational mechanisms. In all vertebrates, furthermore, the neural processes that instantiate these motivational responses are susceptible to learning. For example, single cell recordings in the ventral striatum of monkeys have revealed that certain cells respond to the unexpected occurrence of rewards (Schultz et al., 1992). This prediction error signal appears to serve as a neural learning mechanism that continuously helps update the contingency between a given sensory object and its experienced reward outcome (Schultz et al., 1997). Similar learning mechanisms exist for teaching the organism if behavioural engagement with an object will prove punishing (Cain, 2018; LeDoux, 2012). This observation suggests that motivational responses function as predictors of how rewarding or punishing a sensory object is expected to be based on previous experiences (Pessiglione & Lebreton, 2015). In humans, results from neuroimaging studies suggest that exposure to stimuli that are expected to elicit pleasure activates ventral striatum (e.g., Knutson et  al., 2001, 2005; O’Doherty et  al., 2002; Knutson  & Greer, 2008; Salimpoor et  al., 2011). Nuclei in this part of the evaluation system may therefore encode reward predictions that serve to enhance incentive salience and appetitive motivation for the stimulus being evaluated. Supporting this idea, research by Brian Knutson and others has found that the magnitude of observed neural activity in this region predicts behaviours such as decisions to buy retail goods or invest in monetary gambles (Knutson & Genevsky, 2018; Salimpoor et al., 2013). Human imaging experiments have also found evidence for the encoding of reward prediction errors in the NAcc (e.g., Gold et al., 2019), implicating striatal activity in stimulus-reward learning. Interestingly, these stimulus-reward contingencies also appear to influence liking and disliking evaluations. Thus, studies of music listening have revealed that reward expectations modulate the degree of pleasantness of musical stimuli, even though motivational and hedonic outcomes of the psychological states might be mediated by different processes in ventral striatum and other structures (Cheung et al., 2019; Gold et al., 2019; Salimpoor et al., 2011; Shany et al., 2019). In addition to encoding motivational outcomes of sensory liking evaluations, the mesocorticolimbic evaluative system also harbours processes that track hedonic values of different sensory objects in order for the organism to be able to choose a preferred option. Electrophysiological recordings in nonhuman animals and human neuroimaging have consistently implicated the ventromedial prefrontal cortex, and especially the 44

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OFC, in such comparison computations (e.g., Grabenhorst et al., 2010; Lebreton et al., 2009; Lopez-Persem et al., 2017, 2020; Padoa-Schioppa & Assad, 2006). Existing evidence suggests that OFC neurons contrast the different hedonic values of available options by using a common linear scale, with decisions generally driven by the magnitude of the value assigned to each option (Lebreton et al., 2009; Lopez-Persem et al., 2020; Padoa-Schioppa & Assad, 2006). As noted in the previous section, however, organisms do not always prefer the sensory object they assign the greatest hedonic value to. The reason for this is that behavioural decision-making not only considers hedonic values but also other information of relevance to a given choice dilemma. For example, in addition to promising an expected reward, a given choice option may also come with a potential loss or punishment. In a famous fMRI experiment, Knutson and colleagues (2007) showed participants images of chocolates with and without price information and asked them to decide if they wanted to purchase the chocolate item. Results found NAcc activity to reflect the hedonic value the participants attributed to individual chocolates, while insula activity correlated with the expected loss associated with the price tag. Behavioural choices revealed that purchase decisions weighed both evaluative signals, with participants declining to buy chocolates they desired if these were deemed too expensive. Activity in the OFC tracked this cost-benefit analysis, suggesting that neurons located in this part of the evaluation system compute the integration of different pieces of contextual information that help inform how beneficial or harmful a sensory object is relative to the organism’s current situation. In sum, currently available evidence suggests that several different mesocorticolimbic processes are activated in the course of sensory liking evaluations. These processes appear to code for different functions, including the generation of positive and negative affective states, the elicitation of motivational programs that modulate autonomic physiology, decision-making, motor outputs, and the computation of discrepancies between expected and experienced rewards and punishments. Intriguingly, nuclei distributed across all of the evaluative system’s core anatomical structures, such as striatum, amygdala, insula, and OFC, seem to contribute to the encoding of these mechanisms, although processes located in ventral striatum, amygdala, and insula may be more important to the computation of predictive motivational states compared to processes located in the ventromedial prefrontal cortex that, in turn, play a greater role in integrating contextual information and implementing decisions about how to respond to a stimulus based on sensory liking evaluations (Figure 2.4). However, while it is possible to experimentally distinguish between these different processes, it is unlikely that they occur in isolation from each other during actual evaluative events. Rather, our current understanding of sensory liking computations suggests that individual evaluation events marshal all the mechanisms reviewed here and that the way the different neural processes unfold reflects conditions of relevance to the evaluative event.

The modulation of evaluation events by interoceptive processes A central input to the evaluative system comes from afferent projections from the interior of the body that are routed through brainstem, thalamic, and hypothalamic nuclei (Craig, 2002, 2010; see also Chapter 5). Together, these interoceptive signals constitute a form of visceral sensation that represents body states, including metabolism, muscular sensations, temperature, or touch. Since survival depends on the successful regulation of these physiological states, a core function of sensory liking evaluations is to identify and promote engagement with parts of the environment that can contribute to energy and thermic regulation, pain analgesia, and so on (Berridge, 2004). Obviously, the ability of the evaluative system to take information about internal states into account when assessing the hedonic value of a stimulus improves the organism’s chance of selecting behavioural acts that serve to uphold homeostasis. In humans, evidence suggests that interoceptive signals can significantly influence liking and disliking outcomes by modulating neural activity related to hedonic evaluation in the mesocorticolimbic system. 45

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As described in the previous section, one prominent example of the way sensory liking evaluations incorporate interoceptive information is found in the effect of satiation on food and drink liking (Hayes, 2020). Typically, experienced pleasure for a food item is enhanced when energy deficits are high and diminished when energy levels rise (Small et al., 2001; Kringelbach et al., 2003). Energetic states related to fluid balance, glucose metabolism, and so on are signalled in several different ways to the evaluative system. For example, changes in plasma osmolarity and angiotensin II, a hormone that signals water and salt deficiencies, are projected to neurons in the lamina terminalis of the forebrain (Zimmerman et al., 2017). From here they project to the brainstem and hypothalamus, two anatomical structures that are crucial to homeostatic control (Swanson, 2000). Outputs from these energy balance controllers modulate neural activity in the evaluative system, in part through dopaminergic projections from the ventral tegmental area (VTA) to the striatum and amygdala (Zheng & Berthoud, 2007). Glycemic control, similarly, involves afferent projections to neural circuits in the hypothalamus that provides input to VTA via the parabrachial nucleus (Sternson & Eiselt, 2017). Using fMRI, Morville and colleagues (2021) have shown that neural activity in human VTA, substantia nigra, and the parabrachial nucleus varies systematically with fluctuations in serum glucose. Representations of energy homeostasis in the evaluative system modulate both computations of pleasure and displeasure for food and drink tastes (Kringelbach et al., 2003; Small et al., 2001; Thomas et al., 2015) as well as liking responses to food- and drink-related visual cues (Führer et al., 2008; Siep et al., 2009; Thomas et al., 2015). Thus, diminished pleasantness ratings for stimuli such as chocolate or strawberry liquids obtained during satiated states correlate with reduced BOLD signals in NAcc, insula, and OFC (Kringelbach et al., 2003; Small et al., 2001; Thomas et al., 2015). In contrast, enhanced pleasure experienced for similar stimuli when energy levels are depleted correlates with elevated neural activation in the same regions (Kringelbach et al., 2003; Small et al., 2001; Thomas et al., 2015). By enabling variable hedonic evaluations of a food or drink stimulus, the evaluative system can make better decisions about how to expend resources to pursue and consume specific food items that will meet the organism’s physiological needs (Hayes, 2020; Zheng & Berthoud, 2007). Not only can flexible liking and disliking outcomes regulate intake (Hayes, 2020), variations in liking and disliking for individual chemical compounds can also tailor behavioural responses to specific metabolic needs—for example by favouring foraging for salt over foraging for glucose when sodium levels are low but serum glucose levels are high (Zheng & Berthoud, 2007). Another example of endogenous physiological states modulating sensory liking evaluations is found in the context of mate choices (Ryan & Jordan, 2017). In most sexual species, reproduction is highly regulated, with fecundity being determined, at least partially, by fluctuations in the production of gonadal sex hormones such as androgen, testosterone, oestrogen, or progesterone (Roney, 2018). For instance, in females, a reproductive cycle triggers the production of oestrogen and progesterone at specific moments that not only influence courtship and copulative behaviours but also are critical to parental behaviours such as egg laying, lactation, or parental care (Cheng, 2008; Ryan & Jordan, 2017). Animal experiments also find that oestrogen and progesterone influence how attractive females find male traits (Rosenthal, 2017; Ryan & Jordan, 2017). This effect seems to be a result of oestrogen and progesterone modulating neural activity in the mesocorticolimbic evaluative system. Thus, oestradiol levels have been found in multiple experiments to modulate dopamine production, especially in the striatum (Yoest et al., 2018; Diekhof, 2018). Studies suggest that this modulation of neural activity is associated with enhanced sensitivity, and reaction, to rewards (Sakaki & Mather, 2012; Yoest et al., 2018; Diekhof, 2018). In humans, a few neuroimaging experiments have found activity in the ventral striatum and OFC to be higher for rewarding stimuli during the follicular phase than the luteal phase (Dreher et al., 2007; Frank et al., 2010; Rupp et al., 2009a, 2009b). But a full account of the computational role played by endocrinological inputs to the evaluative system remains a work in progress.

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There is some evidence that changes in hormone production affect the way women’s liking and disliking evaluations unfold. Hence, some studies have found women to attend more and exhibit heightened reactions to liked images during the follicular phase, suggesting that increases in oestrogen enhance motivational responses to sensory objects (Pilarczyk et al., 2019; Roney, 2018). In contrast, women appear to experience enhanced disliking responses to negative stimuli, especially objects and events that signal threat or disgust, during the luteal phase (Fleischman & Fessler, 2011; Masataka & Shibasaki, 2012). However, attempts to show that oestrogen and progesterone directly influence how liked or disliked specific stimulus properties are experienced to be by women have so far proven controversial. For instance, findings suggesting that masculine facial features are more liked by women when oestrogen levels are high (e.g., Johnston et al., 2001; Penton-Voak et al., 1999; Penton-Voak & Perrett, 2000) have failed to replicate (Marcinkowska et al., 2016; Jones et al., 2018; Dixson et al., 2018). On balance, the studies I have reviewed here support the idea that interoceptive information is projected to the evaluative system, where it modulates neural activity related to the encoding of pleasure, displeasure, and motivational outputs. By incorporating information about physiological states such as energy homeostasis or fecundity, the evaluative system can tailor sensory liking evaluations to individual adaptive scenarios.

The modulation of evaluation events by perceptual and cognitive processes In addition to interoceptive signalling, there is mounting evidence that evaluative conditions are reflected by perceptual and cognitive processes involved in the representation of the stimulus being evaluated. These processes allow organisms to represent and integrate contextual information that is of importance to the assessment of a given object’s hedonic value. For example, if a stimulus is unknown, it might be advantageous to assume that it is potentially dangerous and thus code it as unpleasant. Similarly, having specialized knowledge about an object, including knowledge about its provenance, precise identity, or how other conspecifics rate it, can help increase the accuracy of the organism’s evaluative predictions. Research suggests that the human brain contains numerous mechanisms that allow for the representation of such conditions and that liking and disliking evaluations are affected by their input to the evaluative system.

Expectations Predictive coding plays a fundamental role in the representation of contextual conditions (Friston, 2010). Based on previous experiences, perceptual systems predict what information the organism expects to encounter. These predictions constitute a gating mechanism that enables the organism to attend to sensory information that is of relevance to current physiological and behavioural concerns (Alink et al., 2010; Engel et al., 2013; Egner et al., 2010; Murray et al., 2004; Oliva & Torralba, 2007). Predictive coding, in the context of sensory liking, serves to allocate attention to those parts of the sensory Umwelt that are potentially rewarding or punishing and to predict the relevance of a stimulus to ongoing survival needs. Thus, sensory objects predicted to be rewarding attract visual attention (di Pellegrino et al., 2011; Valuch et al., 2015) and are allocated greater computational resources than other sensory objects (Chen et al., 2012; Li et al., 2016; Liu & Chen, 2012). In the auditory domain, work on music perception has revealed that the auditory system represents musical stimuli in part based on predictions of how tones and chords are temporally related (Pearce, 2018). These predictions are rooted in the learning of statistical regularities from repeated exposure to music. Over time, learning of such regularities accumulates into a perceptual model of probabilistic chord, pitch, or tone progressions (Pearce, 2018). Computational modelling has shown that the brain uses these models to predict the onset on musical events when perceiving new musical stimuli, yielding variable states of perceptual uncertainty and surprise depending on whether predictions match sensory events or not

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(Hansen & Pearce, 2014; Pearce et al., 2010). Deviations from model expectations elicit neural activation in a connected network of auditory, hippocampal, and evaluative structures, including the amygdala and NAcc (Cheung et al., 2019; Koelsch et al., 2008). Analysing the relation between perceptual uncertainty, surprise, and liking, Cheung and colleagues (2019) found the interaction between uncertainty and surprise about chord progression to elicit pleasure when a surprising chord occurred with low uncertainty or when an unsurprising chord occurred with high uncertainty. Examination of the visual system in animal models has revealed that neural activity in the visual cortex encodes the reward history of a stimulus (Hikosaka et al., 2006; Serences, 2008; Summerfield & de Lange, 2014). This causes neurons in V1 and V4 to change firing rates for visual stimuli depending on the organism’s reward and punishment history with a given sensory object (Baruni et al., 2015; Gavornik et al., 2009; Goltstein et al., 2018; Shuler & Bear, 2006). Human eye-tracking work has found visual attention to track aspects of the visual scene that the individual finds pleasing (Goller et al., 2019; Leder et al., 2016). There is also emerging evidence that merely attending longer to a stimulus, as indexed by gaze duration, can enhance liking for a visual stimulus (Shimojo et al., 2003). Famously, “mere exposure” to a visual stimulus is enough to modulate sensory liking evaluations, with repeated exposure enhancing how liked a sensory object is experienced to be (Bornstein, & D’Agostino, 1992; Zajonc, 1968). It has been suggested that this effect is a function of biological organisms’ aversion to uncertain or unpredictable sensory events (Zajonc, 1980). In a combined electrophysiology and fMRI study, Herry and colleagues (2007) demonstrated that mice and humans responded aversively to different task assignments, as quantified by enhanced place avoidance in mice and elevated attention to angry faces in humans, when exposed to sequences of neutral sound pulses varying in temporal predictability. This increase in aversive behaviour during unpredictable sound events correlated with higher engagement of the amygdala (Herry et al., 2007). In a follow-up experiment, Ramsøy and colleagues (2012) showed that exposing human participants to the same pulse sequences while they rated how much they liked brand logos and artworks yielded lower liking outcomes during unpredictable perceptual events than during predictable perceptual events. Together, these results suggest that humans and other animals interpret perceptual uncertainty as indicative of potential danger that must be avoided and that, consequently, they evaluate sensory objects affected by this uncertainty as less liked. In sum, research investigating the influence of perceptual predictions on sensory liking evaluations shows that perceptual systems represent how familiar a stimulus is even at the perceptual level. By matching model predictions to actual sensory information, perceptual processes encode if a stimulus is novel, surprising, or known to elicit pleasure or displeasure and project this information to the evaluative system, where it is integrated into evaluations of how pleasing or displeasing a stimulus is.

Knowledge In addition to predictive coding, representations of stimuli are heavily modulated by conceptual knowledge. Semantic and episodic memory of an object influence how it is perceived by activating associative networks of conceptual nodes based on the nature of the sensory input (Buckley & Gaffan, 2006; Miyashita, 2004). These associative networks are encoded by a distributed network of nuclei in different parts of the temporal lobe, including nuclei located in the hippocampal formation, parahippocampal and perirhinal cortices, and fusiform cortex (Devlin & Price, 2007; Henke, 2010). Through activations of this network, sensorycognitive associations can individualize the object in ways that allow for more precise sensory liking evaluations that fit the individual’s needs and behavioural agenda. For example, whether it is prudent to approach or avoid an object can depend on the object’s provenance, identity, or social status. There is abundant evidence that the conceptual knowledge an individual brings to bear on the perceptual and cognitive representation of a stimulus can influence how liked or disliked it is. For example, acquired 48

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knowledge about a stimulus affects the degree to which it is evaluated to be liked or disliked, as evidenced by comparisons of experimental subjects who vary in expertise (Kozbelt, 2020). For instance, while most people like sensory objects with curved contours more than objects with angular contours (Chuquichambi et al., 2022), architects and designers report diminished liking for curved buildings combined with an enhanced liking for angular architectural structures (Palumbo et al., 2020; Vartanian et al., 2019). Kirk and colleagues (2009a) used fMRI to compare neural activity occurring during sensory liking evaluations of buildings in architects and non-architects and found greater activation of hippocampus and precuneus in the architects, a result that might suggest recruitment of stored associations acquired through extensive engagement with architectural objects. This idea is partially corroborated by findings from other neuroimaging experiments that have demonstrated that the density and remoteness of associations generated by object perception can be predicted from the degree to which the entorhinal, parahippocampal, perirhinal, and fusiform cortices are engaged (Friis-Olivarius et al., 2017; Hulme et al., 2014). It should be emphasized, though, that it remains largely unknown how processes encoding stimulus expertise modulate liking and disliking mechanisms in the evaluative system. Experiments have also shown that it is possible to influence liking and disliking for sensory stimuli by manipulating a person’s immediate state of knowledge with respect to a stimulus. This can be demonstrated by furnishing people engaged in sensory liking evaluations with different items of information about the stimulus being evaluated (Okamoto & Dan, 2013). For instance, being told that a stimulus is exclusive rather than generally available, expensive rather than cheap, or fabricated by a luxury manufacturer rather than by a less prestigious producer are all pieces of information that have been experimentally shown to enhance liking despite the fact that the stimulus remains the same under both conditions (Krishna, 2012; Okamoto & Dan, 2013; Fernqvist & Ekelund, 2014; Piqueras-Fiszman & Spence, 2018). Several neuroimaging studies have investigated what happens during such evaluative events and found that object-external information modulates neural activity in parts of the mesocorticolimbic reward circuit, including the OFC (e.g., McClure et al., 2004; Plassmann et al., 2008; Kirk et al., 2009b). In their study, Kirk and colleagues (2009b) also found evidence that the different pieces of semantic information used to describe the stimulus elicited variable activity in the entorhinal cortex and the temporal pole, key anatomical structures involved in the encoding of memory and conceptual knowledge.

Evaluative task conditions Finally, sensory liking evaluations are also modulated by neural mechanisms that represent the task conditions of a particular evaluation event. I have already noted some examples of this phenomenon. For instance, how sexually attractive a courter is deemed to be can vary depending on whether the chooser makes the evaluation early in the evening or just before closing hours (Rosenthal, 2017; Ryan & Jordan, 2017). Similarly, liking outcomes are also modulated by the availability of options: Is the apple being evaluated the only possible source of energy, or are there other, perhaps better, options around? As a general principle, sensory liking evaluations are always embedded within specific task conditions that the organism needs to take into account to make the best behavioural decision. How the human brain accomplishes this computational task remains somewhat unclear, but it is likely that prefrontal structures construct a model of relevant task conditions—for example, what behavioural act the sensory liking evaluation is in service of, what the evaluative anchor being used is, how risky and potentially rewarding or punishing the available options are, and so on—that is used to modulate sensory and evaluative processes (Dayan & Berridge, 2014; O’Doherty et al., 2017). Neuroimaging experiments that have compared explicit evaluation tasks to other non-evaluation tasks have found that attending to a stimulus with the instruction to rate how liked it is according to a specific evaluative anchor enhances neural activity in a broad network of anatomical structures that encode both perceptual information and hedonic values (e.g., Chatterjee et al., 2009; Grabenhorst & Rolls, 2008; Ishizu & 49

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Zeki, 2013; Jacobsen et al., 2006; Kim et al., 2007; Lebreton et al., 2009). This finding suggests that task conditions modulate both the way the stimulus is represented and computations of liking and disliking. Although it is still unclear what functional mechanisms these activation patterns reflect, emerging evidence from experiments examining the impact of different task conditions on liking outcomes suggests that one mechanism involved in the computation of explicit evaluations is a matching of perceptual and emotional states with a model representing task conditions. For example, Sherman and colleagues (2015) measured individual working memory capacity (WMC) for a cohort of participants and asked how different levels of WMC affected explicit evaluations of liking for paintings. Results revealed that participants with greater visual WMC reported elevated ratings for paintings with high degrees of visual complexity. The implication of this finding is that humans hold visual information relevant to the evaluative task present in working memory while making explicit judgment decisions. Che and colleagues (2021) tested this hypothesis directly by asking participants to rate visual art according to two different explicit judgments, beauty and liking. This evaluation task was embedded in a working memory task where participants saw a matrix with 1, 3, or 5 dots before the painting. They were instructed to remember the first matrix while viewing and rating the paintings because they would then have to reproduce it after. This manipulation loaded the participants’ visual working memory during sensory liking evaluations and allowed for a direct test of the idea that people hold visual information relevant to an evaluation task during the decision phase. Results of Che et al.’s (2021) study confirmed that loading of working memory did indeed influence processes associated with evaluation: The participants took significantly longer time to make beauty judgments than to make liking judgments. This finding suggests that beauty evaluations make greater demand on visual working memory than liking evaluations, a conclusion that is further supported by another finding from the experiment, that participants with greater WMC were faster to complete the working memory task after liking judgments than after beauty judgments (Che et al., 2021). One possible reason explicit beauty judgments engage working memory processes more than liking judgments is that people consider beauty a more complex, and possibly more restrictive, evaluative model than liking (Skov & Nadal, 2021). We know from Brielmann and Pelli’s (2019) work that to be judged as beautiful, a stimulus must elicit greater pleasure than to be judged as likeable. This finding indicates that people conceive of beauty as a more restrictive category than liking and that explicit evaluations of beauty use introspection to decide if the pleasure felt for a stimulus matches this high end of the spectrum. It is similarly possible that people conceive of beautiful objects as having specific object properties and that explicit evaluations of beauty involve a process of matching the actual perceptual information to this model. This process, as the previous evidence suggests, likely is mediated by working memory mechanisms. A recent experiment by Che and colleagues (2022) lends tentative support to the idea that people conceive of beauty and liking as different evaluative anchors and that cognitive models of what counts as beautiful or likeable inform explicit sensory liking evaluations that make use of these evaluative anchors. Che et al. (2022) asked participants to rate faces and paintings using both beauty and liking as reported judgments. In one part of the experiment, participants were left to conceive of beauty and liking using their own intuitions. In another part, Che and colleagues (2022) gave the participants specific instructions with respect to how they should think of beauty and liking as evaluative anchors. Liking judgments, the participants were told, are subjective and based on inner feelings of how pleasant an object is. Beauty judgments, in contrast, they were instructed to think of as objective, based on the object’s order or proportion. Comparing ratings from the two test conditions, Che et al. (2022) found beauty judgments to take a markedly longer time and liking judgments to take a noticeably shorter time during the instruction condition than during the noninstruction condition. Beauty ratings were also higher with instructions than without, while liking ratings were lower with instructions than without instructions. It is difficult to see how these results would emerge unless participants model beauty as a more specialized evaluative anchor than liking, with only certain perceptual features and greater pleasure allowing a stimulus to be judged as beautiful. Determining if a stimulus 50

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matches model requirements therefore takes a longer time when people are asked to assess if it is beautiful than when they are asked to decide if it is simply pleasant (Skov & Nadal, 2021). While it remains to be worked out precisely how cognitive mechanisms represent task conditions and how such representations come to modulate evaluative computations in the mesocorticolimbic system, it seems clear that the conditions under which a stimulus is being evaluated are factored into assessments of how liked or disliked it is. Together with perceptual predictions and conceptual associations, such representations of tasks requirements and goals help further contextualize sensory liking evaluations by integrating information about the organism’s previous experiences with the object, its acquired knowledge, and the parameters of the current behavioural task that the sensory liking evaluation is meant to serve.

Conclusion I started this chapter by considering the historical assumption that the key to understanding why humans and other biological organisms like some sensory objects and dislike others lies in unearthing either innate or learned relationships between stimulus features and hedonic reactions. As my overview of experimental work has shown, this assumption is not tenable. Rather, existing evidence strongly compels us to view liking and disliking outcomes as the product of an integration of multiple factors that include, but are not limited to, stimulus information: the physiological states of the organism, its motivational needs, prior experiences with the stimulus, cognitive knowledge, and the weighing of goals and requirements associated with the behavioural act the organism is presently engaged in. The adaptive value of such flexible liking and disliking outcomes is obvious: Instead of being forced to respond in a stereotypical manner to sensory stimulation, flexible liking and disliking responses allow for behavioural responses that are tailored to the organism’s fluctuating survival needs and changes in the environment it inhabits. Centrally, liking and disliking outcomes are flexible because sensory liking evaluations consist of largescale integration of computational mechanisms that represent these contextual conditions (Figure 2.5). As my review of experimental findings demonstrates, how much a stimulus is liked or disliked in a given situation depends upon the specific, combined contribution from all, or most, of these mechanisms. If a stimulus is unknown, it can be less liked than if it is well known. If the organism has previously liked a stimulus, that experience boosts expectations that the stimulus will also be found pleasant in the current situation, and so on. Individual computational mechanisms make contributions that are modulated by the particular state of the evaluative event. It is therefore misleading to conceive of sensory liking evaluations as reflexive reactions to stimulus information. Sensory liking evaluations are temporal events where information from multiple computational nodes is projected back and forth under influence of both the stimulus and the evaluative context. This revised explanatory framework, where sensory liking evaluations are understood as computational events with variable inputs from different mechanisms, raises the question whether it is possible to categorize evaluation events according to their function. In other words, instead of characterizing the function of different forms of sensory liking evaluation by basing definitions on the eliciting stimulus—as we normally do by speaking of food hedonics, sexual preferences, economic utility, or art appreciation—it may make more analytical sense to see different sensory liking evaluations as determined by properties inherent to individual events. For instance, some evaluation events might be more reliant on projections from mechanisms representing energy homeostasis (e.g., when considering what to make for lunch), while others may engage mechanisms representing endocrinological levels to a higher degree (e.g., when looking though a dating app for potential dates). To date, little work, however, has been conducted to answer this interesting question. A shift in focus from the stimulus to evaluative events could be a way for the field of empirical aesthetics to make progress on the longstanding question of whether “aesthetic evaluations” constitute a sui generis form of sensory liking (see Chapter 1). Attempts to define aesthetic liking as distinct from other kinds of 51

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Figure 2.5 Model of computational mechanisms known to be involved in human sensory liking evaluations, based on the empirical evidence reviewed in the chapter. Activation of each mechanism holds the power to modulate liking and disliking for a given stimulus. Current evidence suggests that the specific way the whole system of computational nodes is activated during an evaluative event depends on physiological, environmental, and behavioural circumstances. For example, engagement of processes coding for perceptual and reward predictions is a function of the organism’s previous experience with the stimulus being evaluated. Similarly, whether the organism attends to the stimulus with the express purpose of assessing if it fits task requirements of a prospective behavioural act or if it “passively” computes the object’s hedonic value while focused on another task is determined by the contextual conditions of the evaluation event. Projections from mechanisms representing endogenous and exogenous states of relevance to the individual evaluative event project to neural mechanisms in the evaluative system that code for pleasure, displeasure, and motivational outputs. Liking and disliking outcomes emerge as a result of the connected pattern of neural activity.

sensory liking have traditionally motivated their arguments by appealing to the idea that aesthetic “qualities” constitute a special class of stimulus properties. For example, aesthetic evaluations have been considered distinct because they are directed at certain stimulus features (e.g., Menninghaus et al., 2019). Other theories have proposed that aesthetic stimuli “afford” specific evaluative responses, including especially intense states of pleasure (e.g., Makin, 2017) or more contemplative hedonic states of “being moved” (e.g., Vessel, 2020). However, examining the scientific evidence accumulated over the last 20 years, reveals no empirical support for such claims (e.g., Skov & Nadal, 2020, 2022). An alternative hypothesis could be that certain evaluative events qualify as aesthetic. There is for instance a substantial amount of evidence that emotional responses are attenuated during certain types of sensory liking evaluations. Thus, the encoding of fear appears to be diminished in evaluative situations where people expect the stimulus to be fictive (e.g., Mocaiber et al., 2010; Van Dongen et al., 2016). Similarly, it has been hypothesized that pleasure responses are not accompanied by normal motivational outputs when they occur in the context of liking evaluations people believe are directed at art stimuli (e.g., Sarasso et al., 2020; 52

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but see Skov & Nadal, 2022). If this idea turns out to be true—and the jury is still out on whether it is—it might make theoretical sense to designate evaluation events where motive responses are regulated as a consequence of the belief that potential liking and disliking outcomes do not concern survival needs as “aesthetic” evaluations. Of course, neither emotional regulation nor stimulus attention is a computational mechanism that is unique to the representation of specific behavioural contexts in the same way energy homeostasis might be unique to the representation of food consumption or gonadal hormone levels might be unique to the representation of sexual courtship and reproduction. Regulation of adaptive emotions seems equally important to sensory liking evaluations that take place in service of the appreciation of horror movies and evaluations that occur in the context of enjoying fermented cheeses. Whether we feel the need to think of sensory liking evaluations that rely especially on the attenuation of adaptive emotions as “aesthetic” rests entirely on how we define the concept of aesthetics. In any case, understanding why a sensory stimulus is liked or disliked involves factoring in the computational context of the individual evaluation event. Sensory liking always serves physiological and behavioural needs. The human brain has evolved a large number of neural mechanisms that respond to the varying conditions of different evaluation events. Only by understanding the way these mechanisms communicate with each other and integrate information relative to the circumstances of individual events can we hope to develop a computational theory of why a stimulus become liked or disliked.

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3 THE NEUROBIOLOGY OF LIKING Eloise Stark, Kent C. Berridge and Morten L. Kringelbach

In order to sustain human life, it is an evolutionary imperative that stimuli which promote individual and species survival (such as infant care, food, and sex) are prioritized above less relevant or less important stimuli (Berridge & Kringelbach, 2008, 2015). It is therefore logical to assume that there must be an affective core within the brain, which has the role of paying attention to and evaluating stimuli, assessing their valence as positive or negative, and making them available for conscious appraisal and decision-making (Barrett et al., 2007; Frijda, 1986; Kringelbach & Phillips, 2014; Russell, 2003). The affective valence of a stimulus or event is the quality it has of being perceived as “good” versus “bad,” or hedonically “liked” or “disliked.” The key here is the process of transforming perception into affect, which is an inferential process that actively generates the valence based upon past experience and present context (Friston et al., 2006; see also Chapter 2). This percept is actively generated by the brain and is subsequently translated into hedonic and motivational aspects of valenced reaction (Berridge & Kringelbach, 2015). In this chapter, we concern ourselves with the hedonic impact of a stimulus, namely “liking.” “Liking” is a crucial component of both reward and emotion and is fundamental to human survival by encouraging approach behaviour and consummation of basic rewards, such as food and sex, and higher-order rewards, such as music or visual arts (Kringelbach & Berridge, 2010). Affective valence can be experienced consciously as a subjective experience, which is hard to capture except via subjective self-report but also influences both behaviour and physiology, which can be measured objectively. For example, we can tell whether newborn infants “like” the taste of different foods by giving them a taste and watching their behavioural response. Infants demonstrate facial expressions of “liking” in response to pleasurable tastes, for example, indicated by a relaxed facial expression and rhythmic tongue and mouth movements (Steiner, 1973). We can contrast this to bitter tastes, which elicit “disgust” reactions indicated by a gaping mouth and turning one’s body away. These affective facial expressions to different tastes are conserved across several species, including human infants, apes and monkeys, and even rats (Grill & Norgren, 1978; Steiner et al., 2001). The objective behavioural or physiological reaction may occur with or without conscious subjective feelings (Anderson & Adolphs, 2014; Berridge, 2018; Damasio & Carvalho, 2013; Frijda & Parrott, 2011; Winkielman et al., 2005; Winkielman & Gogolushko, 2018). It is therefore important to distinguish between the two elements of positive affective valence by denoting objective affective reactions, or the core hedonic impact, with quotation marks (“liking”) and subjective hedonic feelings as liking.

DOI: 10.4324/9781003008675-4

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In this chapter, we will review the evidence for the “liking” phase of the Pleasure Cycle and explore how positive affective valence is constructed by the brain. The research clearly shows that hedonic hotspots and coldspots mediate “liking” within the brain and dispel the popular myth that this is a process controlled by dopamine, which is more related to “wanting.” Finally, we build on Aristotle’s distinction between “hedonia” (pleasure) and “eudaimonia” to suggest that eudaimonia and human flourishing might usefully be construed as a brain state which could perhaps best be described as “meaningful pleasure.”

The Pleasure Cycle: “wanting,” “liking,” and “satiety” The Pleasure Cycle can be used to conceptualize the “wanting,” “liking,” and “satiety” from pleasurable stimuli (see Figure 3.1A). Each element has a dissociable neurobiological basis which contributes in a timevarying and interactive way. These phases are not necessarily mutually exclusive, as “liking” and “wanting” can simultaneously overlap. However, each phase may dominate in turn at different temporal moments. The focus of this chapter is on the “liking” phase, which follows on from “wanting” and involves consummation of a stimulus and online evaluation of the stimuli’s ongoing reward value. This is the part of reward processing during which pleasure is at its highest, and may even involve a peak, such as the experience of orgasms (Georgiadis & Kringelbach, 2012; Georgiadis et al., 2012) and “chills” when listening to music (Blood & Zatorre, 2001; Laeng et al., 2016; Panksepp, 1995). Before we go into the neurobiological basis of the “liking” phase, it is important to dispel the common myth that this is coordinated by the brain’s mesolimbic dopamine system. The evidence clearly shows that dopamine is neither necessary nor sufficient for the generation of “liking” within the brain. In particular, the facial expressions that denote a “liking” response are not altered by suppression or activation of mesolimbic dopamine systems. In rodent studies, while intra-accumbens dopamine agonists (e.g., amphetamine microinjections) lead to increases in “wanting,” “liking” remains unaffected by such changes (Wyvell & Berridge, 2000). Other studies have used dopamine antagonists, such as neuroleptic drugs, finding that while “wanting” is decreased, there are no changes in “liking” (Peciña et al., 1997). Large 6-hydroxydopamine (6-OHDA) lesions, which cause vast destruction of ascending dopamine neurons, lead to profound aphagia (refusal to swallow), suggesting diminished “wanting,” but have consistently failed to suppress hedonic reaction patterns to sweet tastes indicative of “liking” (Berridge et al., 1989). Likewise in humans, dopamine-receptor antagonists frequently fail to suppress subjective ratings of pleasure for cigarettes (Brauer et al., 2001), cocaine (Leyton et al., 2005), or amphetamines (Wachtel et al., 2002). Together, these findings strongly contradict the popular view that dopamine is the “neurotransmitter of pleasure,” in the sense that it does not appear to mediate what most people associate most with a pleasurable response: the subjective hedonic value, or “liking.” The true function of dopamine is beyond the scope of the current chapter, but indications are that it has an important role in the “wanting” phase of the pleasure cycle to provide incentive salience to important stimuli (Berridge & Kringelbach, 2008).

Hedonic hotspots and coldspots The brain system subserving “liking” reactions and generating intense pleasure is a far smaller and more functionally fragile system than the sizeable and robust “wanting” system in the brain. The generation of “liking” is more restricted than wanting, both neurochemically and anatomically. Neurochemically, opioid stimulation (but not dopamine stimulation) in specific subregions of brain structures can enhance “liking,” whereas “wanting” is enhanced by both. Anatomically, “liking” is enhanced by opioid-stimulating microinjections in subregional “hedonic hotspots” within an anatomical structure but not in remaining subregions of the same anatomic structure—even if stimulation anywhere in the entire structure can enhance “wanting.” The generation of a “liking” response is also more restricted as a brain circuit, requiring unanimous activation of 64

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Figure 3.1 The Pleasure Cycle. (A) Fundamental (i.e., rewards associated with behaviour necessary for survival of an individual or the species) and higher-order pleasures are associated with a cyclical time course. Typically, rewarding moments go through a phase of expectation or wanting for a reward, which sometimes leads to a phase of consummation or liking of the reward which can have a peak level of pleasure (e.g., encountering a loved one, a tasty meal, sexual orgasm, drug rush, winning a gambling bet). This can be followed by a satiety or learning phase, where one learns and updates predictions for the reward, but note that learning obviously can take place throughout the cycle. These various phases have been identified at many levels of investigation; for example, recent research on the computational mechanisms underlying prediction, evaluation, and prediction error are particularly interesting. Note, however, that a very few rewards might possibly lack a satiety phase (suggested candidates for brief or missing satiety phase have included money, some abstract rewards, and some drug and brain stimulation rewards that activate dopamine systems rather directly). (B) The hedonic hotspots (red) and coldspots (blue) in the rat brain, shown on the coronal, sagittal, and horizontal planes and in 3D fronto-lateral perspective view (clockwise from top left). (C) Similarly, the rendering shows putative human hotspots, extrapolated from neuroimaging literature and the rat causal hotspots. In the perspective views, the tentative interconnected networks between the different hotspots and coldspots have been added to give an impression of the topology of the pleasure network.

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multiple hotspots simultaneously, whereas “wanting” can be enhanced by a single hotspot. In short, enhancement of pleasure “liking” is restricted and fragile, and brain pleasure systems are relatively recalcitrant to activation compared to “wanting” systems. Consequently, our brain mechanisms may consign us more often to states of desire than of pleasure. The “hedonic hotspots” that mediate “liking” anatomically have been compared to “islands” of brain tissue contained within the larger “sea” of a full limbic structure, such as the nucleus accumbens or ventral pallidum (Smith et al., 2010). The size of each hotspot discovered so far is only approximately one cubic millimetre in volume of the brain of a rat. In the human brain, a hotspot is expected to be about a cubic centimetre in volume, extrapolated by considering the difference in ratio between the whole-brain size of rats and humans. One key methodological advance in exploring these hedonic hotspots was the finding that they can generate increases in pleasure “liking” reactions to sweetness when stimulated with apposite neurochemical microinjections (Berridge, 2019). For instance, pleasure-enhancing neurochemicals stimulate opioid receptors, which detect heroin-like neurochemicals in the hotspots. Or they may stimulate endocannabinoid receptors that detect marijuana-like neurochemicals. Importantly, no “liking” enhancement occurs if the same drug microinjections are moved outside the precise boundaries of the hedonic hotspots, even if they are administered within the same brain structure. If this does happen, and the regions surrounding the hotspots are stimulated instead, the microinjections uniformly stimulate intense “wanting” but without evidence of enhanced “liking.” Several of these hedonic hotspots that mediate the “liking” response have been found, dispersed through the rodent brain from the cortex to the brainstem (see Figure 3.1B). They appear to be functionally interconnected like an archipelago of interacting islands. Hotspots are found in the limbic prefrontal cortex, nucleus accumbens, ventral pallidum (the chief target of nucleus accumbens), and the brainstem pons. The entire network may need to activate together as a single integrated circuit in order to magnify sensory pleasures, which thus involves collaboration between the various hotspots. For example, activation of one hotspot by an opioid microinjection automatically recruits activation in other hotspots in different brain structures (Smith & Berridge, 2007). Pleasure magnification requires coordination among all opioid hotspots in the nucleus accumbens and ventral pallidum (Smith & Berridge, 2007). If this coordination is prevented by the suppression of another hotspot with an opioid-opposing drug, a simple hotspot opioid activation will not boost pleasure. Although opioid stimulation of either hotspot would normally be sufficient to increase “liking,” this will not be the case if the larger circuit is not activated. Even if “liking” enhancement is disabled, stimulation of “wanting” persists after either hotspot is triggered. Thus, while partial activation of the limbic circuit is sufficient to induce strong desire, complete activation is required to generate intense pleasure. A growing corpus of research in humans has identified that many rewards, as different and diverse as species-specific pleasures such music and art, compared with fundamental pleasures such as sex and food, all activate and share a reward network comprised of overlapping brain regions (Cacioppo et  al., 2012; Georgiadis & Kringelbach, 2012; Kringelbach et al., 2012; Salimpoor et al., 2011; Vartanian & Skov, 2014; Veldhuizen et al., 2010; Vuust et al., 2021; Vuust & Kringelbach, 2010; this literature is further discussed in Chapters 7–12) (Figure 3.1C). This has sometimes been called the “common currency” reward network, of which one important implication is that experiments exploring one type of pleasure, such as food and sex, should apply to other kinds of pleasure, such as species-specific pleasures like music. This strongly argues against the view of lower and higher pleasures (Crisp & Kringelbach, 2017). Regions within this “common currency” network include anatomical regions of the prefrontal cortex, such as the anterior cingulate, insula, and orbitofrontal cortices, as well as subcortical structures including the ventral pallidum, amygdala, and nucleus accumbens.

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Anhedonia Anhedonia refers to a absence or severe reduction in the experience of pleasure and is a significant feature of neuropsychiatric disorders such as mood disorders, eating disorders, and addiction (Rømer Thomsen et al., 2015). It constitutes a significant burden to such disorders; for example, anhedonia in the context of depression is a predictor of poor treatment response (Spijker et al., 2001), and in addiction it is a predictor of relapse (Koob and Le Moal, 2001; Volkow et al., 2002). It is prescient to study anhedonia as a transdiagnostic symptom, given that it is arguably more likely to be linked to a specific neurobiological underpinning than heterogeneous psychiatric diagnostic categories, such as schizophrenia (Hyman & Fenton, 2003; Insel et al., 2010). The different components of the pleasure cycle require efficient state transitions to move between the different phases, such as from “wanting” to consummation and “liking.” It has been proposed that anhedonia in affective disorders results from perturbations to the orchestration of such state transitions (Rømer Thomsen et al., 2015). To illustrate this proposal, one example of a perturbation to the pleasure system comes from rodent studies where hedonic hotspots are selectively damaged. If you ablate the hedonic hotspot in rodent posterior ventral pallidum, these animals will no longer display positive hedonic reactions when given sweettasting foods, instead displaying a “disliking” reaction such as mouth gapes, which are usually reserved for bitter or noxious tastes (Aldridge & Berridge, 2010; Cromwell & Berridge, 1993; Khan et al., 2020). One case study of a human who underwent a hypoxic episode and subsequent bilateral lesions of the globus pallidus also illustrates this perturbation to the pleasure system, as the patient subsequently reported anhedonia, including a diminished pleasurable response to alcohol (Miller et al., 2006).

From pleasure to meaningful pleasure and human flourishing The transition from the experience of pleasure to well-being is not straightforward (Kringelbach & Berridge, 2009). A lot of pleasure rarely, if ever, translates into states of well-being. Rather, excessive pleasure seeking can often get stuck in maladaptive cyclical addictive behaviours which are seldom pleasurable over the long term. Aristotle distinguished between hedonia and eudaimonia, where the latter is perhaps best thought of as virtue or trait: a way to live well, thrive, and, in the end, have a good life. Equally, however, it also possible to think of eudaimonia and human flourishing as meaningful experiences which tend to be fleeting, transitory states which are hard to reliably invoke. Take for example patients with a life-threatening cancer diagnosis who can be helped by a small dose of psychedelics (Grob et al., 2011). Such carefully controlled psychedelic experiences are often rated by people as among their five most meaningful experiences (Griffiths et al., 2008; Griffiths et al., 2011). As an example, one participant reported “to ‘let go’ and become enveloped in the beauty of—in this case music—was enormously spiritual.” Another reported “I realised I was glad to be alive. I’ve always thought I wouldn’t be able to feel that.” This state of eudaimonia is clearly important to ease these patients’ severe depression and anxiety but, given their transitory nature, difficult to study with scientific means. Over the last few years, we have started to investigate robust ways of getting human participants engaged in meaningful eudaimonic experiences, which has involved special practices and stimuli, including psychedelics but also social interactions, music, and meditation. This experimental approach is then combined with sophisticated whole-brain modelling of neuroimaging data, which allows us to draw causal mechanistic inferences on the underlying brain mechanisms and networks (Deco et al., 2015; Kringelbach & Deco, 2020). Over the coming years we hope to make significant progress in understanding how and why humans are optimized to search for meaning in everything we encounter. Beyond fundamental pleasures keeping us alive,

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we have a deep need to discover meaning in our interactions with the world through our senses, interpreting neural signals from the enchanted loom of the billions of neurons constituting our brain. As such we go through the cycles of life (Ahrends et al., 2021), constantly predicting what might happen and extracting meaning from the events we encounter and appraising them with further meaning. The arts exemplify this process: we inject meaning and complexity into the music, dance, visual art, and poetry we encounter through anticipatory frameworks and neuroaesthetic reactions. A “meaningful” life is one of the distinguishing characteristics of eudaimonia. Importantly, many people claim that they cannot live without music or the arts, that they value it and that it provides them with more than just pleasure. Perhaps art is only incidental, but it may also endure because it exploits the fundamental nature of what makes us human.

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4 DISLIKING From adaptive disgust to ugliness Christoph Klebl, Michael Donner and Indra Bishnoi

Humans and other animals have evolved various mechanisms to deal with threats. For example, the emotion fear has evolved to help us defend against imminent physical threat posed by predators (e.g., lions or snakes), aggressive conspecifics or heights by motivating a fight-or-flight response (LaBar, 2016). Another major selection pressure that has been posed on the evolution of all species, including humans, is threat from pathogens—that is, organisms such as viruses and bacteria that can cause disease ( Janeway et al., 2001; Wolfe et al., 2007). Pathogen threat differs from physical threat posed by predators or aggressive conspecifics because pathogens cannot travel large distances easily. In order to infect a new organism, pathogens often require physical contact between the past and future host through their mouths, skins, anuses or genitals (Tybur et al., 2013). In response to these recurring threats, natural selection has fashioned humans (as well as other animals) with a suite of flexible systems that function to reduce the probability of pathogen infection. Most importantly, animals have evolved immune systems—complex network of cells, proteins and tissues that remove pathogens from the body ( Janeway et  al., 2001)—in order to defend their bodies against diseases. The human immune system is very flexible and can acquire immune responses to defend against novel pathogens ( Janeway et al., 2001). It is, however, energetically costly and might not be able to successfully fight novel pathogens at first exposure. Therefore, humans have evolved other mechanisms—particularly the emotion disgust—which serve as an early line of defence against pathogens (Tybur et al., 2013).

Pathogen disgust With broad agreement that the emotion disgust functions to facilitate disease avoidance (Curtis et al., 2011; Oaten et al., 2009; Rozin et al., 2008; Tybur et al., 2013), it is unsurprising that research into the physiological, psychological and behavioural mechanisms underlying pathogen avoidance has largely been guided by research on disgust. Much of the contemporary theorizing about the function of disgust has been informed by careful consideration of the range of cues that elicit disgust (for a review see, Stevenson et al., 2019). Though there is some disagreement about the number of different elicitor domains of disgust (and thus disagreement about the different functions of disgust), theorists generally agree that disgust is elicited by diseaserelated cues (e.g., rotting flesh, blood, faeces) and that detection of these cues initiates withdrawal responses including oral expulsion (e.g., spitting and vomiting) as well as facial movements that guard the face, mouth and eyes (e.g., wrinkled nose, lip retraction and squinted eyes) (for a review, see Oaten et al., 2009). Disgust DOI: 10.4324/9781003008675-5

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is also characterized by feelings of nausea, prompted as a result of ingesting (or coming close to ingesting) pathogenic substances, as well as decreased heart rate and increased skin conductance response (Stark, Walter et al., 2005). Perhaps most importantly, disgust facilitates the avoidance of physical contact with the disgust elicitor (i.e., removing or distancing oneself from the presence of pathogens), thus neutralizing the possibility of pathogen transmission. These functional, behavioural, physiological and phenomenological properties capture what is commonly referred to as pathogen disgust (the term that we also adopt in this chapter; Tybur et al., 2013) and what others have termed core disgust (Rozin et al., 2008). It is important to note, however, that the mere presence of pathogens does not always lead to a pathogen disgust response in all contexts. Following Tybur et al.’s (2013) evolutionary-computational approach to the emotion of disgust, estimates of the cost of contact with a pathogen elicitor are integrated with estimates of the costs of avoiding contact with the pathogen elicitor (i.e., the benefits associated with contact). For example, the caloric benefit of eating decayed food might outweigh the potential costs of pathogen contaminants in the food for someone in a state of acute hunger. Similarly, mothers are less disgusted by their own baby’s faeces-soiled diapers compared to those of some else’s baby (Case et al., 2006), and people are more comfortable with contacting infectious individuals whom they value interpersonally (Tybur et al., 2020). Thus, whilst the overall function of the pathogen disgust system is to coordinate the avoidance of infectious disease vectors, the system also evaluates a range of other inputs that might confer benefits to an individual, thus allowing flexible responding.

Neurobiology of pathogen disgust The anterior insular cortex is the primary gustatory cortex (see Figure 4.1), involved in taste and flavour (Rolls & Baylis, 1994). As pathogens often require physical contact through orifices like the mouth for infection (Tybur et al., 2013), this cortex has been associated with pathogen-related disgust in mammals (Tuerke et al., 2012; Wicker et al., 2003). The anterior insular cortex takes sensory input and converts it to physiological sensations of disgust, such as nausea and vomiting (Wicker et al., 2003). In mice, the optogenetic activation of the anterior insular cortex elicits disgust-related responses, such as facial expressions (Dolensek et al., 2020). Additionally, lesioning the insular cortex prevents conditioned gaping, a disgust behaviour in rats (Kiefer & Orr, 1992). In humans, electrical stimulation of the anterior insular cortex triggers nausea and sensations of sickness (Krolak-Salmon et al., 2003; Penfield & Faulk, 1955). In functional magnetic resonance imaging (fMRI) studies, visually eliciting pathogen disgust leads to an increase in activation of the insular cortex and

Figure 4.1 Illustration of the human brain with the bilateral insula highlighted in dark orange. The insula is located deep within the lateral sulcus separating the temporal lobe from the parietal and frontal lobes.

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the amygdala (Regenbogen et  al., 2017; Wright et  al., 2004), the brain region responsible for emotional responses and emotion-based learning and memory (Gallagher & Chiba, 1996; Maren, 1999). Projections from the anterior insular cortex to the medial and basolateral amygdala have been found to be involved in disgust-related behaviours, such as aversive taste learning (Kayyal et al., 2019). This relationship may be mediated by the elevation of serotonin in the insular cortex after toxin exposure (Limebeer et al., 2018; Tuerke et  al., 2012). Furthermore, the area postrema, located in the brainstem, is an important brain region for toxin disgust specifically because of its structure, which has a thin blood brain barrier, making it particularly easy for blood borne toxins to infect this region (Dempsey, 1973). Gastrointestinal defence mechanisms like nausea and vomiting are triggered by the activation of the area postrema in humans and rodents (Borison, 1989; Miller & Leslie, 1994), such that lesions to the area postrema inhibit toxin-induced conditioned disgust behaviours in rodents (Eckel & Ossenkopp, 1993; Ossenkopp & Eckel, 1995; Ossenkopp et al., 1994). The induction and expression of disgust involves a number of brain regions within the social decisionmaking network (SDMN). The SDMN merges the mesolimbic reward network with the social brain network (Newman, 1999; O’Connell & Hofmann, 2011), which includes the cingulate and prefrontal cortices, nucleus accumbens, thalamus, dorsal hippocampus, paraventricular nucleus of the hypothalamus, ventral tegmental area, insular cortex, amygdala and sensory regions such as the piriform cortex and occipital lobe (Becker et al., 2016; Borg et al., 2013; Goodson, 2013; Johnson et al., 2017; Marlin & Froemke, 2017). These regions are responsible for encoding sensory and social cues that are useful for the expression of disgust behaviours and their respective reward values (Kavaliers et al., 2021). It is likely that pathogen disgust, as well as sexual and moral disgust, is elicited by coordinating across both overlapping and distinct regions within this network.

Sexual disgust Whilst there is a strong correspondence between cues that indicate disease and cues that elicit disgust, many theorists have suggested that cues that do not contain obvious traces of disease can also activate a disgust response (Ackerman et al., 2007; Lieberman et al., 2007; Rozin et al., 1999, 2008; Tybur et al., 2009, 2013). Indeed, it has been argued that the role of disgust has been expanded, through the evolutionary process of exaptation, to certain components of sexuality (e.g., incest, unwelcomed sexual attention). Building on the pre-existing pathogen disgust system, a sexual disgust system evolved to perform the function of avoiding contact with sexual partners who might jeopardize one’s reproductive fitness (Tybur et al., 2013). Similar to the computational structure of pathogen disgust, the sexual disgust system is designed to estimate the fitness value of an individual as a sexual partner by weighing up the potential costs (e.g., exposure to pathogens through intercourse) and benefits (e.g., reproduction) of sexual contact. From this perspective, every sexual encounter carries with it the risk of novel pathogen exposure, and these costs can potentially outweigh any reproductive benefits associated with having an unrestricted mating strategy (i.e., multiple sexual partners; Tybur et  al., 2013, 2015). Following this logic, sexual contact that risks producing unhealthy offspring (i.e., sex with kin, sex with the elderly) is particularly likely to elicit sexual disgust (Tybur et al., 2013). Thus, to avoid individuals posing a reproductive threat, the sexual disgust system should be particularly sensitive to input cues associated with sex value such as health, age and attractiveness (among others) (Grammer et al., 2003; Thornhill & Gangestad, 2006). Given that the nature of the inputs for the sexual disgust system are qualitatively different from the inputs for the pathogen disgust system (due to their somewhat distinct adaptive problems), pathogen and sexual disgust likely have somewhat different functional, behavioural, physiological and phenomenological properties (Tybur et al., 2013). Nevertheless, while sexual and pathogen disgust are somewhat distinct from each other, they likely overlap in everyday disgust experiences. For example, people report feeling disgusted towards potential sexual partners who display symptoms of illness (Buss & Schmitt, 1993; Symons, 1979). Moreover, across mammalian species, attraction towards a potential 73

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partner is hindered when paired with a potential pathogen threat ( Jones et al., 2013; Kavaliers et al., 2021; Kavaliers et al., 2014). Activation of select regions within the SDMN has been observed in functional magnetic resonance studies where participants were exposed to images of non-preferred sexual stimuli, that is, erotic images of partners of the opposite sexual orientation, hardcore pornographic stimuli and sadomasochism. Specifically, the prefrontal cortex, amygdala, nucleus accumbens, hippocampus, thalamus and olfactory regions are activated during feelings of sexual disgust (Borg et al., 2014; Stark, Schienle et al., 2005; Zhang et al., 2011). Hormonal modulation has widespread effects between these regions (Kavaliers et al., 2021). Male mice with higher testosterone levels, due to sexual arousal and/or higher social hierarchy, show a decreased avoidance of pathogen threat and higher parasite levels (Barnard et al., 1998; Kavaliers et al., 2001; Kavaliers & Choleris, 2018). Additionally, females, who are more susceptible to direct sexually acquired infection, display stronger physiological disgust and greater avoidance behaviours towards infectious conspecifics (Kavaliers et al., 2019; Poirotte & Kappeler, 2019).

Moral disgust A final category of disgust elicitors that is of interest involves moral norm violations. Given what we know about the evolved functions of both pathogen and sexual disgust, it should be unsurprising that certain pathogen-related moral transgressions (e.g., eating worms) and sex-related moral transgressions (e.g., masturbation, homosexuality) elicit disgust. What is striking, however, is that people report feeling disgusted, as well as displaying the canonical disgust facial expression, to moralized actions that are free of pathogen and sex stimuli such as fraud, theft, physical assault and disloyalty (Chapman et al., 2009; Hutcherson & Gross, 2011; Rozin et al., 2008; Tybur et al., 2009, 2013). A key question here is whether these moral violations activate a disgust response (with its signature functional, behavioural, physiological and phenomenological properties) or whether these responses resemble a metaphorical usage of disgust, a contentious issue that has been debated for many years (e.g., Bloom, 2004; Nabi, 2002; Royzman & Kurzban, 2011). Setting this issue aside, it has been proposed by Tybur and colleagues (2009, 2013) that moral disgust functions to coordinate condemnation of people who violate moral rules. In this view, expressing disgust vocally or facially communicates one’s disapproval of a given behaviour (Giner-Sorolla et al., 2018; Kupfer & Giner-Sorolla, 2016; Royzman & Kurzban, 2011; Tybur et al., 2013). An ability to detect others’ expressions of disgust as communicating condemnation also plays a key role in reputation management. Given that maintaining a reputation as a valuable community member is essential for the evolution of cooperation (Nowak & Sigmund, 2005; Trivers, 1972), understanding others’ signals of condemnation (e.g., expressions of moral disgust) enables one to avoid the individual fitness costs associated with punishment and social exclusion (and reap the individual benefits of cooperation). With enough support, these expressions of moral disgust can be effective at coordinating punishment and condemnation of socially undesirable behaviours. The medial prefrontal cortex has been found to be uniquely related to moral disgust. However, despite this and its unique function, moral disgust has been found to share certain brain regions related to the processing of pathogen and sexual disgust, such as the insula and the cingulate cortex (Borg et al., 2008). These brain regions are activated when participants view photos of moral transgressions, including incest, public indecency and murder (Borg et al., 2008; Vicario et al., 2017; Ying et al., 2018). Interestingly, similar facial expressions are evoked by a disgusting taste, observation of a contaminant and unfair treatment in an economic game (Chapman et al., 2009). Additionally, thinking about moral transgressions leads individuals to perceive a neutral-tasting beverage as disgusting (Eskine et al., 2012). As such, while both neurobiologically unique and overlapping regions bring about moral disgust, the resulting behavioural and physiological output is similar to that of other types of disgust. The roles of the insula and medial prefrontal cortex in moral disgust may be to bring about these physiological disgust behaviours and moral cognition and decision-making, 74

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respectively, while the cingulate cortex has been theorized to play a role in moral reasoning (Fumagalli & Priori, 2012; Koenigs et al., 2007; Sevinc & Spreng, 2014; Vicario et al., 2017). Before moving on, it is worthwhile acknowledging that whilst we have attempted to cover some of the key functions and elicitor domains of the emotion disgust that are common to most theoretical models of disgust, there have been other domains proposed that arguably do not cleanly map onto the three disgust categories discussed previously (pathogen, sexual and moral). Of primary relevance here is the specific category of contamination disgust found in Rozin and colleagues’ (1987, 1994, 2008) account of disgust (Haidt, 2001), as well as Olatunji and colleagues’ (2007) revised model of Rozin’s account. Contamination disgust represents the experience of disgust toward a neutral stimulus (e.g., toothbrush) that makes physical (and even imagined) contact with a disgusting stimulus (e.g., faeces). Given this close relationship between contamination and disgust, others have argued that rather than representing a domain of disgust altogether, contamination is best thought of as an accompanying feature of the pathogen disgust system (Tybur et al., 2013).

Individual differences in disgust sensitivity Validation of the various categories of disgust has been examined using measures of individual differences in disgust sensitivity. Tybur and colleagues (2009) developed the Three Domain Disgust Scale (TDDS) to assess individual differences in sensitivity to pathogen, sexual and moral disgust. In line with their general physiological overlap, individuals with higher sensitivity to pathogen, sexual and moral disgust, on average, experience more intense disgust in each domain. However, factor analyses of self-report data from multiple studies confirm the three-factor disgust model (DeBruine et al., 2010; Olatunji et al., 2012; Tybur et al., 2009, 2011), suggesting that pathogen, sexual and moral disgust may be functionally distinct categories. In addition, varying sex differences on these individual difference measures of disgust also indicate that there might be qualitative differences between the domains of disgust. Specifically, although women, on average, report greater disgust responses than men across all disgust domains, the sex difference is markedly larger in the sexual disgust domain compared to the pathogen and moral domains (Al-Shawaf et al., 2018; Fleischman, 2014; Tybur et al., 2009)—a finding that is consistent with theorizing about the disparate (sexual) selection pressures faced by males and females (for reviews, see Buss & Schmitt, 1993; Trivers, 1972). Finally, evidence that pathogen, sexual and moral disgust are somewhat distinct can also be found by considering the divergent correlations they have with other self-report measures, such as the Big Five personality traits (for reviews, see Tybur et al., 2009, 2013; Tybur & Karinen, 2018), further supporting the notion that there may be functionally distinct domains of disgust (Tybur et al., 2013).

The social consequences of the disease-avoidance system In the previous sections, we have argued that the disgust systems serve to motivate avoidance of pathogens and costly sexual encounters and to potentially coordinate reactions to moral infractions. Due to the high costs of failing to detect a threat when one is present, the signal detection process in each disgust system— pathogen, sexual and moral—is hypersensitive to treating a range of cues as sources of threat. More precisely, the disease-avoidance system evolved to minimize errors that would lead to the greatest costs to one’s fitness (Haselton & Buss, 2000). For example, as it is potentially fatal to judge an infected person to be healthy and comparably less costly to judge a healthy person to be infectious, the disease-avoidance system is biased toward avoiding the former error (i.e., false positives; Schaller & Park, 2011). The disease-avoidance system has been argued to be implicated in the stigmatization of individuals that are perceived to pose the risk of transmitting infectious diseases. Particularly individuals that have visible cues of infectious diseases, such as people who suffer from leprosy, are at risk of being subjected to stigmatization (Kurzban & Leary, 2001). However, due to its over-inclusiveness, the disease-avoidance system can also be 75

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activated by people who do not have a contagious disease (Kurzban & Leary, 2001). For example, being perceived to be a source of novel pathogens or being less familiar with cultural norms (Schaller  & Park, 2011) can be perceived to pose a greater disease risk, potentially leading to the stigmatization of outgroup members. Both dispositional and situational feelings of vulnerability to disease positively predict xenophobic attitudes (Faulkner et al., 2004), and women report greater ethnocentric attitudes in the first trimester of their pregnancies—where the maternal and fetal vulnerability to disease is the greatest—compared to the second and third trimester of their pregnancies (Navarrete et  al., 2007). Furthermore, people who have a greater dispositional proneness to experience disgust tend to intuitively disapprove of gay people more strongly (Inbar et al., 2009). It has been argued that the disease-avoidance system also underlies the stigmatization of people that are perceived as unattractive, particularly those with facial and bodily disfigurement (Park et al., 2003). People with facial and bodily differences are avoided in everyday social interactions, leading to them feeling marginalized (Goffman, 1963). For example, people approach individuals with facial disfigurement less often (Kapp-Simon  & McGuire, 1997), they keep greater physical distance from them (Rumsey et  al., 1982) and they exhibit a greater cardiovascular reactivity when interacting with them (Blascovich et al., 2001), compared to individuals without facial disfigurement. Studies have found that people feel disgusted toward and physically avoid facially disfigured individuals to the same degree as people who have influenza (Ryan et al., 2012). Additionally, people avoid physical contact with individuals who have physical disabilities, such as facial birthmarks or obesity (Park et al., 2013). Consistent with this, ugly faces elicit greater disgust than average-looking and attractive faces (Klebl et al., 2021). This bias has social consequences for individuals. People not only attribute less socially desirable traits to unattractive compared to attractive individuals (Dion et al., 1972), but unattractive people also face social disadvantages such as lower success in finding employment (Hosoda et al., 2003) and more severe punishments in jury decisions (Efran, 1974). People’s bias against individuals with disfigurements is also reflected in (and perhaps exacerbated by) mainstream media. For example, the majority of all-time top American film villains, but none of the all-time top American film heroes, have dermatologic conditions (Croley et al., 2017). The hypervigilance of the disease-avoidance system (especially to pathogen and sex-related cues) seemingly also extends to people’s abstract social and political attitudes. Indeed, research over the years has demonstrated that the disgust systems play an important role in political orientation (Inbar et al., 2009; Navarrete & Fessler, 2006; Smith et  al., 2011). One particularly influential line of work considers the political sentiments of individuals who vary in their sensitivity to different domains of disgust. This work has reliably demonstrated that individual differences in disgust sensitivity is associated with greater political conservatism (e.g., Inbar et al., 2009, 2012; Terrizzi et al., 2013). Having established this general relationship, researchers have more recently begun to unpack which domains of disgust relate to which dimensions of political conservatism in order to explain the effect. Of relevance here is the finding that pathogen disgust sensitivity is more closely tied to the aspects of conservatism that are concerned with greater adherence to traditional social norms of the ingroup (i.e., the traditionalism facet of right-wing authoritarianism) in contrast to aspects of conservatism that are concerned with negativity towards ethnic outgroups (i.e., social dominance orientation) (Tybur et al., 2016). This relationship was further clarified by a study that found disgust sensitivity (to pathogens) was related to anti-immigration sentiments when immigrants were described as not assimilating to cultural traditions (testing the ingroup norm account), but not when immigrants were described as assimilating to cultural traditions (testing the outgroup avoidance account) (Karinen et al., 2019). Thus, individuals who have a higher propensity to be disgusted by pathogen cues are more likely to endorse ideologies that preserve cultural norms—a key feature of political conservatism. Although researchers have paid close attention to the dimensionality of conservatism when investigating associations between measures of disgust sensitivity and measures of ideology (for a review, see Terrizzi et al., 2013), less attention has been paid to the dimensionality of disgust sensitivity in this relationship. There are a growing number of studies showing that the association 76

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between pathogen disgust sensitivity and ideological preferences may be mediated by sexual disgust sensitivity (i.e., individual sexual reproduction strategies) (Tybur et al., 2015; Billingsley et al., 2018; but see Aarøe et al., 2020). These findings suggest that individuals who are more prone to experiencing pathogen disgust are also more likely to pursue restricted reproductive strategies, perhaps as a means to minimize the threat of pathogen transmission from sexual interactions. The downstream consequences of increased motivations toward sexual restrictiveness are that individuals choose to endorse or oppose particular policies that favour their reproductive interests. Because political conservatism aligns with rules that set out to constrain others’ sexual behaviours (e.g., contraception or abortion), it has been argued that this might explain, at least in part, why dispositional sensitivity to disgust is associated with more politically conservative ideologies (Kurzban et al., 2010; Tybur et al., 2015; Weeden et al., 2008). As such, it is important to consider the dimensionality of disgust in order to gain a better understanding of the motivational underpinnings of ideological commitments and political sentiments. Paralleling the work linking disgust sensitivity with an individual’s overall political orientation is a related field of research demonstrating that disgust is implicated in moral judgments. Research in this tradition was motivated by sentimentalist theories of moral psychology that claim emotions have an important role to play in people’s evaluations of third-party immoral behaviours (Haidt, 2001; Pizarro et al., 2011). Seminal studies in this area found that exposing participants to noxious odours, dirty desk and bitter tastes increases the perceived wrongness of actions that violate a range of moral rules (i.e., care/harm, fairness/cheating, sanctity/ degradation), and these studies have been taken as evidence that disgust plays a causal role in moral judgement (Eskine et al., 2011; Schnall et al., 2008; Ugazio et al., 2012). It is worth noting that the robustness of these effects has been challenged by a recent meta-analysis which found that inductions of state disgust (i.e., temporarily induced disgust) have a very small effect on moral judgements (Landy & Goodwin, 2015). Importantly, however, this body of work is limited by the fact that studies have only used pathogen disgust elicitors to induce state disgust, thereby overlooking the possibility that other types of disgust—namely sexual disgust and moral disgust—might be implicated in moral judgment. Likewise, early work linking trait disgust sensitivity (i.e., individual differences in disgust sensitivity) to harsher ratings of moral judgments were only concerned with measures of pathogen disgust sensitivity (e.g., Chapman & Anderson, 2014; Horberg et al., 2009; Inbar et al., 2009; Jones & Fitness, 2008). Consequently, moral judgments in these studies were interpreted as being (at least partially) based on diseaseavoidance motivations. Driven by theoretical developments in disgust research, more recent work investigating the motivational and emotional roots of moral judgement have paid attention to the dimensionality of disgust sensitivity. Indeed, in a recent meta-analysis (Donner et al., 2020) it was found that sexual disgust sensitivity is more strongly correlated with condemnation towards a variety of moral transgressions than pathogen disgust sensitivity, suggesting that motivations to avoid unwanted sexual contact also have the potential to structure moral attitudes, beliefs and behaviours. Interestingly, the most robust associations were observed between sexual disgust and the moral value domains of sanctity (i.e., upholding spiritual and physical purity) and authority (i.e., respecting hierarchy and tradition), which are among the most robust correlates of political conservatism (Graham et al., 2011, 2013; Kivikangas et al., 2020), thus hinting at possible pathways by which disgust sensitivity may shape broad left–right political orientations (as discussed previously). Neuroimaging studies reveal that social disgust shares SDMN brain regions related to the processing of pathogen, sexual and moral disgust. Viewing faces from other races reveals the importance of the insula, amygdala and anterior cingulate cortex in racially biased disgust perception (Liu et  al., 2015). Viewing images of stigmatized individuals, including overweight, transsexual and pierced faces, also activates these brain regions, in addition to the prefrontal cortex (Krendl et al., 2006). In primates and rodents, the prefrontal–amygdala pathways play a central role in social decision-making and are influenced by oxytocin (OT; Gangopadhyay et al., 2021). OT is a neuropeptide that is produced in the hypothalamus, such as the paraventricular nucleus (Mitre et al., 2016). Acting in SDMN brain regions, especially the medial amygdala, 77

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OT mediates approach and avoidance behavioural responses, as well as parasite recognition (Kavaliers et al., 2006). Within the nucleus accumbens and ventral tegmental area, OT enables social approach behaviours (Steinman et al., 2019). Mice with a deletion of the OT gene, or those treated with an OT antagonist, display an impairment in social recognition and avoidance of parasitic conspecifics (Kavaliers et al., 2003; Kavaliers et al., 2006; Kavaliers, Colwell & Choleris, 2005; Kavaliers, Choleris, Ågmo et al., 2005). While the SDMN is crucial for disgust, regions such as the ventromedial prefrontal cortex (vmPFC) are likely specific to social disgust (Ciaramelli et al., 2013). The vmPFC has been implicated in social cognition, specifically evaluating social information and social preference (Amodio & Frith, 2006; Mitchell et al., 2006). Lesions to the medial prefrontal cortex in macaque monkeys decrease interest in social, but not object, stimuli (Deaner et al., 2005; Rudebeck et al., 2006). In humans, damage to the vmPFC does not impact pathogen or moral disgust but does increase approach behaviour towards stimuli that elicit interpersonal disgust, while healthy individuals display avoidance behaviours towards these stimuli (Ciaramelli et al., 2013). The vmPFC does not activate when non-social stimuli rated as disgusting, like vomit or an overflowing toilet, are viewed (Harris & Fiske, 2006). Instead, viewing socially desirable people, such as college students and rich people, increases the activation of the vmPFC. However, the activation is low when viewing stigmatized individuals rated as disgusting, such as drug addicts and homeless people, indicating a lower social preference (Harris & Fiske, 2006, 2007). Specific regions like the vmPFC, paired with regions in the SDMN, such as the insula and amygdala, likely play a role in the social consequences of disgust (Harris & Fiske, 2006).

Ugliness judgments and the disease-avoidance system In the previous sections, we have outlined the important role that disgust plays in pathogen avoidance, sexual behaviour, social stigmatization and even morality and politics. We now turn to the domain of aesthetic judgments. Understanding the nature of the disease-avoidance system is crucial for a better understanding of the function of aesthetic judgments—especially ugliness judgments. Based on theorizing on the function of beauty which proposes that beauty judgments alert us to objects that increased the chances of survival and reproduction of our ancestors (i.e., have adaptive value; Dutton, 2009), one might assume that ugliness judgments function to alert humans to objects that are detrimental to survival and reproduction. Anecdotally, however, not all dangerous objects are ugly. For example, while most people would find a rotten corpse (i.e., a disease threat) ugly, many people would judge a tiger (i.e., a physical harm threat) as neutral in appearance or even beautiful. Indeed, there is emerging evidence that ugliness judgments are not generally elicited by dangerous objects but instead have a more specific function—that is, alerting us to objects that may pose a disease threat (Klebl et al., 2021; Park et al., 2013; Ryan et al., 2012). Indirect support for this comes from studies discussed previously which showed that facial and bodily disfigurement provide a heuristic cue for disease threat and elicit the same behavioural and emotional response as individuals with a contagious disease (Park et al., 2003; Park et al., 2013; Ryan et al., 2012). More direct evidence linking ugliness judgments and the disease-avoidance system was provided by a recent series of studies which found that people judge objects that possess cues of pathogen presence as uglier than similar objects without pathogen cues and that this was not due to a generalized negative affect (Klebl et al., 2021). For example, a plate containing a yellowish liquid that looked like bodily fluids was judged as uglier than a plate containing a liquid with a chemical blue dye. Furthermore, ugly human faces were found to elicit greater disgust, but not greater fear or sadness, than beautiful or average-looking faces when controlling for the shared negative valence of disgust, sadness and fear (Klebl et al., 2021). This suggests that there is a unique association between ugliness judgments and the emotion disgust. This association has been found to extend to animals and, to a lesser degree, to buildings whose aesthetic features parallel those of organic entities (Coburn et al., 2019). Specifically, ugly animals elicit greater disgust, but not greater fear or sadness, than beautiful animals, and ugly buildings elicit greater disgust, but not greater fear or sadness, than beautiful 78

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buildings, when controlling for the shared negative valence between ugliness judgments and the negative emotions (Klebl et al., 2021). The association between ugliness and disgust extending to entities that may not pose a risk for infectious disease (i.e., ugly human faces and ugly animals) and non-organic entities that cannot pose a disease threat (i.e., ugly buildings) is consistent with how the over-inclusive disease-avoidance system operates (Haselton & Buss, 2000; Schaller & Park, 2011). To date, the literature provides only initial evidence into the function of ugliness and its role in the disease-avoidance system. However, it hints at the possibility that ugliness judgments constitute the aesthetic dimension of the disease-avoidance system. As ugliness judgments involve attention allocation to the objects perceived as ugly (Faust et al., 2019), they may facilitate the disease-avoidance system response by allocating our attention to potentially infectious objects.

The neuroscience of ugliness As beauty and ugliness are brought about by activation in similar brain regions, an aesthetic continuum has been theorized (Kandel, 2012). Studies have found regions in the SDMN, specifically the occipital lobe, nucleus accumbens and cingulate and prefrontal cortices, to be active when people view both attractive and unattractive faces, music or art (see Chapters 6 and 7). However, the occipital lobe is inversely related, with greater activation in this region when viewing increasingly unattractive faces, while the nucleus accumbens and cingulate and prefrontal cortices seem to be linearly related to faces, music and art (Ishizu & Zeki, 2011; Martín-Loeches et al., 2014). As noted, beauty judgments may alert us to objects that have adaptive value (Dutton, 2009). Like art, attractive faces activate the medial orbitofrontal region of the prefrontal cortex, a region activated by reward value, to a greater degree than unattractive faces (Critchley & Rolls, 1996; Elliott et al., 2000; Francis et al., 1999; Ishizu & Zeki, 2011; O’Doherty et al., 2001; O’Doherty et al., 2003). This effect is strengthened when attractive faces are presented with a smiling facial expression (O’Doherty et al., 2003). In contrast, faces rated as lowly attractive activate the lateral orbitofrontal region of the prefrontal cortex, and the insula and cingulate cortex, more so than attractive faces (Krendl et al., 2006; O’Doherty et al., 2003). However, attractiveness is hindered when it is paired with a pathogen threat in mammals, including humans ( Jones et al., 2013). In mice, females display a reduced preference towards male mice that have been paired with pathogen infection (Kavaliers et al., 2021; Kavaliers et al., 2014). In line with this work, OT antagonists have been found to decrease toxin-induced disgust behaviours that were previously associated with a social partner in male rats (Boulet et al., 2016). Given the neural overlap between the types of disgust (e.g., pathogen disgust) and the aesthetic continuum, recent findings add support to the theory that ugliness alerts us to objects that may pose a disease threat (Klebl et al., 2021; Park et al., 2013; Ryan et al., 2012).

Major challenges, goals and suggestions While the adaptive function of disgust has been extensively investigated in the past 40 years, research on the nature of ugliness and its relationship to the disease-avoidance system is still in its infancy. There is initial evidence suggesting that ugliness judgments and the disease-avoidance system are closely linked with each other (Klebl et al., 2021; Park et al., 2003; Park et al., 2013; Ryan et al., 2012). However, as of yet, there are more questions than answers about the nature of this relationship. Important issues that should be addressed by future research pertain to (1) whether ugliness judgments and the disease-avoidance system are causally related to each other, (2) whether ugliness judgments have evolved as part of the disease-avoidance system, (3) the nature of the relationship beyond the visual domain, (4) the instances in which ugly entities do not elicit a disease-avoidance response and (5) individual and cultural differences in ugliness judgments. First, the exact nature of the link between ugliness judgments and the disease-avoidance system is not yet understood. One possibility is that ugliness judgments and the disease-avoidance system are independent 79

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systems, but both can be elicited by particular lower-level features of objects. For example, facial disfigurement may elicit both disgust and ugliness judgments because it possesses a feature (e.g., the degree of non-prototypicality of an entity; Halberstadt & Rhodes, 2000) that is both correlated with the presence of disease (Kurzban & Leary, 2001) and ugliness judgments. Another possibility is that ugliness judgments and the disease-avoidance system are interacting systems and, as such, are causally related with each other. As outlined previously, ugliness judgments may have the function of allocating our attention to objects that potentially pose a pathogen threat (cf. Klebl et al., 2021). Perceiving an object as ugly may thus facilitate sustained attention allocation to that object which in turn may amplify one’s disgust response and motivated avoidance of a potentially infectious object. Alternatively, disgust experiences may cause people to judge objects as ugly. Second, if ugliness judgments facilitate the disease-avoidance system response, the question of whether ugliness judgments have evolved as a part of the disease-avoidance system or have been co-opted by the disease-avoidance system to facilitate the allocation of attention to potentially infectious objects remains open. Although evolutionary processes cannot be directly investigated, testable predictions can be generated that could provide evidence for either possibility (cf. Confer et al., 2010). For example, if ugliness judgments have evolved to alert us to cues of pathogen threat, we would expect them to be more universal for entities that can be infectious (i.e., organisms such as humans or animals) compared to objects that cannot carry pathogens and may only resemble infectious organisms (i.e., non-organic entities such as buildings or other artefacts; cf. Schaller & Park, 2011) due to a greater adaptive pressure to avoid ugly organic entities. This is consistent with research that showed that there is greater inter-individual variability in people’s preferences for abstract images than for real-world images (Vessel & Rubin, 2010). If ugliness judgments, however, have been co-opted by the disease-avoidance system, one would expect that ugliness judgments have a function across organic and non-organic entities that is independent from pathogen avoidance. For example, ugliness judgments may have the function to identify objects that deviate strongly from prototypical exemplars (cf. Halberstadt & Rhodes, 2000). Third, in order to further illuminate the function of ugliness and its relationship with the disease-avoidance system, future research should investigate ugliness judgments in perceptual domains beyond visual ugliness. For example, while smells and tastes can elicit basic disgust responses (Rozin et al., 2008), it is unknown whether they can be perceived as ugly. Furthermore, while sounds can be perceived as ugly (Ishizu & Zeki, 2011), as well as disgusting—for example, sounds of contact with microbial environments such as squelching (Oaten et al., 2009)—it is unclear whether auditory ugliness is related to auditory disgust. Fourth, future research should investigate instances in which ugliness judgments do not trigger disease avoidance. There is anecdotal evidence that people can have positive responses to ugly objects, such as toward ugliness depicted in art. For example, people may enjoy Marcel Duchamp’s (1887–1986) Fountain or paintings by Egon Schiele (1890–1918) that depict twisted bodies. While more frequent in modern art, the aesthetic appreciation of ugliness is not just a modern phenomenon. For example, the Japanese aesthetic tradition wabi-sabi (侘寂) values imperfections such as repaired broken ceramics with their cracks highlighted ( Juniper, 2011). One reason ugly art may elicit positive emotions is that art appreciation is not limited to the aesthetic dimension of beauty and ugliness (Fingerhut & Prinz, 2018). Ugly art may elicit other experiences such as interest or wonder that may counteract the negative responses linked with ugliness perceptions. Fifth, individual and cultural differences in ugliness judgments should be further investigated. For example, some indigenous cultures in Africa, Melanesia and Australia use scarification (Garve et al., 2017), and women of the Ethiopian Mursi use lip-plates (Turton, 2004) to attract sexual partners, while the same practices might be considered unattractive in other cultures. Furthermore, personality traits such as openness to experience or one’s artistic expertise may influence whether objects are perceived to be ugly (Cotter et al., 2017; Vartanian et al., 2019). 80

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Conclusion Disgust is an emotion that has evolved to protect us from threats posed by pathogens. As such, pathogen disgust involves withdrawal and oral expulsion of toxic substances or objects. Building on this system, disgust has been co-opted into the sexual domain, performing the function of avoiding contact with fitnessjeopardizing sexual partners, as well as into the moral domain, functioning to coordinate the condemnation of people who violate moral rules (Tybur et al., 2013). The evolved functions of the disgust systems have implications for everyday social lives such as people’s political orientation, moral judgments, xenophobic attitudes, and the stigmatization of people with facial differences. Now, disgust has also been found to be closely linked to ugliness judgments (Klebl et al., 2021), opening up new avenues of research into the role of disgust in aesthetic judgments.

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5 THE INFLUENCE OF INTEROCEPTIVE SIGNALS ON THE PROCESSING OF EXTERNAL SENSORY STIMULI Alejandro Galvez-Pol, Enric Munar and James M. Kilner Interoception refers to the set of physiological and cognitive processes involved in sensing, interpreting, and integrating information that arises inside the body, providing a continuous mapping of our ever-fluctuating internal milieu across conscious and unconscious levels (Khalsa et al., 2018). It can be distinguished from exteroception (sensation of the environment) and proprioception (sensation of the body in space). While far more research has focused on how external stimuli are represented by the brain, research on interoception focuses on the effect of ever-fluctuating afferent bodily signals on brain processes. Importantly, these so-called interoceptive signals not only inform the brain about the state of the body but also influence how we relate to our environment; that is, they influence our perception of the world. In this chapter, we focus on this latter facet, with special emphasis on the way the cardiovascular system modulates the processing of external stimuli. First, we outline the historical roots of interoception. Second, we describe how changes inside the body are consciously perceived and such signals influence perception of external stimuli. Third, we describe the physiological pathway of the heart–brain axis and its impact on stimuli processing. Fourth, we review the link between afferent bodily signals and the neural encoding of subjective values and discuss what is known about the way interoception affects hedonic coding of sensory objects. Last, we consider current challenges of the field and how these can be overcome.

A brief history of the study of interoceptive signals on cognition “The stability of the organism’s internal landscape (milieu intérieur) is the condition for the free and independent life” (Bernard, 1878). Although 150 years old, these words resonate well with our current understanding of the way the integrity of living organisms rests upon upholding a homeostatic equilibrium. Some years after Bernard, William James (1884) and Carl Lange (1885) proposed that our emotional experiences originate from responses in the body that accompany the perception of external events. In contrast, Walter Cannon (1927) and Philip Bard (1928) proposed that stimulating neurons in the central nervous system was sufficient to elicit feelings and physical reactions in a simultaneous manner. This debate regarding the cause of emotions—body states vs brain activity—has continued to this day. By the turn of the 20th century, Charles Sherrington (1906) coined the term interoceptor to describe the presence of an internal bodily surface dedicated to the monitoring of changes within the body. By the time Sherrington and Edgar Adrian received the Nobel Prize for their discoveries regarding the functions DOI: 10.4324/9781003008675-6

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of neurons, Cannon further elaborated Bernard’s notion of a milieu intérieur in what he termed homeostasis, intended to describe self-regulating processes that promote survival by maintaining the stability of the organism (Cannon, 1939). During this first half of the 20th century, most studies in interoception were conducted in the Soviet Union, with Pavlov’s work on learnt reflexes and interoceptive processes in preparation for digestion the most famous. However, some of these works remained unnoticed until later decades, when Soviet psychophysiology became more appreciated by international audiences. The advent of operant conditioning and the discovery of various types of interoceptors laid the foundation for our present understanding of interoception. Novel paradigms in cardioception such as the heartbeat detection task allowed scientists to estimate how much interoceptive information reaches awareness (Schandry, 1981). Later, the development of neuroimaging techniques allowed neuroscientists to map the brain activity correlated with these tasks (Craig, 2002), and new theories such as the “Somatic Marker Hypothesis” stressed how subjective value computations integrate bodily representations to form decision values (Damasio, 1999, 1994). Since then, the field of interoception has grown, matured, and diversified. The number of studies has grown exponentially and our understanding of the interaction between interoceptive mechanisms and external perception has improved significantly. The current chapter reviews this body of work and moves beyond the recognized role of interoception for homeostasis, providing insight into the role that afferent interoceptive signals play in the computation of perception, valuation, and reasoning.

Conscious interoception mediates stimulus processing Interoception is multifaceted; it comprises distinguishable dimensions and different physiological systems (cardiovascular, gastrointestinal, hormonal, circulatory) acting across conscious and unconscious levels (Khalsa et al., 2018; Quigley et al., 2021). Yet the primary physiological focus of most interoception studies is the cardiovascular system. This is likely due to the emergence of early evidence for a modulatory effect of the carotid sinus on central and autonomous nervous processes (Koch, 1932; Kreindler, 1946) and the methodological ease of monitoring discrete regular events (i.e., heartbeats) that can be recorded via noninvasive tools, such as electrocardiogram (ECG), pulse oximeters, or wearable heart rate monitors. Most interoceptive processes, such as the monitoring of one’s psychophysiological state, unfold at the unconscious level and often reach awareness only when the system is compromised (e.g., pain, thirst). The study of this facet of interoception usually focuses on the effect of afferent bodily signals upon the processing of external stimuli and brain function. At the conscious level, numerous studies have examined the ability to notice bodily states and fluctuations, mostly in the cardioception domain. Three dimensions of conscious interoception have dominated this research: (i) interoceptive sensibility, which refers to the subjective experience of internal sensations as measured by self-reports; (ii) interoceptive sensitivity, which refers to the objective measure of interoception and involves testing of the capability to perceive bodily changes in behavioural tasks; and (iii) interoceptive awareness, which refers to metacognitive insights about the two former dimensions and is most often operationalized as the distance between one’s beliefs and the person’s actual ability to perceive inner body states (see Garfinkel et al., 2015; Murphy et al., 2019). In the following, we briefly review findings that this body of work has produced with respect to the role these dimensions play in the processing of external stimuli.

Interoceptive sensibility One way to examine the conscious processing of interoceptive signals is by using self-report questionnaires that allow assessing participants’ experience with bodily sensations (interoceptive sensibility), as well as how this sensitivity is related to other cognitive domains. The choice of a self-report questionnaire depends largely on the research question, for example, noticing vs body listening or dissociating adaptive 90

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vs maladaptive interoception (see the Body Perception Questionnaire [Porges, 1993], the Multidimensional Assessment of Interoceptive Awareness [Mehling et al., 2018, 2012], or the Interoceptive Sensory Questionnaire [Fiene et al., 2018]). Self-report measures have shown that participants with a heightened interoceptive sensibility might experience more anxiety, and this is more likely to occur when they have difficulties in identifying and describing emotions. The lack of attributing interoceptive signals to emotional states predisposes participants to anxietyrelated characteristics (Palser et al., 2018). This in turn may intensify the attribution of negative valence to stimuli, as well as exacerbating a negative narrative when processing external events. In this way, self-reports allow us to quickly obtain information about the interoceptive-mediated valuation of external stimuli. Relatedly, Paulus et al. (2019) highlighted two scenarios in which the negative appraisal of stimuli can be mediated by maladaptive interoceptive mechanisms. In the first scenario, people who are exposed to threatening stimuli experience high levels of arousal. If the situation is positively resolved, future related events should be perceived as less hazardous, decreasing sympathetic engagement. However, if people fail to update their beliefs, similar scenarios will continue to elicit heightened visceral responses. In the second scenario, people extrapolate physiological responses from the original context to other situations. Both scenarios involve persistent non-veridical perception that negatively affects the regular function of the viscera, which eventually feeds back to the central nervous system, where it affects the assessment of external stimuli. In sum, if one’s set point (i.e., optimal bodily state) is missattuned, the representation of interoceptive signals becomes imprecise, and the valuation of external stimuli does not necessarily meet the most appropriate behaviour (Linson et al., 2020; Paulus et al., 2019).

Interoceptive sensitivity Another way to examine the conscious processing of interoceptive signals is by using lab-based tasks that allow obtaining more objective measures. This usually involves asking participants to notice bodily changes while their physiological rhythms are being recorded (e.g., counting heartbeats while the ECG is recorded). Then, the difference between participants’ subjective reports and the objective quantification of their bodily changes are compared against each other. The difference between these measures provides an objective estimate of interoceptive sensitivity. This allows “profiling” participants, ranking them on a continuum from poor to good interoceptors, and relating this individual variance to other measures and tasks. For example, good interoceptors display larger electrodermal responses to unfair offers in the context of the ultimatum game (Dunn et al., 2012), have better memory recall for words encoded during the systolic phase of the cardiac cycle (i.e., hearts’ contraction pumping the blood, Garfinkel et al., 2013), exhibit greater sympathetic reactivity during mental stress and subjective arousal during emotional picture viewing (Herbert et al., 2010), and display higher reinforced learning of emotional faces (Pfeifer et al., 2017). Good cardioception has also been associated with stronger expectancy for unconditioned stimuli (Zaman et al., 2016), better learning with the corresponding modulation of hippocampal activity (Stevenson et al., 2018), and higher sensitivity to negative affect but lower accuracy in recognizing faces depicting fear and sadness (Georgiou et al., 2018). A recent study, combining both objective and subjective measures of interoception, showed that participants with better cardiac interoceptive awareness and insight are able to withhold actions and respond more slowly in a Go/NoGo task, while the opposite pattern was observed for participants with poorer interoception (Rae et al., 2020). This suggests that precise afferent input may support sensorimotor decisions. In contrast, noisier signalling could prompt hasty responses to external stimuli. Taken all together, current findings suggest that cardiac sensitivity is related to greater perception and memory encoding of emotional stimuli. However, these results should be considered with caution. More consistent research is needed, and some studies have also shown that interoceptive signals can inhibit stimulus processing (see, e.g., Park et al., 2014; Salomon et al., 2018). Overall, the question of the relation between 91

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interoceptive sensitivity and everyday well-being needs further work. Furthermore, research on atypical interoception, a common denominator in all mental disorders, suggests that either low or amplified functioning in interoceptive sensibility, sensitivity, or awareness could be linked to a maladaptive valuation of stimulus processing (Khalsa et al., 2018).

The influence of afferent bodily signals on the processing of stimuli As noted previously, the primary focus of studies on interoception has been the influence of the cardiac cycle on cognition. To understand the influence of the heart on stimulus perception, in the following we summarize what is currently known about neural signalling from the heart to the brain and how these projections modulate cognition.

The heart–brain axis One of the distinctive features of the heart is that it is endowed with pacemaker properties; that is, the heart can generate its own intrinsic oscillatory electrical activity. In a single heartbeat, two phases are observed: in the systole phase, the heart contracts and ejects the blood, whereas in the diastole phase, the heart expands and fills. Both phases constitute a cardiac cycle, with the R-peak (peak in ECG depicting the contraction at systole) denoting the beginning of a new cycle. During the systolic phase, pressure sensors located in the carotid sinus, coronary arteries, and aortic arch (i.e., baroreceptors) detect changes in blood pressure due to the ejection of the blood from the left ventricle. Baroreceptors convey information to the brain about the strength and timing of the heartbeats during the systolic phase while being quiescent during the diastole phase of the cardiac cycle (Critchley & Harrison, 2013). Many studies have observed that neural and behavioural responses to external stimuli vary according to the phase of the cardiac cycle during which they occur (see, e.g., Azevedo, Badoud et al., 2017; Leganes-Fonteneau et al., 2021). This is usually demonstrated by meticulously locking the presentation of stimuli to the systolic or diastolic phase of the cardiac cycle. Many studies have associated the variation in participants’ responses along the cardiac cycle with the phasic firing of the baroreceptors. Although this is under investigation, it is clear that heartbeats selectively modulate the processing of external stimuli by constantly facilitating, competing with, or inhibiting information processing (see Figure 5.1 and the following section). As an intrinsic oscillator, the heart has an inherent nervous system composed of interconnecting, efferent, and afferent ganglionated nerve plexi (a branching network of intersecting nerves). These project through the spinal cord and the vagus nerve to the nucleus of the solitary tract (NTS) and other autonomic nuclei of the brainstem, which in turn allow for dynamic regulation of efferent1 sympathetic and parasympathetic cardiomotor activity. Interestingly, approximately 80% of the fibres of the vagus nerve are afferent, which makes it more of a listener than a storyteller (as revisited by Wolpert et al., 2020). In the NTS, the convergence of signals from different bodily systems (e.g., cardiac, gastric) projects to viscero-sensitive brain regions such as the thalamus, hypothalamus, amygdala, cingulate cortex, and insula. The insula is considered a major hub for interoceptive information (Craig, 2009). The posterior insula receives inputs from bodily systems and the anterior part re-represents this information with emotional, cognitive, and subjective states. Information from the environment and interoceptive signals seem to be assimilated across this posterior-to-anterior insular gradient (Namkung et al., 2017).

Effect of cardiac phases on stimulus perception Many studies have examined the iterative influence of the cardiac cycle upon the perception of stimuli by presenting these during systolic or diastolic phases (Figure 5.1C). The idea is to exploit these naturally 92

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Figure 5.1 Interoceptive processes and their influence on stimulus processing. (A) Bodily organs endowed with pacemaker properties interact with the brain via afferent and efferent signals. The activity of the stomach, heart, and brain unfolds at various frequencies. Changes in such a complex oscillatory activity affects one another and influence cognition (Azzalini et al., 2019). (B) The brain’s responses to sensory inputs (e.g., external stimuli) not only depend on the stimuli’s properties but also on its own internal state at the time when the stimuli are processed. Conscious perception and awareness of one’s inner bodily states moderates the perception of stimuli and corresponding physiological correlates (interoceptive sensibility and predictions; Murphy et al., 2019). (C) Studies examining the influence of the cardiac cycle on cognition often measure participants’ ECG and present stimuli during time windows phase-locked to systole or diastole. In principle, stimulus presentation is shifted in time to account for the time needed for afferent baroreceptor activity and other physiological changes to reach the brain (~300 ms). (D) An overall trend in studies using cardiac phaselocking shows that motor responses are more prone in the early period of the cycle (systole) whereas sensory processing of stimuli seems enhanced in the later quiescent physiological period (diastole). Icons in all panels are under Creative Commons license CC0 from Pixabay.com.

occurring fluctuations to understand how subsequent variations (e.g., bodily arousal, firing of baroreceptors at systole) affect stimulus processing at the neural and behavioural levels. These studies are usually focused on inspecting the processing of emotional or non-emotional stimuli presented at near or suprathreshold perceptual levels. Depending on the task and stimuli, cardiac phases can both selectively facilitate and inhibit stimulus processing. Systolic modulation of sensory processing has been observed for subjective pain perception and sensitivity to tactile stimuli, which are attenuated during this phase compared to diastole (Wilkinson et al., 2013; Al et al., 2020; Motyka et al., 2019; see also concurrent effects of respiration in Grund et al., 2021). Similarly, the startle reflex, an unconscious defensive response that induces an immediate eyeblink response to sudden or threatening stimuli such as sudden noises or sharp movements, is attenuated by systolic afferent signals (Larra et al., 2020). Startle responses are also modulated by phase respiratory and gastric rhythms (Schulz et al., 2017, 2016). Conversely, enhanced processing at systole has often been linked to negative emotional stimuli such as the detection of fearful faces and memories (Garfinkel et al., 2020; Garfinkel & Critchley, 2016). Likewise, the processing of threatening stimuli associated with racial stereotypes is heightened during 93

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systole (Azevedo, Garfinkel et  al., 2017). Specifically, participants in a first-person shooter task produced more errors (“shooting” un-armed Black vs. White targets) during systole. These results may be associated with a cardiac modulation of error monitoring (Bury et al., 2019), and motor inhibition might be more prone to fail during systole (Makowski et al., 2020; but see Rae et al., 2018). Overall, current research indicates that there is a putative effect of afferent cardiac signalling on stimulus processing (Critchley & Garfinkel, 2018). However, results seem to vary significantly across experimental manipulations. While more consistent research is needed, it is clear that cardiac afferent signals and concurrent physiological changes moderate cognitive processes by competing for the allocation of attentional and representational resources. This in turn can reduce or amplify the sensory processing of stimuli. Sensory processes that are concurrent with the systolic phase of the cardiac cycle (the noisy period of the cycle) seem to be reduced, whereas motor behaviour seems to be facilitated (see the later section “Active Sensing”).

Cardiac-related effects upon the processing of face stimuli Influential theories of emotion highlight that certain emotions are likely coupled with particular bodily states. For instance, disgust is closely coupled with parasympathetic responses, whereas feelings of anxiety or fear are associated with heightened cardiovascular arousal caused by sympathetic activation. In this context, physiological arousal is often understood as a consequence of top-down processes rather than as a cause of emotional experience. Yet several studies have shown that detection and appraisal of facial emotional expressions fluctuate according to the effect of short-term baroreceptors’ activity, that is, a transient increase of visceral arousal. The modulatory effect of the heart upon the processing of face stimuli varies according to both the emotion displayed and the task employed. In a forewarned reaction time task, facial expressions of disgust, but not sad, happy, or neutral expressions, were judged as more intense when presented in systole. Furthermore, the processing of disgust and happy faces resulted in a more pronounced deacceleration of subsequent heartbeats, a mechanism proposed to facilitate perception and appraisal (Gray et al., 2012). In a rapid serial presentation task, the detection and intensity ratings of fearful faces were found to be higher when these were presented in systole, suggesting that heartbeats might facilitate conscious processing of briefly presented and emotionally strong stimuli (Garfinkel et al., 2014). The cardiac interaction between processing of fearful faces and cardiac phase correlated with neural activations in several brain regions with the most prominent found in the amygdala, a structure associated with threat processing and the integration of physiological and affective information. Interestingly, regardless of the emotion, the overall effect of the cardiac cycle on emotional processing was found in the anterior insula. In an emotional spatial cueing task, a systolic effect on attentional engagement to fearful faces was found at different spatial frequency ranges. The systolic phase enhanced the processing of fearful facial expressions at low, but not broad or high, spatial frequencies (Azevedo, Badoud et al., 2017), implying that afferent bodily signals modulate the processing of faces by distinctly influencing the magnocellular and parvocellular pathways at the early stages of the visual processing. More recently, Leganes-Fonteneau et al. (2021) used an emotional visual search task where participants saw a target emotional face on the screen (fearful, happy, sad, or disgust) surrounded by five neutral distractors. An interesting point of this task is that it allows capturing attentional processes allocated to the scanning of faces in a crowd, as well as differentiating between the correct detection of the target stimuli (accuracy in the visual search task) and the correct identification of the emotion presented. Accuracy in the visual search was higher for disgust and happy faces presented during systole, whereas the opposite effect was found for fearful faces. The identification of fearful and sad faces was higher when presented in diastole. Overall, these studies highlight that detection and appraisal of facial emotional expressions are the result of body–brain interactions. The role of cardiac interoceptive signals goes beyond the processing of fearful faces and depends on the core task and emotional expression. Presumably, these effects have been explained as a 94

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consequence of afferent baroreceptor signals conveyed through the brainstem via the vagus nerve. However, other physiological changes are concurrent to heartbeats (Davos et al., 2002), and these may instigate the previous effects by directly or concurrently affecting key neural substrates for face processing.

The self and the heart–brain axis support subjective preferences Research in cognitive neuroscience and psychology have long focused on the significance of the body as the ground of the self—the person as the object of its own reflective consciousness. More recently, the study of the self has focused on the importance of the body from within. It has been proposed that regardless of the bodily state, organs endowed with pacemaker properties such as the heart and the stomach could work as constant transmitters that signal the presence of a body to the brain (Azzalini et al., 2019; Tallon-Baudry et al., 2018). Then, the central nervous system would make use of this information to generate a first-person perspective. Compelling evidence for this mechanism comes from observing that the amplitude of brain responses to heartbeats (heartbeat-evoked responses; HERs) correlates with the self-relatedness of thoughts in the ventromedial prefrontal cortex (vmPFC; Babo-Rebelo et  al., 2016; Tallon-Baudry et  al., 2018). Relatedly, the amplitude of the HER has been linked to the conscious perception of stimuli presented at near-threshold detection (Al et al., 2020; Park et al., 2014; Park & Blanke, 2019). This suggests that brain responses to heartbeats might regulate the perception of near-threshold stimuli by moderating one’s selfconsciousness during the perceptual experience. Following this line of work, it has been proposed that this relationship, between the self and the heart– brain axis, supports the valuation of what we like or dislike. Specifically, it has been exposed that preferencebased decisions about external stimuli such as cultural goods are subjected to the self. For instance, “Do you prefer Forrest Gump or The Matrix? Only you know which movie you like best” (Azzalini et al., 2021, p. 1). These authors examined participants’ brain activity while they were presented with pairs of movie titles. The participants either indicated which movie they preferred or had to discriminate between versions of the title written with different levels of contrast. The results of the study showed that when choosing the participants’ preferred movie, HERs signalled the recruitment of self-reflective processes in vmPFC. Conversely, this association was not observed for the contrast discrimination task. Moreover, the interaction between HERs and subjective value encoding reflected the inter-individual variability in choice consistency and the trial-by-trial fluctuations within participants. These results indicate that the neural monitoring of cardiac signals and the neural encoding of subjective values are related to each other. Considering these findings, the sensory valuation of stimuli appears to depend on the novelty of the stimuli and the use/absence of a subjective approach, including possible re-enactments of autobiographical and bodily memories (Galvez-Pol et al., 2020a; Riva, 2018).

The gut–brain axis The gastrointestinal tract has received increasing attention in recent research on interoception. Similar to the heart, the gut generates its own intrinsic oscillatory electrical activity. The gastrointestinal tract has a rhythm that unfolds in the form of a continuous slow electrical pulse (one cycle every 20 seconds, ~0.05 Hz). This rhythm results from the activity of interstitial cells of Cajal, pacemaker cells that generate rings of electrical waveforms. These cells mediate between the autonomic nervous system and the muscle layers of the stomach, whereby mechanical changes in smooth muscles can be detected by making contact with vagal sensory neurons along with intramuscular arrays (Powley et al., 2016). The gastric rhythm can be non-invasively measured via electrogastrography (EGG), which reflects a combination of the gastric rhythm caused by interstitial cells of Cajal and of gastric smooth muscle contractions (Wolpert et al., 2020). Importantly, the recording of spontaneous brain activity (non–task-related fluctuating 95

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neural activity) has revealed that several brain regions are linked to gastric function (Rebollo et al., 2018). In this so-called gastric network, the gastric phase seems to modulate the neural activity of viscero-sensitive brain areas (somatosensory cortices and parieto-occipital regions). The presence of this gastric network suggests that automatic regulation of basic processes for life such as digestion are linked to complex patterns of brain activity that affect how people could perceive external stimuli. Evidence for this has been shown in a behavioural task where participants were given a dose of domperidone (an antiemetic moderating gastric rhythm) or a placebo while their oculomotor responses to neutral and disgust images were recorded. The study showed that domperidone did not change the subjective ratings of disgust but decreased oculomotor avoidance following incentivized exposure to disgusting stimuli (Nord et al., 2021). Future work is needed to examine the link between gastrointestinal afferent signals and the brain and subsequent behaviour, as well as to study other aspects of the gut–brain axis that are likely to mediate this link (e.g., microbiota).

Active sensing in interoception: beyond the phase-locked presentation of stimuli Many of the findings that we have presented here were obtained by deliberately locking the presentation of stimuli to the different phases of the cardiac cycle. However, in our everyday life, the environment does not normally provide us with sensory information phase-locked to our physiological cycles. Instead, we freely and actively sense the stimuli at hand. Interoception studies in active sensing examine whether participants are naturally prone to sample the stimuli in the environment in a particular phase of the bodily cycles. These types of studies do not impose temporal constraints and allow participants to access the stimuli at their own pace. Simultaneously, participants’ behaviour and their physiological rhythms are coregistered (e.g., ECG, breathing, and participants’ responses). After data collection, researchers work “backwards” in the data by situating each of the participants access to the stimuli along with the recorded continuous rhythms. Then, they analyse whether participants are more prone to sample the stimuli, for instance, in the systolic or diastolic phase of the cardiac cycle (Figure 5.2A, B). A significant coupling between the active sampling of stimuli and cardiovascular oscillations has been found in various tasks. For instance, in a visual search task comparable to a “spot the difference” task, participants’ oculomotor behaviour and ECG were recorded while they searched for differences between two bilateral arrays continuously displayed on the screen (Figure 5.2C; Galvez-Pol et al., 2020b). Across three different analyses, the results showed a significant coupling of saccades, subsequent fixations, and blinks with the cardiac cycle. More eye movements to sample the arrays were generated during systole, which has been reported as the period of the maximal firing of the baroreceptors. Conversely, more ocular fixations were found during the diastole phase (quiescent baroreceptors). Last, more blinks were generated in the later period of the systolic phase. These results show that (1) in a more ecological setting, interoceptive and exteroceptive processes adjust to each other, and (2) the active sampling of external stimuli might occur when more computational resources are available, that is, during quiescent periods of the inner body (Galvez-Pol et al., 2020b). While this latter hypothesis needs further examination, recent research seems to support this idea (see, e.g., Kluger et al., 2021). Beyond ocular sensory sampling, Kunzendorf et  al. (2019) found that participants freely generated a keypress leading to the onset of images in a memory task more often during the systolic phase, though it did not influence memory performance. Similarly, Perl et al. (2019) reported that participants preferred to self-initiate the onset of non-olfactory cognitive tests to coincide with the beginning of the inspiratory phase. Also, recently it has been shown that participants tend to initiate actions during the expiration phase of the breathing cycle. Moreover, the neural marker of these self-initiated movements seems to be modulated by the respiratory phase (Park et al., 2020). Overall, active sensing is entrained by cardiorespiratory fluctuations, which indicates the constant incorporation of bodily signals into one’s engagement with the stimuli in the environment. However, the behavioural relevance of this entrainment remains unclear. 96

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97 Figure 5.2

Active sensing of stimuli in interoception. (A) ECG depicting one cardiac cycle. Here, two events (e.g., saccades to sample stimuli, keypresses leading to stimuli onset, etc.) occur red at the early phase of the cardiac cycle and one at the later phase. (B) Schematic of analysis; from left to r ight, the number of events in this cycle are depicted in phase i) as deg rees of each event relative to the concur rent heartbeat, ii) as counted events binned into time windows along the cycle, and iii) as counted events in the systolic or diastolic phases. After n tr ials, it is possible to compute the frequency of events as a function of phase, the event changing ratio, and their phasic occur rence. (C) Active visual sampling task and results. Participants reported the number of boxes differ ing in colouration between two bilateral ar rays by compar ing each box in the left ar ray with the homologous box in the r ight Ar ray. (D) The succeeding results showed that saccade onset, mean time point of fixations, and blink onset occur red significantly more often in the early, mid, and later per iod of the cardiac cycle, respectively. Adapted from Galvez-Pol et al. (2020b).

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Future research: a more ecological approach to interoceptive processing Many of the studies reviewed in this chapter show that perception, reasoning, and emotional experiences vary according to changes in the body (e.g., heartbeats). Yet most of this research has been conducted by: (1) meticulously locking the presentation of brief stimuli to distinct phases of our bodily cycles, (2) adopting an idiosyncratic perspective: physiological bodily signals serve “one’s purposes,” or (3) somewhat overlooking that humans might relate to stimuli in their environment according to the processing of their physiological signals. Active sensing paradigms tackle the first limitation (see the previous section). Meanwhile, very recent work has shown that dynamic changes in our bodies can be inferred by others (hence, unfolding a dialectical perspective; Galvez-Pol et al., 2021a). Also, recent work has shown that our sense and interpretation of inner bodily signals correlate with how we seek and experience our surrounding environment (Galvez-Pol et al., 2021b). We believe that these lines of research will allow for a better understanding of the physiological mechanisms underpinning sensory processing in real-life scenarios, including work, family life, entertainment, or even art experiences.

Concluding remarks In recent years, the field of interoception has grown, matured, and expanded. Exponential growth in the number of studies has produced a better grasp of the relationship between the perception of the outside world and mechanisms inside the body. These studies have shown that our responses to external stimuli not only depend on the stimuli’s properties but also on our internal bodily state at the time when the stimuli are processed. Internal bodily changes (e.g., afferent signalling from the heart and stomach) moderate cognitive processes by competing for the allocation of attentional and representational resources. This in turn might dampen, enhance, or modify the processing of stimuli. While the field is still in development, more consistent methods, paradigms, and integrated theories are needed. Likewise, the consideration of various physiological systems (beyond the cardiac system, e.g., gastrointestinal, hormonal, respiratory) is a promising avenue for developing next-generation studies. Furthermore, we believe that research in interoception should advance towards a more ecological understanding of how humans function in the real world, that is, the ecological niche in which the brain has evolved. In this setting, including interoceptive signals in the study of sensory acquisition in active sensing is a fundamental step towards a more ecological understanding of exteroceptive and interoceptive processes.

Acknowledgements This work was supported by the Autonomous Community of the Balearic Islands (CAIB). Postdoctoral grant Margalida Comas to Alejandro Galvez-Pol, Ref PD/036/2019.

Note 1 Efferent neurons carry signals from the central nervous system (i.e., the brain) to the body’s muscles, glands, and organs. Afferent neurons project signals from sensory receptors and the autonomous nervous system (i.e., the body) to the central nervous system.

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Impact of cardiac interoception cues and confidence on voluntary decisions to make or withhold action in an intentional inhibition task. Scientific Reports, 10(1), 4184. https://doi.org/10.1038/s41598-020-60405-8 Rae, C. L., Botan, V. E., Gould Van Praag, C. D., Herman, A. M., Nyyssönen, J. A. K., Watson, D. R., Duka, T., Garfinkel, S. N., & Critchley, H. D. (2018). Response inhibition on the stop signal task improves during cardiac contraction. Scientific Reports, 8(1), 9136. https://doi.org/10.1038/s41598-018-27513-y Rebollo, I., Devauchelle, A. D., Béranger, B., & Tallon-Baudry, C. (2018). Stomach-brain synchrony reveals a novel, delayed-connectivity resting-state network in humans. eLife, 7, 1–25. https://doi.org/10.7554/eLife.33321 Riva, G. (2018). The neuroscience of body memory: From the self through the space to the others. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior, 104, 241–260. https://doi.org/10.1016/j.cortex.2017. 07.013 Salomon, R., Ronchi, R., Dönz, J., Bello-Ruiz, J., Herbelin, B., Faivre, N., Schaller, K., & Blanke, O. (2018). Insula mediates heartbeat related effects on visual consciousness. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior, 101, 87–95. https://doi.org/10.1016/j.cortex.2018.01.005 Schandry, R. (1981). Heart beat perception and emotional experience. Psychophysiology, 18(4), 483–488. https://doi. org/10.1111/j.1469-8986.1981.tb02486.x Schulz, A., Schilling, T. M., Vögele, C., Larra, M. F., & Schächinger, H. (2016). Respiratory modulation of startle eye blink: A new approach to assess afferent signals from the respiratory system. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 371(1708). https://doi.org/10.1098/rstb.2016.0019 Schulz, A., van Dyck, Z., Lutz, A. P. C., Rost, S., & Vögele, C. (2017). Gastric modulation of startle eye blink. Biological Psychology, 127, 25–33. https://doi.org/10.1016/j.biopsycho.2017.05.004 Sherrington, C. (1906). The integrative action of the nervous system. Yale University Press. Stevenson, R. J., Francis, H. M., Oaten, M. J., & Schilt, R. (2018). Hippocampal dependent neuropsychological tests and their relationship to measures of cardiac and self-report interoception. Brain and Cognition, 123, 23–29. https:// doi.org/10.1016/j.bandc.2018.02.008 Tallon-Baudry, C., Campana, F., Park, H. D., & Babo-Rebelo, M. (2018). The neural monitoring of visceral inputs, rather than attention, accounts for first-person perspective in conscious vision. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior, 102, 139–149. https://doi.org/10.1016/j.cortex.2017.05.019

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6 NEURAL CORRELATES OF VISUAL AESTHETIC APPEAL Edward A. Vessel, Tomohiro Ishizu and Giacomo Bignardi

The question of how visual experiences acquire aesthetic value is an old one. Many philosophers and poets have mused about what makes a face attractive, what makes a mountain vista beautiful, or how a painting can move us. Not only was the question of visual beauty of prime importance for early work in philosophical and empirical aesthetics (from Kant [1790/1987] to Fechner [1876], Arnheim [1954], and Berlyne [1971]), it was also one of the earliest domains of aesthetic experiences to be systematically explored using the modern tools of cognitive neuroscience. In part, this was due to the fact that in the late 1990s, knowledge of the human visual system was a fair amount ahead of an understanding of other sensory systems, and novel brain imaging techniques and analyses, particularly those for functional magnetic resonance imaging (fMRI), were often pioneered in the visual domain. The existence of theoretical sketches of human visual processes (e.g., Goodale & Milner, 1992; Ungerleider & Mishkin, 1982; Van Essen et al., 1992) served as a springboard for early ideas about how visual experiences might come to be experienced as pleasurable or even beautiful, and fMRI methods allowed for these ideas to be tested. There are a variety of definitions of what constitutes an aesthetic experience (see Anglada-Tort & Skov, 2020). Here, we will adopt a rather wide definition. Visual aesthetic experiences clearly involve perception yet also engage sense making (comprehension). They are evaluative, affectively absorbing, and linked to specific appraisals ( judgments) of a stimulus, such as whether they are pleasing, beautiful, moving, or attractive (Pearce et al., 2016; Vessel, 2020). Visual aesthetic appraisals are a source of hedonic pleasure—a hedonic value that derives from processing the perceptual and conceptual aspects of a visual experience itself (and apart from any value associated with what an image or object may stand for, e.g., the value of a hundred-dollar bill or the sweetness of a particular strawberry). Such appraisals are not confined to encounters with visual art but can also occur during encounters with the natural world, with people, with objects, and even with thoughts and concepts. In order to clarify what is known about visual aesthetic appreciation, we begin this chapter with an overview of relevant behavioural work. Much of the empirical work on visual aesthetics has focused on understanding whether visual aesthetic appraisals, for example, feeling beauty or being moved, can be predicted from properties of an image. More recently, an increasing focus has also been on understanding the highly idiosyncratic nature of visual aesthetic tastes and how differences in context, or in a perceiver’s personality and past experience, interact with a visual stimulus to make for an appealing aesthetic experience. We then turn to focus on what is known about the neural mechanisms that support aesthetic appreciation. Much recent work in visual neuroaesthetics has focused on understanding how visual aesthetic appraisals are DOI: 10.4324/9781003008675-7

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represented in the brain. What neural systems contribute to visual aesthetic appeal? Does the brain represent aesthetic appeal in a domain-specific or domain-general fashion? Are aesthetic appraisals automatic, or do they require explicit judgment? Finally, we briefly outline some initial thoughts on perhaps the most challenging question in the field— why do we like what we like? Unfortunately, there is very little direct evidence that bears on the question of why humans find certain visual experiences aesthetically appealing; why they can be so impactful; and what evolutionary benefit, if any, such a mechanism might afford. We therefore present a necessarily selective and tentative account that is informed by recent empirical and theoretical work in empirical aesthetics both in vision and beyond (see also the discussions of this issue in Chapters 2 and 11).

Measuring visual aesthetic appreciation Before diving deeper into the neural correlates of visual appreciation, we first summarize a body of behavioural research in order to better clarify the relevant constructs. We begin by describing types of measurement that have been employed by researchers to assess the neural correlates of visual aesthetic appreciation to highlight sources of variation that might affect interpretation and synthesis of findings. Figure 6.1 shows the two main types of measurement that have been employed to assess appraisal in neuroaesthetic studies, direct and indirect, with the first being used more frequently than the second. Other measures, such as production or adjustment tasks, are also possible, but given the minor role they have played in neuroimaging studies, they will not be reviewed here (the curious reader can refer to Palmer et al., 2013). Direct measurements quantify appraisal that is directly reported by participants. They can be collected when participants are appraising images by means of dichotomous (e.g., “no,” “yes”; Jacobsen et al., 2006), ordinal (e.g., from “ugly, “indifferent,” “beautiful”; Ishizu  & Zeki, 2011), interval, or continuous rating scales (e.g., aesthetically “moving,” from low to high; Vessel et al., 2019) and when participants are expressing their preferences by choosing between two images (e.g., Kim et al., 2007) or through ratings obtained continuously over time (e.g., Belfi et al., 2019; Isik & Vessel, 2021). Indirect measures, on the other hand, are collected using a variety of alternative tools that are intended to measure an appraisal indirectly, with the goal of measuring appeal in a manner that is unmediated, or at least only partially mediated, by conscious reflection and labelling of experience. Examples include measurements of viewing time (e.g., with or without a requirement to expend “effort,” Aharon et al., 2001), reaction time (e.g., Lopez-Persem et al., 2020), arousal (e.g., as measured by skin conductance changes, Salimpoor et al., 2011), pupil dilation (e.g., Laeng et al., 2016), heart rate (e.g., Tschacher et al., 2012), and “willingness to pay” (e.g., Smith et al., 2010). It is worth noting, however, that while for some aesthetic domains, there is evidence that indirect measures can be used to assess liking (e.g., skin conductance response to asses pleasure from music; see Fleurian & Pearce, 2020), there is so far little consensus regarding which indirect measures are reliable indicators of visual appraisal. Further, an additional source of variation between studies is whether the task involves explicit appraisal, for example, whether participants are asked to express their appraisal while viewing the stimuli (and thus also during brain imaging). Alternatively, researchers study implicit appraisal by not imposing a rating task during brain imaging, relying instead on pre- or post-session ratings or ratings from other observers. While explicit appraisal is important to establish neural correlates of appreciation, it is important to investigate whether the neural correlates of implicit appraisal differ from the ones of explicit appraisal, even when individuals are focused on a non-aesthetic task such as identity recognition (Chatterjee et al., 2009) or perceptual judgments (Kim et al., 2007). To make things more complicated, not only do studies employ different measures and/ or tasks, but they also introduce other sources of methodological variation by asking a variety of questions. For example, when asked to judge images, participants can be asked questions ranging from how attractive (Chatterjee et al., 2009) or how liked images of faces are (Lopez-Persem et al., 2020) to how beautiful (Ishizu & Zeki, 2011) or how moved participants feel when viewing a painting (Vessel et al., 2012). 104

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105 Figure 6.1 Methodological “sources of variation.” Different types of questions and measurements (direct and/or indirect) are used to assess visual aesthetic appeal. To establish the neural correlates of visual appreciation, such measures can be employed during (explicit) or before or after (implicit) neuroimaging experiments. L = low; H = high; t = time.

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Finally, even when participants are asked the same question, such as how attractive a face is, the instructions are sometimes different, with some implying personal attraction and potential mating desire for the person depicted (Kocsor et al., 2013) and others ignoring it (Chatterjee et al., 2009). While it is the case that different types of measures are strongly related, such as interval ratings being predictive of choice (LopezPersem et al., 2020), it is possible that one can influence the other (see Ariely & Norton, 2008; Izuma et al., 2010), and even when different questions lead to highly correlated ratings, such as pleasure and beauty (Brielmann & Pelli, 2019), the relationship is rarely perfect. All in all, given that only a few studies have investigated such differences, it is important to take these considerations in mind when interpreting results from different studies.

Aesthetics from below A central question in visual aesthetics is how representations of visual features and content map onto representations of value or appreciation. To address this question, it is helpful to recall how visual perception and representation is structured. Vision is generally divided into low-, mid-, and high-level processes. Low-level vision includes the computation of local features such as contrast, orientation, colour, and motion. Mid-level vision is conceptualized as organizational processes such as element grouping, contour completion, surface segmentation, and depth assignment. Finally, high-level vision includes processes such as object and scene recognition that require making contact with stored representations. In empirical aesthetics, there is a strong tradition of an “aesthetics from below” approach that seeks to explain aesthetic preferences for a visual experience as the combination of preferences for a variety of objectively measurable low-, mid-, and high-level features. Yet while there do indeed appear to be a number of stimulus dimensions that are hedonically “marked,” this approach has yielded mixed results. Two of the most studied features in this regard are symmetry and angular versus curved contours. Across a wide variety of stimuli from abstract geometrical patterns ( Jacobsen & Höfel, 2002) to faces (see Rhodes, 2006), symmetry positively predicts average preferences (Bertamini et al., 2019). Similarly, smoothly curved contours tend to be preferred over angular and/or jagged contours for a variety of visual stimuli, including images of objects and abstract patterns (Bar & Neta, 2006), closed shapes (Bertamini et al., 2016), product packaging (Westerman et al., 2012), and architectural interiors (Vartanian et al., 2013). In addition, a number of other low- and mid-level features have also been claimed to predict preferences, such as contrast and clarity (Reber et al., 1998; Tinio & Leder, 2009), the presence of certain colours (McManus et al., 1981), and aspect ratio (McManus, 1980). Beyond localized features, a number of global features of images, such as anisotropy (inhomogeneity of orientation, such as the predominance of vertical and horizontal features in city scenes) and self-similarity (similar structure at multiple scales, such as fractals) have been explored under the hypothesis that such features reflect key aspects of the statistics of natural images to which the visual system is sensitive (e.g., Mallon et al., 2014; Redies, 2007; Spehar et al., 2016). Researchers have also investigated configural or holistic features that could be subsumed under the label of “good composition,” such as balance (Hübner & Fillinger, 2019) and positioning in a frame (Sammartino & Palmer, 2012). Yet this approach has some clear limitations. Often, such features fail to show a clear relationship to aesthetic preferences, and within a single study, the effects are often quite small, with a large degree of variance from person to person (e.g., McManus et al., 2010; Spehar et al., 2016).

High-level aesthetic domains and shared versus individual taste Research that specifically measures the degree of “shared” versus “individual” taste across a set of observers has found that people often don’t agree in what they find visually appealing. Vessel and Rubin (2010) 106

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reported a study in which observers made preference judgments on a set of photographs of real-world scenes and, separately, a set of abstract images that contained variation in low- and mid-level features but lacked any shared semantic interpretations. While the observers’ judgments revealed a strong degree of shared taste for real-world scenes, the same observers were much more idiosyncratic in their ratings of the abstract images. That the presence of visual features alone did not lead to consistent ratings across observers highlights the importance of higher-level, semantic meanings and associations for appreciation of scenes. A study of simple colour preferences reached a similar conclusion, finding that differences in colour preferences were well predicted by participants’ liking of objects associated with those colours (e.g., positive associations of sky with blue but negative associations of excrement with yellow-brown; Palmer & Schloss, 2010). This issue becomes even more apparent when one turns to a defining feature of high-level vision—the specialized processing of object and scene features in category-selective visual regions. The human ventral occipitotemporal cortex (VOT) contains distinct regions that respond selectively to faces, bodies, scene layout and identity, objects, and symbols (see Grill-Spector & Weiner, 2014 for a review). Domains of aesthetic evaluation (e.g., faces, natural landscape, dance, fashion, architecture, visual artwork, design objects) do not neatly map onto these visual categories in a one-to-one manner (for example, architectural images may contain aspects of scene layout but also object-like properties). Yet it has become increasingly clear that appreciation of visual images that fall under different domains, such as faces and paintings, results in different levels of agreement across individuals (Bignardi et al., 2021; Leder et al., 2016; Vessel et al., 2018). Specifically, individuals tend to agree more on what they like when they are appraising images of natural kinds such as faces and landscapes than when they are judging cultural artefacts such as architecture and paintings (see Figure 6.2; variance component analysis of data adapted from Vessel et al., 2018; Martinez et  al., 2020). What leads to these differences is still a matter of debate. One possible explanation is that inherited versus acquired brain concepts play a role when appraising visual images of biological versus nonbiological kinds (Zeki, 2011; Zeki & Chén, 2020; see Zeki et al., 2020 for a definition of brain concepts). An alternative suggestion is that judgments of natural kinds have greater behavioural relevance for everyday decisions compared to cultural artefacts (Vessel et al., 2018); over many years of interaction, this could lead different people to base their appraisals on similar information. These differences in shared taste provide a way to organize findings from studies of visual appreciation in a manner that links to the organizational principles of high-level vision. While this approach has limitations (e.g., scenes may contain a mixture of naturalistic and human-made content), we will use this taxonomy from naturalistic (people, natural landscapes) to artefactual images (human-created objects) to describe the findings from visual appreciation. Among naturalistic visual aesthetic domains, appreciation of faces has the highest level of agreement across individuals (see Figure 6.2; Vessel et al., 2018). There is a strong degree of shared taste for evaluations of faces, and individuals have a good estimation of imagined ratings of others (Bignardi et al., 2021; Hönekopp, 2006; Leder et al., 2016; Vessel et al., 2018). Moreover, individuals from different cultures tend to agree on which faces are on average considered more attractive, regardless of the ethnicity of the faces being rated, and infants tend to look more at faces that are indeed rated more attractive by adults (Langlois et al., 2000; Slater et al., 1998). Several features of faces, including degree of symmetry, averageness, and statistical typicality more generally, can account for some of this agreement across observers (see Rhodes, 2006 for a review; see Ryali et al., 2020 for details for statistical typicality). Although visual scenes (or “places”) are often considered a category from the perspective of visual perception, this may be too general from the perspective of aesthetics, especially as it cuts across the natural versus artefactual distinction. Rather, we will discuss natural landscapes separate from humanmade architectural settings (exterior and interior). Natural landscapes are another category for which agreement is substantially shared, though to a lesser extent than for images of faces (see Figure 6.2, Vessel et al., 2018). Within the environmental assessment 107

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Figure 6.2 Amount of “shared taste” across observers varies by visual aesthetic domain. Variance component analysis (upper panel) of aesthetic ratings for images of faces, places, exterior and interior architectural places, and paintings. By using a multilevel intercept-only null model (Martinez et al., 2020), it is possible to partition the percentage of variance in (1) appraisal that is common across individuals (image, here represented in yellow, that is variance accounted for by item level characteristics, or better by the shared experiences evoked in the individuals by such characteristics), (2) appraisal that is unique to the individual (participant, here represented in green, that is the overall individual participant appraisal averaged across images within a domain), (3) appraisal that emerges as an interaction from the individual appraising and the image appraised (interaction, here represented in purple), and (4) error (residual, here represented in gray). CI represents the confidence interval, obtained following a bootstrapping procedure (2000 bootstrap samples, following Sutherland et al. [2020]). The lower panel shows the proportion of variance expressed only over non-residual variance which is accounted for by shared (“shared taste,” yellow) and unique (blue) components. “Shared taste” decreases along the axis that goes from natural to artefactual (faces to paintings), while “idiosyncratic taste” tends to follow an inverse trend. Reanalysis of visual appreciation ratings from Vessel et al.(2018). Data at: https://edmond.mpdl.mpg.de/imeji/collection/dMlhGcI642YmIIF2.

literature, there is a strong tradition of investigating the presence of natural versus humanmade intrusions (Biederman & Vessel, 2006; S. Kaplan et al., 1972), the presence of refuge or an expansive view (Appleton, 1975), the likelihood of new information emerging (“mystery”; Kaplan, 1992), and the presence of specific biomes (see Kaplan & Kaplan, 1995; see also Chapter 10). More recently, some of these measures have been related to spectral measures of scene shape (e.g., openness, Oliva & Torralba, 2001; Pegors & Epstein, 2011). Yet while aesthetic ratings for naturalistic images show higher agreement than artefactual ones, higher doesn’t mean universal. For example, even for faces, only about half of the variation in appraisal can be explained by shared preferences (Germine et al., 2015; Hönekopp, 2006; Vessel et al., 2018). That is, while it seems that agreement on visual appraisal follows a gradient from shared to idiosyncratic—from naturalistic to artefactual, respectively—idiosyncrasies still tend to emerge. As Hönekopp (2006, p. 208) said “(almost) all people are good-looking—at least to some.” This is also another descriptive finding from the field of empirical aesthetics, namely that variation in appraisal is the norm, not the exception. In contrast to natural landscapes, images of architecture produce quite idiosyncratic ratings of appeal across individuals (both interior and exterior; see Figure 6.2; Vessel et al., 2018), despite being photographs of real-world “places.” Factors such as angular (rectilinear) versus curved interiors, open versus closed spaces, and ceiling height have been found to have an impact on average aesthetic judgments of architectural interiors (Coburn et al., 2020; Vartanian et al., 2013) and also to affect approach-avoid judgments in non-experts 108

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(Vartanian et al., 2017). A recent study has proposed that the aesthetic experience of interior architecture can be organized along dimensions of “fascination,” “coherence” (ease of understanding the organization of a scene), and “hominess” (Coburn et al., 2020; though it should be noted that hominess was highly correlated with naturalness; see Chapter 10 for more information). Finally, aesthetic judgments of images of visual artwork (paintings) tend to produce highly individual ratings; it is typical that less than 10% of repeatable variance can be accounted for by a shared factor (Leder et al., 2016; see Figure 6.2, Vessel et al., 2018). Of course, artworks are a very heterogeneous category of stimuli. Yet even within artworks, representational works produce higher agreement than abstract works (Schepman et al., 2015). This agreement has been related to similarity in liking for associated semantics (Schepman et al., 2015) and for objects contained in paintings (Levitan et al., 2019). Ambiguity and multiple-interpretability can also play a central role (Muth et al., 2015), lending artworks multiple levels of meaning. Taken together, this body of work suggests that especially for human artefacts, a purely stimulus-driven approach that seeks to compute liking based solely on an analysis of visual features is limited in what it can achieve. Not only is it unable to capture the wide degree of individual differences observed in aesthetic preferences, it may not even be able to account for a majority of variance in average ratings for some aesthetic domains.

The interactionist view A second approach emphasizes the role of the idiosyncratic experience of the perceiver in aesthetic appraisal: How does a visual stimulus interact with an existing mind/brain, whose goal is to make sense of the world? This approach can be characterized as essentially about information processing and recasts aesthetic appreciation as pleasure from understanding. This approach also has a long history. Constructs such as novelty, familiarity, complexity, and order have received quite a bit of attention, particularly by Zajonc (1968; the mere exposure effect), Berlyne (1958, 1970, 1974), and their contemporaries in the mid-20th century.1 In the environmental aesthetics literature, Kaplan (1992) summarized a variety of findings on landscape preferences in a theory that emphasized the roles of exploration and understanding of one’s environment. More recently, processing fluency (Reber et al., 2004), which holds that perceptual and cognitive experiences that are processed more easily are preferred, has received widespread attention, particularly as it is capable of incorporating both preferences for previously exposed stimuli and also preferences for a number of specific stimulus dimensions (e.g., higher contrast, prototypical shape). We note, though, that processing fluency has difficulty accounting for the fact that people often exhibit novelty preferences or in fact seek out, and get more pleasure from, complex, challenging, or informationally rich experiences (e.g., Muth et al., 2015; see also Graf & Landwehr, 2015). Another line of work has emphasized the role of meaning and semantic associations (Biederman & Vessel, 2006; Levitan et al., 2019; Martindale, 1984; Palmer & Schloss, 2010; Vessel & Rubin, 2010) and measures that more directly relate to information processing, such as expectations, surprise, uncertainty, entropy, compressibility, and learnability (Biederman & Vessel, 2006; Schmidhuber, 2010; Schoeller & Perlovsky, 2016; Silvia, 2005; see Chapter 26). Although details vary across several formulations, the central theme is that the brain is constantly engaging in prediction of its sensory world (Clark, 2018; Friston, 2010), and violations of those expectations (surprise) engage mechanisms that attempt to make sense of the unexpected input. Both the initial surprise (Loewenstein, 1994; triggering interest/curiosity; Silvia, 2008) and the potential subsequent “click of comprehension” from successful sense-making (Biederman & Vessel, 2006; Muth & Carbon, 2013) are experienced as pleasurable. This approach has gained significant momentum and support, particularly in the music domain (e.g., Cheung et al., 2019; Gold et al., 2019; see also Chapters 7 and 16). Two key insights of this class of 109

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theories are that (1) higher-order (semantic, conceptual, personal) information matters more than lowerorder information, reflecting the hierarchy of sensory processing, and (2) that people get the most pleasure from experiences that provide the greatest opportunity for learning, because they offer novel information that is richly interpretable to the perceiver. While some of these measures may reflect common aspects of human experience and could therefore be potentially computed solely from a stimulus (or statistics of occurrence over a set of stimuli), the majority of these measures, and the finer aspects of all of them, can only really be computed by taking into account the interaction of a particular stimulus with the specifics of an observer’s past history (exposure), associations, beliefs and expertise. Indeed, even low- and mid-level predictors like contrast, symmetry, and angularity can likely be recast in terms of access to information content. Visual appreciation is less about specific features and more about one’s ability to predict, make sense of, and learn about our world.

How the brain supports visual aesthetic appreciation How does the brain get from a neural representation of what we see to a representation of what we like? One of the primary topics tackled by neuroaesthetics research is the question of whether there is a specific pattern of brain activity associated with the psychological state of finding something aesthetically appealing. As reviewed in the previous section, it is clear that these states can be elicited by different experiences for different people. Many experiments in neuroaesthetics have used fMRI to identify brain regions that are activated in response to the subjective experience of high aesthetic appeal, variously characterized as the feeling of beauty, “being moved,” or liking. These experiments aim to detect correlations between brain responses and subjective states by computing statistical contrasts between average patterns of blood oxygen level–dependent (BOLD) signal for different experimental conditions (e.g., “beautiful” versus “ugly” or “highly moving” versus “not moving”) or by measuring an association between BOLD signal and a continuously varying parameter (e.g., rated enjoyment). While less often the focus, several studies also have sought to identify brain systems that support explicit judgments of aesthetic appeal by comparing aesthetic versus non-aesthetic judgment tasks. More recently, the question of how the brain transforms a neural representation of what we see into a representation of what we find appealing has also come into sharper focus, though at this stage, it is fair to say that an answer remains far off. Based upon comparisons of neural activation for high versus low aesthetic appeal, as well as studies that compare aesthetic versus non-aesthetic tasks, there is evidence that several large-scale brain systems contribute to visual aesthetic appreciation. In addition to the visual system and subcortical brain systems for valuation and reward, there is also evidence for engagement of medial and inferior lateral prefrontal cortical areas, the default-mode network, and in some cases somato-motor systems. In the following, we will discuss findings related to specific brain systems, followed by a discussion of integration across networks.

The visual system It is clear that visual experiences with aesthetic material can engage most, if not all, of the visual system, depending on the type of stimulus (static/dynamic, scenes versus objects or bodies, moving through space, etc.) What is less clear is how a neural representation of what we see, as is thought to exist in feature- or category-selective regions of the ventral visual pathway (Grill-Spector & Weiner, 2014; Kanwisher, 2010; Pitcher et al., 2009), is related to representations of aesthetic or affective dimensions. Is aesthetic value represented only in core “valuation” regions such as the striatum or orbitofrontal cortex? Or are there already signals present in visual regions reflecting aesthetic value? 110

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Early visual processing As discussed previously, there is evidence that at least some visual feature dimensions correlate with aesthetic appeal, though with the caveat that such evidence is often seen at the level of average preference, and often with small effect sizes. However, there do not exist many neural studies that directly link such preferences to neural activation. For example, higher-contrast stimuli lead to higher responses in early visual regions (Boynton et al., 1999), and this sensitivity is reduced in higher-order regions such as the lateral occipitotemporal cortex (LOC; Avidan et al., 2002), but no link has been made between these findings and preferences for high-contrast images. Symmetry responses first appear in V3, with strongest responses in area VO1 (Keefe et al., 2018; Sasaki et al., 2005). However, an experiment that specifically looked at aesthetic judgments of symmetrical versus asymmetrical stimuli found no differences for either symmetry nor aesthetic appeal of symmetric stimuli in visual regions ( Jacobsen et al., 2006), though these authors did report higher activity in early visual regions for more complex stimuli. On the other hand, contour curvature strongly modulates neural responses in V4 (Pasupathy & Connor, 2002), and angular (versus curved) stimuli lead to greater activation in a number of early visual areas (Bar & Neta, 2007). Within the domain of motion, a study of kinetic

Figure 6.3 The visual system and aesthetic appreciation. On both (A) lateral and (B) ventral surfaces of the brain, a variety of feature- and category-selective visual regions have been identified. These include the fusiform face area (FFA), parahippocampal place area (PPA), lateral occipital complex (LOC), occipital face area (OFA), and extrastriate body area (EBA). In addition, a number of retinotopic visual regions (V1–V4) as well as V5/MT also show selectivity for a variety of low- and mid-level visual features. A number of studies have reported modulations of these regions by aesthetic appeal. Also shown are approximate locations of regions within ventral occipital and inferior temporal areas, such as the ventral occipital temporal cortex (VOT), inferior temporal cortex (IT), fusiform gyrus (FG), lingual gyrus (LG), and parahippocampal gyrus (PHG). Regions can also be organized into broader visual streams (dotted arrows), including the ventral (“what”) pathway, the dorsal (“where/how”) pathway, and a recently identified middle pathway subserving social vision including the posterior superior temporal sulcus (pSTS). To date, most work on visual aesthetics has focused on the ventral pathway. The regions consisting of the action observation network (AON), including the parietal cortex, premotor cortex, and inferior frontal gyrus (IFG), are thought to be engaged by aesthetic appeal of dance and body movements. The upper and lower arrows denote the dorsal and ventral streams, respectively, and the middle arrow, through V1/V2, EBA/V5, and pSTS, denotes the recently proposed “social perception” pathway (Pitcher & Ungerleider, 2021).

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dot patterns reported greater activity for aesthetically preferred patterns in an area of visual cortex likely overlapping with motion-sensitive regions (V5/middle temporal (MT) area; Zeki & Stutters, 2012), though the relationship between observers’ preferences and measures of motion energy was not entirely clear (for an in-depth discussion of the neural correlates of motion appreciation, see Chapter 9). In contrast to motion, an fMRI study on harmonious colours found no significant difference in activity within V4 between aesthetically preferred colour patches and non-preferred ones (Ikeda et al., 2015). Overall, there is very little brain imaging work that attempts to link low- and mid-level features, and their fMRI correlates, directly to aesthetic appeal, and what work there is does not provide a coherent picture. One possible interpretation of the literature is that more activity in visual regions is generally preferred. With the potential exception of angularity, where angular stimuli were associated with greater activity (Bar  & Neta, 2007), most of the existing work is not inconsistent with this hypothesis. A more parsimonious summary of the existing evidence is that activity in early visual cortices does not generally bear a strong, consistent relation to aesthetic preferences.

Higher-level visual processing Across a number of visual aesthetic domains, there is evidence for activity changes in higher-level portions of ventral, lateral, and medial occipito-temporal cortex correlated with aesthetic appeal. The exact location of these modulations depends on the stimulus domain, though there has generally been a greater focus on parts of the ventral visual pathway, including the ventral occipitotemporal cortex, thought to support object and scene recognition (e.g., the “what” pathway; Goodale & Milner, 1992; Ungerleider & Mishkin, 1982). For faces, the most well-studied domain of visual aesthetic appraisal, there is evidence that the intensity of aesthetic appeal is correlated with activity in portions of a face-selective network that is composed of a series of patches in the occipital and the temporal lobes. Two of these regions, the occipital face area (OFA; Gauthier et al., 2000) in the inferior occipital gyrus and the fusiform face area (FFA; Kanwisher et al., 1997) in the fusiform gyrus (FG), are part of the ventral visual pathway, while a third, the superior temporal sulcus (STS; Haxby et al., 2002), is part of a recently suggested “social perception” visual pathway (Pitcher & Ungerleider, 2021). BOLD activity in the FG and/or the STS correlates with the attractiveness of faces as reported by participants (Kranz & Ishai, 2006; Pegors et al., 2015; Winston et al., 2007). For example, Kranz and Ishai (2006) reported enhanced activation in all of the face-specific brain network areas for attractive faces when compared to neutral or non-attractive faces. Moreover, a correlation with attractiveness was observed in the FFA even when participants were not explicitly rating the attractiveness of the face, suggesting that the beauty of a face is accessed by the human brain automatically (Chatterjee et al., 2009; Kocsor et al., 2013). Several more advanced methods have also been used to probe facial attractiveness representations in the visual system. For example, a study using multivariate pattern analysis (MVPA), a technique that assesses spatial patterns of BOLD signal rather than activation per se, found that patterns of BOLD signal in FFA encode the perceived beauty of faces (Yang et al., 2021). Further, using representational similarity analysis (RSA) with electroencephalographic (EEG) data, Kaiser and Nyga (2020) found that facial attractiveness judgments are reflected in signals recorded as early as 150 ms after stimulus onset. However, other studies, including meta-analytic studies, did not find any relationship between activity in face-selective regions and the appraisal of faces (Bzdok et al., 2011; Chuan-Peng et al., 2020). In addition, the strong link between stimulus features of a face and attractiveness ratings (and thus high “shared taste” across observers) makes it difficult to assess the degree to which these reported modulations reflect stimulus differences or true sensitivity to aesthetic appeal. Similar to faces, a number of studies using visual scenes have also found modulations of ventral visual regions correlated with aesthetic appeal. In one study, a mixture of indoor and outdoor scenes rated as 112

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pleasing were found to correlate with higher activity in a region of the parahippocampal gyrus (PHG; Yue et al., 2007) that shows selectivity for scenes (the parahippocampal “place” area; PPA; Epstein & Kanwisher, 1998). Yet given the important distinction between natural kinds and cultural artefacts, it likely makes sense to separate out effects observed with natural landscapes versus those with humanmade environments and architecture. Within studies that have specifically focused on natural landscapes, one study that asked observers to rate how “sublime” the landscapes were found that more sublime images were correlated with more activity in an extensive portion of VOT stretching from the FG to PHG and the hippocampus (Ishizu & Zeki, 2014). Another study also found that attractive landscapes were correlated with greater activity in the PHG, but the activity differences only survived a stringent threshold in an object-selective region (lateral occipitotemporal cortex) and not in the PPA per se (Pegors et al., 2015). Overall, the precise relationship of appeal-related activity to scene-selective regions remains unclear. In a study using movies of natural landscapes rather than images, Isik and Vessel (2021) found that aesthetic appeal was associated with greater activity in regions of PHG but also with the middle occipital sulcus (MOS) and posterior middle temporal gyrus (pMTG) on the lateral surface. These activations were immediately adjacent to scene-selective occipital place area (OPA) and motion-sensitive MT, but these regions themselves (identified using independent localizers) were not significantly modulated by appeal. In contrast to natural landscapes, architecture is the aesthetic domain that generally encompasses humanmade places such as building exteriors or interior spaces, and ratings of aesthetic appeal of architecture tend to be characterized by strong individual taste (e.g., Figure 6.2; Vessel et al., 2018). In a study using images of building exteriors, Kirk et al. (2009) did not report any modulation by aesthetic appeal in early visual regions. However, Vartanian et  al. (2013) found that activity in the middle occipital gyrus (MOG) was correlated with beauty ratings of interior spaces and also that curvilinear spaces (which are preferred) led to greater activation than rectilinear spaces in early visual regions (calcarine sulcus, lingual gyrus). Using the same data, Coburn et al. (2020) additionally found that ratings of fascination, coherence (the degree to which a scene could be easily understood), and “hominess” (which correlated strongly with naturalness), factors that reflect peoples’ assessments of interior architectural spaces, were correlated with differences in activity in early visual regions. Turning to artworks, another visual aesthetic domain that shows a very low degree of shared taste across observers (Leder et al., 2016; e.g., Figure 6.2; Vessel et al., 2018), there are again a number of reports of modulations in visual regions by aesthetic appeal. Correlations with aesthetic appeal have been reported in the inferotemporal sulcus (ITS; Vessel et al., 2012), FG (Vartanian & Goel, 2004), and PHG (Vessel et al., 2012). The heterogeneity of the types of artworks used (from representational paintings of landscapes, people, and still lifes to surrealist or abstract works with few to no identifiable objects) makes it difficult to identify a systematic relationship between the features of an artwork and where one might expect modulation by appeal. On the other hand, the low degree of agreement in Vessel et al. (2012) on which particular paintings people found aesthetically moving makes it more likely that the observed activations reflected aesthetic appeal per se and not visual features of the artworks. In a study comparing several visual aesthetic domains (natural landscapes, architecture, visual artwork), Vessel et al. (2019) found that category-selective regions in VOT tended to be more active for appealing images of all three domains. In addition, multivoxel patterns across VOT contained a small but significant degree of information about aesthetic appeal for all three domains. However, these patterns were only weakly predictive of appeal and highly domain specific: the spatial pattern of activity that predicted appeal for architecture, for example, was not predictive of appeal for artwork or landscapes (and vice versa). Watching and enjoying a dance performance critically depends on seeing and interpreting bodies and body movements. As such, it is not surprising that the extrastriate body area (EBA), a part of the actionobservation network (AON), is engaged by watching dance and is also modulated by rated appeal of short 113

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dance movements (Calvo-Merino et al., 2008; Cross et al., 2011). As the growing body of research on the neuroaesthetics of dance is covered in a separate chapter (Chapter 16), it will not be dealt with in depth here. There have been several meta-analysis studies which attempt to isolate brain systems that are reliably activated across neuroimaging studies in response to visual aesthetic appreciation regardless of task differences (e.g., Vartanian & Skov, 2014; Boccia et al., 2016). A recent study applied activation likelihood estimation (ALE) in order to identify the neural correlates of reactions to specific visual categories of artworks (portraits, scenes, abstract paintings, body sculptures). It showed different content-dependent areas of the VOT are involved in aesthetic appreciation; for example, the FFA is involved in evaluating beauty of a portrait, and a few additional frontal and subcortical areas are also involved (Boccia et al., 2016). But other ALE studies primarily emphasize the engagement of frontal and subcortical regions with aesthetic appreciation (e.g., Brown et al., 2011; Chuan-Peng et al., 2020; Kühn & Gallinat, 2012; see subsequently). Taken together, this body of literature suggests that aesthetic appeal can lead to modulations of portions of the visual system in a manner that appears dependent on the precise nature of the stimulus being considered. However, it remains unclear how these activations relate to the well-documented feature and category selectivity of the higher-level visual cortex and what exactly is being computed or represented in these regions modulated by aesthetic appeal.

Subcortical reward circuitry Reward processing, particularly hedonic pleasure but likely also reward prediction and learning, are core aspects of visual aesthetic appreciation. Like other abstract rewards such as money (Delgado et al., 2000) and music (Blood & Zatorre, 2001), appealing visual images can activate regions of the basal ganglia associated with reward processing (Aharon et al., 2001; Vartanian & Goel, 2004; Yue et al., 2007). While the potentially rewarding properties of an attractive face or a resource-rich location may make it seem obvious that such stimuli would engage the brain’s reward circuitry, it is in fact quite significant that visual stimuli such as artwork, which have no a priori survival value associated with them, can also engage this same system (Lacey et al., 2011). Within the basal ganglia, modulations by aesthetic appeal appear most often in portions of the striatum. The main components of the striatum are its dorsal part, composed of portions of the caudate and the putamen, and its ventral part, primarily the nucleus accumbens (NAcc). This subcortical structure is one of the major dopamine-containing areas in the brain, receiving most of the dopaminergic projections from the substantia nigra in its dorsal part and from the ventral tegmental area in its ventral part (Purves et al., 2018). It is also one of the brain regions with the highest density of mu-opioid receptors, especially in its ventral section (Meier et al., 2021). Across a variety of natural and artefactual visual aesthetic domains, BOLD activity in the striatum has been found to be modulated by aesthetic appeal. In several studies, activation in the striatum has been found to correlate linearly with the attractiveness of faces (Aharon et al., 2001; Cloutier et al., 2008; Kim et al., 2007; T. Wang et al., 2015), and there is some evidence that activation of the ventral striatum occurs early and in a relatively automatic fashion (Kim et al., 2007). Similarly, pleasing scenes (Yue et al., 2007), sublime natural landscapes (Ishizu & Zeki, 2014), and aesthetically appealing movies of natural landscapes (Isik & Vessel, 2021) have also been found to modulate striatal activity. For artefactual categories, the aesthetic appeal of artworks has been repeatedly associated with activity in the striatum (Belfi et al., 2019; Vartanian & Goel, 2004; Vessel et al., 2012), and the beauty of interior architecture has been reported to modulate activity in the globus pallidus (Vartanian et al., 2013), an adjacent region of the basal ganglia. It has also been claimed that interactions with artwork engage the striatum compared to non-art images regardless of aesthetic appeal, reflecting the rewarding nature of engaging with artwork (Lacey et al., 2011). There is generally a lack of

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reports of aesthetically appealing dance sequences leading to striatal activation, though it is the case that higher NAcc activity was observed for liked dance sequences before individuals were trained on the dance moves (but not after; Kirsch et al., 2015). However, the precise location of activations within the basal ganglia, particularly with respect to ventral (NAcc) versus middle or dorsal (putamen, caudate) striatum, is less clear. The NAcc has been tightly linked to reward processing, together with the medial orbitofrontal cortex (mOFC)/ventromedial prefrontal cortex (vmPFC), and is considered a key node of the reward circuit, along with the interconnected medial, dorsolateral prefrontal, and orbitofrontal areas; the amygdala; and dopaminergic midbrain nuclei (Heekeren et al., 2007; O’Doherty, 2004; Yacubian et al., 2007). BOLD increases in NAcc have been reported both for preferred faces (Kim et al., 2007) and for moving artwork (Belfi et al., 2019). Yet for visual images, activations in the dorsal striatum are perhaps even more common, appearing for preferred faces (T. Wang et al., 2015), scenes (Yue et al., 2007), and artwork (Vessel et al., 2012, 2019). In the decision-making literature, NAcc has been identified with “critic” functions by representing actual reward and reward-prediction errors (Schultz et al., 1992; Setlow et al., 2003; Wan & Peoples, 2006; though see Knutson & Greer, 2008), whereas dorsal activations have been identified with “actor” functions of learning and habit implementation (Maia, 2009) and reward expectation (Delgado et al., 2000, 2003). Within the music literature, it has been suggested that NAcc activation reflects peak moments of (consummatory) hedonic pleasure, while dorsal striatal activation reflects pleasure from anticipation (e.g., prediction) of such moments (Blood  & Zatorre, 2001; Salimpoor et  al., 2011). Yet the results of studies with visual stimuli would argue against such a strict interpretation: aesthetically appealing artworks and landscapes have been more directly associated with modulation in dorsal striatum, and the pleasure derived from visual aesthetic experiences cannot be considered purely anticipatory. In addition, recent imaging work using poetry found activation in NAcc preceding the reported onset of chills (Wassiliwizky et al., 2017). The precise function of these different structures in aesthetically appealing experiences thus remains an open question. Another question that remains empirically unanswered is whether the neurotransmitters that play a significant role in striatal and especially NAcc functioning (i.e., dopamine, GABA, and opioids) also play a role in visual aesthetic appraisal (Spee et al., 2018). On the one hand, while dopaminergic signalling has been shown to broadly play a role in reward-based associative learning (Schultz, 1998) and has already been linked to appealing music (Ferreri et al., 2019; Salimpoor et al., 2011), its link with visual aesthetic appraisal is yet unknown. On the other hand, there is at least some evidence that the endogenous opioid system may play a role in visual appraisal. In a study where males were asked to rate female faces, blocking opioid activity (by naltrexone) decreased liking and wanting to highly attractive faces, while increasing opioid activity (by morphine) led to an increased liking for the most attractive faces and an increase in exerted effort to both see highly attractive and avoid highly unattractive faces (Chelnokova et al., 2014; though we note that the effects were modest; see Meier et al., 2021). However, it is yet unclear whether opioidergic modulation of neural activity impacts visual aesthetic appreciation by exerting a direct effect in the ventral striatum or if it also impacts activity in the sensory cortices, as has been previously suggested (Biederman & Vessel, 2006) based on their distribution in these areas (Wise & Herkenham, 1982). Another subcortical region that seems to encode information relevant for aesthetic appeal is the amygdala. In primates, the amygdala has been shown to track the reward value associated with abstract visual stimuli (Paton et al., 2006). Yet reports of amygdalar activation positively associated with aesthetic appeal in humans are more rare. BOLD activity in the amygdala has been related to differential activity for images of angular (versus curved) objects (Bar & Neta, 2007), to disharmonious (versus harmonious) colour combinations (Ikeda et al., 2015), to beautiful versus ugly sculptures (Di Dio et al., 2007), and to facial attractiveness (though in a nonlinear manner potentially reflecting arousal; Bzdok et al., 2011).

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Figure 6.4 Prefrontal and subcortical structures implicated in visual aesthetic appeal. (A) On the medial surface of the cortex, the divisions of the medial prefrontal cortex are visible, including the anterior cingulate cortex (ACC); medial prefrontal cortex (mPFC); and, below the gyrus rectus, the ventromedial prefrontal cortex (vmPFC), which is contiguous with the medial orbitofrontal cortex (mOFC). (B) In a coronal section, the substructures of the basal ganglia are visible, including the caudate, putamen, and nucleus accumbens (NAcc; one of the structures of the ventral striatum). (C) On the ventral surface, the medial (mOFC) and lateral (lOFC) divisions of the orbitofrontal cortex are visible.

Prefrontal cortex Across a variety of sensory modalities, several regions of the prefrontal cortex are thought to mediate important aspects of aesthetic experiences (e.g., Brown et al., 2011; Vessel, 2020). Yet, while visual appreciation is no exception, as will be discussed in the following, it is also the case that there is a remarkable degree of heterogeneity in reported engagement of the prefrontal cortex for aesthetically appealing images. Primary among the prefrontal regions implicated by aesthetic processing are those thought to support valuation, representation of conscious feeling/emotion, and integration of valuation with ongoing state and goals. In particular, the ventral portion of the medial prefrontal cortex, including the ventral and anterior medial prefrontal cortex (vMPFC, aMPFC), anterior cingulate (ACC), especially the ventral subgenual portion, and medial orbitofrontal cortex, have been repeatedly identified as central for aesthetic appeal. We note that summarizing fMRI findings from mOFC and vMPFC can be challenging. Not only are labelling conventions for this part of the brain inconsistent across different subfields and labs (Ishizu, 2019; Vessel, 2020), the orbital and medial prefrontal cortex exhibit large anatomical variability across individuals (e.g., Fornito et al., 2008). To make matters worse, these regions are highly susceptible to spatial distortions during fMRI due to their location above the eyes and sinus cavities; these distortions can lead to mislocalization of an activation by up to several centimetres. Over a decade of research suggests a close relationship between aesthetic appeal and activity within the mOFC and vMPFC using a wide range of stimuli. Modulations of fMRI activity by aesthetic appeal have been reported below the superior rostral sulcus (SRS) for faces (Kim et al., 2007; O’Doherty et al., 2003), artwork (Kawabata & Zeki, 2004; Lacey et al., 2011) scenes containing central objects (Kirk, 2008), and buildings (Kirk et al., 2009). In turn, modulations by aesthetic appeal have been reported in or above the SRS for faces (O’Doherty, 2004; Smith et al., 2010); artwork (Lacey et al., 2011; Vartanian & Goel, 2004; Vessel et al., 2012); abstract patterns ( Jacobsen et al., 2006); art and music (Ishizu & Zeki, 2011); bodies 116

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(Martín-Loeches et al., 2014); architecture (Vartanian et al., 2013); motion stimuli (Zeki & Stutters, 2012); harmonious versus disharmonious colours (Ikeda et  al., 2015); and non-visual domains, including mathematical beauty (Zeki et al., 2014). A more dorsal portion of the MPFC has also been reported to correlate with aesthetic preferences, at least for paintings (Vessel et al., 2012). The relationship between rated appeal and mOFC or MPFC activity has generally been reported to be positive and monotonic, though the precise shape is less clear. There is a notable lack of reports of modulation by aesthetic appeal in the MPFC/mOFC for several visual aesthetic domains, notably natural landscapes (Isik & Vessel, 2021; Yue et al., 2007) and dance stimuli. Yet given the well-documented role of this region in valuation more generally (Bartra et al., 2013; O’Doherty et al., 2003), it would be surprising if it were to play no role for certain types of aesthetic valuation. One potential explanation for this discrepancy may be the spatial scale at which aesthetic appeal is represented, making it difficult to detect using activation methods. Another possibility is that the lack of MPFC/ mOFC activation for some domains stems from methodological or analytical differences, such as how BOLD responses to temporally extended stimuli (e.g., dance, music) are modelled. Studies using multivariate analysis methods, which generally do not average signal over neighbouring voxels but rather assess whether spatial patterns across a region of cortex can be related to a psychological state, have also identified the mOFC and vMPFC as important for a variety of sensory valuations. Multivoxel patterns across the OFC and vMPFC were able to predict valence judgments (pleasant-tounpleasant) for both visual and gustatory stimuli, whereas other regions (ventral temporal cortex and anterior insula) contained patterns that were specific to either vision or taste, respectively (Chikazoe et al., 2014). A study of faces and natural landscapes found that a multivoxel spatial correlation measure across the vMPFC was predictive of attractiveness ratings for both domains (though there was also a degree of domain specificity; Pegors et al., 2015), and a study of natural landscapes, architecture, and visual artworks also found that multivoxel patterns in parts of the aMPFC and dMPFC (belonging to the default-mode network; see the following) represented aesthetic appeal in a domain-general manner (Vessel et al., 2019). A recent study using intracranial EEG found that high gamma band activity in the vMPFC and OFC—a measure of high frequency neuronal oscillations thought to reflect coordinated firing of local neural ensembles (Lachaux et al., 2012)—was related in a domain-general fashion to the likability that participants expressed for images of faces, paintings, and food (Lopez-Persem et al., 2020). Critically, neuronal activity was related to rated appreciation even when individuals were not explicitly rating appeal, supporting the hypothesis that assessments of visual appreciation occur implicitly, even when not required. Interestingly, the authors also reported that baseline activity in the vMPFC and OFC before image presentation was significantly related to rated appreciation, potentially reflecting the influence of an observers’ internal state on visual appreciation. While it is difficult to obtain strong causal information on brain function in humans, two studies have used brain stimulation to explore the potential causal role of the mOFC/vMPFC in aesthetic appeal. They report that transcranial direct current stimulation (tDCS) designed to enhance the excitability of the MPFC increased aesthetic ratings of paintings (Cattaneo et al., 2019; Nakamura & Kawabata, 2015). Given the difficulty in modelling how tDCS current flows through the cortex, a note of caution is warranted in interpreting these results. Another approach to studying causal relationships between cognition and brain function are lesion studies. Although there are no neuroaesthetics studies exploring change in aesthetic evaluation by patients with an mOFC/vmPFC lesion, one study reported that patients with injuries to the ventral PFC (including the mOFC/vmPFC) reportedly suffered impairments when making moral judgments (Young et  al., 2010). Considering the fact that this region is engaged when judging moral beauty (Tsukiura & Cabeza, 2011), it would be interesting to test whether such a patient population would show alteration or impairment in other aesthetic domains. 117

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Together, this breadth of research links many diverse and highly subjective experiences of feeling beauty, pleasure, and being moved to activity in the mOFC/vMPFC and suggests that this part of the brain contains a representation that can function as a “common currency” in hedonic appraisals independently of the source (Bartra et al., 2013; Chikazoe et al., 2014; Levy & Glimcher, 2011; O’Doherty, 2004; Pegors et al., 2015). This common neurological basis for valuations may allow for comparison of all manner of experiences derived from different sources. Yet there is also evidence that signals in parts of the OFC and MPFC can represent differential aspects of various flavours of hedonically rewarding experiences (Grabenhorst & Rolls, 2011; Sescousse et al., 2013), such as those derived from primary rewards (e.g., sucrose), through association (e.g., money), or through engaging in sense making (e.g., aesthetic experiences). How these different representations interact to support both subjective experience and the coordinated control of behaviour during aesthetic experiences remains to be seen. Beyond the medial and orbital surfaces, several other prefrontal regions also appear to play important roles in visual aesthetic appraisals. On the lateral surface, the inferior frontal gyrus (IFG), particularly the pars triangularis or opercularis segments, as well as the insula, have received quite a bit of attention. As in the vMPFC/ mOFC, anatomical localization and labelling are an issue for these regions: the IFG lies on top of the insula, and activations often appear to straddle both cortical surfaces, a problem that is exacerbated by the moderate to high degree of spatial blurring that is often applied before averaging across observers. Modulation by aesthetic appeal has been reported here for artwork (Vessel et al., 2012), abstract patterns ( Jacobsen et al., 2006), faces (Chatterjee et al., 2009; Kim et al., 2007; O’Doherty et al., 2003), human bodies (Di Dio et al., 2011), and abnormal scenes (containing an out-of-context object; Kirk, 2008). Interestingly, a meta-analysis of aesthetically appealing stimuli across four modalities (vision, audition, gustation, olfaction) and 93 studies identified the anterior insula, rather than the mOFC/MPFC, as the region most consistently engaged by aesthetic appeal (Brown et al., 2011; though we note that many of the included studies used stimuli that are not typically evaluated in an aesthetic manner, such as glucose, water, and sexually arousing images). While it is unclear whether these findings reflect engagement of the anterior insula, IFG, or both, the status of the anterior insula as a hub of the “salience network” (or ventral attention network; Seeley et al., 2007; Thomas Yeo et al., 2011) and also as important for interoception (Critchley et al., 2004; see also Chapter 5) makes it an important target for future study. Also on the lateral surface, activations in the lateral OFC have been reported for aesthetically appealing faces (O’Doherty et al., 2003; Pegors et al., 2015), as well as for artworks (Vessel et al., 2012). One magnetoencephalography (MEG) study (Cela-Conde et al., 2004) and a few brain-stimulation studies (see Cattaneo, 2020 for a review) have suggested that the dorsolateral PFC (dlPFC) plays a role in aesthetic processing, though few other reports have corroborated such a link (though see a recent study on musical pleasure; Mas-Herrero et al., 2021). The frontopolar and frontomedian cortex have been found to respond more to scenes containing an out-of-context object (Kirk, 2008) and to curvilinear versus rectilinear architecture (Vartanian et al., 2013) and also to contain multivoxel information correlated with aesthetic appeal for art and architecture (Vessel et al., 2019). The superior frontal gyrus was found to be modulated by aesthetic appeal for artworks (Vessel et al., 2012) and architecture (Vartanian et al., 2013).

Subcortical vs. cortical representations of hedonic pleasure Both the striatum and OFC/MPFC are key nodes of the brain’s reward circuitry. However, their functions within that network appear to differ. In one fMRI study, both expert architects and non-experts were asked to aesthetically evaluate images of buildings (Kirk et al., 2009). While both NAcc and OFC were modulated by appeal, only cortical regions, namely the OFC and ventral ACC, were modulated by expertise. A study by Wang et al. (2015) also found differential profiles in the striatum and OFC. They compared qualitatively different types of beauty judgments, facial beauty (physical attractiveness), and moral beauty 118

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(abstract and higher-order beauty) and found that both types of beauty judgments commonly engaged the mOFC/vMPFC, consistent with previous neuroaesthetics research. However, only facial beauty judgment engaged the striatum. Activity in the striatum and OFC may also occur at temporally dissociable stages. Kim and colleagues showed that when participants evaluate the attractiveness of a face, increases in activation in the NAcc were detectable during initial brief exposures to the image, whereas OFC activation was more dominant for later exposures (2007). These findings are generally consistent with the view that the ventral striatum functions as a “hedonic hotspot” supporting core pleasure (along with the ventral pallidum), whereas the OFC supports conscious representations of affect that can influence decisions (Berridge  & Kringelbach, 2013), potentially in concert with interoceptive information coming from the anterior insula (Brown et al., 2011). Interestingly, it has also been suggested that different dopamine pathways, which play an important role in the experience of pleasure, may differentially affect activity within the striatum and OFC. The OFC and vmPFC are targets of both the mesolimbic and mesocortical pathways, whereas the striatum is strongly modulated by the mesolimbic pathway alone. The mesolimbic pathway projects to the ventral striatum (primarily NAcc) and hippocampus, as well as the OFC and vmPFC, whereas the mesocortical pathway transmits dopamine from the ventral tegmental area to the PFC, especially the dlPFC, and eventually through the parietal, occipital, and temporal areas. These different projections of the dopaminergic system may contribute to the temporal dissociation between the striatum and OFC (Spee et al., 2018).

The default-mode network and strongly moving aesthetic experiences Beyond activations of localized brain regions, strongly moving aesthetic experiences with visual art involve activation of a brain system known as the default-mode network (DMN). Nodes of the DMN are typically suppressed during tasks requiring external focus, such as looking at images, and are engaged by tasks that require internally directed or self-generated thought such as autobiographical memory, prospective thinking, or judgments of self-relevance (Andrews-Hanna et al., 2010; Axelrod et al., 2017; Fox et al., 2005; Raichle et al., 2001). Whereas disliked artworks and artworks rated as pleasant are accompanied by suppression of the DMN, viewing of artworks rated as strongly moving results in a release from this suppression and engagement of the DMN by ongoing visual processing (Belfi et al., 2019; Vessel et al., 2012, 2013). Evidence for likely DMN engagement has also been found using MEG: a pattern of coherence across brain regions consistent with the DMN emerges 1000–1500 ms after onset of an image rated as beautiful (Cela-Conde et al., 2013). While the precise role played by the DMN in moving experiences remains unsettled, its central role in self-generated and internally directed thought suggests that it may become engaged by aesthetic experiences that trigger an assessment of self-relevance or that resonate with aspects of self identity and autobiographical memory (Vessel et al., 2013). Interestingly, while modulation of DMN regions by aesthetic appeal have also been reported for other visual stimuli such as faces (O’Doherty et  al., 2003), abstract visual patterns ( Jacobsen et  al., 2006), and architecture (Vartanian et al., 2013), DMN activation has not been robustly reported for appealing landscapes (e.g., Isik & Vessel, 2021) or dance and was reported to be deactivated by awe-inducing landscape movies (van Elk et al., 2019). It remains an open question whether the DMN is engaged by all moving aesthetic experiences or only in a restricted set of circumstances. On the other hand, it has been shown using multivariate methods that the DMN contains a representation of aesthetic appeal that generalizes across multiple visual aesthetic domains, even if it is not, on average, more active. Multivoxel activity patterns in DMN can be used to predict observers’ aesthetic responses, and these patterns are consistent across visual artworks, architecture, and natural landscapes (Vessel et al., 2019). In contrast, while multivoxel patterns in higher-level visual regions can also predict a degree of aesthetic appeal, the predictive patterns in these regions are unique to each domain. 119

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Figure 6.5 The default-mode network (DMN) is a network of highly interconnected brain regions that are thought to support self-referential and inwardly directed thought and are typically suppressed by tasks that require external focus (Andrews-Hanna et al., 2010; Fox et al., 2005; Raichle et al., 2001). However, aesthetically moving visual artworks engage DMN regions, which have also been shown to contain multivoxel patterns that can be used to decode aesthetic appeal across multiple visual domains (Vessel et al., 2012; Vessel et al., 2019). The core regions of the DMN include the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC)/precuneus on the cortical midline. The DMN also includes regions in the inferior parietal lobule (IPL) lateral temporal cortex (LTC), and hippocampus (not shown).

Coordination across different brain systems Given their highly integrative nature, it is not surprising that visual aesthetic experiences engage multiple brain systems. Several models have tried to make sense of interactions amongst these many systems. One popular model highlights an “aesthetic triad” of processes engaged during aesthetic experiences (Chatterjee & Vartanian, 2014; see also Chapter 10): sensory-motor (sensation, perception, motor system), emotion-valuation (reward, emotion, wanting/liking), and knowledge-meaning processes (sensemaking, context, culture, expertise). We would note that while a fair amount is known about the brain systems that mediate the first two sets of processes, much less is known about knowledge-meaning neural processes during aesthetic experiences. A second emerging model proposes a difference between ordinary pleasures that engage sensory and reward networks and more intense or contemplative aesthetic experiences that cross a threshold to become strongly moving (Brielmann & Pelli, 2018; Pelowski et al., 2017; Vessel et al., 2013). Whereas externally focused (sensory) and internally focused (DMN) brain systems are typically anticorrelated (Fox et al., 2005), strongly moving experiences may result from change in these large-scale network dynamics, bringing these two systems into closer coordination (Belfi et  al., 2019; Vessel et al., 2013). Such coordinated activity would provide a pathway by which representations of an external stimulus could mutually interact with internally generated states, generating insight and pleasure not just from an analysis of the stimulus but also from the interplay between a stimulus and internal thought. Although visual neuroaesthetics research has heavily relied on regional/inter-regional BOLD measurements, it is also important to consider time frames of information processing to understand such coordination across large systems. Recent studies argue that aesthetic appraisal may rely on the activation of

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two different networks—an initial aesthetic network and a delayed network—engaged within distinct time frames (e.g., Cela-Conde et al., 2013). This study suggests that the DMN may correspond mainly to the delayed aesthetic network processing with more cognitive analysis of a stimulus in a comparatively late stage (1000–1500 ms). This can be compared with the mOFC/vmPFC, which may have an important role in the initial aesthetic network occurring in the earlier stage where a fast aesthetic appreciative perception is formed. It could be interesting to combine fMRI and M/EEG with network/connectivity analyses in order to investigate directional influences of one brain system on another. Given the degree of variability seen in neural responses for different visual aesthetic domains, it may be that the degree of cross-network integration may differ depending on the domain. For example, a visual domain like natural landscapes may be more driven by exogenous, stimulus-derived factors and thus require less input from self-generated or self-referential processes (Isik & Vessel, 2021). Such differences in the balance between bottom-up and top-down activation may additionally be related to differences in the state of mind evoked by a particular aesthetic task or context (e.g., Herz et al., 2020)

Why do we like what we like? Why we like what we like is a hard question and is one that may not have any single answer. Given the current “cartographic” state of knowledge in the field of visual neuroaesthetics (and more generally in cognitive neuroscience; Poeppel, 2008), it is difficult to say much about why certain visual experiences come to be felt as beautiful or moving. Keeping this in mind, we will outline some of the explanations put forward for why humans like what they like and put forward some explanations of our own as well. From an evolutionary perspective, it is clear that there must be some adaptive benefit for our ability to make appraisals of a diverse array of visual stimuli, from the face of a person we see on the street, to a remote mountain meadow, to a piece of contemporary art. Classical theories have often focused on why humans have preferences for specific features within specific classes of objects. For example, it has been suggested that preferences for certain facial features may reflect implicit knowledge about markers for health or desirable genetic qualities (e.g., Thornhill & Gangestad, 1999; see also Chapter 11). Similar claims have been made about the potential advantage of a preference for curved over angular visual features, as an implicit mechanism for avoiding sharp objects or dangerous places (Bar & Neta, 2007). Within environmental aesthetics, preferences for certain types of landscapes, such as ones that afford a view or that signal resource availability (e.g., water, flora and fauna), have been touted as conferring a potential survival advantage (Orians & Heerwagen, 1992). The aesthetic appraisal in this sense seems to play an essential role as a deciding factor in the decisions we face in various situations. In Plato’s theory of ideas, truth, goodness, and beauty are universal values that we pursue as ideals. According to this view, humans have a cognitive bias that links the good with the beautiful and the true with the beautiful, which is pervasive in human societies (Dion et al., 1972). Here, one could say that aesthetic appraisal has the function of bringing in a kind of emotional information that allows us to judge the “rightness” or “goodness” of things or situations. This determinant can be applied to a variety of entities, not only faces or landscapes but also morality or arts. However, this approach often suffers from equivocal empirical evidence and extensive post hoc reasoning and largely ignores the fact, highlighted in this chapter, that people often don’t agree on what they like. Even for faces, one of the visual domains that elicits a high degree of shared taste, evolutionary predictions are sometimes far off from the observed data. For example, sexual facial dimorphic traits seem to be preferred in a culturally specific fashion, suggesting cultural, and not evolutionary, pressure to influence shared liking (Scott et al., 2014). Although there is some evidence suggesting that variation in preferences for specific features can be accounted for by genetic variance (e.g., sexual dimorphism in faces; Zietsch et al., 2015), the

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assumption that evolutionary pressures acted on the formation of preferences for very specific features has so far received little support. If anything, we now know that preferences are influenced mainly by environmental, and not genetic, factors, as studies on the heritability and development of preferences suggest (Germine et al., 2015; Rodway et al., 2016; Sutherland et al., 2020). Further, even when individuals tend to agree on what they like across cultures, the empirical evidence for a link between such shared appraisals and signalling properties of what is appraised is contradictory. For example, the link between rated attractiveness of faces (or the trait predictor of attractiveness, e.g., symmetry) and actual health has been recently disputed (Foo et al., 2017; Pound et al., 2014). Here we would argue that a set of alternative views, some of which have been proposed to account for non-human animal appraisal, could potentially explain aspects of why individuals like what they like (Ryan & Cummings, 2013). First, it is possible that sensory biases, as in the non-human animal kingdom, could account for some of the shared appraisals in humans. For example, the female tungara frog auditory system tends to be biased toward conspecific calls with a lower-than-average frequency range (Ryan et al., 2019). These sets of biases constitute part of the experiential world of non-human animals (their umwelt or environment; Ryan, 2011). Being animals, it is thus possible that similar biases constituting the human environment could potentially and partially explain why individuals tend to show some shared preferences. However, here it is important to note that, as for animals, these biases would constitute only an initial set of pressures from which environmental exposure and contextual information would then operate (Ryan et al., 2019), making aesthetic evaluations a more flexible and time-dependent (Nadal & Chatterjee, 2019), though not entirely unconstrained, mechanism (Bignardi et al., 2020). More generally, we would argue that preferences for specific features may not be at the right level of granularity for linking to evolutionary pressures and that evolution may instead have acted upon more general-purpose mechanisms. A  mechanism that promotes learning about, and modelling (Brielmann  & Dayan, 2021), one’s environment is a promising potential target for such evolutionary pressures. The goal of such a mechanism would be to enable better predictions in the future. According to this view, curiosity about our world, and the pleasure we derive from making sense of it, act as a motivational drive that pushes humans to keep exploring, keep learning, and keep developing new ways of parsing our world (Biederman & Vessel, 2006; Koelsch et al., 2018; Loewenstein, 1994; Schoeller & Perlovsky, 2016). The consequence is that humans can get pleasure from understanding and also that what we find pleasurable can change as we learn. First, optimal interest is directed to potential sources of learning, often corresponding to moments of high surprise or high uncertainty. Then, the subsequent decrease in entropy that comes from making sense (“aha” moments associated with resolution, the creation of new categories, paradigm shifts, etc.) is experienced as pleasurable and signals that something has potentially been learned (Van de Cruys et al., 2021). Although such information-seeking behaviours, which incur the cost of receiving information that will not directly result in receiving rewards, can also be observed in animals (Wang & Hayden, 2019), they are likely most elaborated in humans. Such explanations, including learning-based theories for visual aesthetic appeal, can potentially explain appraisals that happen at many different levels of representation, interacting with different types of environmental exposures over the lifespan. An infant may get pleasure from learning about simple shapes and their parent’s faces. A child may actively seek certain environments over others, exposing themselves to new sources of visual input. A teenager may find that expressionist art, subversive graffiti, or Japanese manga resonates with their rapidly changing view of their place in the world. A neuroscientist captivated by complexity may find that two-photon microscopy captures neural signalling in a way they had not previously considered. While it may not be the case that every experience of beauty is also an “aha” moment, relating aesthetics to information processing is a promising direction for future research. Our proposed explanations are far from complete. Testing whether such explanations carry true explanatory power will require neuroaesthetics to develop new paradigms and increasingly quantitative models. The 122

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task of understanding “why we like what we like” is a complicated puzzle that still requires many pieces to be completed.

Some concluding remarks and open questions Interest in the neural basis of visual aesthetic appreciation has increased dramatically over the past 25 years, and while the field is still relatively small, much has been learned. Despite the highly individual and subjective nature of aesthetic experiences, a host of tools and methods have been developed that are facilitating systematic study. The psychological nature of aesthetic appeal has itself been much better clarified, and there exists a general outline of the brain systems that are involved. Yet it is clear that there is still very much to learn. One crucial limitation, highlighted here at the beginning of the chapter, is represented by the same strength that facilitated the rise of the systematic study of aesthetic appeal: “methodological variation.” In particular, the array of different questions and tasks that researchers have introduced, and the clear lack of a priori agreement on the terminology used, puts constraints on the possibility of interpreting results from different studies. In spite of such weaknesses, we now have a general map of where in the brain one can find modulations by aesthetic appeal. Yet we still understand very little about the underlying computations and representations that can be found in these regions. In the following, we highlight some key open questions in visual neuroaesthetics: • • • • • • • • • • • • •

How do representations of what we see inform representations of aesthetic appeal? How does activation in visual pathways relate to aesthetic appeal? Is appeal computed or represented in these local regions, or is appeal computed elsewhere? How much of the variability in reported activation of the mOFC/vMPFC and IFG/insula is due to methodological concerns versus reflective of true differences in mechanisms? Are the MPFC and/or the DMN engaged for all aesthetically appealing visual experiences, or for only for a subset? What are the precise roles of various brain regions (MPFC, OFC, IFG/insula) in computing and representing aesthetic appeal? What brain structures support knowledge-meaning processes during aesthetic experiences? How might we expect neural engagement to change when moving from images to more ecologically valid aesthetic experiences? How are differences in brain structure and function linked to individual differences in aesthetic experiences? What are the etiological (i.e., genetic and environmental) sources of variation in aesthetic appeal? How can empirical findings from neuroaesthetics be translated into humanities and philosophy to contribute to our understanding of human’s aesthetic and artistic activities? What are the differences, if any, between the neural systems supporting experiences of beauty, pleasure, and reward? Is it possible to study aesthetic appraisals/beauty judgements in infants and animals? How can neuroaesthetics benefit from increasing incorporation of neuropsychopharmacological and causal (lesion, TMS) approaches?

Acknowledgements We thank Margherita Bignardi for help with Figure 6.1 and MacKenzie Trupp for thoughtful discussions and for editing an earlier version of this manuscript. This work was partially supported by the BMBF and Max Planck Society to G.B. 123

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Note 1 We note that complexity and order have been investigated using both objective and subjective frameworks. The former approach seeks to quantify complexity and order from a stimulus, while the latter posits that these constructs can only be understood by taking the perceiver’s subjective judgment into account.

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7 AUDITORY PLEASURE ELICITED BY MUSIC Ernest Mas-Herrero

Sounds warn us of potential dangers (e.g., a storm approaching, a snow avalanche, or the presence of a predator) and help us detect possible resources (e.g., a stream of water or prey). In addition, our ability to produce a large variety of sounds and our capacity to extract and learn regularities has given rise to language: crucial for social interaction, communication of complex thoughts, and, ultimately, essential for the spectacular success of our species. Besides its direct and pragmatic survival utility, sounds may also be the source of intense pleasurable aesthetic responses. For example, natural environmental sounds not only alert us of potential threats or benefits but may also be found relaxing and pleasant. Who has not been hypnotized by the sound of waves breaking along the shore, the leaves rustling in the wind, or the rain falling on the roof ? Similarly, we may find some voices particularly appealing and relaxing (e.g., the voice of our favourite radio/podcast host or our favourite actor). However, if there is one auditory aesthetic response that stands out above all the others, it is, without doubt, the one elicited by music. Nowadays, it is almost impossible to spend a day without listening to music, either at home, on TV, shopping, or while having drinks at a bar. Music constantly surrounds us. However, the presence of music in our societies is not new. Indeed, human beings have been enjoying music for a long time. The earliest musical instruments found in archaeological excavations (a bone flute and two ivory flutes) date back to the Upper Palaeolithic period, more than 40,000 years ago (Conard et al., 2009; see also Chapter 29). These findings demonstrate that humans had already developed a musical tradition by that time, one that has endured until this day. Despite music’s ubiquity and universality, the reason music evolved and prospered remains a matter of debate, one that has intrigued many scientists and philosophers. Some authors have suggested that music appeared as a prelinguistic form of communication (Fitch, 2006). Others hold that music has a sexual-selection basis similar to songbirds’ songs (Miller, 2000; Ravignani, 2018). And others posit that music is nothing more than a consequence of the adaptations required for language and other high-order cognitive functions, devoid of evolutionary purpose in and of itself (Pinker, 1997). However, what is indubitable is that music strongly impacts us. Indeed, music is generally ranked as one of the most pleasurable experiences in life (Dubé & Bel, 2003; Mehr et al., 2019). But how does the human brain translate a structured sequence of sounds, with no apparent biological advantage, into pleasure? The investigation of music from a neuroscientific perspective is indeed a challenging enterprise. Because of the lack of animal models, the neural mechanisms of music-induced pleasure have remained elusive until the 134

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development of modern neuroimaging techniques that allowed for the non-invasive measurement of brain activity in humans. Although this field is still in its early stages (at least compared to the neuroscience of primary pleasures such as food), a reasonable number of studies have addressed this question over the last 20 years. In the following sections, I review the main findings to emerge from this body of work.

Music perception Music appreciation begins with the decoding of acoustic features and encoding them into neural firing patterns (for a detailed description of this process, see Chapter 15). This process is essential to the representation of sounds in our brains, categorizing and identifying the tones we are listening to, establishing temporal and structural patterns, and so on. Sounds are nothing more than vibrations propagating as waves through the air or other media (e.g., water). And musical sounds are not that different from other sounds at the stage of early perception, sharing most of the initial processing mechanisms with other auditory stimuli. Striking the strings of a guitar makes them vibrate and generate musical sound waves that propagate through the air and ultimately hit the listeners’ cochlea. There, through a complex cascade of mechanical reactions, air vibrations are transformed into neural (electrical) signals. This information then travels via the auditory nerve to the central nervous system, where it is progressively transformed in the brainstem and the thalamus until it reaches the auditory cortex (located in the superior temporal gyrus; STG). This auditory-specific cortical region is located in temporal cortices and responsible for the more fine-grained analysis of acoustic features. Notably, contrary to speech, music is preferentially processed in the right hemisphere (Zatorre et al., 2002). This distinction may be driven by complementary specialization of the two hemispheres for fine acoustic modulations in the temporal vs. the spectral domain, with the right side showing the highest sensitivity to spectral information crucial for pitch-based aspects of music (Albouy et al., 2020; Zatorre & Belin, 2001). It is important to note that music, unlike other sounds, relies not only on the processing of discrete features (e.g., pitch, time, or timbre) but also on identifying temporal and structural patterns. To do so, the auditory cortex does not work in isolation but through collaboration with many different brain regions located in the cortex, particularly the posterior parietal cortex, the inferior frontal gyrus, and motor regions. Communication among this set of cortical areas is crucial for identifying temporal and structural patterns of pitch and rhythm to ultimately create musical percepts. For instance, the interplay between the STG and inferior frontal gyrus (IFG) is involved in the recognition of musical patterns, melodic expectations, and auditory working memory (Albouy et al., 2019; Herholz et al., 2012). Likewise, interactions with parietal, frontal, and motor regions support the perception of rhythm and rhythmic expectations and predictions (Chen et al., 2008; Thaut et al., 2014). A great model for understanding the neurobiology of music perception is provided by individuals suffering from amusia or tone-deafness. Congenital amusia is a lifelong musical disorder characterized by pith perception deficits, with neuro-genetic underpinnings, that affects 1.5% of the population, according to recent reports (Peretz & Vuvan, 2017). Individuals suffering from amusia have difficulties identifying out-of-tune and off-key notes and distinguishing familiar melodies without the aid of lyrics. They just do not get music, no matter how much time they invest in musical training (Lebrun et al., 2012; Wilbiks et al., 2016). Yet they may correctly recognize voices or environmental sounds and have no apparent deficits in language processing and speech. These deficits are associated with a reduced communication between frontotemporal cortices, including the IFG and the STG, particularly in the right hemisphere, and likely occur because of abnormal developmental processes (Loui et al., 2009). Impaired music perception in the amusical brain may limit the appreciation of music as an aesthetic experience. Indeed, amusical individuals are less interested in musical activities and are less likely to enjoy music than control-matched individuals (Mcdonald & Stewart, 2008; Omigie et al., 2012). Other evidence comes from patients developing amusia after brain damage (also known as acquired amusia; Sihvonen et al., 2019), 135

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which may be concurrent with a sudden inability to experience pleasure from music (Hirel et al., 2014; Mazzucchi et al., 1982; Satoh et al., 2016). However, there may still be some room for musical pleasure to occur in these individuals since some amusical individuals do report experiencing considerable pleasure from exposure to music (Omigie et al., 2012; Wilbiks et al., 2016). Particularly astonishing is the case of Tim Falconer, a unique case in Wilbiks and colleagues’ (2016) study of a self-described “musicophile” despite suffering from amusia. His motivation was such that he followed an 18-month program of formal vocal training to overcome his condition. Unfortunately, despite his efforts, Tim did not show substantial improvement in rhythm and meter perception after his training (Wilbiks et al., 2016). The mechanisms through which individuals like Tim may drive pleasure from music despite their striking musical perceptual deficits are still unclear but point to at least a partial dissociation between music perception and appreciation.

Neural circuits involved in musical pleasure The common reward circuitry The cortical network previously described plays a key role in auditory cognition and perception. It helps us understand and make sense of music, but this is only one part of the story. What circuits are behind the rewarding feelings of music? Psychologists and neuroscientists have been particularly intrigued by how rewards are processed in the brain, especially in response to primary sensory pleasures (e.g., food) that (1) possess clear evolutionary advantages and (2) are shared with other organisms. A great deal of research in both humans and nonhuman animals supports the existence of a core reward system in the brain involved in many facets of reward, from motivation to pleasure and from pleasure to learning (Berridge & Kringelbach, 2015; see Chapter 3). The identification of an anatomically identifiable reward circuit in the brain dates back to 1954 when James Olds and Peter Milner reported one of the most important studies in the neuroscience of reward. They surgically implanted electrodes into rats’ brains through which electrical impulses were delivered. The rats were placed in a cage where a lever controlled the delivery of current to the electrodes so that rats could self-stimulate their brains by simply pressing that lever. By testing different parts of the brain, Olds and Milner observed that at specific locations, a rat would press the lever “to stimulate itself in these places frequently and regularly for long periods of time if permitted to do so” (Olds & Milner, 1954). Subsequent studies have shown the rewarding effects of stimulating various subcortical and cortical sites in several species, including humans (Olds, 1969). Intracranial recordings in nonhuman primates and human neuroimaging studies investigating brain responses to both primary (e.g., food and sex) and secondary rewards (e.g., money) have further shown that these different reward experiences, irrespective of their nature, engage a common set of brain regions, constituting what is known as the reward circuit (Bartra et al., 2013; Sescousse et al., 2013). The reward circuit is highly consistent across species, phylogenetically old, and responsible for the feelings of pleasure and the reinforcement of biologically relevant behaviours such as eating or sex. This circuitry includes several brain regions, but the striatum (particularly the nucleus accumbens; NAcc), the orbitofrontal cortex (OFC), and dopaminergic neurons located in the ventral tegmental area/ substantia nigra appear especially important (Haber & Knutson, 2010; Kelley & Berridge, 2002). Does this phylogenetically ancient reward circuitry respond to high-order pleasures such as music? The first study investigating this question was conducted by Blood and colleagues in 1999. The authors used positron emission tomography (PET) to examine regional cerebral blood flow changes—considered a proxy of brain activation—elicited by affective responses to music. Ten participants listened to a novel melody and five versions of that melody in which different chords, differing in their degree of dissonance, were used as accompaniment. Using this approach, the authors aimed to modulate the pleasantness of the melody by adding more or less dissonance to the melody’s harmonic structure. Their results revealed that changes of activation in the OFC were associated with the subjective degree of pleasure reported by the participants. Thus, 136

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the study showed, for the first time, that despite its complexity and sophistication, the functional mechanisms employed by music reward were comparable to that of other, adaptive sensory pleasures, tapping into a common neural circuitry for computing pleasure and reward. In this first attempt, the authors elegantly manipulated the stimuli’s unpleasantness by manipulating dissonance. Their approach, however, was not optimized to maximize the individual participant’s pleasure but rather their displeasure. So, although individuals reported liking more the consonant condition than the different dissonant versions, the stimuli used hardly evoked intense feelings of pleasure in the listeners. One way to solve this problem is to use real music known to elicit strong emotional reactions. Yet individuals vary greatly in their musical preferences: a piece of music can be highly pleasant for one individual but unpalatable to another. To overcome this limitation, Blood and Zatorre conducted a second study two years later (2001). They now asked participants to bring their own favourite music, known to evoke intense feelings of pleasure, into the lab. Specifically, the researchers asked for music that evoked chills, goosebumps, or shivers down the spine. Because chills and goosebumps are clear, discrete, and highly reproducible events that are generally associated with particularly intense pleasurable music excerpts, they are often used as an index of musical pleasure experiences (Grewe et al., 2005; Pelofi et al., 2021; Salimpoor et al., 2009). As a control, each participant also listened to one of the other participants’ pieces which they knew would not elicit chills in them. That way, the authors ensured that differences in acoustic features that could characterize chillinducing music did not drive the neural responses associated with chills; instead, those would be directly related to each individual’s subjective experience. The results of the study revealed that the occurrence of chills was associated with the engagement of a distributed network that involved cortical and subcortical brain regions engaged in the computation of reward and emotion, such as the insula, the anterior cingulate cortex (ACC), and the orbitofrontal cortex, as well as the ventral striatum. Since these first seminal studies, a large body of neuroimaging research has investigated the neural correlates of music-induced pleasure. In a recent meta-analysis, we showed that independently of the experimental design used, intense feelings of pleasure to music are consistently associated with the engagement of the core reward circuitry, particularly the insula, striatum (specifically the NAcc), and orbitofrontal cortex (Mas-Herrero, Maini et al., 2021). Notably, these structures were also engaged in studies investigating foodinduced pleasure, consistent with the existence of a common set of brain regions underlying the rewarding feelings of any rewarding event and experience—from primary to aesthetic rewards. Another piece of evidence supporting the role of the phylogenetically ancient reward circuitry in musical reward comes from an investigation by Salimpoor and colleagues in 2011. Here, the authors aimed to investigate whether music could lead to the release of dopamine, a neurotransmitter well known for its role in reward processing. The different regions of the reward system communicate through dopamine, and dopamine is also responsible for the addictive properties of drugs (Volkow et al., 2017). Would dopamine also mediate the rewarding properties of music? To test this hypothesis, Salimpoor and colleagues used PET to non-invasively measure dopamine release in a group of volunteers while listening to either pleasant or non-pleasant music, using a similar approach as that pioneered by Blood and Zatorre (2001). Their results elegantly revealed that highly pleasurable pieces of music induced greater dopamine release in both ventral and dorsal portions of the striatum compared to the neutral control music. Notably, dopaminergic release levels associated with music listening predicted the number and intensity of participants’ chills: the greater the release of striatal dopamine, the more pleasure participants experienced. This study further supported the hypothesis that musical reward relies on similar brain mechanisms as those identified in studies of food or drug pleasures. We have recently extended these findings by showing that dopamine is not only correlated with musical reward, as shown by Salimpoor and colleagues, but that dopamine release is causally related to music enjoyment. Neuroimaging methods are correlational, and thus they are not capable of teasing apart those brain regions that are directly involved in generating the hedonic experience of music from those that are only 137

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modulated by this experience (e.g., responding as a consequence of pleasure rather than being its cause). To disentangle whether dopaminergic striatal circuits are the cause or consequence of music reward experiences, it is necessary to modulate the functioning of this circuitry and investigate if that leads to changes in rewardrelated measures. Nowadays, it is possible to modulate human brain activity non-invasively by applying transcranial magnetic stimulation (TMS). TMS is a non-invasive brain stimulation technique that uses magnetic fields to cause temporal electric current changes in specific brain areas. It is basically a big magnet that generates magnetic pulses, which will go through the scalp, hit the target region, and there cause a change in electric current. Yet TMS has a limited range of action and is only effective to stimulate cortical areas, and therefore, we cannot directly stimulate subcortical structures such as the striatum. Nevertheless, because the striatum is highly connected with cortical brain regions (Haber & Knutson, 2010), striatal functioning can be indirectly modulated by applying TMS over certain cortical regions, specifically over the left dorsolateral prefrontal cortex (DLPFC; Strafella et al., 2001). Stimulation of the DLPFC may propagate until it reaches subcortical striatal regions via white matter paths connecting both, resulting in alterations of these regions as well. Taking advantage of this procedure, we recently tested whether modulation of striatal circuits through excitatory and inhibitory TMS stimulation over the left DLPFC could actually enhance or disrupt music-induced pleasure, respectively (Mas-Herrero, Dagher et al., 2021; MasHerrero et al., 2018a). In two independent studies, over three separate days, two samples of 17 participants received either excitatory, inhibitory, or sham stimulation (as a control) over the left DLPFC, counterbalancing for the order. Following each stimulation, the participants listened to a set of musical excerpts while providing continuous real-time ratings of experienced pleasure. Stimuli consisted of five self-selected favourite songs and 10 experimenter-selected songs that were likely to be experienced as positive (since they matched participants’ musical taste). In addition, the participants had the opportunity to purchase our music selection through an auction paradigm procedure in which participants could bet their own money to buy any of the musical items they would find appealing. Finally, we recorded neuroimaging data and objective physiological measures of emotional arousal, such as electrodermal activity (EDA). Previous studies have shown that EDA increases as a function of perceived pleasure, with a maximum during the occurrence of chills, particularly in those cases where pleasure is experienced as highly intense (Benedek & Kaernbach, 2010; Mas-Herrero et al., 2014; Salimpoor et al., 2009). Our results showed that exciting fronto-striatal pathways via TMS significantly increased subjective reports of pleasure, psychophysiological measures of emotion, and participants’ motivation to purchase our music selection. In contrast, inhibition of this system reduced all these measures. Notably, neuroimaging results indicated that the NAcc was the key brain structure underlying these modulations of pleasure and motivation. Changes in the functioning of the NAcc (but not other brain regions) due to TMS stimulation predicted TMS-induced changes in participants’ pleasure and motivation. These findings were further replicated by directly modulating systemic dopamine levels through a pharmacological intervention (Ferreri et al., 2019). In a double-blind, within-subject pharmacological design, 27 healthy participants were engaged in a similar music listening paradigm as used in our previous TMS experiment. Yet now participants either received a dopamine precursor (levodopa), a dopamine antagonist (risperidone), or a placebo (lactose) on three different days. Consistent with the previous TMS findings, participants reported experiencing more pleasure, and more willingness to spend their own money buying music, following the administration of the dopamine precursor levodopa. In contrast, the dopamine antagonist risperidone reduced the number of chills experienced and participants’ motivation to buy music from our musical list. These two studies represent the first direct demonstration that affective reactions to music can be manipulated by the stimulation of dopaminergic striatal circuits, providing strong support for the hypothesis that this circuitry mediates feelings of pleasure induced by music. Interestingly, these findings seem to contradict previous evidence from nonhuman animal studies using primary rewards such as gustatory sweetness showing that dopamine’s role is limited to the motivational aspects of reward but does not affect pleasure itself (Berridge & Kringelbach, 2015; see also Chapter 3). Dopaminergic manipulations do 138

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not change liking responses in rodents (the pleasure remains intact, no matter the dopamine levels). Still, it influences the amount of work they are willing to exert to get more sweet pellets (they are more motivated when dopamine levels are up). Therefore, together, these findings may suggest a possible dissociation between primary and abstract pleasures, with the latter requiring dopaminergic transmission for pleasure to happen—at least in humans.

Music-specific pathways If the pleasure we experience in relation to music is driven by an ancient circuitry that we share with many other organisms, how is it that it seems a uniquely human trait? If other animals possess this “hardware,” why is musical pleasure only evident in humans? Indeed, studies performed in nonhuman primates indicate that they generally prefer silence to music (McDermott & Hauser, 2007), even though they may be responsive to aspects of sound relevant to music (Izumi, 2000; Wright et al., 2000). How does the human brain turn music into pleasure? To answer this question, we have to consider that the reward circuitry does not work in isolation but is highly interconnected with the rest of the brain. Notably, as animals become more evolved, the connections between this circuitry and the rest of the brain, particularly the cortex, gain complexity and are more numerous (Kaas, 2000, 2005). Specifically, humans show the largest interconnections with temporal and frontal cortices (Balsters et al., n.d.; Gordon et al., 2021), which, in turn, are particularly evolved in humans (Dick & Tremblay, 2012; Friederici, 2017; Smaers et al., 2011). These cortico-striatal connections may have provided alternative ways to trigger reward-related signals in humans, expanding the range of events and experiences we may find pleasurable. Evidence for the existence of music-specific reward pathways involving cortical brain regions comes from patients who develop alterations in their aesthetic appreciation of music after brain damage. The most intriguing example of this is found in patients who, after brain damage, lose the capacity to experience pleasure in connection with music listening despite a preserved ability to perceive music (i.e., they are not amusical) and form reward-related responses to other pleasant stimuli. This condition is known as acquired specific musical anhedonia. Remarkably, most patients with musical anhedonia exhibit lesions in cortical areas—including temporal, frontal, and parietal cortices—but not in reward-related structures. This acquired condition is actually rare (Belfi et al., 2017). The first case reported was a 24-year-old male who, after a haemorrhage in the right tempo-parietal lobe, lost the capacity to experience aesthetic pleasure from music (Mazzoni et al., 1993). Notably, before the vascular accident he was an amateur guitarist. Yet, on the day of the accident he realized that he had difficulties responding to music in the way he was used to. As he would report later: “my perception is changed . . . it’s flat, it’s no longer 3-dimensional; it’s only on two planes . . . there’s no emotion.” Interestingly, his musical abilities were intact: he could perfectly perceive rhythm, melody, and harmony, yet the emotional feelings were gone. The second case study concerned a 52-year-old radio announcer reported by Griffiths and colleagues (2004). Following an infarction of the left insula extending into the frontal lobe and amygdala, he experienced a general loss of pleasure to music. However, he had preserved music perceptual abilities and hedonic reactions to other rewarding activities. Finally, Satoh and colleagues reported a 71-year-old retired teacher who suffered an infarction of the right tempo-parietal lobe (2011). After this injury, he was unable to enjoy music, including the music of his favourite artists. However, as in the previous cases, he was perfectly capable of driving pleasure from other activities and did not present musical perceptual deficits. Notably, other patients may develop a specific craving for music, known as musicophilia. Indeed, this seems to be a common phenotype in some patients suffering from frontotemporal lobe dementia (FTLD; Fletcher et al., 2013, 2015). A fascinating case was reported by Geroldi et al. (2000). The patient was a 73-year-old woman suffering from FTLD who never really enjoyed music in her life. Yet, one year after the onset of FTLD, she developed a strong interest in Italian pop bands and singers. As reported by the authors, “she 139

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listened to Italian pop bands and singers, commenting that they had beautiful voices and played good music with nice rhythms.” Although she developed apathy and lost interest in other activities due to her dementia, she suddenly started enjoying music. This particular musicophilia phenotype in FTLD has been associated with structural changes to a widespread network, including the right temporal cortex, frontal areas, and striatum (Fletcher et al., 2013, 2015). Another example of musicophila was reported by Rohrer and colleagues in 2006. EEG of a 65-yearold woman treated for partial epilepsy revealed focal discharges in temporal cortices, particularly in right frontotemporal regions (Rohrer et al., 2006). After being diagnosed with partial epilepsy, she was treated with lamotrigine, a medication generally prescribed to treat certain types of epileptic seizures. Before the treatment, she barely listened to music and never showed any interest in music-related activities. Indeed, she seemed to actively avoid music. As the author reported: “she would shut the door to avoid hearing her husband playing piano music, and found choral singing irritating.” Yet, after some weeks of treatment, she suddenly developed a craving for music. She would now spend many hours listening to music on the radio and TV and developed a strong preference for classical music. Music was not irritating anymore; instead, she would now be extremely annoyed by people talking in a concert. Notably, this change in behaviour turned out to be specific to music appreciation, since she and her family did not notice any other evident changes in behaviour or personality, and she did not lose or gain interest in any of her previous hobbies either. Many of these case studies share the fact that disruptions or increases in sensitivity to music were driven not by alterations to reward-related structures but rather involved damages to frontal and temporal cortices, particularly in the right hemisphere. As we have seen in the first section of this chapter, the right superior temporal gyrus and inferior frontal gyrus play key roles in music perception and musical pattern recognition. Yet the lesions did not affect music perception, suggesting that these structures were functioning well. Nor was dysfunction to the reward circuitry observed, with affective reactions to other rewarding stimuli preserved. Thus, one possibility is that the patients’ lesions left unaltered the local functioning of cortical and reward-related regions but impaired the coupling between both systems. To directly test the role of cortico-striatal interactions in music-induced pleasure, Salimpoor and colleagues investigated the connectivity patterns between the nucleus accumbens and cortical regions during music listening (2013). Participants were scanned while listening to a list of novel music that they had not heard before but matched their musical preferences. After listening to each piece, participants were given the chance to purchase the music in an auction paradigm. The authors used participants’ bids to measure the reward value associated with each piece of music. In accord with previous studies, the results showed that while listening to those songs on which participants would later bet higher, the participants’ striatum, especially the NAcc, was more activated than during those songs towards which they did not show any interest. Yet, the most relevant finding was that highly rewarding musical pieces were also associated with an increased coupling of the STG and frontal areas (including the IFG), on the one hand, and the NAacc, on the other. Altogether, accumulating evidence from neuroimaging and lesion studies indicates that music-induced pleasure relies not only on the engagement of the common reward circuitry but also on the crosstalk between this circuitry and higher-order cortical regions involved in auditory cognition and learning, which are phylogenetically newer and especially well developed in humans. Indeed, these cortico-striatal interactions appear to be a hallmark of musical reward compared to other reward types and provide a biological basis for why music is found pleasurable in humans but not in other species with whom we share a similar reward circuitry.

Do we all love music? The fact that music is present in all cultures and societies and plays an essential part in most people’s lives has led to the general and implicit assumption that everybody loves music. Yet, as we saw in the previous section, there are indeed individual differences in how rewarding music is experienced to be, even in the healthy 140

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population. Some do not find music appealing at all, while others spend most of their time and money engaging in music-related activities. In addition, musical pleasure is a complex, multifactorial construct since it provides different pleasure sources: from the mere fact of passively enjoying a highly emotional musical excerpts to dancing or social bonding in concert settings. In 2013, we developed a psychometric instrument (the Barcelona Music Reward Questionnaire; BMRQ) to provide a fine-grained description of the different factors that contribute to music reward and allow for an assessment of individual differences in music reward sensitivity (Mas-Herrero et al., 2013). Using factor analysis on data collected from around 1600 participants, we identified five main factors that critically contributed to music reward. We named these factors Music Seeking, Emotion Evocation, Mood Regulation, Sensorimotor, and Social Reward. Music Seeking refers to the experience of pleasure associated with discovering novel compositions and bands/singers, or music information seeking. Not surprisingly, musicians generally score higher on this factor than non-musicians. The Emotion Evocation factor measures music’s emotional impact, that is, the degree to which individuals get emotional listening to music, experience chills, or even cry when listening to certain pieces. The Mood Regulation factor captures the rewarding feelings elicited by music when it efficiently regulates our mood by relaxing and comforting us when stressed, anxious, or depressed. The Sensorimotor factor refers to the pleasure of spontaneously synchronizing and coordinating our body to music by either tapping, humming, or dancing. Finally, the Social Reward facet measures the reward of social bonding through music, either by attending concerts, sharing music, or just sharing discussions of music-related topics. In this first study, we also administered standardized scales related to individual differences in general reward processing in order to measure reward sensitivity to other rewarding stimuli (e.g., food, sex, sports, or social activities, among others). In accordance with the idea that rewards are processed by a common reward circuitry, we found a strong correlation between these measures (Mas-Herrero et al., 2013). Individuals who were more sensitive to reward in general were also more likely to display a high sensitivity to music reward. However, although these different measures were significantly correlated across the population studied, some individuals did not follow this rule. Concretely, about 5% of the studied population presented average scores on sensitivity to non-music rewarding stimuli yet presented low scores on the BMRQ. That is, 5% of the studied population reported not enjoying music despite being perfectly capable of experiencing pleasure in response to other rewarding stimuli. These preliminary findings suggested the existence of specific musical anhedonic individuals in the healthy population. Yet we could not rule out that these individuals were not actually suffering from amusia, which could explain their specific low sensitivity to music. In addition, this initial finding was completely dependent on self-reported questionnaires (which might be subject to bias or even malingering). It was then necessary to provide further validation with behavioural and physiological responses to music and other rewarding stimuli to ultimately conclude that healthy individuals with specific musical anhedonia existed. To overcome these limitations, we conducted a second study in which we initially administrated the BMRQ, other scales of reward sensitivity, and tests of music perceptual abilities in a large sample (MasHerrero et al., 2014). Based on participants’ scores on this battery of tests, we selected three groups of individuals (10 participants each) who reported high (music hyperhedonics; HHDN), average (music hedonics; HDN), and low (music anhedonics; ANH) sensitivity to music but similar sensitivity to other rewards and no music perceptual difficulties. Therefore, the differences in sensitivity to musical reward among groups could not be attributed to a general lack of sensitivity to reward (general anhedonia or inability to feel pleasure) or deficits in music perception. In other words, their lack of sensitivity to music could not be driven by a general dysfunction of either the reward circuitry or auditory cortical regions. Next, the three groups of participants performed two tasks: a music task in which they had to indicate in real time the degree of pleasure they experienced during music listening and a monetary task in which the participants had to respond as quickly as possible to a target to either win or avoid losing money. For the music task, and following the procedure 141

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introduced by Blood and Zatorre, we asked the participants to bring in their three favourite musical excerpts, those evoking the most intense feelings of pleasure. However, because, by definition, specific musical anhedonics do not find music appealing in general, we anticipated that selecting highly pleasurable music excerpts would be challenging for this group of individuals. Therefore, we performed a survey in more than 200 individuals of similar age and socioeconomic status in which we asked them to list those musical excerpts that they found most pleasurable. Next, we selected the 20 musical pieces that were most frequently reported by our sample. Finally, an independent sample of 45 participants rated the degree of pleasure experienced while listening to the 20 selected excerpts. We then selected those excerpts that were consistently rated as pleasurable by most participants, leading to a final selection of 13 musical pieces that were “universally pleasing.” The three groups of participants, therefore, in addition to self-selected excerpts, listened to this set of music pieces. To assess whether the differences among groups also translated into objective physiological responses associated with emotional intensity, we measured participants’ EDA response and heart rate while participants performed both tasks. As we expected, the group with low sensitivity to musical reward had trouble selecting their favourite music. Indeed, one participant of the ANH group was unable to provide any piece of music, two participants only provided one, and two more asked for help from their family or friends since they reported not having a strong preference for any music in particular. Furthermore, the ANH group rated all the music as less pleasurable and reported chills less frequently and of lower intensity than the other two groups (regardless of music selection). These differences were also reflected in their physiological responses. While HDN and HHDN groups displayed the expected increase in EDA and heart rate as a function of the pleasure experienced, the ANH group did not show any significant changes in these measures throughout the task. Thus, although some ANH participants reported experiencing chills while listening to the music, those responses were not accompanied by changes in physiological responses as expected. Notably, and in contrast to the differences found among groups in the music task, the three groups performed similarly when playing for monetary rewards, and physiologically, the three groups exhibited similar EDA and heart rate responses to monetary reward-predicting cues. Therefore, although the ANH groups did show lower hedonic reactions and attenuated physiological responses to music, they showed average performance and physiological responses when receiving monetary rewards. In a second session, conducted a year later, we replicated the behavioural results obtained in the music task, suggesting that the effects were stable over time. Furthermore, we verified that the observed effects were not explained by differences in familiarity with our music list (all groups showed the same familiarity with the melodies presented). Finally, we verified that the participants with low sensitivity to musical reward correctly recognized the emotions conveyed by the melodies ( joy, sadness, etc.), even when they did not experience such feelings. Altogether, these results demonstrated that the lack of musical pleasure reported by these people was not driven by a general inability to feel pleasure, music perceptual deficits, differences in exposure, or inability to recognize emotions from music. In subsequent studies, we have also shown that this condition does not reflect a more general deficit affecting either aesthetic rewards in general or other types of emotional sounds (Mas-Herrero et al., 2018b): Specific musical anhedonics demonstrated average emotional reactions to pictorial art (e.g., the Guernica of Picasso) and emotional sounds (e.g., baby crying and sexual moans). What, then, is going on? Based on previous neuroimaging and lesion studies, we hypothesized that this particular lack of sensitivity to music rewards could be driven by altered connectivity between the nucleus accumbens and frontal and temporal cortices. To test this hypothesis, we selected three new groups of participants with high, average, or low sensitivity to music but with similar sensitivity to other rewarding stimuli and no music perceptual deficits, using the same battery of test used before (Martínez-Molina et al., 2016). These three groups of participants also performed a music and a monetary task but now inside a magnetic resonance imaging scanner to investigate the neural correlates of specific musical anhedonia. Besides replicating our 142

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previous behavioural and physiological findings, we also found that those with specific musical anhedonia exhibited a reduced activity in the NAcc while listening to pleasant music. Notably, this reduction was not related to a general dysfunction of this brain region. When playing for money, activations of the nucleus accumbens did not differ from the other groups. Why, then, did the nucleus accumbens fail to respond to music in the members of the ANH group? In answer to this question, we observed decreased functional connectivity between the right STG and the NAcc during music listening in the ANH participants compared to the other two groups. It is important to highlight that the pattern of activity in the STG while listening to music was similar among the three groups, consistent with the intact music perceptual abilities of the ANH group. Therefore, the problem does not reside in auditory regions but in how these structures communicated with the NAcc. These findings fit very well with the idea that music-induced pleasure may be mediated via cortical pathways involved in auditory cognition in interaction with reward-related structures. However, although these findings suggest that communication between temporal cortices and the NAcc is necessary for music reward to emerge, it is important to notice that these two regions are only sparsely connected through direct anatomical pathways. It remains possible that music information processed by temporal cortices accesses the NAcc through a third region. Here, the orbitofrontal cortex represents an ideal candidate for the mediation of this interaction. The OFC is a key structure in the reward circuitry that receives inputs from all sensory modalities and projects to the ventral striatum, including the NAcc (Haber & Knutson, 2010). In this way, the OFC acts as a multisensory hub, integrating signals from sensory and perceptual regions into the reward circuitry. To investigate the mediating role of the OFC in the interaction between the STG and the NAcc, we used diffusion tensor imaging (DTI)—an MRI technique that allows for measuring the integrity of white matter fibre tracts—in a subset of participants from our previous fMRI study (Martínez-Molina et al., 2019). Specifically, we studied the relationship between music reward sensitivity (measured by the BMRQ) and the white matter microstructure of the pathways connecting the STG and the NAcc via the OFC (STG–OFC and OFC–NAcc connections). The results revealed that white matter microstructure in the right STG–OFC and the OFC–NAcc connectivity predicted individual differences in music reward sensitivity. Remarkably, the integrity of the entire pathway connecting the STG and NAcc via the OFC (STG–OFC–NAcc) explained individual differences in NAcc activation during music listening. These results extended the previous findings by suggesting that the OFC plays a pivotal role in the interaction between high-order cortical brain regions and the NAcc in music-induced pleasure. Overall, research on specific musical anhedonia has provided significant insights into the brain mechanism involved in musical pleasure, validating the idea that music-induced pleasure arises through a complex interplay between perceptual, integrative, and reward systems.

Major challenges, goals, and suggestions Despite the significant advances in understanding the neural basis of music-induced pleasure during the last 20 years, there are still many open questions requiring further research. First, although the literature reviewed here indicates that the interactions between auditory cortical regions and the reward system play a crucial role in music reward, it is still unclear what the directionality and the dynamics of these interactions are. Is the reward system controlled by auditory cortical signals, or is this coupling only reflecting greater auditory processing because of the pleasure experienced? The implementation of new connectivity methods to estimate directed functional (causal) connectivity, such as Granger causality analysis, as well as new techniques to improve the temporal resolution of MRI data acquisition, may provide important insights into this topic. Second, it remains to be seen if specific musical anhedonia is a heritable trait. Investigating the genetic underpinnings of this condition may provide important advances in understanding the evolutionary trajectory of music reward sensitivity. Twin or family studies may be the first step to determining whether the predisposition to developing musical anhedonia is genetic. If so, genome-wide association studies (GWA or 143

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GWAS) may enable us to map the genetic loci for musical anhedonia and, through comparative genetics, investigate evolutionary trajectories. Third, as we have seen in this chapter, we now have a pretty good picture of the brain circuits involved in musical pleasure. Yet we do not fully understand what it is about music that triggers these circuitries (see also Chapter 15). Why do we like some music and detest other music? Why are there such large individual differences in musical tastes and preferences, even within the same culture and society? Theoretical models have long posited that the key may rely on music’s power to generate predictions and expectations through its temporal and structural patterns. According to these models, either the fulfilment or violation of our expectations may give rise to pleasure peaks. However, large individual differences are present in musical preferences indicating that the balance between predictability and surprise may differ across individuals. For instance, some people might enjoy music characterized by repetitive patterns (pop, techno, hip hop, etc.). In contrast, others may prefer musical genres involving improvisation and surprise ( jazz, experimental music, among others). Are these differences in musical tastes a matter of exposure? Or do they reflect individual differences in auditory cognition and perception shaping our preferences for more or less music complexity? Studying the development of musical tastes and the brain changes associated with it as a function of individuals’ auditory abilities may provide an excellent model to understand how music-induced pleasure arises. In addition, the implementation of computational models to estimate music’s ongoing expectations and surprises may be an excellent tool to assess music complexity objectively and thus investigate the role of surprise, uncertainty, and predictability in music-induced pleasure.

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8 ODOUR AESTHETICS Hedonic perception of olfactory stimuli Gulce Nazli Dikecligil and Jay A. Gottfried

Olfaction is a key sensory modality that detects and interprets volatile chemical compounds to ultimately inform, guide and shape an organism’s behaviour in response to its environment. Many species rely on their sense of smell for finding and selecting food (Arenas & Farina, 2012; Laidre, 2009; Raghuram et al., 2009; Stutz et al., 2016), avoiding predators (Amo et al., 2008; Amo et al., 2011; Ferrero et al., 2011; Kelley & Magurran, 2003; J. J. Li et al., 2014; Roth et al., 2008; Sündermann et al., 2008), mating (Clarke et al., 2009; M. Keller et al., 2009; Keverne, 2004; Mclennan, 2003), communicating with conspecifics (Arakawa et al., 2008; Banks et al., 2016; Chauvin & Thierry, 2005; Kullmann et al., 2008; Logan et al., 2012; Ryan et al., 2008), and spatial navigation (Buehlmann et al., 2015; Jacobs et al., 2015). In humans, the sense of smell has historically been considered an archaic and weak sense that has relatively little influence on cognition and behaviour and is deemed inferior to the olfactory abilities of other mammals (McGann, 2017), yet studies in the past decades have consistently shown that humans, much like other mammals, have an acute sense of smell (Bushdid et al., 2014) that informs a rich spectrum of behaviour from food and mate selection to spatial navigation ( Jacobs et al., 2015; Shepherd, 2004; Stevenson, 2010). The importance of olfaction for human behaviour has been evident in studies focusing on smell disorders: It has been shown that impoverished olfaction negatively impacts daily functions such as cooking, eating, detecting hazardous chemicals, and assessing foul odours (Croy et al., 2014; Miwa et al., 2001; Temmel et al., 2002). Furthermore, smell disorders can decrease the pleasure and reward associated with eating, as they diminish the complex flavour experiences associated with food consumption (Blomqvist et al., 2004; A. Keller & Malaspina, 2013; Nordin et al., 2011). Collectively, the consequences of impoverished smell contribute to decreased quality of life (Blomqvist et al., 2004; Miwa et al., 2001; Nordin et al., 2011) and correlate with increased likelihood of depression (Bonfils et al., 2005; Croy et al., 2012; Frasnelli & Hummel, 2005; Neuland et al., 2011; Smeets et al., 2009; Temmel et al., 2002). These findings highlight that the human sense of smell, in addition to its crucial role in supporting survival functions via odour detection, discrimination, and identification, also contributes to human well-being through its role in appreciation of the surrounding chemosensory landscape. This crucial role of olfaction in human pleasure and reward and how it relates to the field of aesthetics is often overlooked in favour of work that centres on audition and vision. Despite being relatively understudied as a subject of aesthetics, olfactory pleasures (and displeasures) form the backbone of many industries ranging from culinary arts and oenology to cosmetics. Olfactory appreciation builds on affective assessments of scents, which is one of the key functions of the olfactory system to inform approach or avoidance behaviours. A mouse smelling peanut butter might 148

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navigate towards the odour source to locate its meal for the day, or a human sitting next to foul-smelling garbage might decide to relocate a different bench in the park. It is important to note that appetitive and aversive odours can shape behaviour either because they act as a cue that is associated with a positive or negative outcome, respectively (e.g., peanut butter scent signals the presence of a higher-calorie meal), or because they act as reinforcers in and of themselves (e.g., the unpleasant smell of sweaty socks can cause someone to move away merely because it is unpleasant, and likewise the scent of an amber candle might cause someone to linger in a room merely because they enjoy the scent itself ). Beyond approach and avoidance behaviours, our affective assessments of odours can bias our perceptions and decisions even in non-olfactory domains; pleasant odours have been shown to improve pain tolerance (Bartolo et al., 2013; Jo et al., 2020; Prescott & Wilkie, 2007) and to modulate interpretation of facial expressions (Cook et al., 2017; Leleu et al., 2015; Poncet et al., 2020). These findings support the notion that odour valence perception is not only crucial for well-being but also can shape cognition and decision making in non-olfactory domains such as vision and touch. Aesthetics of olfaction, while interconnected with simple hedonic judgements of odours and learned odour-outcome associations, extends beyond mere assessments of whether an odour smells good or bad or whether it signals approach or avoidance behaviour. Olfactory aesthetic appreciation, much like aesthetic preferences in other sensory modalities, is influenced by stimulus novelty; complexity; familiarity; unexpected features of the stimulus; interplay between elements of appetitive, neutral, and aversive components; the context in which the odour is experienced; and importantly the identity of the odour object as well as the social and cultural associations the odour quality evokes. Interestingly, aesthetic appreciation of odours is not necessarily aligned with approach or avoidance behaviours either; for example, the smell of gasoline may both elicit liking and avoidance, whereas the smell of blue cheese may elicit dislike and approach. In a similar dissociation, odour hedonics judgments and odour aesthetic appreciation do not always necessarily run parallel either. An odour that is disliked may still be aesthetically appreciated, whereas an odour that is liked may be found to have low aesthetic value. In other words, olfactory aesthetics does not necessarily go hand in hand with the perceived hedonic value (e.g., smells good or bad), utility of the odour object and its source (e.g., this scent indicates there is food nearby), or appropriate odour response (e.g., this scent means I should avoid this area) and can be affected by a multitude of cultural factors, personal experiences, stimulus properties, and metacognitive processes. While olfactory aesthetics is the overarching topic of this chapter, we will mainly discuss literature on odour valence perception, and its neural basis, as a building block for understanding the mechanistic underpinnings of olfactory aesthetic appreciation, given that a much greater number of neuroscientific studies focus on valence perception rather than the more complex and subjective topic of odour aesthetics per se. We will begin the chapter with discussing the basics of odour perception and how odour valence perception relates to odour quality perception. In the next section, we will give an overview of the olfactory system and neural circuits of odour quality coding. We will then highlight factors such as odour stimulus parameters and cognitive factors that are known to shape odour valence perception. We will finish the chapter with neural mechanisms that underlie affective assessment of odours.

General principles of odour perception in humans The human olfactory system is excellent at detecting the presence of odours even at concentrations as low as 10 parts per billion (Cain et al., 2007). Once an odourant is detected, the olfactory system can extract many attributes from an odour stimulus: how intense it is, how long it lasts, what perceptual qualities it has (e.g., fruity, flowery, fishy, etc.), how familiar it is, and how pleasant it is. Of these attributes, assessing the perceptual qualities and characteristics of an odourant (e.g., “this is a smoky odour”) and comparing the odour-evoked percept to existing olfactory templates (e.g., “does this smell like burning rubber?”) perhaps serves the most crucial function for odour-guided behaviour, allowing organisms to identify and in return 149

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act appropriately to odour stimuli (to run away from fire or prepare for eating popcorn, depending on odour identity). Not only can the human olfactory system identify and differentiate odours arising from distinct odour sources, but it can even learn to discriminate between pairs of enantiomers that were initially indistinguishable through association of one enantiomer with an aversive outcome (Li et al., 2008), suggesting that odour perceptual qualities can be specific to even small molecular differences and yet malleable as new associations are acquired. Despite highly sensitive odour detection and discrimination abilities, humans are surprisingly limited in naming odours when these are presented without any contextual cues (i.e., sights and sounds that may typically accompany the odour source). Identification performance increases when subjects are presented with multiple-choice options instead of doing free recall (Cain, 1979; Cain et  al., 1998; Lawless & Engen, 1977). While naming odourants is challenging, assessing odour pleasantness comes easily; humans can rapidly report how much they like an odour and use odour pleasantness to categorize odours (Berglund et al., 1973; Schiffman et al., 1977; Zarzo, 2008). These findings have led researchers to reason that difficulties in odour-naming might reflect limitations in odour quality perception and propose that odour valence instead may be the key feature that dominates odour perception (valence-centred approach) (Yeshurun & Sobel, 2010). An alternative model proposes that difficulties in odour naming may reflect an evolutionary and ecological disconnect between language and olfactory systems (Olofsson  & Gottfried, 2015) rather than a difficulty in odour quality perception per se and argues that odour perceptual qualities are the primary defining axis of odour objects (object-centred approach). Specifically, object-centred approaches suggest that odour quality coding and perception occur earlier in the olfactory processing stream, and the emerging odour-object representations inform the downstream affective assessments of the odour object (Gottfried, 2010; Olofsson et al., 2013; Olofsson et al., 2012). Experiments directly testing this hypothesis of whether odour quality perception arises prior or after odour valence perception (Olofsson et al., 2013; Olofsson et al., 2012) have shown that, in case of familiar odours, humans can identify odours faster than they can assess odour valence. These studies suggest that while odour valence is a crucial dimension of odour perception, it hinges on odour-quality representations that then inform affective assessments of odour quality. From an evolutionary and behavioural standpoint, it is easy to see why odour-valence information, in the absence of odour-quality information, would be insufficient for odour-guided behaviour; for example, while both garlic bread and a jasmine-scented candle might smell equally pleasant, they smell pleasant in distinct ways that inform the subject on how to interact with the odour source. While both pleasant odours may generate an approach response, the organism uses the odour quality information to determine what specific approach behaviour it will follow (e.g., initiate food consumption or light the scented candle).

Neural mechanisms of odour identity coding and perception What are the neural mechanisms that underlie the translation of myriad volatile compounds arising from a wide range of odour sources into complex olfactory percepts? This section gives a brief overview of the current understanding of the neuroanatomy and neurophysiology of the olfactory system and how it encodes odour information. Volatile odorous compounds (“odourants”) can access the olfactory receptors and initiate odour processing through two different routes, the orthonasal route, where odourants are inhaled into the nasal cavity through the nostrils, and the retronasal route, where the odourants emitted by food and drinks in the mouth travel through the back of the oral cavity and into the nose during exhalation. Furthermore, in rodents, odourants reaching the nasal cavity can be processed through either the main olfactory system and/or the accessory olfactory system, depending on the odour source and the volatility of the odourant. However, it has been shown that the vomeronasal organ (VNO; the olfactory sensory organ of the accessory olfactory system) is not present in primates and humans (Salazar & Quinteiro, 2009). Therefore, we will focus our discussion on the main olfactory system. 150

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Figure 8.1 Illustration of two differing models of odour valence perception. Object-centred models propose that the olfactory system identifies the odour object first and then uses this information to determine the hedonic value of the odour object. Conversely, the valence-centred approach proposes that odour hedonic perception arises prior to odour object identification and is necessary for odour object identification. Figure modified with permission from Olofsson, J. K., Bowman, N. E., Khatibi, K. & Gottfried, J. A. “A Time-Based Account of the Perception of Odor Objects and Valences.” Psychol. Sci. 23, 1224–1232 (2012).

Orthonasal olfactory processing in the main olfactory system begins with the inhalation of odourants into the nasal cavity. Once inside the nasal cavity, odourants bind to olfactory receptors (Ors) expressed on the cell membrane of olfactory sensory neurons (OSNs) residing in the olfactory epithelium (OE). The binding of odourants to G-protein–coupled Ors initiates the electrochemical signalling in OSNs. Each OSN expresses a single OR (Serizawa et al., 2004, 2005) from a repertoire of ~350 Ors in humans (Malnic et al., 2004) or ~1000 Ors in mice (Godfrey et al., 2004) (the size of the olfactory gene repertoire varies across species [Keller & Vosshall, 2008; Niimura et al., 2014]). However, a given OR can bind multiple monomolecular odourants, and, likewise, a given monomolecular odourant can bind to multiple Ors, albeit with different affinities (Malnic et al., 1999). Furthermore, odourants can also act as antagonists for Ors (Oka et al., 2004; Reddy et al., 2018). The electrochemical signals initiated in OSNs travel down the axons of OSNs and target the olfactory bulb (OB). In this first stage of olfactory processing, axons of OSNs expressing the same OR converge onto a small number of spherical structures called glomeruli such that each glomerulus represents one OR. Within the glomeruli, OSN axons synapse onto the dendrites of output neurons of the OB called the mitral/tufted cells (M/T cells), which then project to downstream olfactory regions. In addition to OSN inputs and mitral/tufted cell outputs, OB neural circuits contain local neurons (granule cells and juxtaglomerular cells); top-down projections from the cortex (Boyd et  al., 2015; Matsutani, 2010; Matsutani  & Yamamoto, 2008; Rothermel & Wachowiak, 2014); and extrinsic neuromodulatory inputs from serotonergic, cholinergic, and noradrenergic projections as well as intrinsic dopaminergic neurons (Linster & Cleland, 2016; McLean & Shipley, 1987; McLean et al., 1989; Petzold et al., 2009). 151

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As a result of converging projections from OSNs expressing the same OR, each monomolecular odourant binds to a small subset of Ors and consequently gives rise to a sparse pattern of glomerular activity (Davison & Katz, 2007). However, animals and humans rarely encounter monomolecular odours in isolation and instead interact with natural scents that are complex odour mixtures composed of a large number of monomolecular odourants (e.g., hundreds of organic volatile compounds have been identified in relation to the aroma of strawberry [Ulrich et al., 2018]). Such odour mixtures elicit complex spatiotemporal patterns of glomerular activity (Chae et al., 2019; Gschwend et al., 2016; Vincis et al., 2012). Both the spatial and temporal patterns of odour-evoked responses in the OB carry odour identity information (Uchida et al., 2014). It is important to note that glomerular activity reflects the first stage of olfactory processing, where the information relayed by the OSNs is shaped by local OB circuits (specifically, lateral inhibition plays an important role in early odour processing [Cleland & Linster, 2012; Geramita et al., 2016; Najac et al., 2015; Shao et al., 2012; Urban, 2002; Zavitz et al., 2020]), top-down projections [Boyd et al., 2015; Boyd et al., 2012; Markopoulos et al., 2012; Matsutani, 2010; Matsutani & Yamamoto, 2008; Rothermel & Wachowiak, 2014], and neuromodulatory inputs [D’Souza & Vijayaraghavan, 2014; Escanilla et al., 2009; Guérin et al., 2008; Linster & Cleland, 2016; Liu et al., 2012; Mandairon et al., 2006; McLean & Shipley, 1987; Petzold et al., 2009; Suyama et al., 2021]). Olfactory information diverges onto multiple downstream regions after the first stage of processing in the OB; outputs of the OB, mitral/tufted cells, project to cortical and subcortical regions: anterior olfactory nucleus (AON), tenia tecta (TT), olfactory tubercle (OT), amygdala, entorhinal cortex, and piriform cortex (PCx). The direct monosynaptic projections from the OB to the PCx are a unique neuroanatomical feature of the olfactory system, as ascending sensory information in all other sensory modalities is relayed to the primary sensory cortices through the thalamus. While neighbouring mitral/tufted cells in a given glomeruli receive converging information from the same OR, this spatial organization of olfactory information in the OB does not hold for the next stage of processing in the PCx. M/T cells receiving input from the same OR project their axons in a seemingly random fashion to the PCx (Ghosh et  al., 2011; Igarashi et  al., 2012). As a result, nearby PCx neurons receive input from a spatially distributed set of glomeruli (Miyamichi et al., 2011). Furthermore, a given PCx neuron receives converging inputs from multiple M/T cells arising from distinct glomeruli, which suggests that PCx neurons integrate information across distinct glomeruli and hence Ors (Apicella et al., 2010). This neuroanatomical organization results in PCx odour responses lacking a clear topographical organization. Electrophysiological and imaging studies in PCx of rodents (Illig & Haberly, 2003; Rennaker et al., 2007; Roland et  al., 2017; Stettler  & Axel, 2009) and neuroimaging studies in humans (Gottfried, 2010) have shown that odours activate distributed ensembles of neurons with unique spatiotemporal activity profiles. While PCx neural activity patterns can be used to decode odour identity information, studies to date suggest that PCx does not have a chemotopic map (the physical locations of the neural responses do not carry information about the spatial, physiochemical, or perceptual features of the odour stimuli). The absence of topographic organization in PCx is yet another feature that differentiates the olfactory system from the auditory, visual, and somatosensory systems, where neurons responding to similar features of sensory stimuli are spatially clustered together. However, similar to the piriform cortex, the primary gustatory cortex also lacks a chemotopic organization, and single gustatory cortex neurons can respond to broad range of tastants (Chen et al., 2021; Dikecligil et al., 2020; Fletcher et al., 2017), which suggests that the cortical representation of chemical senses is not organized spatially according to the chemical or perceptual features of the stimuli. In addition to the diffuse ascending projections from the OB, the PCx receives extensive local inputs from other PCx neurons, the AON, and the orbitofrontal cortex (OFC), as well as the amygdala and entorhinal cortex (Bekkers & Suzuki, 2013). Given this connectivity, the odour information arriving to the PCx is interpreted and processed in context of these extensive top-down and auto-associative projections and hence does not simply reflect the same information arriving from the OB. 152

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From the PCx, odour information further diverges; PCx axons project to the OB, AON, and other olfactory regions, including those that receive direct OB input as well as the OFC, hypothalamus, amygdala, entorhinal cortex, and mediodorsal thalamus. In addition to the monosynaptic connections listed previously, the PCx has indirect connections with key regions involved in emotion, memory, hedonics, and cognitive processing, including the insula, cingulate cortex, nucleus accumbens, hippocampus, and ventral putamen. Many of these regions are reciprocally connected among themselves as well. Among the higher-order regions that receive PCx input, OFC is considered the secondary olfactory cortex, as it receives dense projections from PCx (Carmichael et al., 1994). This projection from the PCx to OFC provides a pathway for olfactory information to reach neocortex without going through a thalamic relay. In the past two decades, neuroimaging studies in humans have shown that odour stimuli consistently evoke activity in the OFC, frequently in a bilateral fashion (Gottfried et al., 2002; Gottfried et al., 2003; Small et al., 1997; Sobel et al., 1998; Zatorre et al., 1992). Electrophysiology studies in animals have shown that OFC neurons not only encode odour identity but can respond to olfactory context cues and odour cue-outcome contingencies (Rolls et al., 1996; Rolls et al., 1996). Current theories suggest that the OFC plays an important role in integrating odour identity information with expected outcomes to guide odourrelated behaviour and decision making (Gottfried, 2007; Gottfried  & Zald, 2005; Howard et  al., 2015; W. Li et al., 2010). Overall, the olfactory system, while specializing in detecting and identifying a vast array of volatile compounds, does so through highly interconnected olfactory and non-olfactory regions that reciprocally exchange olfactory, contextual, and neuromodulatory information that ultimately leads to interpretation of

Figure 8.2 Neuroanatomy of the human olfactory regions. (A) A ventral view of the human brain in which the anterior part of the right temporal lobe has been resected in the coronal plane to better visualize the underlying olfactory and limbic regions. (B) Close-up of the outlined area in panel (A). The outputs of the olfactory bulb (OB) pass through the lateral olfactory tract (LOT) and send parallel projections to many downstream olfactory and limbic regions, including the olfactory tubercle (OT), piriform cortex (PCx; anterior portion of PCx shown in purple and posterior portion of PCx shown in blue), amygdala (AM), and entorhinal cortex (EM). Other key regions involved in processing higher-order olfactory information, the hippocampus (HP) and olfactory portion of the orbitofrontal cortex (OFC), can also be seen in the ventral view. Figure modified with permission from Gottfried, J. A. “Central mechanisms of odour object perception.” Nat. Rev. Neurosci. 11, 628–641 (2010).

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olfactory information in context of the organism’s current physiological, emotional, and cognitive states as well as past experiences and memories.

Factors that influence odour valence perception As discussed earlier in the chapter, odour valence perception is one of the key functions of the olfactory system that complements odour quality perception to guide behaviour. While there are evolutionarily conserved appetitive and aversive odours that are hardwired in many species (Ferrero et al., 2011; Hallem & Sternberg, 2008; Kobayakawa et al., 2007; Li et al., 2013; Papes et al., 2010; Semmelhack & Wang, 2009; Stowers & Logan, 2010; Suh et al., 2004), such as fox odour (Endres & Fendt, 2009; Saito et al., 2017) or volatiles found in mouse urine (Li et  al., 2013) that promote avoidance and approach behaviours, a much greater number of odourants acquire their hedonic value through learning (Chao et al., 2004; Li & Liberles, 2015; Logan et al., 2012; Zhang et al., 2005). Furthermore, the hedonic value of an odourant is not fixed for a given organism but can be further shaped by contextual cues, multisensory inputs, an organism’s physiological state, and the properties of the odour stimulus such as its duration and concentration (Barkat et al., 2008; Bensafi et al., 2007; Chao et al., 2004; Linster & Cleland, 2016; Luisier et al., 2019; O’Doherty et  al., 2000; Poncelet, Rinck, Bourgeat et  al., 2010; Poncelet, Rinck, Ziessel et  al., 2010; Small et al., 2001). The olfactory system, in concert with neural circuits that mediate cognitive, limbic, and homeostatic functions, integrates across these factors to determine the hedonic value of an odour for that given moment, and hence, as the organism and the circumstances around the odourant change, so does its potential hedonic interpretation. In the following, we will discuss some of the well-studied factors that contribute to odour hedonic perception. These factors can be broadly grouped into three categories: (1) properties of the odour stimulus itself, (2) environmental and contextual cues accompanying the odour, and, finally, (3) the cognitive and physiological state of the organism that is interacting with the odourant. We note that social and cultural factors also play a very important role in learned odour-outcome and odour valence perceptions; however, this topic is beyond the scope of the chapter, and we direct readers to recent articles on this topic (Coppin et al., 2016; Ferdenzi et al., 2017; Ferdenzi et al., 2013; Ferdenzi et al., 2011).

Effects of odour stimulus properties on odour valence perception One of the puzzling aspects of olfaction compared to other sensory systems is the lack of a clear relationship between odour stimulus parameters and odour quality perception (Sell, 2006). In vision and audition, wavelength of light and sound are the basic building blocks of sensory processing and resulting perceptions. In contrast, there is no evident parameter of monomolecular odours (e.g., molecule size, functional groups, carbon chain length, etc.) that can reliably predict aspects of the resulting percept such as odour quality and pleasantness across individuals. A handful of studies in recent years (Khan et al., 2007; Koulakov et al., 2011; Ravia et al., 2020; Snitz et al., 2013) has aimed to assess if large data sets using hundreds of molecular and perceptual features could unveil any relationship between groups of physiochemical and perceptual features such as odour valence. One of the first studies using this approach asked subjects (perfumers and olfactory scientists) to rate how well 160 unique odourants matched each of the 146 available verbal descriptors (e.g., “fruity,” “floral,” “woody,” etc.). After using dimensionality reduction approaches, the authors found that odour pleasantness explained 30% of the variance in the perceptual space (Khan et al., 2007). Additionally, the physiochemical features that accounted for the largest variance in the molecular space were correlated with odour pleasantness. These findings suggest that there might be groups of physiochemical properties partly associated with odours that are found to be pleasant, although molecular properties alone cannot account for the other 70% of variance 154

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in odour perception. A similar study where subjects sampled and rated 476 odour molecules for their perceived intensity, pleasantness, and semantic category showed that machine learning algorithms trained on a subset of the molecule-percept relationships could predict, above chance level, the perceptual outcomes of novel molecules that the algorithm had not seen before (Keller et al., 2017). Specifically, physiochemical properties could be used to predict the pleasantness of novel molecules using the algorithms trained on the existing pairings. Overall, these studies do not necessarily suggest that physiochemical properties dictate odour percepts per se but that there may be broad associations between groups of physiochemical properties and general odour quality perceptions. It is possible that these associations arise because odourants from similar odour sources (e.g., flowers) tend to share molecular features (Dudareva et al., 2006; Dudareva et al., 2004; Tholl et al., 2011) and are perceptually grouped together due to the similarities in the ecological function of the odour source (i.e., flowers eliciting approach behaviour). In other words, the olfactory system might be grouping odour objects based on the functional and ecological relevance of the odour source rather than the molecular features, but the underlying correlations in molecular features of odours coming from similar sources might lead to predictable patterns between molecular features and odour quality and valence percepts.

Odour stimulus intensity, duration, and frequency Beyond the molecular composition of the volatiles entering the nose, odour stimuli are also defined by the concentration of the molecules, the duration of the odour plume, and the frequency with which the same odour object is encountered in a short amount of time. Concentration of odourants in the natural environment is subject to change depending on proximity to the odour source, wind, and temperature. Such changes in odour concentration typically alter the perceived intensity of a given odourant (although the relationship between concentration and perceived intensity is not linear, and can vary widely across odourants) (Cain, 1969; Moskowitz et al., 1974). Studies investigating whether changes in odour intensity alter odour valence perception have reported conflicting findings, with some studies showing that increasing odour intensity results in decreased odour pleasantness (Henion, 1971; Moskowitz et al., 1974; Moskowitz et al., 1976) and others showing both increased and decreased odour pleasantness depending on the odour identity being tested (Doty, 1975). Yet another study using food odours (sweet and savoury) reported that higher concentrations of the same odour were rated to be more pleasant compared to lower concentrations, suggesting that food odours that are already considered appetitive in low concentrations may become more pleasant with increasing odour intensity (Howard et al., 2015). A possible explanation is that odour intensity informs the organism about the relative proximity of the odour source and the appropriate behavioural response that should be taken in response to the odour object depending on this proximity; therefore, the potential changes in stimulus–outcome relationships as odour intensity changes may alter the hedonic value of the odourant. For food odours, high odour intensity means that the food is close by, and this may increase the motivation to seek and consume the food and can alter the hedonic value of the associated food odour. Likewise, a low concentration predator odour may not be salient enough to generate an escape response, as it signals that the predator is far away and may be considered hedonically neutral, whereas high-intensity predator odour might trigger an avoidance response and consequently alter the hedonic value of the odour (Ferrari et al., 2006; Hacquemand et al., 2013). It is important to note that once inside the nasal cavity, many odorous molecules activate both the olfactory and the trigeminal nerves (e.g., acetone, ammonia) and can lead to unpleasant non-olfactory sensations such as stinging or burning that can contribute to decreased odour valence perception with increasing concentrations (Doty et al., 1978). Taken together, these findings suggest that odour pleasantness may be altered by the changes in the intensity of the odourant in an odour identity–dependent manner, with effects mediated by the simultaneous stimulation of the trigeminal nerve by the odorous molecules. 155

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In the natural environment, organisms often encounter odourants within complex, turbulent air streams called odour plumes. Humans and animals can sample odour plumes repeatedly across multiple sniff cycles to extract valuable information about the location and proximity of the odour source (Raithel & Gottfried, 2021). How, then, does repeated exposure to the same odourant, such as what might happen when a mouse is sniffing the air for fox odour or a human smelling baked goods from a nearby pastry shop, affect the valence of the odourant? One study in humans showed that an odour that is initially pleasant becomes less pleasant with repeated exposure, whereas an odour that is unpleasant becomes less unpleasant (W. S. Cain & Johnson, 1978). This observation that the hedonic value of the odour becomes more neutral with repeated exposure was found to be consistent with affective habituation, where repeated exposure diminishes the affective response (Leventhal et al., 2007). It is possible that the observed decreases in odour valence occur as a result of odour habituation. It has been shown that repetitions of the same odour stimuli evoke decreased responses in the OSN, OB (Dalton & Wysocki, 1996; Kurahashi & Menini, 1997; Scott, 1977; Zufall & Leinders-Zufall, 2000), and PCx (Li et al., 2006; Sobel et al., 2000).

The effect of internal states and external cues on odour valence perception While odour stimulus parameters might play a minor role in odour valence perception, a much greater variance in affective assessment of odourants arises from the cognitive and emotional state of subjects and the external cues that provide contextual information within which odour stimuli are interpreted. Perception of both odour quality and hedonic value are highly dependent on the surrounding sensory and contextual cues that accompany the odour. Presenting the same odour mixture with different labels can influence familiarity, intensity, edibility, and pleasantness ratings (Djordjevic et al., 2008; Herz, 2003; Herz & von Clef, 2001; Manescu et al., 2014). In one study, subjects smelled the same set of odours under three different semantic labels; for example, juniper berry odour was either labelled as green mango (a positive label), hospital disinfectant (a negative label), or the number “21” (a neutral label) (Djordjevic et al., 2008). Subjects reported the odours to be more pleasant when they were paired with a positive label compared to a negative or neutral label. Subjects also showed greater sniff volume and decreased skin conductance measurements for odours that were labelled with positive names, suggesting that the odour labels affected both perceptual and physiological responses. This effect generalized to 13 out of the 15 odours that varied across the pleasantness spectrum, implying that the ability of labels to bias odour hedonic perception is not limited solely to pleasant or unpleasant odours. In the following, we will highlight how contextual information arising from other sensory modalities can influence odour valence perception.

Multisensory inputs Sensory information arising from a given sensory modality does not lead to fixed percepts but is rather interpreted within the context of a cacophony of multimodal sensory cues, giving rise to dynamic, contextdependent percepts. For the olfactory system, the interpretation of an odour stimulus can be influenced by the accompanying sights, sounds, and tastes (Dubose et al., 1980; Jadauji et al., 2012; Manesse et al., 2020; Morrot et al., 2001; Osterbauer et al., 2005; Seo & Hummel, 2011; Seo et al., 2014; Spence et al., 2010; Zellner et al., 1991). For example, a sour cheese odour can be aversive if one is walking by a dumpster in a dark alley, or it could be pleasurable if one is having a cheese plate at their favourite restaurant. How does information arriving from other sensory systems contribute to odour processing and specifically to the hedonic assessment of odourants? What are the neural mechanisms that might contribute to this dynamic interpretation of odour hedonics? Interactions between taste and olfaction, sensory systems that specialize in translating chemical information into actionable percepts, inform decisions about which foods to seek, consume, and return to. During 156

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food consumption, while tastants stimulate the taste receptor cells on the tongue, volatile molecules emitted from food can travel through retronasal and orthonasal pathways and bind to olfactory receptors. This simultaneous activation of the olfactory and gustatory systems (along with the oral somatosensory system) forms the basis of a unitary flavour percept. The frequent pairing of taste–odour combinations during food consumption (e.g., pairing of banana odour with sweet taste each time someone eats a banana) leads to strong associations that can bias odour and taste perception when the taste or odour stimuli are presented individually. For instance, presentation of just the banana odour can yield a “sweet” percept, as banana odour has almost exclusively been paired with one sweet taste. Likewise, the hedonic value of one modality can bias the hedonic perception, such as perceiving an otherwise neutral odour as pleasant when it has been paired with a sweet taste. How quickly can such taste–odour pairings emerge and modulate odour hedonic perception? In humans, neutral odours that were paired with a sweet solution were later reported to be more pleasant in as few as three repetitions of the odour–sweet solution pairings (Barkat et al., 2008). A similar study in rodents using an odour–taste association task (Blankenship et al., 2019) delivered odours either via the orthonasal or retronasal route and paired them with either water (neutral taste) or sucrose solution (pleasant taste). Following three days of training where the animals were presented with the pairings, the authors tested rats’ odour preference. Odour preference was acquired faster through the retronasal route compared to the orthonasal route, and preferences acquired through retronasal odour–taste pairings did not generalize to the same odour delivered via the orthonasal route. Interestingly, it was shown that the inactivation of primary gustatory cortex selectively impaired retronasal (but not orthonasal) preferences. These findings suggest that the odours processed through the retronasal route might have a privileged association with taste stimuli due to the frequent co-occurrence of retronasal odours and gustatory stimuli during food consumption. Not surprisingly, electrophysiology studies in rodents have shown that gustatory stimuli can activate piriform cortex neurons (Maier et al., 2012), and conversely, olfactory stimuli can activate gustatory cortex neural activity (Samuelsen & Fontanini, 2017) suggesting that cross-modal chemosensory stimuli are represented at the primary sensory cortices. Furthermore, olfactory cues predicting taste stimuli can be acquired faster than visual, auditory, or somatosensory cues predicting taste stimuli and can be represented more extensively in the primary gustatory cortex (Vincis & Fontanini, 2016). Collectively, these findings show that taste–odour associations can form rapidly and influence the odour hedonic perception. Olfactory encounters are often accompanied by visual cues that can shape expectations and subsequent olfactory perceptions. A behavioural study has shown that artificially red-coloured white wine was described just as if it was a red wine by a panel of wine professionals, suggesting that the colour cues could strongly bias odour identity perception (Morrot et al., 2001). Other studies have also shown that the colour of the odour source can influence odour identification and furthermore alter odour hedonic perception: orangecoloured cherry drinks were reported to be orange flavoured (Dubose et al., 1980), and red-coloured strawberries were reported to be more pleasant compared to green-coloured strawberries (Zellner et al., 1991). Given the strong behavioural influence of visual cues on odour perception, it is not surprising that early neuroimaging studies have shown that olfactory tasks can activate visual cortices (Qureshy et  al., 2000; Zatorre et al., 2000), suggesting that subjects may be forming a mental image of the odour source that may aid in olfactory task performance. In a simple odour-detection task (Gottfried & Dolan, 2003), congruent picture–odour pairing (e.g., displaying orange pictures while delivering orange odour) versus incongruent picture–odour pairings (e.g., displaying lettuce picture while delivering orange odour) facilitated rapid odour detection. Furthermore, congruent pairings activated the anterior hippocampus and OFC, suggesting that the cross-modal interaction between the two senses might be facilitated by these regions. A different study also provided complementary evidence where congruent bimodal stimuli (visual and olfactory) resulted in increased activation in the OFC and insular cortex, key regions for multimodal integration of olfactory and gustatory inputs to inform flavour perceptions (Osterbauer et al., 2005). A causal study investigating whether visual cortex activity can influence odour perception ( Jadauji et al., 2012) reported that repetitive transcranial 157

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magnetic stimulation (TMS) of the early visual cortex, but not auditory cortex, improved performance in an olfactory discrimination task. Other studies investigated the role of olfactory cues in modulating visual perception and found that odours can influence binocular rivalry (Zhou et al., 2010), visual attention (Seo et al., 2010), and visual processing of faces (Leleu et al., 2015), raising the possibility that bidirectional interaction between olfactory and visual systems can meaningfully inform multisensory perception. Similar to gustatory and visual stimuli, auditory inputs have also been shown to influence olfactory perception and hedonic assessment. In a study where odours were paired with either congruent (pairing of Christmas songs with cinnamon smell) or incongruent sounds (pairing of a coffee advertisement with cinnamon smell), the subjects reported the odours to be more pleasant when they were paired with congruent sounds (Seo et al., 2014). In a similar experiment, subjects were presented with either a pleasant or unpleasant sound prior to or during the odour presentation. Irrespective of the hedonic value of the odour stimuli, the subjects’ hedonic judgements were modulated by how much they liked the sound, such that when pleasant sounds were paired with unpleasant odours, the subjects reported the odour as being pleasant (Seo & Hummel, 2011).

Neural circuits mediating odour hedonics Neural mechanisms of rewarding and aversive stimuli have been extensively studied in both human and animal models. While the nuances of how each brain region contributes to hedonic processing is an area of active research, many regions, including the dorsal and ventral striatum, substantia nigra, orbitofrontal cortex, insula, and anterior cingulate cortex, have consistently been shown to be involved in assessing and guiding subjective value judgement and related decision making. Many of these regions are also involved in odour hedonic processing, and in the following, we will discuss some of the well-studied brain regions in context of odour valence coding. We will discuss findings from both human and animal studies; while experiments in humans can provide greater insight about the subjective perceptual experience, animal studies can provide finer details about the underlying neural circuits given the wider range of experimental techniques available to study the brain. However, unlike human studies, where subjects can be asked to report their subjective experience of an odourant, animal studies must rely on behavioural assays such as odour investigation time, odour conditioned place preference, and other quantifiable approach and avoidance behaviours to interpret whether an animal finds an odour appetitive or aversive. It is important to note that the results of these assays may be confounded with other behavioural factors and psychological states that may drive approach and avoidance behaviours such as curiosity, anxiety, hunger, exploration, or neo-phobia. Furthermore, animals may spend more time investigating odours that they have a harder time identifying due to the speed–accuracy trade-off (Rinberg et al., 2006), and hence odour investigation time may not correlate directly with how much an animal likes an odour stimuli per se. These behavioural caveats should be considered carefully when interpreting the results of animal studies. In recent years, one region in the olfactory system has consistently been shown to be involved in odour valence processing in rodent studies, the olfactory tubercle (OT). The OT receives direct projections from the OB and is reciprocally connected with many regions in the olfactory network (OB, AON, PCx, OFC), as well as regions associated with the reward circuitry, including the ventral tegmental area (VTA) (Z. Zhang, Zhang et  al., 2017). The neuroanatomical connectivity of the OT supports integration of olfactory and reward-related signals to influence behavioural output. Furthermore, modulation of OT activity can alter motivated behaviours such as mating (Hitt et al., 1973) or preference for conspecific chemosignals (DiBenedictis et  al., 2015). Single neuron recordings from OT of awake rodents have found that OT neurons preferentially encode odours that predict a reward outcome, and these reward-specific responses in the OT emerge prior to motor movement (Gadziola et al., 2015). This finding suggests that OT plays an important role in encoding odour–reward contingency and can be informing subsequent decision making. Another 158

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study using c-Fos activation to assess neural activity has shown that when mice were conditioned to associate odours with either a reward or an electric shock, odours that predicted reward versus aversive outcomes activated distinct areas of the OT (Murata et al., 2015). These findings suggest that subregions of the OT can encode odour-associated outcomes according to their hedonic value, independently from odour identity. A recent study investigated how learned odour–reward associations emerged in the OT and whether the OT and posterior PC encoded odour–reward associations in a similar way (Millman & Murthy, 2020). The authors found that mice could learn odour–reward contingencies within a single experimental session, and OT neurons showed both odour identity–specific and reward-outcome–specific responses as early as 50 ms after the odour inhalation. In contrast, neurons in the posterior PC, while encoding odour identity, did not show reward-specific responses. Other studies have also investigated VTA to OT projection and its role in reward-related behaviour and odour hedonics. Optogenetic activation of VTA to OT projections could elicit preference for an otherwise neutral odour, and blocking OT dopaminergic receptors prevented the formation of preference for neutral odours (Zhang, Liu et al., 2017). Interestingly, this pathway may be important for maintaining the learned odour preference, as inactivation of dopaminergic input from VTA to OT eliminated the odour preference that was already acquired. Appetitive or aversive associative olfactory learning where organisms learn the relationship between a neutral odour stimulus and a positive or negative outcome is a key mechanism that shapes hedonic value of odours and odour responses (Barkat et al., 2008; Gottfried et al., 2002; Herz, 2005; Li, 2014; Li et al., 2008). Animals can learn within a few trials that an odour may predict an electric shock response and display avoidance response to the odourant (Herzog & Otto, 1997; Kucharski & Spear, 1984; Raineki et al., 2010). Alternatively, an animal can rapidly learn that an odour predicts sucrose reward and display anticipatory mouth movements in response to the odour (Vincis & Fontanini, 2016). Across different sensory stimuli and paradigms, it has been shown that acquisition and retention of associations involves brain regions involved in sensory, emotion, and memory networks (Fanselow & Poulos, 2005). While distinct experimental paradigms differentially engage the previously mentioned networks, both the hippocampus and amygdala have been shown to play a key role across a wide range of associative learning paradigms (Cousens & Otto, 1998; Davis et al., 1994; Grace & Rosenkranz, 2002; Phillips & LeDoux, 1992). The hippocampus receives olfactory information through the amygdala and entorhinal cortex, structures that both receive direct input from the OB and PCx (Insausti et al., 2002; Keshavarzi et al., 2015; Schwerdtfeger et al., 1990; Uva & de Curtis, 2005). Hippocampal lesions in rodents impair olfactory memory (Eichenbaum, 1998; Ergorul & Eichenbaum, 2004) and odour–place associative memory (Gilbert & Kesner, 2002). Studies in humans have shown that patients with bilateral hippocampal lesions can identify odours just as well as control subjects but perform poorly in an odour–place association task where they need to learn the pairing between an odour and a specific location (Goodrich-Hunsaker et al., 2009). An fMRI study (Yeshurun et al., 2009) in humans investigated whether the temporal order and hedonic value of an odourant affected strength of associative learning. The authors paired a sequence of pleasant or unpleasant olfactory (or auditory stimuli) with visual stimuli and found that first pairings were better remembered as well as pairing with unpleasant olfactory stimuli. Furthermore, the strength of activity in the hippocampus predicted the recall of olfactory–visual associations (but not audio–visual associations). These studies highlight that the hippocampus plays an important role in olfactory associative learning in both humans and animals, and the valence of olfactory stimuli can affect the strength of the learned associations. The amygdala, one of the most-studied brain regions involved in processing of emotional and salient stimuli, receives direct projections from both the OB and PCx, and animal recordings have shown that amygdala neurons respond to odours and these responses are modulated by learning (Cain & Bindra, 1972; Gore et al., 2015; Grace & Rosenkranz, 2002; Schoenbaum et al., 1999; Sevelinges et al., 2004). Furthermore, lesions of the lateral amygdala prevent olfactory associative learning (Cousens & Otto, 1998; Sevelinges et al., 159

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2009; Sevelinges et  al., 2004). Intracellular recordings from basolateral amygdala of anesthetized rodents showed that when an odour is paired with a foot shock, the neural responses to the conditioned odour is stronger and the repeated presentation of this odour does not lead to habituation as it does in unconditioned odours. Interestingly, the administration of a dopamine antagonist prevents the acquisition of new odour– foot shock conditioning but does not interfere with the previously acquired odour–shock pairings (Grace & Rosenkranz, 2002; Rosenkranz & Grace, 2002) suggesting that dopamine-mediated basolateral amygdala activity is crucial for acquiring new odour associations. A qualitative meta-analysis of human olfactory neuroimaging studies using functional magnetic resonance imaging (fMRI) and positron emission tomography imaging (Patin & Pause, 2015) reports that a wide range of pleasant odours evoke bilateral amygdala activation, although in some studies, responses to pleasant odours were limited to either the left or right amygdala only. Unpleasant odours, including mild- and moderate-strength stimuli, were also found to evoke bilateral amygdala activation across many studies. Across studies, odours with higher concentrations and greater emotional salience led to greater amygdala activation compared to lower concentration odours and hedonically neutral odours. In one neuroimaging study investigating how odour valence is encoded in the human brain and whether it is dissociated from odour intensity, the authors reported that amygdala activity is associated with odour intensity, whereas orbitofrontal cortex activity is associated with odour valence (Anderson et al., 2003). However, later studies have shown that the amygdala does respond to a range of odour valences, suggesting that both regions may be involved in assessing and responding to odour valence ( Jin et al., 2015). Similar to animal studies where hedonic representations in the amygdala have been shown to be malleable, human studies also showed that top-down cognitive factors can influence olfactory hedonic judgement and related amygdala activity. One study investigated whether subjects’ hedonic perception and corresponding amygdala activation could be altered by presenting the same odour mixture under different labels (isovaleric acid and cheddar cheese flavour mixture presented as either “body odour” or “cheddar cheese”) (de Araujo et al., 2005). Subjects rated the odour mixture as being more unpleasant when it was paired with a “body odour” label, whereas the level of amygdala activity (blood-oxygen-level dependent [BOLD] signal) was positively correlated with pleasantness ratings. Collectively, these findings suggest that the amygdala plays an important role in evaluation of olfactory stimuli in context of past experiences and present cognitive context. The OFC, given its extensive inputs from the PCx and higher-order cortical regions, is critically involved in integrating olfactory information with cognitive processing and value information. Early electrophysiology studies in monkeys showed different subsets of OFC neurons respond to odour–identity and odour–taste associations (Rolls, Critchley, Mason et al., 1996). A separate study showed that OFC neurons that respond to odour–outcome associations on the basis of the outcome rather than odour identity can also change their responses when associations are reversed (Rolls, Critchley, & Treves, 1996). Meta-analysis of human neuroimaging studies using pleasant odours reported consistent OFC activation across studies (pleasant versus neutral or pleasant versus no odour contrasts), and the activity was greater when subjects were asked to report their subjective experience compared to when they were passively smelling (Zou et al., 2016). This finding suggests that conscious access to the olfactory hedonic value might depend on the OFC activity. In fact, a case study in a patient with right OFC traumatic brain injury provides supporting evidence to this role of OFC (Li et al., 2010); an otherwise healthy 33-year-old patient with no history of smell loss developed anosmia upon traumatic brain injury limited to the right OFC. Surprisingly, the patient displayed differential physiological and neural (PCx) responses to unpleasant odours in contrast to neutral odours as assessed by skin conductance measurements and fMRI, but when asked, he was unable to detect presence of odours above chance level. These findings suggest that even though the early stages of the olfactory system are intact and responsive to odourants, the right OFC lesion interfered with the patient’s ability to consciously access and report their odour experience. Similar to single neuron studies in animals showing plasticity of OFC reward value representations, human neuroimaging studies have also shown that OFC can dynamically update reward contingency of odours. Sensory-specific satiety studies (Gottfried et al., 2003; O’Doherty et al., 2000; Small et al., 2001), where the 160

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subjects consume a food item until it is no longer palatable, report that the odour associated with the satiated food item decreases in pleasantness and, in conjunction, the OFC activation in response to the satiated food odour is reduced. Does OFC activity merely encode information about the rewarding properties of odours (affective value), or does OFC also encode information pertaining to what the stimulus is (e.g., is it chocolate cake or pizza)? A human neuroimaging study investigated this question using a classic conditioning paradigm where the subjects were presented with visual cues that predicted four distinct odour outcomes (sweet or savoury food odours) at two different concentrations (low or high concentration, which were respectively perceived as moderately or very pleasant) (Howard et al., 2015). The authors studied the patterns of OFC activity evoked by visual cues in anticipation of the rewarding food odours and found that the OFC can indeed encode odour identity information for odours that were rated to be equally rewarding. This finding, in combination with the aforementioned single neuron recordings, suggests that the OFC simultaneously represents both predicted reward value of odours and their identity. One framework (Berridge et al., 2009; Pool et al., 2016) on reward-related behaviour proposes that reward behaviour can be studied as two dissociable psychological and neurobiological constructs that are distinct yet highly correlated: liking and wanting (see also Chapter 3). Liking corresponds to the subjective experience associated with a reward (e.g., how likeable the chocolate flavour is when it is in the mouth), whereas wanting corresponds to the anticipatory motivational aspect that drives approach behaviour to expected rewards (e.g., the motivation to seek and consume chocolate). In an fMRI experiment investigating whether wanting and liking of food odours recruit distinct neural circuits ( Jiang et al., 2015), participants were asked to rate the pleasantness of food odour (liking) as well as their desire to consume the food item corresponding to the odour stimuli (wanting) either in a hungry or satiated state. The authors found that when subjects were rating how much they liked the food odours, there was greater activity in nucleus accumbens (NAcc) compared to the condition in which subjects were rating how much they wanted to consume the food item. Interestingly, this effect was reversed when subjects were hungry, with the NAcc showing greater activity in response to the ratings of wanting. Furthermore, when subjects were hungry, there was greater activity in the ventral pallidum for the wanting condition compared to liking ratings, and this effect was not present when subjects were satiated. These findings suggest that the neural circuits mediating odour liking and wanting recruit different areas in a motivational state–dependent manner.

Conclusion The extent to which odours are found to be rewarding, aversive, or neutral varies greatly across individuals, cultures, and even within an individual depending on the individual’s cognitive and emotional state and the signals surrounding the odour stimuli. The olfactory system, in concert with emotion, memory, and cognitive systems, integrates across a multitude of past and current factors in the context of an organism’s goals and motivations to determine the hedonic value of the odours it encounters. The complexity and nonlinearity of the factors that contribute to odour hedonics perhaps make it such that it may be nearly impossible to predict the hedonic judgement of odourants in a deterministic fashion. We argue that this is a strength rather than a weakness of studying odour hedonics; it opens a window into how the physical world is interpreted and reinterpreted in the nose of the beholder, unfolding the mysteries of how our internal state, experiences, biases, and goals shape our subjective experience and allowing for a scientific framework to investigate the basis and complexity of aesthetic preferences.

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9 MOVEMENT APPRECIATION Kohinoor M. Darda, Ionela Bara and Emily S. Cross

The speed and agility of a white-tailed deer; the dramatic falling waters of the Niagara; the powerful takeoff of the Concorde’s supersonic airliner; Anna Pavlova’s gentle yet fluid pirouette in The Dying Swan; and the dynamicity of the Laocoön, the group statue at the Vatican Museum—movement, in all its splendour, has captured people’s, and artists’, imagination for millennia. Many artists have sought new ways to give visual expression to movement, while much more recently, scientists have begun to investigate how we perceive and why we appreciate real and apparent motion. Artists have discovered how to exploit the visual form in order to capture motion’s vitality in a wide range of styles, including conceptual, abstract, and realistic artistic representations. The successful depiction of motion in visual art represents a milestone in human creation and has shaped the way we understand, appreciate, and disseminate artworks today. Yet the first instances of studying aesthetic appreciation of movement from a psychological scientific perspective only date back to the early 20th century. With the advent of more refined neuroimaging technologies over the past several decades, the field of neuroaesthetics has contributed to our understanding of how the human brain perceives and appreciates movement in art. In this chapter, we discuss movement appreciation from a neuroaesthetic perspective, with a special focus on movement representations in visual art. We begin with a short history of movement representation in art images. We continue to outline the antecedents and consequents of movement appreciation before moving on to discuss the neurobiological processes, functions and mechanisms that underlie movement appreciation in different contexts.

History In the field of neuroaesthetics, the study of movement appreciation has been primarily investigated using dance as stimuli and/or dancers as participants, as dance is an art form whose very essence is the human body in motion (Cross & Ticini, 2012). A number of studies have investigated cognitive and neural processes engaged when observing and appreciating movement as a starting point to understand the aesthetic experience triggered by watching dance (Christensen & Calvo-Merino, 2013). The depiction of movement in paintings, sculpture, and photography, however, brings its own set of challenges—how does one represent movement and dynamism in a static medium? Photographers have long used suspended movement to give the impression of motion to their viewers—the ability of the camera to freeze a literal split second and catch details imperceptible to the human eye gives the viewer a strong sense of the (implied) motion that would happen if the moment in the photograph were “un-frozen” (Scharf, 1962; see Figure 9.1B). In addition, 172

DOI: 10.4324/9781003008675-10

Movement appreciation

Figure 9.1 The representation of motion in photography, paintings, and sculpture: (A) Use of motion blur in photography, (B) use of suspended movement in photography, (C) Edgar Degas’ Ballet Scene (1879) depicting ballerinas in motion, (D) Wassily Kadinsky’s Yellow-Red-Blue (1925) depicting the use of action lines to imply motion, (E) Bhimbetka Rock painting (c. 12,000–8000 BCE) showing a humanoid being attacked by a wild boar (photo taken by Rhodia Colomes, published with permission from photographer), (F) an example of extreme contrapposto—Myron’s Discobolus by Giovanni Battista Piranesi (18th century), and (G) an example of Tribhanga (triple bend): statue of an Indian deity from the Halebidu temple in India (circa 12th century; photo taken by Dr. Saurabh Kadekodi, published with permission from photographer).

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motion blur, action sequencing, and visual flow, when incorporated deliberately in a photograph, can further contribute to striking representations of visual dynamism (see Figure 9.1A). Long before the invention of photography, however, artists were confronted with the challenge of representing movement in static sculptures and paintings. That dynamism or motion in artwork is important and appreciated is well evidenced—art theorists like Arnheim (1951) supported the idea that motion cues in visual art might be salient and more engaging and therefore inextricably linked to aesthetic appreciation. Artist and inventor Leonardo Da Vinci called rigid, static sculptures and paintings “doubly dead”—dead once because artworks are after all an impression (of something or someone) and “doubly dead” because they do not imply movement of the mind or of the body ( Justi, 1923). In addition, Gombrich (1964) and Wölfflin  (1942/2012) have pointed out that  the use of motion cues in visual art shaped the standards in art production and perception and contributed to a better recognition and classification of art styles. Both Gombrich and Wölfflin discussed how Italian Renaissance paintings used closed lines to accentuate the repose and stability of forms, while Baroque paintings emphasized dynamic effects by using strong diagonal lines and open dynamic forms. Some of the earliest examples of the depiction of movement or actions in static media can be found in the Bhimbetka paintings of Madhya Pradesh, India, likely dating back to the Mesolithic period (12,000– 8000 BCE; see Figure 9.1E). Over time, as an alternative style to depicting rigid forms in sculptures and paintings, Indian artists introduced the concept of tribhanga (“triple bend,” circa 2300 BCE)—a position where the body bends in one direction at the knees and in the other direction at the hips and then at the shoulders and the neck, an early example of how body asymmetry can be used to depict movement (Chakraborty, 1986; see Figure 9.1G). The Greek equivalent is contrapposto (or counterpoise, circa 500 BCE), the positioning of the human figure (in a sculpture or a painting) with the shoulder and arms turned in a different direction to the hips and legs—a position that suggests action and reaction in various parts of the figure, lending it naturalness and dynamicity (Summers, 1972). An example of extreme contrapposto is Myron’s Discobolus, a sculpture depicting a Greek discus thrower whose body looks like a tense spring set to uncoil in a furious burst of purposeful energy (Unger, 2014; see Figure 9.1F). In all these artworks, the dynamism perceived, although not real, is a stylistic impression of motion. It is conveyed to the viewer through several associations, regularities, and structural characteristics that the human brain is able to rapidly and easily comprehend.

Form features of implied motion in art Art theorists like Arnheim (1974) have suggested that perceived motion is derived not only from the inherent characteristics of the subject that is depicted (for instance, a running human or a waterfall) but also from the compositional features of the artwork as a result of the use of lines, shapes, and particular directions. For example, in Edgar Degas’ Balletprobe (1873; see Figure 9.1C), we perceive the dancers to be in a state of motion because we understand they cannot hold a position with one leg in the air for too long. In contrast, Kandinsky’s Yellow-Red-Blue (1925; see Figure 9.1D) features strong directional, diagonal, and gestural lines; repetition; and object placement to imply dynamism/motion in the artwork. Apart from action lines, artists use a variety of form features or cues to enhance or even create implied motion in an artwork and to convey information about direction and speed (Pavan et al., 2011; Krekelberg et al., 2005). These form cues include (but are not limited to) broken symmetry (i.e., an asymmetrical relationship between different parts of the body), blur (i.e., a blurring of the subject or of the background), stroboscopic effects (i.e., superimposing different moments of a moving object in the same painting), forward lean, and/ or dynamic balance (Cutting, 2002; see Figure 9.2 for an illustration of typical implied motion cues used in visual art).

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Figure 9.2 Typical motion cues used in visual art. (A) Blur—Rain, Steam, and Speed by William Turner (1844); (B) forward lean—Liberty Leading the People by Eugene Delacroix (1830); (C) stroboscopic effects—Dynamism of a Dog on a Leash by Giacomo Balla (1912); (D) The Wind by Hans Hofmann (1942). The use of form features to convey implied motion in artworks reached its peak in the early 20th century—the representation of movement became the central issue of the poetics of the Futurist avant-garde. Futurists wanted to capture the remarkable speed and dynamism that characterized their time: “the splendour of the world has been enriched by a new beauty: the beauty of speed” (Futurist Manifesto; Marinetti, 1908; p. 286). This is represented in Giacomo Balla’s painting Dynamism of a Dog on a Leash (1912; see Figure 9.2C), which uses stroboscopic images as a means to communicate motion—one can almost feel the frantic energy of the dog and its walker trying to keep up. Similarly, another paradigmatic example of using form features to convey implied motion even in the absence of recognizable content or objects is the abstract action painting style developed towards the middle of the 20th century, primarily associated with artists such as Jackson Pollock, Franz Kline, and Hans Hofmann (see Figure 9.2D).

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Movement appreciation—antecedents and consequences As Figures 9.1 and 9.2 show, implied motion cues have long been used in all types of artwork, irrespective of content. At the most fundamental level, content in visual art concerns the depiction of form: humans, still lifes, landscapes, or natural scenes, as well as non-figurative or abstract representations (Harrison, 2009). Evidence suggests that a majority of people tend to place higher aesthetic value on landscape paintings compared to paintings with human content and that such preferences are stable over time (Vessel & Rubin, 2010; Augustin et al., 2012; Leder et al., 2016; Pugach et al., 2017; Vessel et al., 2018). This preference also applies to artworks that depict implied motion. While both paintings portraying dynamic human forms and dynamic landscapes are rated as more aesthetically pleasing than paintings depicting static landscapes and human bodies, dynamic landscape paintings are rated as more aesthetically pleasing than dynamic paintings with a human content (Cazzato et al., 2012; Nather et al., 2014; Orgs et al., 2013; Palmer & Langlois, 2017; Massaro et al., 2012; Bara et al., 2021). Non-figurative or abstract art also contains implied motion cues, even when the artwork contains no representational or figurative content (see Figure 9.1D; Cutting et al., 2002). Overall, abstract art is preferred less than representational art (Uusitalo et al., 2009; Vessel & Rubin, 2010; Brinkmann et al., 2014; HaynLeichsenring et al., 2020). This preference is, however, modulated by art expertise, such that the preference for abstract art is higher for art experts compared to art-naïve participants (Pihko et al., 2011; Van Paasschen et al., 2015; Bimler et al., 2019). So far, the aesthetic appreciation of abstract artworks with implied movement has only been modestly researched, but preliminary evidence suggests that dynamic abstract artworks might be preferred to static abstract artworks (Cattaneo et al., 2017). In sum, behavioural evidence suggests that across different types of content, dynamic artworks are rated as more beautiful and more aesthetically pleasing than static artworks, suggesting a relationship between implied motion perception and aesthetic appreciation (Bara et al., 2021; Massaro et al., 2012; Mastandrea & Umilta, 2016). Emerging evidence also suggests that changing a painting’s title to a metaphorical title that is in congruence with its meaning or content can enhance the perceived aesthetic value of the painting (Millis, 2001; Leder et al., 2006; Mastandrea & Umiltà, 2016). In the context of this work, Mastandrea and Umiltà (2016) found that when titles of Futurist artworks with movement-related words were manipulated such that some titles were “increased” to enhance the sense of dynamism, and some titles were “decreased” to diminish the sense of dynamism, paintings with the “increased” titles were rated as more dynamic. Paintings with more movement (irrespective of title) were liked more than paintings with less movement. This finding suggests that ratings of movement or dynamism are somewhat flexible and can be influenced by extraneous factors and contexts. Limited behavioural evidence also suggests that the level of dynamism or implied motion in a painting can influence participants’ subjective estimation of time. Participants exposed for variable periods of time to abstract and representational paintings varying in dynamism reported that they had been exposed to the dynamic paintings for a longer duration than actually happened, suggesting that participants’ perception of time is affected by the observation of implied motion cues (Bueno & Nather, 2012; Nather et al., 2012; Nather et al., 2014). In summary, the literature reviewed here suggests that implied motion cues affect the aesthetic appreciation of visual artworks. Taken together, the findings we have highlighted suggest that dynamic artworks, irrespective of whether they depict human bodies, landscapes, or abstract forms, are preferred to static artworks.

Neural mechanisms of movement appreciation Visual neuroaesthetics has studied the brain-based foundations of aesthetic appreciation across a diverse set of art forms, ranging from paintings to watching professional dance performances (see Chapter 6). Most of 176

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this research has focussed on understanding the response to visual properties such as form, colour, symmetry, complexity, luminance, or contrast (Bar & Neta, 2006; Jacobsen & Höfel, 2003; Jacobsen et al., 2006; Palmer & Schloss, 2010; Nadal et al., 2010; Vartanian et al., 2013; Bona et al., 2015; Graham et al., 2016; Van Geert & Wagemans, 2020; Hayn-Leichsenring et al., 2020; Iigaya et al., 2021). Only recently has this research been expanded to also include studies that explore the way motion cues influence neural activity associated with aesthetic appreciation (e.g., Kim & Blake, 2007; Di Dio et al., 2016; Thakral et al., 2012; Bara et al., 2021). Neuroimaging studies that have examined this question have used abstract and representational paintings as well as sculptures (Kim & Blake, 2007; Di Dio et al., 2011; Di Dio et al., 2016), dynamic and static photographs of people (Proverbio et al., 2009; Kourtzi & Kanwisher, 2000; David & Senior, 2000), glass patterns (Krekelberg et al., 2005), and line cartoons such as the Hokaisai manga (Osaka et al., 2010) as stimuli. In the following sections, we first briefly summarize what is known about the general neural mechanisms of aesthetic appreciation. We then review what we have learned about neurobiological processes specifically involved in the appreciation of different types of movement stimuli.

Models of aesthetic appreciation In psychology, aesthetic appreciation has been modelled as a system of information processing hubs, where aesthetic responses to individual stimuli are considered an emergent function of both bottom-up and topdown processing stages (e.g., Pelowski, Markey et al., 2016; Spee et al., 2018). These models often distinguish between automatic and controlled processes. Thus, many existing models assume that stimulus properties prompt a number of automatic processing stages involved in representing perceptual features such as motion, shape, colour, symmetry, and complexity. The result of these processing stages then engenders a number of controlled processing stages during which cognitive processes help extract the perceived aesthetic value and meaning of the artwork (Leder & Nadal, 2014; Locher et al., 2010; Pelowski & Akiba, 2011; Locher et al., 2010; Silvia, 2005; Graf, & Landwehr, 2015; Redies, 2015). In addition to these psychologically based models, neuroimaging experiments have examined the neurobiological mechanism associated with aesthetic appreciation. Based on this work, Chatterjee and Vartanian (2014) have proposed that aesthetic experiences emerge from an interplay between sensory–motor, emotion–valuation, and meaning–knowledge processes (the so-called “aesthetic triad”; see also Chapter  10). Consistent with the aesthetic triad model, neuroimaging studies have demonstrated that aesthetic responses to art are not limited to the engagement of only single brain regions. Rather, they depend upon a distributed network across both hemispheres (Boccia et al., 2016). For example, aesthetic appreciation of visual artworks shows engagement of the occipital-temporal brain regions when appreciating visual features such as shape, colour, and symmetry (Vartanian  & Skov, 2014), whereas evocative artworks engage the bilateral insula, precuneus, and anterior cingulate cortex (Cupchik et al., 2009; Di Dio et al., 2007; Huang et al., 2011; Vartanian & Goel, 2004). Since the experience of art has been described as pleasurable and gratifying (Dutton, 2009), the engagement of the reward brain circuit when evaluating aesthetics of an artwork is reasonably expected. Indeed, several studies have emphasized that aesthetic judgments are associated with the activation of the mesolimbic reward circuit (for reviews, see Boccia et al., 2016; Brown et al., 2011; Di Dio & Gallese, 2009; Kirsch et al., 2016; see also Chapters 2, 3, 6, and 7 in this volume). Taken together, these findings suggest that an extended network of brain regions known to be involved in sensory, perceptual, cognitive, and reward processes all play a role in aesthetic appraisal.

Neural correlates of real and implied motion perception in general Motion plays a critical role in our survival as a species, and it is thus unsurprising that across millennia, humans have developed robust abilities in perceiving, interpreting, and responding to motion (Grossman 177

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et  al., 2000; Thompson  & Parasuraman, 2012). The neural substrates of motion perception have been investigated extensively in both animals and humans. Evidence from human neuroimaging and brain stimulation studies suggests that the medial temporal complex (MT+/V5) plays a key role in motion processing (Dupont et al., 1994; Goebel et al., 1998; Grossman et al., 2000; Watson et al., 1993; Zeki et al., 1991). Neurons located within MT+/V5 are also involved in decoding the speed and direction of objects in motion (Beckers & Zeki, 1995; Born & Bradley, 2005) and unify motion signals from primary visual cortex (V1) into coherent global motion (Snowden et al., 1991). Interestingly, MT+/V5 is not only engaged when perceiving real motion but also illusory (Zeki et al., 1993; Kobayashi et al., 2002), imagined (Goebel et al., 1998; Winawer et al., 2010), and implied motion (Kourtzi & Kanwisher, 2000; Proverbio et al., 2009; Osaka et al., 2010; Senior et al., 2000). Converging findings from a number of neuroimaging studies suggest that images that incorporate motion cues and induce motion perception in observers also engage the same cortical regions (MT+/V5) responsible for processing real motion (Kourtzi  & Kanwisher, 2000; Proverbio et  al., 2009; Osaka et  al., 2010; Senior et al., 2000). Using functional magnetic resonance imaging (fMRI), these studies have reported a higher blood-oxygen level dependent (BOLD) signal response in MT+/V5 to images that imply movement (e.g., a ballet dancer executing pirouettes or ocean waves breaking) compared with static images without implied movement. In addition, Senior et al. (2002) conducted a brain stimulation study to investigate the causal role of medial temporal cortex in implied motion processing. They disrupted neural activity in MT+ by applying transcranial magnetic stimulation (TMS) pulses over this area of the brain, which led to a reduction in participants’ perception of implied motion. Such techniques enable researchers to better understand the important functional contribution made by MT+ to implied movement processing.

Neural mechanisms of movement appreciation One of the fundamental aims of neuroaesthetics is to examine the neurobiological bases of aesthetic experience, and most research paths are focused on investigating the aesthetic response to a varied set of art forms, spanning visual artworks and dance performance to non-art forms, such as natural landscape or face aesthetics (Pearce et al., 2016). Given that the appreciation of dance is covered in depth elsewhere in this volume (Chapter 16), the rest of the chapter focuses on aesthetic appreciation of implied movement, primarily in visual arts. In one of the first neuroimaging studies investigating movement appreciation in visual art, Kim and Blake (2007) investigated the extent to which the BOLD response within brain region MT+ is related to the impression of movement from motion cues in art and also to its aesthetic appreciation. Higher aesthetic evaluation scores were linked to increased motion perception, but only among experienced art viewers. Overall, MT+ showed more engagement when participants viewed abstract artworks with implied motion cues compared to more static artworks. However, from this study, it was not clear whether MT+ is more engaged because it is involved in the aesthetic appreciation of the artwork, or simply because participants pay more attention to dynamic artworks. Using dynamic and static figurative artworks, Di Dio et al. (2016) reported a more robust BOLD response in MT+ both when participants were making judgements about the dynamism of a painting as well as when they were engaged in the task of assigning an aesthetic value to it. This finding consequently suggests that visual motion areas may contribute to both aesthetic and non-aesthetic (motion) judgements. In contrast, however, Thakral et al. (2012) measured BOLD responses in MT+ while participants viewed Van Gogh’s dynamic paintings and found no correlation between MT+ responses and pleasantness ratings. Further support for the involvement of the MT+ in aesthetic processing comes from a TMS study conducted by Cattaneo et al. (2017). They applied TMS over either the MT+ or vertex while participants viewed dynamic and static paintings and made judgements about implied motion and aesthetic appreciation. 178

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The results showed that compared to vertex stimulation, TMS over the MT+ decreased implied motion perception in dynamic artworks overall, as well as participants’ aesthetic preference for abstract artworks. However, TMS over the MT+ did not have any influence on participants’ preference for figurative artworks. These findings further elucidate the causal role of the MT+ in aesthetic appreciation and suggest that the contribution of the MT+ to aesthetic appreciation may depend on the type or content of the artwork.

Neural correlates of movement appreciation modulated by content Previous neurobiological evidence demonstrates that the aesthetic appreciation of movement begins in brain regions associated with visual processing. Besides motion cues, this work suggests that other basic elements of visual processing, such as colour, luminance, shape, and orientation, as well as higher-order object recognition (i.e., identifying faces, bodies, and scenes), are all decoded within visual brain regions before signals are transferred to brain areas involved in extracting affective and semantic value (Chatterjee & Vartanian, 2014; Kirsch et al., 2016). In order to understand the neural correlates of movement appreciation, it is thus vital to consider an integrated approach, which recognizes the importance of content representations and how distinct type of content representations engage different neural correlates.

Neural correlates of human movement appreciation The cognitive and neural representations we hold for the human body (for ourselves and others) continuously evolve and are updated following developmental, cultural, cognitive, and emotional changes. Human body forms are rich vehicles mediating signals that relate to the self and others and promote social interactions (Featherstone et al., 1991). The human body also fosters hedonic values associated with body aesthetics (Aglioti et al., 2012; Gallese, 2003; Gallese & Di Dio, 2012). In most cultures, body attractiveness is associated with physical standards of body preference. The idea that a beautiful body can be objectively measured is deeply rooted within European culture and can be traced back to ancient Greece (Clark, 1984; Livio, 2003). Across multiple depictions, from visual art to dance, theatre, and film representations, the human body captures attention and is thought to convey a mixture of meaning-attractiveness-affective signals that ultimately impact a perceiver’s aesthetic experience. Body movements such as walking, running, and dancing provide various cues about a person’s health, age, sex, emotions, and attractiveness (Kozlowski, & Cutting, 1977; Montepare, & Zebrowitz-McArthur, 1988; Loula et al., 2005; Dittrich et al., 1996; Fink et al., 2015). Our understanding of the brain mechanisms involved in perceiving (and appreciating) the human body in motion is inherently linked to the neural correlates of body and face processing. Previous neuroimaging findings have found the visual presentation of human body forms to engage specific cortical regions, such as the extrastriate body area (EBA) and the fusiform body area, two patches of cortical tissue located on the ventral surface of the temporal lobe (Peelen & Downing, 2007). In addition to engaging EBA, representations of the human body displaying (implied or real) dynamic postures recruit additional brain regions, including the superior temporal sulcus (STS; Grossman & Blake, 2002; Peuskens et al., 2005; Peelen et al., 2006) and large portions of the fronto-parietal brain system (Urgesi et al., 2006, 2007; Candidi et al., 2008; Rizzolatti & Craighero, 2004; Chong et al., 2008). Several hypotheses explaining the relationship between body movement and aesthetic preference have been put forward. One, taking its departure from Darwinian aesthetics (Grammer et al., 2003), proposes that body movement aesthetics is linked to mate choice and sexual selection rituals (see also Chapter 11). It has been suggested that the more extravagant the body postures and movements, the higher the reported ratings on the subjective perception of fitness and consequently aesthetic preference. Another hypothesis has linked the aesthetic appreciation of dynamic human body movements to the role played by mirror neurons in the neural coupling of perception and action (Di Pellegrino et al., 1992; Gallese 179

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et al., 1996). Mirror neurons, originally discovered within sensorimotor cortices of the non-human primate brain, respond both when an action is performed and when the same action is observed being performed by another actor. Since the initial discovery of mirror neurons in the monkey brain, considerable work has examined the extent to which homologous regions of the human brain link action with perception. This work has given rise to what is termed the action observation network (AON), comprising sensorimotor brain regions, including premotor, parietal, and occipitotemporal cortices, that is believed to be engaged when humans watch others engaged in action (Cross et al., 2009, 2011). The discovery of mirror neurons and the subsequent characterization of the AON, including the role played by physical embodiment in shaping this network (e.g., Cross et al., 2006), has informed an influential aesthetics theory proposed by Freedberg and Gallese (2007). This theory places the body and bodily experiences of the perceiver at the forefront of aesthetic experience. Known as the embodied simulation theory of aesthetics, it posits that the engagement of the perceiver’s body in simulating real or implied emotions or actions plays an important role in aesthetic processing. For example, Freedberg and Gallese (2007) hypothesized that when viewing actions depicted on a canvas, either in the form of human body representations or brushstrokes representing the artists’ movements, viewers covertly simulate the actions being observed, with this simulation contributing to aesthetic appraisal. In support of this idea, Leder et al. (2012) found that when participants performed hand movements congruent with the type of brushstroke used in a painting, their aesthetic appraisal of that painting increased compared to situations where they made incongruent movements. Although the embodied theory of aesthetics was initially based on work involving responses to paintings, it is also extremely relevant in the domain of performing arts (especially dance), as many researchers have since suggested and investigated (Calvo-Merino et al., 2008; Calvo-Merino et al., 2010; Cross et al., 2011; Kirsch et al., 2015). In the first-ever study to examine naïve observers’ aesthetic preferences for dance movements, CalvoMerino et al. (2008) used fMRI to examine brain responses while participants viewed dance clips relative to a control task. Their findings showed stronger engagement of the right premotor and occipital cortices while participants observed stimuli they later rated as beautiful. Following this work, Cross et al. (2011) investigated the role of embodied simulation, in particular the involvement of sensorimotor brain areas in aesthetic appraisal of dance performance, and how engagement of these brain regions related to dance-naïve observers’ actual physical abilities. The results demonstrated that higher activity in occipito-temporal and parietal regions correlated with higher aesthetic appreciation ratings, especially for movements which were rated as more difficult for observers to reproduce themselves. Taken together, these two studies suggest that aesthetic appreciation of dynamic body forms involves sensorimotor portions of the brain associated with the AON, even when the observed movements extend far beyond the observer’s own physical abilities. Support for the role played by sensorimotor embodiment in the aesthetic appreciation of visual artworks comes from studies showing engagement of motor and premotor cortices when participants engage with these types of artworks. For example, motor simulation, as indexed by cortico-spinal excitability from a wrist extension muscle, was facilitated when participants viewed a painting showing a hand extension movement (Michelangelo’s Expulsion from Paradise) compared to other paintings with no hand movement being displayed (although the researchers did not assess excitability during explicit aesthetic appreciation per se; Battaglia et al., 2011). In a similar vein, an electroencephalography (EEG) experiment found suppression of the mu rhythm (a measure of motor activation) during passive observation of Lucio Fontana’s slashed canvases (where the actions of the artist can be inferred) but not during observation of modified versions of the same canvases (where the actions of the artist cannot be inferred). A related fMRI study conducted by Di Dio et al. (2011) examined the brain response when participants observed classical sculptures compared with real photographs of athletes in two conditions: canonical versus modified proportions and dynamic versus static postures. Although the authors did not explicitly compare dynamic versus static body postures, they found an overall higher activation in the STS for dynamic 180

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sculptures compared to baseline. In addition, by using dynamic and static paintings with human and landscape content, Di Dio and colleagues (2016) found activation of parietal and temporal areas mainly driven by paintings depicting human content with implied motion compared to more static paintings which did not show humans in action. Thus, converging evidence from human neuroscience techniques spanning fMRI, TMS, and EEG suggests that motor simulation of implied motion in artworks critically shapes their aesthetic appreciation. With that being said, it is important to emphasize that engagement of sensorimotor brain regions alone cannot explain the whole story of aesthetic appreciation. According to the neurobiological models already mentioned, it is realistic to assume that visual areas, sensorimotor brain areas, and brain regions known to be involved in emotion-hedonic valuation all inform aesthetic appreciation (Chatterjee & Vartanian, 2014; Martin-Loeches et al., 2014). Accordingly, a recent brain imaging study examined the extent to which aesthetic appreciation for implied movement in art that features the human body engages brain regions that span different circuits, including the EBA, MT+, and the reward brain circuit (Bara et al., 2021). During scanning, participants made aesthetic and motion judgments of paintings representing human bodies in dynamic and static postures. Using functional region-of-interest and Bayesian multilevel modelling approaches, the results revealed no unique functional contribution within or between the main brain systems of interest—EBA, MT+ and reward circuit—to aesthetic appreciation of paintings with implied motion. Instead, exploratory whole-brain connectivity analyses showed functional coupling between neural systems associated with body perception and dorsal parietal cortex for aesthetic appreciation of artworks. These results, although suggestive, are consistent with hierarchical models of aesthetic processing which assume a continuous interaction between perceptual and attentional neural systems (Iigaya et al., 2021). Overall, the neuroimaging findings discussed in this section suggest that while theoretical accounts proposing an interplay between perceptual and affective networks hold intuitive appeal, nascent empirical efforts to characterize these relationships are far from straightforward and require further investigation.

Appreciation of dynamic landscapes The pictorial tradition of depicting natural scenes has a rich history, with origins tracing back to 4th century Chinese art and the European Renaissance in the 15th century (Clark, 1976). Behavioural evidence suggests that landscape content images are appreciated more than images with human and abstract content (Vessel & Rubin, 2010; Augustin et al., 2012; Leder et al., 2016; Pugach et al., 2017; Vessel et al., 2018). One possible explanation for this finding is that, for adaptive reasons, people prefer savannah-like landscapes with unhindered views and a presence of animal life and water bodies because such vistas have been associated with safety, shelter, and access to food and water (Voland & Grammer, 2003; Heerwagen & Orians, 1993). An alternative hypothesis has proposed that, compared to other types of content, landscape images are more easily perceived because of their visual symmetry and simplicity, leading them to be preferred over these other types of content (Mayer & Landwehr, 2018). In addition to behavioural evidence, neuroimaging studies have suggested that highly preferred and beautiful landscape images engage the parahippocampal place area (PPA) as well as the reward circuit compared to less preferred and less beautiful landscape images or images featuring other types of content (Kawabata & Zeki, 2004; Yue et al., 2007; Boccia et al., 2016). Given people’s aesthetic preference for landscapes, it is surprising that the aesthetic appreciation of natural scenes as a distinct aesthetic category beyond arts was recognized only in the 18th century (Brown & Dissanayake, 2017; Seeley, 2014). With the attention given to natural landscapes by poets, painters, and photography (Kemal & Gaskell, 1993) and over time by cinematic genres, such as Westerns, as well as by travel and adventure documentaries, a shift occurred in the aesthetic appreciation of landscapes from static paintings to more dynamic natural scenes: increasingly, static viewpoints became replaced by more dynamic motion displays (Sitney, 1993). 181

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Although there is some behavioural evidence suggesting that paintings with dynamic landscapes are found more aesthetically pleasing than those with static landscapes (Di Dio et  al., 2016; Massaro et  al., 2012), dynamic representations of landscape scenes have only been modestly investigated from a neuroimaging perspective. An fMRI study conducted by Di Dio et al. (2016) sheds some light on the aesthetic processing of dynamic landscapes. Comparing landscape scenes and human figures in dynamic and static artworks, Di Dio and colleagues found that participants behaviourally preferred landscape paintings to human figure paintings and also reported that dynamic landscapes were preferred over dynamic human paintings. Neuroimaging results revealed more engagement of the inferior and middle temporal sulci as well as the posterior parietal and intraparietal sulcus bilaterally for dynamic artworks compared to static artworks, irrespective of content. In addition, static compared to dynamic landscapes engaged the central and posterior insula. The authors suggest that the central insula may represent a locus for sensorimotor processes to interact with internal affective states. Based on this idea, they speculate that the perception of static paintings might involve additional internally generated sensorimotor processing associated with the imaginary exploration of the depicted scenery—an idea in line with Freedberg and Gallese’s (2007) embodied theory of aesthetic appreciation. Evidence for this hypothesis remains to be seen, but recent TMS work (Finisguerra et al., 2021) has found late muscle-specific activation to correlate with the observation of landscapes painted with a brushstroke style rather than a pointillist-like style, suggesting that motor simulation of the painters’ movements might be essential to subjective aesthetic preference. Taken together, neuroimaging evidence suggests that aesthetic appreciation of dynamic landscapes likely involves an extensive network that includes the visual occipital areas and the parahippocampal place area, as well as the sensorimotor and reward brain circuits.

The role of abstract stimuli in movement appreciation Previous studies investigating aesthetic appreciation of abstract art compared to figurative art found that abstract art is less preferred to figurative art (such as landscapes and portraits; e.g., Uusitalo et  al., 2009; Vessel & Rubin, 2010; Brinkmann et al., 2014; Hayn-Leichsenring et al., 2020). One potential explanation for this observation is the so-called fluency theory, according to which stimuli that are easier to process are more liked (Reber et al., 2004). In the case of abstract art, evidence shows that both perceptual fluency and conceptual fluency (i.e., the ease with which one can extract meaning), is reduced. Possibly, the effortful and laborious processing associated with abstract art affects the viewer’s interest and motivation, ultimately diminishing the pleasure they feel (Graf & Landwehr, 2017; Mayer & Landwehr, 2018). In one of the first neuroimaging studies using abstract art, Vartanian and Goel (2004) investigated brain activity associated with aesthetic judgments of figurative and abstract artworks. These authors found greater activation in the bilateral occipital gyrus, fusiform gyrus, and precuneus for figurative art, suggesting that non-abstract art engages brain systems previously associated with object perception. Cattaneo et al. (2015) found that applying TMS pulses over the lateral occipital cortex during an aesthetic appreciation task selectively interfered with aesthetic preference for figurative art but not for abstract art. Overall, this evidence supports the idea that figurative art appreciation relies on neural systems involved in object recognition, whereas the lateral occipital cortex does not play a causal role in abstract art appreciation. Additional evidence suggests that aesthetic preferences for abstract paintings engage the posterior cingulate cortex, a brain region associated with the default mode network (Vessel et al., 2012). This finding implies that abstract art may relate more to conceptual and internally directed processing of thought. Contrasting neuroimaging evidence comes from investigations of implied movement in abstract artworks. Artists have long used visual form cues to convey a sense of movement, such as stroboscopic effects or the direction of speed cues (Cutting, 2002). While some early neuroimaging evidence suggests that implied motion cues in abstract art activate the MT+ (Kim & Blake, 2007), the aesthetic appreciation of abstract 182

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forms implying movement has not been extensively investigated. One example that may suggest a link between aesthetic preference and kinetic abstract patterns comes from Zeki and Stutters’s (2012) study, which reported that participants’ subjective preference for moving abstract patterns correlated with higher engagement of visual areas, as well as with activity in the medial orbitofrontal cortex. Together, the authors interpreted this pattern of findings as support for the hypothesis that aesthetic appreciation for abstract movement involves shared mechanisms with other forms or stimuli that imply motion. Furthermore, a recent study by Cattaneo et al., 2017 examining the causal contribution of the MT+ to aesthetic appreciation of dynamic figurative and abstract art found that applying TMS pulses over the MT+ reduced aesthetic preference for abstract artworks but not for figurative artworks. The authors interpreted this finding as evidence that the aesthetic appreciation of abstract art is mostly driven by sensory perceptual features, such as motion, shape, and colour. A recent study by Humphries et al. (2021) using “high-motion” Jackson Pollock and “low-motion” Piet Mondrian abstract paintings showed altered art appreciation among patients diagnosed with Parkinson’s disease that was specifically linked to their altered ability to translate implied motion cues from abstract artworks into movement representations. This suggests involvement of the motor system (which is disrupted in people with Parkinson’s disease) in representing movement from motion cues in abstract art. The authors further suggest that the motor system may integrate low-level visual features of the artwork to form abstract representations of movements rather than simulating them as actions (as proposed by the embodiment simulation account of aesthetics; Freedberg & Gallese, 2007). In summary, the findings summarized in this section suggest that aesthetic evaluation of dynamic abstract art engages more visual than sensorimotor areas in the brain. In contrast, the aesthetic evaluation of paintings with dynamic representational content (especially human content) seems to involve the sensorimotor system, supporting theories of embodied aesthetics that suggest motor responses are linked to aesthetic pleasure, even in the context of visual art such as paintings and sculptures. However, this has been challenged by recent evidence that shows no straightforward relationship between sensorimotor responses and aesthetic appreciation ratings for visual art (Bara et al., 2021; Humphries et al., 2021). Instead, recent findings suggest that a broad fronto-parietal network is involved in aesthetic processing, and the aesthetic appreciation of artworks relies on an interplay between regions of the visual ventral stream and regions in the prefrontal and parietal areas, which are also part of the default mode network (Di Dio et al., 2016; Bara et al., 2021; Iigaya et al., 2021; Vessel et al., 2019).

Summary of neural mechanisms underlying movement appreciation Advances in human neuroimaging techniques during the past few decades have spurred the development of a cognitive neuroscience of aesthetics, increasing our understanding of the underlying cognitive, affective, and neural mechanisms that shape aesthetic experiences (Cela-Conde et al., 2011). Most models of aesthetic appreciation recognize an interplay between perceptual, cognitive, and emotional or reward neural networks and stages to aesthetic processing, even at the level of low-level feature or object processing (Cupchik et al., 2009; Kirk et al., 2009; see also Brieber et al., 2014; Chatterjee, 2014; Leder et al., 2013). Movement perception and appreciation represents a meaningful instance of such interactions—the perception and appreciation of movement involves the processing of both low-level features such as orientation and colour, as well as high-level features such as content represented in the art. Neuroimaging evidence suggests that, in addition to motion-sensitive visual areas such as the MT+, higher-level visuo-motor areas such as the motor and premotor cortices, as well as the temporal and parietal cortices, are engaged in the evaluation and appreciation of movement (Thakral et al., 2012; Cattaneo et al., 2015; Proverbio et al., 2009; Battaglia et al., 2011; Di Dio et al., 2011, 2016; Concerto et al., 2016). Most studies that have investigated human movement in art have also found engagement of higher visual areas (EBA, STS) along with areas involved in motor-mirror 183

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mechanisms (Di Dio et al., 2007; Proverbio et al., 2009; Calvo-Merino et al., 2010; Cross et al., 2011) and their interaction (Concerto et al., 2016). Thus, the interplay between regions that encode the human body and those implicated in motor processing is evident in the appreciation of human movement depicted in the visual and performing arts. However, the specifics of this relationship remain unclear at this stage and are especially unclear with respect to artworks that lack a depiction of the human form (such as landscapes or non-figurative abstract forms; Di Dio et al., 2016; Kim & Blake, 2007). Neuroimaging evidence continues to accrue that supports the suggestion that aesthetic appreciation of visual art is brought about by an interplay of different networks in the brain that span sensorimotor, visual perception, and reward networks (see Figure 9.3; Kurth et al., 2010; Brown et al., 2011; Boccia et al., 2016; Vartanian & Skov, 2014; Kirsch et al., 2016). More recent evidence, however, suggests that the aesthetic appreciation of artworks with implied movement in both abstract art and art with representational human content relies less on sensorimotor systems and more on an interplay between regions of the visual ventral stream and regions in the prefrontal and parietal areas, which also make up the default mode network (Bara et al., 2021; Humphries et al., 2021; Vessel et al., 2019). Overall, therefore, this section highlights the fact that investigations on movement appreciation are raising more questions than they are answering, and future work will be essential in gleaning a more meaningful and nuanced understanding of movement appreciation.

Figure 9.3 Brain networks implicated by previous research in movement appreciation include the perceptual/visual areas, sensorimotor network, reward network, and regions of the default-mode network. Current evidence, however, does not allow us to determine whether particular brain areas perform specific functions (e.g., the role of the vmPFC) or whether regions (such as the MT+) encode movement in a domain-general or domainspecific manner. Abbreviations—vmPFC = ventral medial prefrontal cortex, mmPFC = medial medial prefrontal cortex, dmPFC = dorsal medial prefrontal cortex, ACC = anterior cingulate cortex, NAcc = nucleus acumbens, AMG = amygdala, PCC = posterior cingulate cortex, M1 = primary motor cortex, S1 = primary sensory cortex, PMC  =  premotor cortex, PPA  =  parahippocampal place area, EVA  =  early visual areas, FFA = fusiform face area, EBA = extrastriate body area, LTC = lateral temporal cortex, IPL = inferior parietal lobule, pSTS = posterior superior temporal sulcus, aI = anterior insula, OFC = orbitofrontal cortex.

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Major challenges, goals, and suggestions In this section, we summarize the major challenges, goals, and suggestions for future research not just with respect to the field of movement appreciation but also as they apply to the field of neuroaesthetics as a whole.

Functional specificity in movement appreciation and the development of a cumulative science The extant literature provides us with a foundation for the neural mechanisms underpinning movement appreciation. A  more specific and functional characterization of neural networks underlying movement appreciation, however, remains far from clear, at present. As an example, very few studies in the field have defined the regions of interest functionally, making claims about functional specificity difficult. For instance, the medial prefrontal cortex (mPFC) is known to play a role in internally driven thoughts as well as reward processing more generally. However, engagement of the mPFC for aesthetic appreciation does not indicate whether it is involved in reward processing or processing of internally driven thoughts (or both), and authors often pick the interpretation that most closely fits their theory or hypothesis, without regard for what they can actually conclude from their findings, given their study design (see Figure 9.3). Interpreting findings in such a way is a classic example of reverse inference (Poldrack, 2006). Such approaches suffer from poor functional specificity, given the macroscopic resolution and heterogeneous nature of swathes of neural tissue that are typically under investigation when using techniques like fMRI. In a similar vein, claims regarding the role of the frontoparietal cortex in “mirroring” processes, as well as a role for MT+ in sensitivity to motion and aesthetic preferences (e.g . Di Dio et al., 2016), could also be explained by processing demands such as attention. While existing studies have provided us with a sound framework within which to work, the field of movement appreciation, and neuroaesthetics as a whole, can benefit from moving toward more sophisticated analysis pipelines that increase functional sensitivity such as by first identifying functional units and then testing how they respond during an aesthetic judgement task ( Julian et al., 2012; Nieto-Castañón & Fedorenko, 2012). To add on to univariate analytical approaches, which can test relatively simple models of brain organization, a move toward more multivariate and connectivity analyses would enable researchers to test more complex models of movement appreciation (e.g. Cela-Conde et al., 2013; Orgs et al., 2016; Bara et al., 2021; Vessel et al., 2019; Iigaya et al., 2021). For instance, in the domain of movement perception, one important question will be to clarify whether the MT+ or sensorimotor areas contain a domain-general representation of movement appreciation across different types and forms of art or whether they represent aesthetic appeal in a domain-specific fashion (see Figure 9.3). In addition, given recent concerns over questionable research practices and low levels of reproducibility in the fields of psychology and neuroscience, neuroaesthetics as a whole, and movement appreciation more specifically, can also benefit from practices that encourage the development of a cumulative science of movement appreciation (Button et al., 2013; Open Science Collaboration, 2015; Simmons et al., 2011). Before moving on to construct elaborate theories, test complex models, and/or attempt to synthesize findings, it may be sensible for the field to use more robust methodological approaches and embrace the credibility revolution in order to develop a more credible foundation for future studies to build upon (Ramsey, 2020; Vazire, 2018).

A more general and inclusive model Overall, the aesthetic preference or appreciation of movement would gain more insight by investigating stimuli across different domains, as well as by including populations from different cultures and ages and

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with different neurological conditions. For instance, some evidence suggests that art and non-art images that imply movement engage different neural regions (e.g. Di Dio et al., 2011). A comparison between art and non-art stimuli would help assess different aesthetically pleasing motion cues and patterns that are common across all stimuli, as well as identifying patterns (if any do indeed exist) that may be specific to art. In addition, movement appreciation may differ across cultures. For instance, the western world is thought to have a strong preference for intense dynamic activity in the context of sports (Proverbio et al., 2009). Moreover, an important aspect that might be addressed by future studies in movement appreciation is to examine how movement appreciation changes during the life span from childhood through to advanced age. Some evidence suggests that musical and visual art aesthetic preferences tend to be formed between 20 and 30 years of age and that adults present higher stable aesthetic preferences than adolescents (Holbrook & Schindler, 1989; Pugach et al., 2017). More research into the stability of aesthetic movement preference would deepen our understanding into how the aesthetic preference is computed and the required heuristics needed to maintain these preferences. Although there have been some accounts taking neuropsychological perspectives toward art cognition (Chatterjee, 2014; Lauring et al., 2019; Humphries et al., 2021), the field of neuro-aesthetics and especially the aesthetic appreciation of movement would also benefit from research quantifying how art production and art appreciation change as a function of neurological conditions. Such investigations can also serve as a useful way of identifying the functional contributions of brain regions and networks in movement appreciation. For instance, due to motor and spatial deficits, Parkinson’s disease patients might show different preferences for more dynamic artworks compared to controls, further elucidating the role that motor processing systems may play in the aesthetic appreciation of movement (e.g., Humphries et al., 2021).

Moving towards a neuroaesthetics of the performance space The field of neuroaesthetics in general will benefit from encouraging more research to be undertaken within real-world contexts, such as museums or theatres, especially as people tend to place greater aesthetic value on artworks presented in art galleries rather than in laboratory environments (Brieber et al., 2014, 2015; Locher et al., 2001; see also Chapters 22 and 24). A few early studies have investigated the neural correlates of aesthetic appreciation in more naturalistic contexts by using functional near infrared spectroscopy (fNIRS), a non-invasive and portable imaging technique, with higher temporal resolution and tolerance for motion compared to fMRI (Cui et al., 2011; Pelowski, Oi et al., 2016; Kaimal et al., 2017). fNIRS thus offers an exciting methodological opportunity in exploring the aesthetic experience in general, especially the aesthetic experience associated with observing and performing movement.

Conclusion Given the ubiquitous presence and influence of movement in our lives, it is not surprising that researchers in the domain of neuroaesthetics have investigated the neurocognitive underpinnings of movement appreciation. In this chapter, we have described considerable advances made in order to illuminate the complex cognitive and neural mechanisms that subserve movement appreciation. With a focus on movement appreciation in visual art, we first showed the importance of different implied motion cues that successfully create an impression of dynamism and their link with aesthetic preference. Second, we discussed the neural correlates of movement appreciation across different kinds of stimuli and content. Finally, we outlined future goals and new avenues for the field, highlighting that research in the developing domain of movement appreciation more specifically and neuroaesthetics more generally will benefit from more inclusive, naturalistic, and robust methodological approaches in order to generate a more holistic and complete understanding of the aesthetics of movement.

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Movement appreciation Van Geert, E., & Wagemans, J. (2020). Order, complexity, and aesthetic appreciation. Psychology of Aesthetics, Creativity, and the Arts, 14(2), 135–154. https://doi.org/10.1037/aca0000224 Van Paasschen, J., Bacci, F., & Melcher, D. P. (2015). The influence of art expertise and training on emotion and preference ratings for representational and abstract artworks. PLOS ONE, 10(8), e0134241. https://doi.org/10.1371/ journal.pone.0134241 Vartanian, O., & Goel, V. (2004). Neuroanatomical correlates of aesthetic preference for paintings. NeuroReport, 15(5), 893–897. https://doi.org/10.1097/00001756-200404090-00032 Vartanian, O., Navarrete, G., Chatterjee, A., Fich, L. B., Leder, H., Modroño, C., Nadal, M., Rostrup, N., & Skov, M. (2013). Impact of contour on aesthetic judgments and approach-avoidance decisions in architecture. Proceedings of the National Academy of Sciences of the United States of America,  110(2), 10446–10453. https://doi.org/10.1073/ pnas.1301227110 Vartanian, O., & Skov, M. (2014). Neural correlates of viewing paintings: Evidence from a quantitative meta-analysis of functional magnetic resonance imaging data.  Brain and Cognition,  87, 52–56. https://doi.org/10.1016/j.bandc. 2014.03.004 Vazire, S. (2018). Implications of the credibility revolution for productivity, creativity, and progress. Perspectives on Psychological Science, 13(4), 411–417. https://doi.org/10.1177/1745691617751884 Vessel, E. A., Isik, A. I., Belfi, A. M., Stahl, J. L., & Starr, G. G. (2019). The default-mode network represents aesthetic appeal that generalizes across visual domains. Proceedings of the National Academy of Sciences of the United States of America, 116(38), 19155–19164. https://doi.org/10.1073/pnas.1902650116 Vessel, E. A., Maurer, N., Denker, A. H., & Starr, G. G. (2018). Stronger shared taste for natural aesthetic domains than for artifacts of human culture. Cognition, 179, 121–131. https://doi.org/10.1016/j.cognition.2018.06.009 Vessel, E. A., & Rubin, N. (2010). Beauty and the beholder: Highly individual taste for abstract, but not real-world images. Journal of Vision, 10(2), 1–14. https://doi.org/10.1167/10.2.18 Vessel, E. A., Starr, G. G., & Rubin, N. (2012). The brain on art: Intense aesthetic experience activates the default mode network. Frontiers in Human Neuroscience, 6, 66. https://doi.org/10.3389/fnhum.2012.00066 Voland, E., & Grammer, K. (Eds.). (2003). Evolutionary aesthetics. Springer-Verlag. Watson, J. D., Myers, R., Frackowiak, R. S., Hajnal, J. V., Woods, R. P., Mazziotta, J. C., Shipp, S., & Zeki, S. (1993). Area V5 of the human brain: Evidence from a combined study using positron emission tomography and magnetic resonance imaging. Cerebral Cortex, 3(2), 79–94. https://doi.org/10.1093/cercor/3.2.79 Winawer, J., Huk, A. C., & Boroditsky, L. (2010). A motion aftereffect from visual imagery of motion. Cognition, 114(2), 276–284. https://doi.org/10.1016/j.cognition.2009.09.010 Wölfflin, H. (1942/2012). Principles of art history. Dover Publications. Yue, X., Vessel, E. A.,  & Biederman, I. (2007). The neural basis of scene preferences. NeuroReport, 18(6), 525–529. https://doi.org/10.1097/WNR.0b013e328091c1f9 Zeki, S., & Stutters, J. (2012). A brain-derived metric for preferred kinetic stimuli. Open Biology, 2(2), 120001. https:// doi.org/10.1098/rsob.120001 Zeki, S., Watson, J. D., & Frackowiak, R. S. (1993). Going beyond the information given: The relation of illusory visual motion to brain activity. Proceedings of the Royal Society of London. Series B: Biological Sciences, 252(1335), 215–222. https://doi.org/10.1098/rspb.1993.0068 Zeki, S., Watson, J. D., Lueck, C. J., Friston, K. J., Kennard, C., & Frackowiak, R. S. (1991). A direct demonstration of functional specialization in human visual cortex. Journal of Neuroscience, 11(3), 641–649. https://doi.org/10.1523/ JNEUROSCI.11-03-00641.1991

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10 HOW ARCHITECTURAL DESIGN INFLUENCES EMOTIONS, PHYSIOLOGY, AND BEHAVIOR Alex Coburn, Adam Weinberger and Anjan Chatterjee

Over the past decade, a new wave of research has emerged investigating the impact of architectural design on human emotions, physiology, and behavior. This research, termed the neuroscience of architecture, leverages evidence-based methods to explore how the human brain interacts with the built environment and to identify key features of buildings and neighborhoods that promote wellness and human flourishing. We begin this chapter by outlining the historical context that motivated this field, including the importance of aesthetics in vernacular architecture, the devaluation of humanistic design principles in mid-20th century construction, and the birth of environmental psychology and evidence-based design movements in response to post-war architectural mass production. We then review empirical findings that have emerged from research on the neuroscience of architecture, including investigations of aesthetic responses to architectural design features; the potential benefits of biophilic design for stress reduction, health, and wellness; and the impact of sensory features on movement and navigation. Finally, we discuss how the probing of specific dimensions of emotional experience in the built environment—including fascination, coherence, and hominess—can promote more evidence-based design practices and foster more human-centered buildings. People often spend most of their lives inside buildings (Evans, 2003). The design of the built environment has a profound impact on people’s mental states and sense of well-being. Here, we review empirical research that links these impacts to brains and behavior. Design can affect how comfortable (Baker  & Standeven, 1995; Brager et al., 2004) or focused (Mehta & Zhu, 2009) people feel and modulates excitement, fear, and awe (Coburn et al., 2020); feelings of trust (Zhang et al., 2014); stress-related hormonal patterns (Fich et al., 2014a; Küller & Lindsten, 1992; Tyrväinen et al., 2014; Valtchanov et al., 2010); and even complex social behaviors such as criminality (Kotabe, 2016). Over time, repeated exposures to specific environments may also affect health outcomes such as speed of recovery from surgery (Ulrich, 1984) and cardiac health (Kardan, Gozdyra et al., 2015). These claims come from findings in several fields, including environmental psychology, cognitive neuroscience, and neuroaesthetics (Coburn et al., 2017). Researchers try to define key aspects of psychological experience that are affected by the built environment and identify important architectural variables and design strategies that enhance wellness on a large scale. A challenge in this approach is the difficulty of operationalizing relevant features of the built environment (Cooper, 2014). Past studies have often assessed the psychological impact of easily quantifiable environmental variables, such as type of ventilation (i.e., mechanical vs. natural), number of windows in a room, and intensity of ambient noise in a building (Hygge, 2003; Johnson, 2000; Lercher et al., 2003). These variables can be operationalized and lend themselves easily to scientific inquiry. However, such isolated measures are 194

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often too simple to capture complex real-world environments that people actually experience (Alexander, 2002). The limited external validity of these measures may be why evidence that emerges from this literature is often contradictory (Cooper & Burton, 2014). Aesthetic qualities of the built environment may predict psychological experience and overall wellness better than any single design variable measured in isolation (Adams, 2014; Brown, 2014; Ellaway, 2014; Kyttä et  al., 2011; Kyttä  & Broberg, 2014). Environments that are perceived as attractive and high quality, for example, are consistently associated with positive mental health outcomes (Ellaway et al., 2005; Evans, 2003; Kyttä et al., 2011). Several studies report strong associations between the perceived attractiveness of residential neighborhoods and self-reports of health, quality of life, and wellness after controlling for socioeconomic and demographic factors (Coles, 2014; Kyttä et al., 2011). In a large-scale study in Finland, perceived environmental quality was strongly linked to perceived happiness, health, and quality of life, whereas no significant associations were found between quantifiable environmental measures and wellness (Kyttä & Broberg, 2014). This research underscores the importance of moving beyond simple quantitative measures of architectural spaces and developing more nuanced ways of defining and operationalizing aesthetic qualities of the built environment (Cooper & Burton, 2014). To give a concrete example of an “aesthetic quality,” one promising category relates to naturalness. Natural environments, as well as naturalistic features of the built environment, promote wellness across a wide range of contexts and populations. The cognitive benefits of exposure to natural spaces include improving mood (Barton & Pretty, 2010; Bowler et al., 2010; Valtchanov et al., 2010), reducing stress (Valtchanov et al., 2010; Villani & Riva, 2011), heightening concentration and working memory (Berman et al., 2008, 2012; Berto, 2005; Bratman, Daily et al., 2015; Bratman et al., 2012; Bratman, Hamilton et al., 2015; Kaplan, 1995), increasing self-esteem (Barton & Pretty, 2010; Pretty et al., 2005), enhancing vitality and energy (R. M. Ryan & Huta, 2010), and increasing self-perceptions of health (Kardan, Gozdyra et al., 2015). Buildings and rooms that offer views of nature are also linked to reduced criminality in residential neighborhoods (Kuo & Sullivan, 2001), faster recovery from surgery in hospitals (Ulrich, 1984), more charitable giving behaviors, and higher levels of trust (Zhang et al., 2014). Simply looking at images of nature and virtual representations of natural landscapes may promote many of these benefits (Berman et al., 2008; Berto, 2005; Valtchanov et al., 2010; Valtchanov & Ellard, 2015). Although the natural environment is often framed as a distinct category of space from the built environment (Kaplan, 1995), many buildings also contain natural visual and sensory features. Examples of these features include actual vegetation (i.e., plants, trees, water), symbolic references to nature, and abstracted natural patterns such as fractal scaling (Capo, 2004; Goldberger, 1996; Hagerhall et al., 2004), color contrast, and high density of curved edges in a scene (Coburn et al., 2019a; Ibarra et al., 2017; Kardan, Demiralp et al., 2015).1 The incorporation of natural elements and patterns into the built environment is often referred to as biophilic design ( Joye, 2007a; Kellert, 2003, 2015; Ryan et al., 2014). Biophilic design is associated with improved mood and cognitive functioning and may confer similar psychological benefits as interacting with natural landscapes (Coburn et al., 2019a; Kotabe, 2016; Kotabe et al., 2017, 2016; Lavdas & Schirpke, 2020a; Salingaros, 2020a, 2020b). Specific aesthetic qualities of the built environment, including perceived beauty and naturalness, can therefore enhance an individual’s psychological experience within an architectural space and may contribute to better mental health and wellness. The idea that aesthetic qualities of the built environment can impact wellness is not new. For millennia, civilizations across the globe sought to understand how to optimize designs of buildings and landscapes in order to improve social, functional, and spiritual aspects of the human experience. From ancient Rome to Imperial China, cultures around the world developed sophisticated aesthetic rules to guide the construction of buildings, neighborhoods, and cities, motivated by the belief that these aesthetic principles are as much as form of science as they are a form of art (Coburn et al., 2017; Mak & Thomas Ng, 2005; Patra, 2009; Vitruvius Pollio et al., 1914). 195

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However, around the middle of the 20th century, a seismic shift occurred in Western culture that led to the widespread rejection of these humanistic principles of construction in favor of a new set of design rules in which measurable variables such as cost, speed, and efficiency were prioritized above less easily quantifiable factors, including aesthetics and occupant experience. Although this shift achieved some progress in urban development, it also brought about unintended social consequences ( Jacobs, 1992). The evidence-based design movements of the 1970s arose in response to mid-century architectural mass production and paved the way for research in urban sociology and environmental psychology. This early scholarship exploring the effects of architectural design on social behavior and psychological experience laid the groundwork for recent research on the neuroscience of architecture. In the following section, we outline the historical context for the rise of research in the psychology and neuroscience of architecture. We then summarize the key evidence that has emerged to date. Finally, we discuss the prospects and limitations of translating evidence-based research into environmental design strategies and architectural practice and discuss the future directions of this emerging area of study.

Historical context Aesthetics and vernacular architecture For most of human history, architectural design and aesthetics developed hand in hand. From medieval Iran, to ancient Japan, to indigenous North American cultures, the construction of vernacular buildings was closely linked with advanced aesthetic principles of architectural design. Among the best known sets of design principles are the Chinese feng shui and the Indian vaastu shastra (Mak & Thomas Ng, 2005; Patra, 2009). These design-oriented philosophies sought to create harmony and comfort in the built environment using wisdom derived from empirical observation. These complex systems of environmental aesthetics emerged gradually, over the course of centuries, by integrating trial and error, spiritual reflection, and intellectual scholarship. In many cases, the buildings generated by these design principles remain among the most important cultural artifacts of the civilizations that built them. Aesthetics was also integral to the development of Western European architecture. Vitruvius, the influential Roman architect and writer, declared that successful construction relies on three closely related principles: functionality (utilitas), structural integrity (firmitas), and beauty (venustas) (Vitruvius Pollio et al., 1914). These three principles, known as the Vitruvian triad, not only reflected a design philosophy of the ancient world but also served as the foundation for the architectural philosophy of Renaissance Europe. Indeed, the Renaissance not only witnessed significant advances in structural engineering (firmitas) and efficient urban planning (utilitas), it also brought about remarkable achievements in aesthetics (venustas) of the Vitruvian triad. Humanistic principles of architectural aesthetics emerged through the prolific drawings and writings of great Italian architects like Palladio, Michelangelo, and Alberti. At the same time, novel mathematical concepts, such as the Golden Ratio, influenced the design of buildings large and small across Western Europe, North Africa, and the Middle East. To the extent that the iconic buildings produced by these cultures were a reflection of skilled engineering, they were just as much a product of sophisticated advances in the science of architectural aesthetics.

Industrialization and the modernist aesthetic In the mid-20th century, the dimension of aesthetics was forcefully rejected. Following the second Industrial Revolution, advocates of the International Style, including Mies van der Rohe and Le Corbusier, spread a new architectural gospel: buildings should be designed as “machines” for living and working (Corbusier, 1927). This new philosophy was based on the idea that “form follows function.” It declared that a building’s 196

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design and appearance should no longer be shaped by humanistic principles of comfort, aesthetics, and harmony but should instead conform to industrial values like cost, speed, density, and efficiency (Rosner, 2020). As part of this new way of thinking about buildings, the founders of Modernism rejected architectural ornament and embraced new mottoes like “less is more” and “death to decoration” ( Johnson, 1947). In a similar vein, these architects abandoned basic tenets of proportion and human scaling that had for centuries guided human construction around the world (Alexander, 1979, 2002). In this way, the Vitruvian principle of venustas was turned on its head, and architectural beauty was reformulated to signify the manifestation of functionalist design. This shift in thinking led to the widespread adoption of a new and highly specific aesthetic ideal. This ideal embraced minimalist, reductive forms and celebrated materials that conformed easily to these shapes, such as glass, steel, and concrete (Rosner, 2020). This movement spread internationally after the Second World War, and its rapid proliferation was inseparable from the economic context out of which it was born. This approach to architectural design depended on the availability of mass-produced, synthetic materials and large-scale manufacturing (Murphy, 2012). Rapid architectural fabrication was promoted by industrial leaders, who profited financially from its rise, and by political leaders, who embraced mass-produced buildings as a large-scale and low-cost solution to post-war reconstruction. Important social factors also contributed to the international proliferation of the neo-Modernist aesthetic. Whereas design principles of vernacular architectural traditions developed gradually, over centuries and across many different cultures, the aesthetic ideals of neo-Modernism were invented in the 1930s by an elite group of white European men ( Johnson, 1947). In the following decades, these ideals were institutionalized by city governments, urban planners, and real estate developers wherever low-cost property and perceived poverty or social unrest could be found. In the United States, this paternalistic sensibility led to the demolition of entire neighborhoods and privately owned homes inhabited largely by black and brown communities and the construction of massive, monolithic apartment towers in their place (Austen, 2018).2 The application of neoModernist design principles to these “urban renewal” projects generated substantial revenue for developers and municipal governments by increasing the density of taxpayers and renters occupying each square foot of real estate. The tower-block model also facilitated the geographic segregation of black and white communities into distinct buildings and neighborhoods as part of a larger set of systemically racist American housing policies (Rainwater, 2006). The tower-block model also gained popularity in European cities as a means of class-based, rather than race-based, segregation. In Copenhagen and Amsterdam, for instance, mass-produced housing was used by municipal governments to move working-class people out of downtown neighborhoods and into suburban high-rise apartments (Helleman & Wassenberg, 2004). Across the Western world, the neoModernist aesthetic therefore served as an effective tool for both financial gain and social control. The rapid spread of these aesthetic principles contributed to the international standardization of architectural form (Alexander, 2002). Supported by powerful socioeconomic forces, the neo-Modernist doctrine pushed competing philosophies of design to the side and morphed into an international monopoly of architectural ideology (Salingaros, 2007). The spread of these principles was not driven by their grassroots popularity or demonstrated social benefit. Instead, it was based on their ability to serve the financial and political interests of a powerful minority of stakeholders. To be clear, this was not the intention of the Bauhaus architects who invented Modernism. They saw their movement as a force for equity and social progress. However, these a priori theories were not tested in the real world, and the evidence supporting them amounted to little more than idealistic conjecture.

Urban sociology and evidence-based design Starting in the 1960s, the principles of Modernism began to face empirical scrutiny. In The Death and Life of Great American Cities, Jane Jacobs (1992) documented how urban renewal projects were impoverishing the 197

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lives of individuals and eroding the vibrancy of urban communities. This research challenged the foundational tenants of neo-Modernist urbanist theory and led to grassroots political efforts opposing a number of significant urban design projects. Among other projects, these efforts prevented the construction of a planned expressway and real estate development in lower Manhattan that would have destroyed large swaths of Soho and Little Italy (Flint, 2009). Christopher Alexander, a professor of architecture at Berkeley, later embarked on a decade-long research project investigating the social and psychological effects of different “pattern languages” of architectural design. Using case studies of buildings and neighborhoods from around the world, he developed a system of empirically derived design principles to improve human experience in the built environment (Alexander, 1979). Alexander employed these principles in several international projects, including the University of Oregon campus design, residential houses in northern Mexico (Alexander et al., 1985), and Eishin School in Japan (Alexander et al., 2012). These empirically driven research and design efforts served as the foundation for evidence-based architectural practices like New Urbanism, which gained traction in the 1970s and 1980s and challenged the previous aesthetic dogma of neo-Modernism (Mehaffy, 2017).3

Environmental psychology and neuroscience In the early 1980s, research arose in the emerging field of environmental psychology, including the Kaplans’ groundbreaking work on evolutionary and environmental aesthetics (Kaplan, 1973; Kaplan & Kaplan, 1989; Kaplan, 1987) and Ulrich’s seminal studies linking the aesthetics of healthcare spaces to health and wellness (Ulrich, 1977, 1983, 1984; Ulrich et al., 1991). Harvard biologist E.O. Wilson published his foundational biophilia hypothesis around this time (1984), which outlined novel evolutionary arguments illustrating the psychological and spiritual connection between humans and natural life forms and landscapes. In collaboration with Yale professor Stephen Kellert, Wilson later applied these broad ideas to architecture and urban design (Kellert, 2003, 2005; Wilson & Kellert, 1995) and inspired biophilic architecture, a growing movement of evidence-based design practices (Berto & Barbiero, 2017; Joye, 2007a; Kellert, 2015; Ryan et al., 2014; Salingaros, 2020a). In the early 2000s, this foundational literature on evolutionary and environmental psychology motivated a new wave of research exploring links between architectural design and the human mind and brain (Eberhard, 2008, 2009, 2004; Eberhard & Patoine, 2004; Whitelaw, 2013). A new field of research thus emerged, often referred to as the neuroscience of architecture. This field draws from several scientific disciplines, including experimental psychology, empirical aesthetics, and cognitive neuroscience (Coburn et al., 2017). The purpose of this field is to investigate the neurobiological underpinnings of architectural and aesthetic experience and to measure the effects of architectural design on human experience using a combination of behavioral studies, brain imaging, and other physiological biomarkers (e.g., tracking eye movements, measuring stress hormones). Whereas previous research in environmental psychology was largely limited to self-report surveys and observations of human behavior, the neuroscience of architecture has enabled researchers to move beyond descriptive and observational measures in order to understand the neural structure and cognitive mechanisms that mediate aesthetic experiences of the built environment. With this historical context in mind, we offer a more detailed review of this nascent field of research.

Neuroscience of architecture The neuroscience of architecture has begun to advance knowledge about how and why specific architectural features affect people in specific ways. This section will begin by reviewing neuroscientific research that has shown how properties of the built environment influence aesthetic experiences and instantiate psychological

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and emotional responses. This research is placed within the theoretical framework of the aesthetic triad, a neuroscientific model originally developed for neuroaesthetics (Chatterjee & Vartanian, 2014), which was then reformulated for the neuroscience of architecture (Coburn et al., 2017). As part of this discussion, we will also consider how individual-level characteristics moderate responses to the built environment, recognizing that some aspects of architectural experience vary from person to person, while others are more stable across most people. The design of built environments can also have a meaningful impact on mood and health. Consequently, this section concludes by highlighting important connections between architectural design and wellness. Research on how built environments influence movement behavior is discussed in detail in Chapter 17. At the outset, it is important distinguish between descriptive and experimental neuroscientific claims. The former uses observational knowledge about brain functioning to qualitatively map the biological or cognitive nature of an aesthetic experience and to potentially develop testable theories and hypotheses. For example, some neurons in primary visual cortex respond preferentially to edges and high visual contrast (Brady & Field, 2000; Ramachandran & Hirstein, 1999). Thus, neuroscience has descriptively demonstrated that the brain is sensitive to certain visual features. Experimental neuroscience, by contrast, directly tests hypotheses, makes predictions, and yields quantitative data. For instance, Vartanian and colleagues (2013) identified increased anterior cingulate activation when participants made judgments of interior spaces during functional magnetic resonance imaging (fMRI), experimentally demonstrating the involvement of emotion and reward pathways. Whereas much of the early research on the neuroscience of architecture took a descriptive approach (see Barbara & Perliss, 2006; Eberhard 2004, 2008, 2009; Eberhard & Patoine, 2004), research over the past decade has shifted towards using experimental methods (see Coburn et al., 2019, 2020; Lavdas & Schirpke, 2020; Vartanian et al., 2013, 2015). Recognizing the value of both types of research, we will return to this distinction at various points throughout this chapter.

The aesthetic triad We developed a brain-based model of architectural experience (Coburn et al., 2017) by applying the aesthetic triad (Chatterjee & Vartanian, 2014) to architecture. Using this model, we proposed that human–building interactions are mediated by three large-scale networks in the brain: the sensory-motor, knowledgemeaning, and emotion-valuation systems (Figure  10.1). The sensory-motor system addresses “bottom-up” processing of the features of buildings, including visual (color, shape, size, materiality), as well as acoustic, tactile, and even olfactory features of the built environment. The brain’s knowledge-meaning circuitry plays an important role in mediating “top-down” processing of architectural environments. The brain’s baseline response to a building’s sensory features may be either dampened or enhanced by an individual’s cultural background, identity, and education, as well as their knowledge about a space. Finally, the emotion-valuation system integrates information from the sensory-motor and knowledge-meaning systems, leading to aesthetic experiences. These experiences may range from profound feelings of joy or delight to interest or even fear and disgust. We postulated that these three systems interact closely to create a holistic sense of architecture. In the following discussion, we review research on the psychology and neuroscience of architecture using the aesthetic triad as an organizing framework. First, we outline key sensory features of architectural spaces that influence psychological and neural responses across a wide range of building typologies, with a particular emphasis on biophilic design. Next, we discuss emotional responses to architectural spaces and examine the neural pathways that mediate different emotional experiences in the built environment. Finally, we discuss key dimensions of the knowledge-meaning neural systems that contribute to individual differences in certain aspects of architectural experience.

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Figure 10.1 The Aesthetic Triad proposes that the human brain processes architectural environments via interactions between sensory-motor, emotion-valuation, and knowledge-meaning neural systems. Chatterjee & ­Vartanian, 2014. Reprinted with permission from Trends in Cognitive Sciences.

Sensory perception of the built environment We begin with a discussion of the sensory features of architectural design that are most closely linked to positive aesthetic experiences, as well as the mechanisms of human perception that mediate these experiences. A logical starting point is the perception of nature and naturalistic features of the built environment (i.e., biophilic architecture), given the wealth of research related to this topic. The natural environment has served as one the most important sources of architectural inspiration throughout history. The Hanging Gardens of Babylon—one of the Seven Wonders of the Ancient World—was characterized by rich greenery. Gardens were featured prominently in ancient Egyptian and Chinese societies. In the modern era, housing costs fluctuate based on proximity to recreational natural spaces (Crompton, 2001), and vacationers spend more money for rooms with ocean views (Fleischer, 2012). These trends reflect a long-standing desire to maintain contact with nature while being in the built environment (Ulrich, 1993). It comes as no surprise, then, that some of the most prominent neurobiological accounts of architecture are concerned with natural qualities and aesthetics. For example, consistent with the biophilia hypothesis, empirical evidence suggests a widespread human preference for natural environments compared to urban spaces (Berman et  al., 2008; Kaplan, 1995), although recent work suggests individuals’ preferences may gradually emerge over the course of development (Meidenbauer et al., 2019). People show a strong tendency to interact with nature for recreation and relaxation, a preference observed globally and across cultures (Chang et al., 2020). Interactions with nature are also salubrious, producing positive effects on physiological, emotional, and cognitive functioning ( Joye, 2007b). Kaplan’s attention restoration theory (ART; Kaplan, 1995) argues that the ameliorative effects of nature stem from alterations to neurocognitive processing—specifically, an improved ability to focus. ART draws from William James’ distinction between involuntary attention—which is automatically captured by surrounding stimuli—and directed forms of attention that rely on cognitive control ( James, 1985), the latter of which is effortful but replenished by interactions with nature. More precisely, ART indicates that natural settings reduce the burden otherwise placed on effortful attention mechanisms (i.e., in an urban environment), thereby allowing directed-attention mechanisms to restore (because there is nothing demanding 200

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them; Berman et al., 2008; Joye, 2007; Kaplan, 1995). Crucially, this restoration occurs independently of emotional changes or aesthetic preferences (Kaplan, 1995; Meidenbauer et al., 2020). Consistent with ART, experimental research indicates a modest improvement in attention after exposure to natural environments (e.g., Berman et al., 2008; Bowler et al., 2010), although questions remain about individual differences and proper control conditions. Several evolutionarily based accounts argue that preferences for natural spaces are affectively and aesthetically driven. Ulrich’s psychoevolutionary stress-reduction framework (1993) posits that, in our evolutionary history, humans were frequently confronted by threatening stimuli, leading to a rapid cortisol response, which persisted until the setting became unthreatening. Unthreatening settings were typically open, calm, and warm. Present-day environments rich in these attributes (among others) may reduce stress and improve affect. The architect Don Ruggles emphasizes the importance of designing buildings that increase the baseline tone of the parasympathetic autonomic nervous system as a target to reduce stress (Ruggles & Boak, 2020). Similarly, the “prospect-refuge” theory (Dosen & Ostwald, 2016) argues that environments which are both open (i.e., prospect) and convey feelings of safety (i.e., refuge) were evolutionarily beneficial and, therefore, remain aesthetically preferred. Although empirical evidence demonstrating one-to-one association between specific natural features (e.g., “openness”) and stress reduction is lacking ( Joye & De Bloack, 2011), a wealth of research reports that exposure to nature is associated with reduced stress and negative emotions, such as anger and sadness (Bowler et al., 2010). The neuroscience of architecture also draws from theories of aesthetic experience more broadly. In particular, Rolf Reber’s theory of “processing fluency” proposes that aesthetic experience fluctuates based on how efficiently an observer can processes the properties of an object. Objects—for example, built environments—are experienced as pleasurable if they contain some complexity but can still be fluently processed. In this way, processing fluency can “bridge the gap” between cognitive (e.g., ART) and affective (e.g., stress-reduction) frameworks, as the theory describes both a reduction in cognitive resources (consistent with attention restoration theory) and hedonic value associated with the condition (in line with stressreduction frameworks). Descriptively, an extensive body of neuroscience research has demonstrated sensitivity to low-level stimulus features. For example, brains are especially attuned to edges and high contrast (Brady  & Field, 2000; Geisler, 2008), which likely evolved to support object identification. Other low-level features such as luminance, color, and motion are initially processed in primary sensory areas (Chatterjee, 2003), before being processed in higher-level regions, such as the parahippocampal place area (PPA)—which responds preferentially to scenes and buildings (Epstein & Kanwisher, 1998)—as well as the hippocampal and entorhinal cortices that are crucial for spatial navigation (Spiers & Barry, 2015). Other research indicates sensitivity to visual symmetry (Bertamini et al., 2019; Gartus & Leder, 2013; Rhodes et al., 1998). These features are fluently processed and, therefore, plausibly contribute to aesthetic experience. Scenes with repeating low-level features like those mentioned previously are judged as more fascinating and coherent ( Joye & van den Berg, 2011), putatively because of easy recognition and error-free processing (Clark, 2013; Reber et al., 2004). These sensitivities could explain aesthetic appreciation for rhythmic architectural designs such as alternating columns or color patterns in stained glass windows (Alexander, 2004; Coburn et al., 2017). Natural environments, specifically, are characterized by a host of reoccurring low-level features, including non-straight edges, low color saturation (Berman et al., 2014), and greater contrast (Coburn et al., 2019). The prevalence of predictable low-level features may explain why humans are able to process natural scenes more rapidly than human-made structures (Greene & Oliva, 2009; Rousselet et al., 2005). Given this preference for nature—that is, biophilia—many designers have sought to bring a natural aesthetic into the built environment. Many global design initiatives encourage the use of natural elements in architecture (Living-Future.Org, 2020; International WELL Building Institute, 2020). One way to do this is to incorporate nature directly into the built environment. This use of natural elements could be as straightforward 201

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as adding plants, water features, or small fires ( Joye, 2007b). Drawing on the ideas of prospect-refuge, architects can also incorporate large windows or balconies that provide extensive vistas of the outdoors. Even more simply, people can arrange pictures or photographs of the outdoors around the home. These approaches, however, may be overly simple. For one, inhabitants of such spaces may not judge the water, plants, or pictures as being natural; they are aware that these elements were inserted into a built space. Furthermore, merely placing an otherwise unnatural structure into a natural landscape is also unlikely to meet biophilic ambitions; if there is nothing inherently natural about the structure itself, buildings may integrate poorly with the surrounding environment and actually detract from the cognitive and affective benefits of an otherwise natural setting. An alternative approach is to incorporate low-level features or patterns that occur frequently in nature into the design of human-made structures and spaces. Indeed, philosophers and artists have long posited that people are innately drawn to human-made objects that echo organic, natural qualities. In one study, participants who evaluated 240 interior and exterior architectural scenes based on perceived naturalness and preference were found to exhibit strong preferences for buildings that contained high densities of naturalistic visual patterns, such as edge density and contrast (measured quantitatively using image statistics). Notably, these two nature-like patterns explained the most of the variance in preference ratings of both architectural facades and interior scenes, even after controlling for the quantity of actual vegetation that was visible in each scene (Coburn et al., 2019). These findings suggested that the degree of implicit naturalness perceived in an architectural environment might be just as important as (and possibly independent from) the amount of explicit nature (i.e., water, plants, trees) conveyed in that scene. This study builds on previous research showing that rooms with properties typical of the natural environment (e.g., non-straight edges) are preferred to spaces with unnatural features (e.g., straight edges; Vartanian et al., 2013). The salubrious effects of interacting with nature may stem largely from these preferences; in one recent study, when participants were presented with equally preferred urban and natural images, no differences in affective state were observed (Meidenbauer et al., 2020). That is, people experienced positive emotions in natural settings because of the prevalence of preferred visual inputs rather than because of unique qualities of nature itself. Thus, low-level features characteristic of natural environments may evoke positive affect and aesthetic appreciation if they are incorporated into the built environment, even without the presence of explicitly natural elements. Crucially, low-level stimuli can elicit neural responses that characterize the “whole.” For instance, brains contain a powerful face detection mechanism—instantiated in the fusiform face area (FFA)—that responds strongly to faces. Yet the region not only responds to actual human faces but also to basic, low-level stimulus features that make up a face (Yue et al., 2011). The FFA is activated by sparse schematic representations of a face (i.e., a smiley face) or even the front of a car (Kühn et al., 2014; Windhager et al., 2010). Moreover, these low-level features can induce emotional responses similar to these evoked by real human faces (Aiken, 1998; Joye, 2007b). Analogous results have been experimentally demonstrated for scene perception. In one study (Kravitz et al., 2011), participants viewed a series of built and natural spaces during fMRI. Results indicated that the primary factor that influenced PPA activation was not whether the space was natural or human-made but rather the extent of openness conveyed in each image. That is, the PPA was sensitive to a particular feature (i.e., openness of the space) regardless of image categorization (i.e., natural vs. built). Viewing open spaces is also associated with activation of temporal lobe structures sensitive to visual motion (Vartanian et al., 2015), suggesting a connection between openness and a desire to move in space (Coburn et al., 2017). Consistent with this interpretation, open interior spaces are rated as more natural (Coburn et al., 2019) and beautiful (Vartanian et al., 2015) and are preferred over enclosed environments (Dosen & Ostwald, 2016). Other work indicates that binocular eye movements may be specifically attuned to statistical regularities in the natural environment (Gibaldi & Banks, 2019), further suggesting that humans are highly sensitive to the 202

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occurrence of certain low-level features prevalent in nature. Similarly, aesthetic preferences for scenes with a high density of curved edges and color contrast have been demonstrated in several studies and across multiple environmental contexts, including natural landscapes (Berman et al., 2014; Ibarra et al., 2017; Kardan, Demiralp et al., 2015), architectural facades (Coburn et al., 2019a; Ibarra et al., 2017; Kardan, Demiralp et al., 2015; Lavdas & Schirpke, 2020b), and building interiors (Coburn et al., 2019a, 2020; Vartanian et al., 2013). Taken together, these findings point to the powerful influence of low-level features on aesthetic experience and further indicate that built environments rich with such features found in nature may trigger biophilic responses. A key low-level feature that influences aesthetic experience is fractal scaling (i.e., “fractals”). Fractals, a hierarchy of self-similar patterns that repeat at increasingly fine sizes, provide a sense of “organized complexity.” Fractals are common in nature; clouds, trees, plants, waves, fire, lightning, and mountains are all composed of repeating patterned elements.4 When incorporated into the built environment, fractals evoke feelings of naturalness and are consistently preferred compared to non-fractal design ( Joye, 2007b; Lavdas & Schirpke, 2020b; Taylor, 2021). Historically, fractal design has been prevalent across many civilizations and architectural practices. As detailed by Richard Taylor (2021), fractals are seen in traditional African settlements, 8th-century temples, 13th-century castles of the Holy Roman Empire, Gothic-period cathedrals, Buddhist temples, Islamic minarets, Gaudi’s Sagrada Familia, and the organic houses of Frank Lloyd Wright. More recent initiatives have incorporated fractals into the design of floors, carpets, walls, solar panels, and window shades. Several contemporary architecture firms have also incorporated fractal scaling into their design practices (e.g., Light Earth Designs, Center for Environmental Structure), although non-fractal design practices are more prominent in contemporary Western architecture ( Joye, 2006; Salingaros, 2007). Neuroscience research—both descriptive and experimental—provides insight into the historical appeal of fractals. First, it is clear that the visual system is proficient at grouping together repeating elements (Biederman, 1987; Reber et al., 2004) and process fractals automatically and fluently (Spehar et al., 2015). This is true for humans as well as primates (Finn et al., 2019). Moreover, neurons in the primary visual cortex appear to show a preference for fractals, suggesting that fractals may play a crucial role in adapting the visual system to the natural environment (Yu et al., 2005). Consistent with these findings, patients with neurological damage to higher-order visual processing regions do not show any deficits in fractal gaze dynamics (Marlow et al., 2015). Fractals may also partially explain associations between nature (which is often rich in fractal patterns) and stress reduction ( Joye, 2007b); results using EEG report heightened attentional states when people view naturally patterned fractals (Hagerhall et al., 2008). All of these findings are broadly consistent with attention restoration theory, stress reduction, and processing fluency perspectives. It is also evident that higher-level visual and semantic features of an environment influence aesthetic experience. In a recent fMRI study, researchers identified decodable neural representations of architectural styles and buildings in high-level visual regions but not cortical regions devoted to low-level features such as the primary visual cortex (Choo et al., 2017). Ibarra et al. (2017) also demonstrated that high-level visual features play an important role in aesthetic evaluations of urban and natural landscapes. They found that high-level scene features—such as the shape and undulation of the skyline, the presence of water in the scene, and the distribution of buildings—mediated the relationship between low-level scene features and aesthetic preference ratings by explaining over half of their shared statistical variance. Thus, aesthetic judgments of built environments may involve complex interactions between low-level and high-level stimulus features. Aesthetic experiences may also be influenced by design features related to spatial navigation. The grid cells of the hippocampus create a “cognitive map” of one’s environment to facilitate navigation (McNaughton et al., 2006; O’Keefe & Nadel, 1978) and may be retrieved during subsequent re-exposure to a given location (Astur et al., 2002; Maguire et al., 2000). Grid cell encoding is influenced by the shape of the environment, with distinct patterns for different geometrical spaces (Krupic et al., 2015). Notably, these differences 203

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have relevance to the ease with which one is are able to navigate their environment, with orienting abilities significantly worse in circular rooms with rotational symmetry (Kelly et al., 2008). An additional class of neurons (head direction cells) signal an animal’s directional position and are especially critical for navigation (Knierim et al., 1995; Taube, 1998). Environmental information can also be found in medial temporal lobe, where representations have been found to be modulated by the spatial location of other individuals (Stangl et al., 2021). Homogenous spaces (e.g., uniform coloring, no obvious landmarks) detract from the functioning of these neural processes and complicate the extent to which one is able to form a complex mental representation of their spatial location (Evans & McCoy, 1998). Feeling lost in one’s surrounding environment is a negatively valenced experience. Other research has found that beauty judgments of buildings vary with neural activity in the global pallidus (Vartanian et al., 2013), a brain structure responsible for regulating voluntary movement. Collectively, these findings suggest that the ease with which one can move through a space may influence aesthetic experiences of built environments. Analogous to perceptual fluency, motor fluency might influence aesthetic experiences. Architects should consider how specific design features can facilitate more fluid spatial navigation. In a study of nursing home design, monotony of architectural composition and absence of reference points and signage were found to impede wayfinding and induce anxiety among patients with advanced dementia (Passini et al., 2000). In another study of corridor design and navigation among healthy adults, hallways with warmer colors were found to more memorable and attractive than hallways with cooler colors (Hidayetoglu et al., 2012). These design factors are also likely to be crucial for large-scale built environments—such as train stations or hospitals—in which people typically move about rapidly. Neuroscience research has also begun to examine how non-visual environmental properties influence aesthetic experience. For instance, specific environmental odors may trigger memories and past experiences, putatively because of the anatomical connections between the olfactory and limbic systems (Ward et  al., 2015). Historically, olfactory considerations played an important role in architectural design and provided an important sensory and emotional link between people and places. Contemporary efforts to eliminate odors and sterilize architectural interiors may contribute to feelings of sensory isolation and detachment in institutional settings such as hospitals, schools and apartment buildings (Barbara & Perliss, 2006). Acoustic features are also relevant to aesthetic experience. Loud environments increase blood pressure (Payne et  al., 2014) and disrupt neural development (Gilbert & Galea, 2014). Several studies have investigated how the acoustic design of classrooms influences childhood learning. Acute exposure to classroom noise can impair speech recognition ( Johnson, 2000), decrease children’s performance on complex listening tasks, and interfere with memory encoding processes (Hygge, 2003). Designs that attenuate noise might therefore be desirable, particularly in urban settings. Other features of a building, such as temperature and ventilation strategies, can influence occupants’ perceptions of comfort and even contribute to perceptions of spatial beauty (Nicol & Humphreys, 2002; Thorsson et al., 2007).

Emotional responses to architecture Humans experience deeply emotional responses to beautiful objects, including architecture (Chatterjee & Vartanian, 2014). Stress-reduction frameworks argue that certain visual properties convey feelings of calmness or warmth, leading to improved affect (Tyrväinen et al., 2014; Ulrich, 1983; Ulrich et al., 1991). The prospect-refuge theory expands on the evolutionary bases for this association. Processing-fluency accounts indicate that efficiently processed stimuli are hedonically marked. These theoretical perspectives are supported by empirical findings linking architectural experiences with neural pathways related to reward and stress response. A meta-analysis of 93 neuroimaging studies pointed to a crucial role of the right anterior insula in positively valenced aesthetic appraisals (Brown et al., 2011). The anterior insula has been highlighted as an 204

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important brain area for processing negative emotions (e.g., disgust, sadness, pain), as well as the regulation of the autonomic nervous system (Cechetto, 2014; Nagai et al., 2010), suggesting a link between aesthetic experience and stress reduction. Further, anterior portions of the insula are also part of the gustatory cortex and show activation when individuals are presented with pictures of food. Thus, aesthetic “taste” and gustatory taste may share neural substrates. While somewhat speculative, it is plausible that a neural system initially evolved to appraise food sources may have been co-opted for the appraisal of aesthetics. Aesthetic appraisals also engaged the orbitofrontal cortex (OFC), a core region of the brain’s emotional and reward circuitry (Bechara et al., 2000; see also Chapter 3). Research on architectural aesthetics has extended these findings. In one study examining approachavoidance responses, the anterior midcingulate cortex (aMCC) was engaged when people viewed enclosed interior spaces that elicited exit decisions (Vartanian et al., 2015). Because the aMCC receives projections from the amygdala—indicating a potential role in fear processing—negative emotions may be involved in processing architectural spaces, particularly ones that people wish to leave. Other work report heightened fear, stress, and cortisol levels when people are immersed in a virtual simulation of an enclosed room (Fich et al., 2014b). Together, this work highlights an underlying negative emotional component (i.e., fear) that may drive aesthetic experiences of the built environment. These findings are broadly consistent with stress-reduction frameworks that emphasize affective responses to the environment based on automatic, evolutionarily evolved processing of the environment (Ulrich, 1993). It is important to note, however, that emotional responses are not automatic; the involvement of prefrontal and hippocampal brain regions in beauty judgments of architecture suggest that conscious reasoning and memory retrieval exert top-down influences on initial, automatic emotional reactions (Coburn et al., 2017; Vartanian et al., 2013) Following up on this work, Coburn et al. (2020) conducted a study in which participants evaluated 200 interior architectural scenes across a variety of aesthetic rating scales. Principal component analysis was conducted to search for statistical patterns of overlap among thousands of ratings. Nearly 90% of the variance in responses was explained by just three underlying psychological dimensions: coherence, fascination, and hominess. Coherence describes the degree to which a space feels organized to the viewer. Fascination refers to the visual richness and complexity of a space and is closely linked to a viewer’s sense of excitement and desire to explore it. Hominess represents the extent to which a space feels comfortable, personal, and “home-like” to the viewer. As a reliability check, the same experiment and analysis was repeated with a separate group of 600 participants. Again, the vast majority of variation in aesthetic responses was explained by the same three dimensions. Coherence, fascination, and hominess have also been identified for images of building exteriors (Weinberger et al., 2021), further suggesting that these three dimensions may broadly applicable to the built environment. Taking these analyses a step further, the authors also examined whether these psychological dimensions were associated with specific neural signatures (Coburn et al., 2020). This hypothesis was tested by integrating the PCA scores of the architectural scenes with fMRI data from Vartanian and colleagues (2013), who had previously evaluated the same images in the scanner via approach-avoidance and beauty judgment tasks. The degree of fascination covaried with neural activity in the right lingual gyrus for both tasks. Coherence was associated with neural activity in the left inferior occipital gyrus only when participants judged beauty, and hominess covaried with activation of the left cuneus exclusively for the approach-avoidance task. Critically, these neural data were collected years before the three psychological dimensions had been identified and in a separate group of participants. The authors concluded that the three dimensions of psychological experience may be hardwired into the visual cortex, with each dimension carrying its own distinct neural imprint. If these insights are shown to extend beyond the specific stimuli used in these studies, and to generalize to other architectural spaces, they could critically inform how buildings and urban environments are designed and evaluated. 205

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Knowledge, meaning, and individual variability The neuroscience of architecture is also broadly interested in how differences at the individual level relate to aesthetic experience. Within the framework of the aesthetic triad (Chatterjee & Vartanian, 2014), individual differences are likely to involve knowledge-meaning systems (Coburn et al., 2017). Some general factors that moderate individual-level differences in architectural experience include familiarity, expertise, context, and cultural upbringing. Here, again, we begin by considering findings from neuroscience and neuroaesthetics broadly. The mere exposure effect, for instance, is a domain-general phenomenon in which people show a preference for stimuli that they see repeatedly (Bornstein & D’agostino, 1992; Montoya et al., 2017). But there is also a serial effect; people prefer art that is preceded by an attractive painting compared to when it is preceded by a less attractive piece (Kim et al., 2019). Aesthetic experience can also be modulated by context and culture, such that people provide more favorable artistic judgments if presented with information about a piece’s cultural value (Kirk et al., 2009a). Differences in preference are evident in greater activity of the medial orbitofrontal and ventro-medial prefrontal cortex, pointing towards the role of memory and expectation. More recent work indicates that hedonic responses to specific architectural styles are associated with differences in anterior prefrontal cortex grey matter volume (Skov et al., 2021), further pointing towards the role of executive control on aesthetic experience. Knowledge about a building’s function may also influence aesthetic experience, with buildings devoted to more positively valenced purposes (e.g., an art museum or temple) likely to receive more positive judgments than negatively valenced ones (e.g., a prison or funeral home; Coburn et al., 2017). Other research suggests that expertise within a specific artistic or stylistic domain biases the ways in which a viewer attends to a stimulus (Seeley, 2013). Taken together, these findings speak directly to the knowledgemeaning component of the aesthetic triad. The upshot is that these factors vary widely across individuals. Person-to-person variability in familiarity, knowledge, memory, and expectation are all likely to moderate the ways in which they engage with—and respond emotionally to—the built environment. That is, the history of an individual’s exposure to a wide array of environments could result in profound differences how they view a particular architectural space. To illustrate this point more concretely, consider again the previously mentioned findings involving spatial navigation. Notably, grid cells encode more than visual features alone; they are also used to represent memories in a given environment (Eberhard, 2009). Thus, when people return to a familiar locale, they are likely to recall their emotional states during prior visits. Frustrating past encounters (e.g., trouble navigating a space) could plausibly lead one to develop negative feelings towards that space, or even other environments with shared physical features. In this way, past exposure mediates responses to a given environment (Eberhard, 2009). This perspective has been broadly supported by experimental neuroscience research; participation in a virtual spatial learning task was associated with increased resting-state functional connectivity between left posterior hippocampus and dorsal caudate, brain regions with well-established roles in spatial navigation (Woolley et al., 2015). Because the observed effects occurred at rest—that is, when participants were not in the virtual space—this study demonstrated that repeated engagement of brain regions for specific purposes may alter “baseline” functional connections between them (Wig et al., 2011). Perhaps experiences in a built environment have lasting ramifications on neural organization and future interactions in that space, as well as other similar environments. Other experimental work has demonstrated this concept more directly. In one study, architecture students showed lower “neural cost” when viewing buildings than students from other disciplines. That is, they recruited fewer brain regions upon repeated presentations of buildings (Wiesmann & Ishai, 2011). This is largely consistent with notions of fluent processing; given their expertise, the architecture students were able to process the architecture images more efficiently. In another study comparing architects and controls, architects exhibited greater engagement of the reward pathway when making aesthetic judgments about buildings

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(Kirk et al., 2009b). They also showed greater activation of memory structures, consistent with the theory that these differences are experience and/or memory based. The extent to which these effects are driven by top-down or bottom-up processes remains an open question, although results from other domains of empirical aesthetics suggests both are likely to play a role (Leder, 2013).

Health and wellness Last, a growing body of work has begun to indicate how specific design features of the built environment can influence psychological functioning and wellness. Exposure to nature appears to yield wide-ranging benefits ( Joye, 2007b), such as greater happiness (MacKerron & Mourato, 2013), improved attention (Berman et al., 2008; Bowler et al., 2010), enhanced creativity (Mehta & Zhu, 2009), and better working memory (Bratman et al., 2019). The use of brief exposure to natural environments may also improve outcomes in clinical populations (e.g., Beute & de Kort, 2018; Roe & Aspinall, 2011). Biophilic design—either by inserting greenery, allowing views of the outdoors, or incorporating low-level features prevalent in nature—may be one way to access these salubrious effects in human-made spaces. Additionally, some research suggests that lighting conditions influence mood and academic performance, although the results of other studies run contrary to these claims. Buildings that provide access to daylight may improve circadian rhythms and sleep quality (Dutton, 2014) and benefit student learning. However, excess natural light can also cause unwanted effects like glare if improperly designed. Research on spatial navigation is also relevant to wellness. For instance, circular rooms with rotational symmetry impede orientation efforts and can lead to higher levels of stress (Kelly et al., 2008). This adverse effect may be heightened when visual reference points and/or exterior views are absent (Passini et al., 2000). It is also clear that open and closed spaces differentially engage fear and emotional neural circuitry, with closed spaces associated with a host of negative emotions and increased stress (Brown et al., 2011; Fich et al., 2014b; Vartanian et al., 2013). Architects may be able to improve the mental health of inhabitants by designing buildings that are open and facilitate fluid navigation. Such designs may be especially useful in clinical populations such as people with Alzheimer’s disease who might be inclined to wander and be frustrated by barriers to movement (Passini et al., 2000). It is important to note, however, no one-size-fits-all framework will apply to how architecture can improve well-being. For one, we have highlighted ways in which individuals vary in response to architectural design. What works for one person may not work for all. Furthermore, responses to the environment can even vary within an individual. Such variability could stem from differences in the knowledge-meaning or emotion-valuation systems, but responses may also vary based on the function of the space itself. For instance, Graham and colleagues (2015) identified characteristics that people generally valued in home ambiances, such as invitingness, organization, and relaxation. However, they also observed a strong effect of room type. That is, participants endorsed different desired aesthetic qualities based on which room they were considering. Different parts of a building—or even different spaces within a single room—will be experienced differently. Assuming that pairing a room with its desired ambiance or aesthetic is beneficial for the inhabitants’ well-being, this research clearly indicates the need for adaptable and fluid design features. Consistent with this perspective, there is also evidence demonstrating that humans prefer settings with varied stimulus properties, especially those over which they are able to exert some amount of control. For instance, although neutral luminance levels are widely viewed as comfortable, stimulating environments characterized by varying levels of lighting are more favorable for productivity (Gou et al., 2014). Similarly, the ability to control the temperature of one’s environment is associated with greater feelings of comfort, and occupants with greater freedom to open and close windows in naturally ventilated buildings feel comfortable at a wider range of temperatures (Brager et al., 2004). Personal choice has been found to activate the extended reward network, with diminished activation for people showing depressive symptoms (Romaniuk et  al., 2019),

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consistent work showing increased stress and feelings of helplessness when exposed to uncontrollable environments (Evans & McCoy, 1998). Thus, designs that provide diverse kinds of stimulation and/or the ability for occupants to regulate sensory qualities may reduce feelings of stress.

Challenges and future directions Insights gained from the neuroscience of architecture can be used to improve models of how and why specific architectural features affect people in specific ways. However, as we have previously detailed (Coburn et al., 2017), a number of challenges must be addressed to more clearly establish how buildings can be constructed to improve human experience and well-being. Here, we briefly touch on these outstanding issues and highlight potential strategies to resolve them. Architectural spaces encompass a wide range of functions and settings. Thus, features that relate to aesthetic experience of the built environment may not be universally shared. There are also practical limitations based on physical setting; an architect cannot merely insert a large window that overlooks water or a forest into every building. Physical and financial constraints further limit potential design choices. The function of a space also cannot be overlooked, both in terms of the design features and the experiences of the inhabitants. For instance, the experiences of a patient in a hospital are distinct from those of a student in a school or someone inside their own home. Design elements that foster wellness are unlikely to be consistent across these different settings, which complicates the ability to apply findings from one set of stimuli to another or to make broad generalizations. Challenges also relate to the measurement of aesthetic experience. To date, the most studies of neuroaesthetics have used 2D images (oftentimes while participants lie horizontally in an MRI scanner). This approach, while practical, overlooks features such as scale and texture and introduces additional confounding factors associated with presenting works of art on a computer screen and in a loud machine. Experimental stimuli are also carefully selected and/or modified to control for potentially confounding variables like lighting and pixilation. While this approach makes it easier to identify the source of an observed effect (e.g., differences in beauty judgments are not related to the “crispness” of an image), generalizability becomes more difficult. These problems are further complicated for the study of architecture. The reasons for this are fairly obvious; buildings are three dimensional, immersive, interactive, and multisensory. There are also a wide range of contextual (e.g., outside noise, surrounding environment) and functional (e.g., hospital, museum, school) factors that cannot be adequately conveyed by images. Advances in virtual reality have the potential to mitigate some of these problems but are still unlikely to fully capture the multidimensional nature of architecture. Another challenge concerns temporal dynamics of aesthetic experiences. Repeated viewings of the same stimulus are associated with more positive appraisals (Bornstein & D’agostino, 1992); thus, spending more time in a building is likely to be associated with fluctuations in aesthetic judgments. Most research studies present participants with an image for only a few seconds, even though it is fairly well established that aesthetic experiences vary over longer durations (Chatterjee & Vartanian, 2014; Coburn et al., 2017). This raises issues both methodological (e.g., How long should participants view an image?) and theoretical (e.g., When can aesthetic experience be most accurately measured? Do we need to obtain multiple aesthetic judgments at different timepoints?). Perhaps the best way to address the previous issues is to move experiments out of the laboratory and into the “wild.” Rather than presenting people with images, data can be collected at actual buildings or structures. This approach has been successfully used to measure feelings of nostalgia at heritage sites (Prayag  & Del Chiappa, 2021). Experience sampling methods could be paired with this approach to obtain aesthetic judgments on different spatial and temporal scales. For instance, participants could respond to a series of prompts on their cell phones at specific times or locations. Further, thanks to recent advances in mobile EEG and 208

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fNIRS, researchers can pair these behavioral ratings with neural data across a wide range of settings. Mobile imaging techniques have provided novel insights about neural processing in art museums (Kontson et al., 2015) and collaboration in the classroom (Dikker et al., 2017) and are now being applied to architecture (Djebbara et al., 2021; see Chapter 17). While these data will be inherently “messier” and complicate the separation of neural signal from noise, results gained in the field may be more ecologically relevant than those collected in the laboratory.

Conclusion The neuroscience of architecture is a very young field that has witnessed significant progress over the past decade. In this chapter, we discussed the historical and economic context that led to the mass-standardization of architectural aesthetics in mid–20th-century Western cultures. We then explored how evidence-based design and research movements emerged as a reaction to these trends in building design and urban development. The neuroscience of architecture represents an important arm of evidence-based architectural research that focuses on understanding sensory, emotional, and psychological dimensions of human experiences in response to architectural design. Many subtle aspects of architectural and aesthetic experience were previously difficult to measure and validate using research methods of behavioral science and environmental psychology; the research tools of neuroimaging and cognitive science, by contrast, have enabled researchers to increase the magnification of their lens, as it were, and observe more closely the mechanisms of the mind and brain that mediate human-architectural interactions. These tools have also enabled researchers to move beyond descriptive and theoretical approaches in order to test specific hypotheses about how people perceive and respond to buildings. Among the most promising areas of research discussed in this chapter include explicit investigation of the aesthetic experience of built spaces; the potential benefits of biophilic design for stress reduction, wellness, and health; the importance of sensory features of spaces that facilitate wayfinding and navigation; and the probing of specific dimensions of emotional experience in the built environment—such as hominess, fascination, and coherence. We also explored factors of the individual that contribute to person-level variation in architectural experiences, including culture, expertise, and familiarity. These individual differences underscore the importance of considering the specific needs of prospective building occupants when designing a space rather than taking a one-size-fits-all approach. Yet a significant body of evidence also suggests that certain types of aesthetic experiences, such as sensory perceptions of biophilic design features, may be shared by many types of building occupants across different spaces. This research highlights the potential value of integrating evidence-based design principles into architectural education, training, and practice. In this way, the neuroscience of architecture stands at the frontier of architectural innovation and is poised to contribute to the design and construction of human-centered buildings and urban spaces.

Notes 1 See the section “Neuroscience of Architecture” for further discussion of fractal scaling and other natural patterns in architecture. 2 One such community, DeSoto-Carr in St. Louis, was a black neighborhood bulldozed in the early 1950s to make way for the infamous Pruitt-Igoe housing project. Designed in the classic Le Corbusian aesthetic, these three dozen monolithic high-rise towers soon degenerated into some of the most derelict and poverty-stricken buildings in America. All 33 buildings were demolished within 20 years of their construction, meeting the same eventual fate as hundreds of similar towers in Chicago, Philadelphia, and Baltimore, among other American cities. 3 Alexander’s projects were also an early model for the design-build method of architectural construction, which has gained popularity since the turn of the millennium. A design-build firm takes responsibility for all facets of planning, construction, and financing of a project, instead of dividing the work among subsets of specialists (i.e., architects, contractors, and sub-contractors).

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Coburn, Weinberger and Chatterjee 4 An oak tree is an intuitive example of fractal scaling found in nature. Self-similar shapes and colors can be found at many scales in the tree, including the large trunk, the medium-sized branches, and the smaller twigs. Even the tiny veins of an oak leaf echo the curvature and shape of the larger trunk and branches, creating a sense of self-similarity and unity across all scales of the structure.

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11 SEXUAL SELECTION, AESTHETIC APPRECIATION AND MATE CHOICE Michael J. Ryan

Darwin proposed a theory for the evolution of sexual beauty in animals—sexual selection (1859, 1871). Darwin’s emphasis was on one particular mechanism of sexual selection, mate choice modulated by what he called “a taste for the beautiful” (1871, p. 63). Darwin was also prescient about the role of underlying neural biases promoting the evolution of sexual beauty: When male animals utter sounds in order to please the females, they would naturally employ those which are sweet to the ears of the species; and it appears that the same sounds are often pleasing to widely different animals, owing to the similarity of their nervous systems, as we ourselves perceive in the singing of birds and even in the chirping of certain tree-frogs giving us pleasure. (1872, p. 91) It has taken more than a century and a half, but recently neuroaesthetics has become an important, although sometimes implicit rather than explicit, approach to understanding where all this beauty comes from. To explore these ideas, we first have to have an understanding of the fundamental tenets of Darwin’s theory of sexual selection.

Sexual selection Beauty abounds in nature (Figure 11.1). One type of natural beauty that for some of us, including Darwin, is the most enchanting includes the melodious songs of birds, the striking colours of coral reef fishes, and the nocturnal flashes of fireflies. These and many other exemplars of animal beauty share something in common: they are all recruited in the service of sex. The discussion of beauty in this chapter refers to animal sexual beauty. The peacock is something of a mascot for sexual beauty, and it is all because of his tail. He has 200 feathers up to 4 feet long. These feathers are adorned with eye-like spots and have an iridescent sheen that causes them to sparkle brilliantly in the sunlight. While courting, he develops an erection, and once those feathers are erect, he shakes, rattles, and rolls them, causing the tail to hum like an engine and his eye-spots to vibrate hypnotically. A true thing of beauty to most of us, including female peahens, but surprisingly not to Darwin. In 1860, he wrote to Asa Gray, a famous North American botanist, “The sight of a feather in a peacock’s tail, whenever I gaze at it, makes me sick!” How is it that Charles Darwin, the co-founder of the 218

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Figure 11.1 Examples of sexually selected traits. (from top left to right): A calling male túngara frog at a typical breeding site in central Panama. The male’s large vocal sac is as distinctive as his complex call (photo by Ryan Taylor). A male peacock erecting its tail feathers or train while courting a female. Although the tail is enchanting to females, it is this beautiful structure which, Darwin declared, made him sick every time he saw it (photo by Jyshah Jysha). The golden-headed lion tamarin is an endangered species found in lowland tropical forests in the state of Bahia, Brazil. They live in social groups in which both males and females care for the young and for juveniles. Little else is known about their mating system. It is considered by some the world’s most beautiful primate (photo by Steve Wilson). The red bird of paradise is a native of Indonesia. The males, one of which is shown here, are characterized by a pair of long tail wires. During courtship, these tail wires seem to outline the male in the middle of a heart (photo by Tim Laman). (from middle left to right): The quetzal is the national bird of Guatemala, and its image adorns the country’s coat of arms. Some consider the male resplendent quetzal the world’s most beautiful bird. My binoculars began to shake in my hands the first time I saw one (photo by Dominic Sherony). A male swordtail characin, right, a native of Trinidad, Tobago, Venezuela, and Colombia, extends its pectoral fin-ray with a piece of flesh that resembles a food item to the female, on the left. The female is attracted to this faux food item, at which time the male initiates courtship (photo by Nicolas Kolm). Male and female fireflies engage in spectacular nocturnal visual displays. As with many other courtship displays, the patterns of flashes are distinctive for each species. This image is a time-lapse photograph of synchronous fireflies from the Great Smoky Mountains National Park, near Elkmont, Tennessee (photo by Radim Schreiber). A  male hairy caterpillar extruding its hair pencils. The tubes, or coremata, are inflated by blood pressure, causing sex pheromones to be secreted through the hairs (photo by Rodney and Smudge Foster Rentz). A bee orchid pseudo-copulating with an orchid. Although this behaviour appears maladaptive, it makes perfect sense in the context of the bee’s strategy for finding females. As females are few and far between, it behoves the male bee to copulate with anything resembling a female (photo by Nicolas J. Vereecken). (from bottom left to right): Sexual selection often results in extreme differences between males and females. In many species, the male is more adorned than the female, as seen here in the collared lizard in which the more colourful male, top, is contrasted with the less colourful female, bottom (photo by A.K. Lappin). The peacock spider is a type of jumping spider; the male’s colourful display is reminiscent of a peacock. His beautifully adorned abdomen is only raised when the male courts the female, at which time he waves it back and forth in an invitation to mate (photo by Jurgen Otto). Guppies are known not only for their spectacular colours but also for incredible variation in those colours, especially among streams in Trinidad. Only a small sample of the striking variation is shown here (photo by Cara Gibson and Anne Houde). (From Ryan, 2018).

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theory of evolution by natural selection, could be turned off rather than turned on by such a magnificent sight? Charles Darwin and Alfred Wallace proposed natural selection as a mechanism that caused the evolution of traits that enhance the survivorship of their bearers. Three steps were necessary for this to happen: there had to be variation in the physical attributes of individuals, that variation had to result in some individuals having a higher survivorship than others, and there needed to be a genetic basis to this physical variation. In short, evolution by natural selection requires variation, selection, and heritability. So why such consternation over the peacock’s tail? Yes, a male peacock displaying his wares to a female is a magnificent sight to us and to his females, but when watching that same male trying to flee from a predator, he seems pathetic. The tail slows down the male whether he is running or flying, and it is clearly a detriment to survival. This is what concerned Darwin. If natural selection is a powerful force in nature, how to explain the evolution of traits, such as the peacock’s tail, that are clearly detrimental to survival? To reconcile the seeming contradiction, Darwin proposed another theory to supplement natural selection—sexual selection. The two most important components of fitness are survival and reproduction. You can’t reproduce if you don’t survive. But if you survive and don’t reproduce, you might as well be dead— at least from a Darwinian perspective. These types of sexually selected traits, the beautiful ornaments rather than the powerful armaments, are common throughout most sexually reproducing animals yet quite diverse in their form: songs of birds, crickets, and frogs; brilliant colours of butterflies, fish, and some primates; and striking odours of moths, deer, and human perfumes. These traits all seem to share some similarities: they are clearly maladaptive for survival, they are involved in sexual behaviour, and they are usually more extremely developed in males than in females, although there are quite a few exceptions (Rosenthal, 2017). Like natural selection, three steps are necessary for evolution by sexual selection: variation in traits, differential mating success due to these traits, and genetic variation underlying these traits. Thus some traits enhance survivorship, while other traits enhance mating success, and these two types of selection can impose conflicting forces on the evolution of a trait. The male peacock can evolve tails that become more and more attractive until the benefit of this elaborate trait is confounded by the costs that compromise survivorship.

Why males are often the sex with armaments and ornaments Darwin noted that it was usually males that have become elaborated under sexual selection. The underlying theory for this was not well developed until Trivers (1972) suggested his idea of parental investment, an idea that arose from experiments on fruit flies conducted by Bateman in the 1940s (1948). Bateman showed that male reproductive success increased with his number of matings, while this metric had no important effect on the number of females’ offspring. Despite a number of exceptions (Arnqvist & Rowe, 2005), a recent meta-analysis has shown strong support for this generalization from Bateman ( Janicke et al., 2016). But why this big difference in the effect of mating success on reproductive success? The answer all boils down to gamete size (Trivers, 1972). In many animals, the largest cell in the species is the female’s egg, and the smallest cell in the same species is the male’s sperm. The result is that males produce many more gametes than do females; in humans the difference is billions of sperm versus hundreds of eggs. The result is that the reproductive success of sperm is limited by the relative scarcity of eggs. Thus males are under selection to compete for access to females. This competition can take two forms: combat between members of the same sex or mate choice exerted by one sex on the other sex. Males have evolved a number of sexual traits that are used for combat with other males to gain access to females; these include claws, canines, antlers, and a myriad of other armaments (Emlen, 2014). Alternatively, males can evolve sexual ornaments. These are the traits of sexual beauty which Darwin and legions of naturalists find so enchanting. 220

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Why do females show preferences for sexual ornaments? Darwin’s suggestion that males evolved weapons for sexual combat was readily accepted by his contemporaries. But the idea that males evolved ornaments to enchant females was rejected by many, including his most ardent supporter, Alfred Wallace (Richards, 2017). There are several reasons this might have happened. Certainly, empowering females to make mating decisions ran contrary to then-current Victorian social mores (Cronin, 1991; Richards, 2017). But there were also some scientific objections, the main one being no cogent argument as to why females would evolve preferences for elaborate ornaments that would hasten the demise of the males that bore them. Of course, as noted previously, Darwin did provide an explanation, that of female aesthetics. But this was viewed as merely kicking the can down the road and begs the question of why females possess these sexual aesthetics. There are now a few theories, not necessarily mutually exclusive, that can explain the evolution of these female preferences (reviewed in Rosenthal, 2017; Ryan, 2018; Rosenthal & Ryan, 2022). In some cases, a male’s ornaments might indicate direct benefits to females, an increase in the number of offspring birthed. For example, male birds with brighter colours might have greater physical vigour and thus defend a larger territory with more food for the females. Alternatively, the same brighter colours might also indicate those males might have good genes that promote both greater vigour and survivorship, which the female would pass on to her offspring. This is classified as an indirect benefit since her reproductive success does not vary with the attractiveness of the male, but the offsprings’ later survivorship does. A tricky hypothesis, called runaway sexual selection, assumes that both sexes possess genes for male traits and female preferences for those traits but only express the gene appropriate for their sex. In this scenario, the preference genes and the colour genes become linked in the genome. The female preference causes the evolution of brighter male colours, and then the preference itself increases in frequency as it hitchhikes through the generations with the male colour genes. This is also sometimes called the sexy son hypothesis since the indirect benefit gained by the female derives from the production of more attractive male offspring. Darwin’s hypothesis was far less utilitarian. He suggested that females had aesthetic preferences, much like our own, and as the previous quote illustrates, males would evolve traits that the females found attractive. This is Darwin’s hypothesis of neuroaesthetics, which has been resurrected more than 100 years later under the rubric of sensory drive, sensory bias, and sensory exploitation.

Sensory biases and sexual beauty All of us species of animal reside in our own sensory world. Standing in the middle of a tropical forest, our senses are bombarded by a diversity of stimuli: the scents of numerous flowers, the flashing of fireflies, and the chirping of katydids. But we are anosmic, blind, and deaf to the dense cloud of moth pheromones, the ultraviolet signage on flowers that advertise to pollinators, and the echolocation calls of the bats swirling all around us. In the late 1800s, Jacob von Uexküll described these sensory worlds as the animal’s Umvelt. This is now one of the fundamental guiding principles of sensory ecology, and in the last decade, it has finally infiltrated how we think about sexual beauty (Ryan, 2011; Caves et al., 2019). There is no beauty without a brain, just as there is no sound when a tree falls in the forest if there is no ear to hear it (Ryan, 2018). Courtship traits are only salient if they can be detected and perceived by choosers. We must have some understanding of the species’ perceptual and cognitive biology to understand why it is that some courtship traits are more attractive than others. In a sense, we need to view these traits through the eyes, ears, and nares, or better yet the brains, of the beholders. Nevertheless, there are a few generalities of what sexual traits are found more attractive. In hundreds of cases, choosers preferred sexual traits of courters that were greater in magnitude: song and call amplitude and complexity, tail length, colour contrast, and quantity of odours are only a few of the examples (Ryan & Keddy-Hector, 1992; Andersson, 1994; Rosenthal, 2017). 221

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In the 1990s, studies of sexual beauty started to show a shift in emphasis from the potential information sexual traits might transmit to the choosers to understanding how those traits interacted with the chooser’s brain. Several similar hypotheses emerged almost simultaneously: sensory traps (West-Eberhard, 1979), sensory biases (Endler, 1992), pre-existing biases (Basolo, 1990b), and sensory exploitation (Ryan, 1990). The similarities and differences among these hypotheses have been described elsewhere (Endler, 1998; Ryan & Cummings, 2013; Cummings & Endler, 2018); they all combine to point out the importance of the environment and the brain on the evolution of sexual traits and preferences for these traits. Interestingly, many of the sensory, perceptual, and cognitive biases that lead to trait preferences are domain general and not specific to the task of mate choice. In many cases, courters evolve traits that exploit pre-existing or hidden preferences in choosers. Although olfactory, electrical, and tactile stimulation are all quite important in sexual behaviour (Rosenthal, 2017), I will restrict this discussion to the two modalities in which we know the most about sexual attraction: sights and sounds.

Visual beauty The sensory end-organs, eyes, ears, olfactory, electrical, and taste receptors, are the portals by which environmental stimuli enter the animal’s sensory, perceptual and cognitive systems. Responses of all sensory end-organs to external stimuli are nonlinear. All sense organs respond more to some stimuli than they do to others. Traits that elicit substantial stimulation of end-organs are not necessarily considered sexually attractive, especially if they have nothing to do with sex (Rosenthal, 2018). Nevertheless, there is often a tight fit between the tuning of sensory organs and the properties of sexually attractive traits. Many studies of sensory biases focus on the sensory receptors, and many of these studies are conducted with fish. Cummings and Endler (2018) suggest this is probably because fish reside in environments in which the ambient light conditions can vary substantially among habitats. This will result in strong selection on photoreceptors to be able to detect food items in a given light background that, in turn, will result in males evolving courtship traits that match the tuning of the photoreceptors. Cummings (2007) has shown that this is exactly what happened in surf perch in the kelp forests off the coast of California. Variation in background light might also play a role in the spectacular burst of speciation in cichlids in the great African Lakes. Seehausen and his colleagues (2008—see also Maan et al., 2006) have shown that eutrophication and water depth contract the visible light spectrum, which also influences photoreceptor tuning, which in turn influences female mating preferences based on male colour. This interaction of ambient light environment, photoreceptor tuning, and mate choice preferences conspired to influence the rate at which speciation takes place in these animals. Guppies, Poecilia reticulata, are small brightly coloured fish that are well known in the pet trade, but their natural environment is in streams and small rivers on the island of Trinidad. They are one of the most variably coloured of all vertebrates, and this variation seems to result from an interaction of predation pressure and female preferences for more colourful males, especially males exhibiting more orange (Houde, 1997). Not only does the amount of male orange colouration vary amongst populations, but so does the female’s strength of preference for orange (Endler & Houde, 1995). Where in the sensory system does this preference come from? Oranges and yellows that adorn the skin of many animals are usually due to carotenoid-based pigments, and most animals cannot synthesize carotenoids but must obtain them from the environment, usually by feeding on small invertebrates. The classic explanation of the female guppy’s orange-based preference was that orange was an “indicator” trait; it indicated to females the foraging ability of males. By being attracted to males with more orange, the argument went, females might be mating with males with genes that promoted foraging abilities (Endler, 1983).

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Archer et al. (1982) used microspectrophotometer studies to show that the spectral sensitivity of longwavelength cones varied substantially among individual females, and they suggested that this variation in spectral sensitivity contributes to the variation among females in their preference for orange colouration. More recently, Sakai and colleagues (2018) showed that differences in longwave-sensitive opsin genotypes interact with variation in the light environment to result in variation in opsin gene expression. They also showed that the expression of multiple opsin genes correlates with the strength of female preference for orange males. Thus the aesthetic preference for orange males, like many preferences for beautiful traits, can be a bit idiosyncratic, and in these fish, this can be linked to variation in gene expression of the photopigments that determine visual sensitivity. Just because a preference for orange is exhibited in the domain of mate choice, it does not necessarily mean that this preference is domain specific. In fact, Rodd and her colleagues (1999) presented compelling evidence that this preference originated in the foraging domain and then had pleiotropic effects on colour preference for mates. These authors noted that guppies often feed on small orange fruits that float on the water’s surface. They presented guppies from different populations a series of chips of different colours and measured the amount of time that both males and females spent inspecting the chips. They showed that both sexes exhibited a preference for orange, and the strength of the preference for orange varied with the strength of the female preference for orange males in that population. If the preference for orange had evolved to promote mate choice, there is no reason to think that males would show the same preference for inanimate orange objects that females express. Bourne and Watson (2009) showed similar results with another closely related fish, Poecilia parae, in the same genus as guppies. Furthermore, Cole and Endler (2015) conducted artificial selection experiments in guppies and showed a response to selection for sensitivity to red wavelengths related to food after only five generations. The heritabilities (h2; the proportion of phenotypic variation explained by additive genetic variation) in the two lines tested were 0.25 and 0.30. As with the previous studies by Rodd et al., there is no difference between the sexes in their response to selection; thus, the evolution of red sensitivity probably has nothing to do with mate preference, but it is assumed it would influence mate preference as a pleiotropic effect. So we know that female guppies prefer males with more orange, there is variation in the strength of this female preference, this preference variation seems to be related to variation in photopigment sensitivity and gene expression, sensitivity to red has a heritable genetic component, and that this chain of related phenomena probably evolved to enhance foraging for orange fruit with the incidental consequence that orange males prove more attractive. One final question is when did this preference for orange evolve? Clearly it did not evolve only in guppies, since one of their close relatives expresses the same preference. How far back in time and how deep in the phylogenetic history of these fishes was there this general preference for orange? Foisy (2017) collected male colouration data for 232 species of poeciliids, the family in which guppies resides, as well as some closely related species. He used some sophisticated phylogenetic analyses that predicted orange tended to evolve in males when there is evidence of a pre-existing bias for the colour orange. Using this phylogeny, he identified 14 species lacking orange but for whom it was predicted there should be a hidden preference for orange. Ten of the 14 species exhibited this hidden preference. The pre-existing preference might be even more widespread, as Spence and Smith (2008) showed that zebrafish have a preference for red fish even though this species lacks red. Therefore, not only do guppies prefer orange because they evolve the preference for orange fruit, but this general preference for orange and also for red seems to be true of many fish, including those in which males lack orange or red courtship colours. These preexisting preferences set the stage for the rapid evolution of these courtship colours. A male in an otherwise bland species that evolves orange or red colouration will have a ready appreciator in the audience of females. Some studies also suggest that there can be pre-existing preferences for the existence and size of ornaments in fish. Basolo (1990a) studied two types of fish in the genus Xiphophorus, swordtails and platyfish.

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Although it has been difficult to resolve in detail the precise phylogenetic relationships within this genus (Cui et al., 2013), in general it is thought that swordtails are one monophyletic group and the platyfish another monophyletic group. The swordtails have extended caudal appendages, the swords, and females show preferences for swords and sometimes for longer swords (Rosenthal et al., 2001). The platyfish lacks swords. This suggests that swords evolved at the base of the swordtail clade after this clade diverged from the platyfish clade. By attaching plastic swords to male platyfish, Basolo showed that females had a pre-existing preference for swords even though their males lacked swords. This pre-existing preference even extends outside of this genus into some closely related genera (Basolo, 1995). Gould (1999) came to a similar conclusion about the ubiquity of hidden preferences with a different set of experiments. Mosquitofish, Gambusia, lack ornamented males, who mostly rely on forcing copulation with females. The researchers presented females with a model of a typical male compared to tens of other models that had exaggerated fins, the addition of swords, and various kinds of patterning. In many of these cases, females preferred models with these novel traits to the model of the typical male. Thus the lack of ornamented males in this species is not due to lack of interest by females but lack of evolutionary innovation by males. The females have a pre-existing bias for a whole range of ornaments.

Sounds of beauty Compared to eyes, ears have not been as rich in the insights they provide into sexual beauty, despite the fact that acoustic sexual displays are widespread throughout the animal kingdom. An exception is the mating calls of frogs and the inner ears that respond to them. There are about 7000 species of frogs, most of them produce loud and conspicuous mating calls, and each species has its own unique call. There is strong selection on female frogs to mate with males of their own species: mating with a male of another species rarely produces viable offspring. This species recognition function is facilitated by a pair of matched filters in the frog’s inner ear (Gerhardt & Huber, 2002). Unlike birds and mammals, frogs have two inner ear organs that are sensitive to airborne sounds, the amphibian papilla (AP) and the basilar papilla (BP). There are a number of differences between these two end-organs, the most critical one being that the AP is most sensitive to lower frequencies, usually below 1500 Hz, and that the BP is tuned to higher frequencies, usually above 1500 Hz. The tuning of one or both of these end-organs matches the distribution of spectral energy in that frog’s mating call. The calls of some frogs have only lower emphasized frequencies, which match the AP; others have only higher emphasized frequencies, which match the tuning of the BP; and some frogs have a pair of emphasized frequencies, one of which matches the AP and one of which matches the BP (Gerhardt & Schwartz, 2001; Figure 11.2). The túngara frog has a mating call that is about 300 ms in duration, sweeps from a high to low frequency, and has a dominant frequency of about 700 Hz—this is the whine. The whine is both necessary and sufficient to elicit a sexual response from the female, but males can make their calls more attractive by adding chucks, a sound that is about 35 ms in duration, with a large number of harmonics, and a dominant frequency around 2200 Hz. Males can add up to seven chucks to their whine (Ryan et al., 2019; Figure 11.2). The males have an unusual larynx that permits these two call components that are acoustically so different from one another. The whine results from vibration of the vocal cords, as most frog calls are produced, but the chuck results from vibration of a relatively large fibrous mass that hangs from the vocal cords (Griddi-Papp et al., 2006; Kime et al., 2019; Figure 11.2). There is congruence between the whine’s dominant frequency and AP tuning and the chuck’s dominant frequency and BP tuning (Ryan et al., 1990). Immediate early gene studies have shown that the simultaneous stimulation of both inner ear organs results in greater neural activation, as estimated from gene expression levels, in the frog’s main auditory nucleus, sensorimotor areas, and in some brain nuclei that are part of the mesolimbic reward system (summarized in Wilczynski & Ryan, 2010). The whine-chuck call is five times more attractive than that same whine by itself. Unfortunately for the calling 224

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Figure 11.2 (A) The túngara frog has an unusual larynx characterized by a large fibrous mass (FM) that protrudes from the vocal cords (VC). (B) Comparative studies (Ryan, 1990), biomechanical models (Kime et al., 2019), and ablation of the fibrous mass (Griddi-Papp et al., 2006) all show that the vocal cord vibration is primarily responsible for the whine (blue circle) and the vibration of the fibrous mass for the production of the chuck (red circle). (C) The dominant frequency of the whine, about 750 Hz, matches the average most sensitive frequency of the amphibian papilla (AP), while the dominant frequency of the chuck, about 2500 Hz, is a close match to the average most sensitive frequency of the basilar papilla (BP), the location of these two sensory end-organs in the inner ear are indicated by the blue and red arrows, respectively (Ryan et al., 1990). (D) Information from the two inner ear-organs enter the brain via the VIIIth cranial nerve. As the information ascends through the brain it is processed in the sensory, sensorimotor, and motor areas of the brain and results in movement of the female to the call, i.e., phonotaxis (see details in Wilczynski & Ryan, 2010). (E) Stimulation of both inner ear organs by the whine-chuck results in a fivefold increase in the attractiveness of the call compared to the whine only (Ryan et al., 2019). Females prefer males making complex calls; they choose these males as mates; and together with the male, they construct a foam nest with approximately 250 fertilized eggs (Ryan, 1985) (from Ryan, 2021).

males, chucks also increase the male’s attractiveness to frog-eating bats by about the same proportion (Tuttle & Ryan, 1981; Ryan et al., 1982, 2019). All frogs have both an AP and a BP, even if one of those inner year organs is not used in communication. There is only one relative of the túngara frog that facultatively adds a chuck-like syllable to its whine-like introductory call (Boul et al., 2007). The other species all have only whines, and the dominant frequencies of their whines match their AP tuning. None of these frogs have call frequencies that would substantially excite the BP. Interestingly, almost all of these frogs have BPs of the same tuning (Wilczynski et al., 2001). Combining the neurophysiological data on the auditory system with the phylogeny of these frogs and the calls they produce shows that túngara frogs evolved chucks to match a pre-existing bias in the female’s auditory system; that is, the tuning of the BP (Figure 11.2). 225

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The matched filter hypothesis of anuran acoustic processing (Capranica, 1965; Capranica & Moffat, 1983) predicts a tight fit between the response properties of the auditory end-organs and the acoustic properties of the species-specific advertisement call. A similarly tight fit between a neural template somewhere in the central nervous system and acoustic properties of songs is made about song recognition in birds (Marler, 1997). But that fit might not be so tight. The permissive aesthetics that Gould et al. uncovered in female mosquitofish has also been demonstrated in túngara frogs. Recognition of and attraction to the whine appears not to be permissive. Substantial change to the whine will disrupt its saliency (Rand et al., 1992). The female aesthetics for the chuck, however, is quite different. Ryan et al. (2007) showed that dozens of sounds attached to the whine in place of the chuck are as attractive as the chuck. Interestingly, these studies did not reveal any supernormal stimuli, that is, syllables that are more attractive than the chuck (Ryan et al., 2007). Thus the sexual potency of the chuck—remember that it increases male attractiveness fivefold—is not unique to this particular sound. A  variety of other sounds would have been just as effective in increasing the male’s sexual beauty; it is just the chuck that happened to evolve first. In game theory jargon (Maynard Smith, 1982), the chuck is an evolutionarily stable strategy in that once established in the population, it cannot be invaded by other types of calls since they are as attractive but no more attractive than the chuck. Crickets also exhibit hidden preferences for some call characteristics that have yet to evolve. Gray et al. (2016) showed that two species of field crickets that produce slow pulse rates have hidden preferences for faster pulse rates than exist in their species. The songs of songbirds are much more complex than the calls of frogs or the chirping of crickets. Although there are some exceptions (Feng et al., 2002), the acoustic repertoires of frogs and crickets usually consist of only a handful of different components or syllables (Gerhardt & Huber, 2002). Song repertoires in birds, however, can be quite extensive, and many studies have shown that female songbirds find larger song repertoires more sexually attractive (Catchpole & Slater, 2003). Hartshorne (1956, 1973) suggested the monotony threshold hypothesis to explain the evolution of song repertoire. The hypothesis speaks to territorial interactions between males but, as we will see, is also applicable to female preference for song repertoire. He suggested that if the male produces a simple song defending his territory, then eventually his neighbours will habituate to that song and aggressive interactions will occur. Habituation is less likely when a male produces more and different syllables in his songs. There is support for the monotony threshold hypothesis both in the context of male–male interactions (Kroodsma, 1978) and female song preferences in songbirds (Searcy, 1992; Catchpole & Slater, 2003; Lyu et al., 2016). Moreover, there is some understanding as to how increased diversity within a song repertoire influences the underlying neural and genetic bases for song preferences. Studies of zebra finches and canaries, both of which habituate to repetition of identical song syllables, show that both electrophysiological responses and gene expression also habituate to the same repeated song stimuli (Stripling et al., 1997; Dong & Clayton, 2009). In addition, the molecular changes that are involved when birds go from experiencing silence to novel calls to habituation are extreme. Exposure to novel songs results in changes in expression of thousands of RNAs, while habituation leads to a very different gene expression profile. We have now begun to understand the behavioural, neurophysiological, and gene expression changes that occur when birds become bored with a male’s song (Dong et al., 2009).

Music It is tempting to draw comparisons between birdsong and music (e.g., Taylor, 2017). A mascot for this relationship is the story of Mozart’s starling, which was not only a pet but also a muse for the composer (Haupt, 2017). Both song in birds and music in humans are known to modulate emotions. Centuries ago, Christian

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Schubart described the emotional correlates of different musical keys in his Ideen zu einer Ästhetik der Tonkunst (translation by Steblin, 2002): D Major, The key of triumph, of Hallelujahs, of war-cries, of victory-rejoicing. Thus, the inviting symphonies, the marches, holiday songs and heaven-rejoicing choruses are set in this key; D Minor, Melancholy womanliness, the spleen and humors brood; F# Minor, A gloomy key: it tugs at passion as a dog biting a dress. Resentment and discontent are its language, A♭ Major, Key of the grave. Death, grave, putrefaction, judgment, eternity lie in its radius. And finally, getting closer to our own concerns, A Major, This key includes declarations of innocent love, satisfaction with one’s state of affairs; hope of seeing one’s beloved again when parting; youthful cheerfulness and trust in God; B♭ Major, Cheerful love, clear conscience, hope, aspiration for a better world. (from Ryan, 2018, pp. 102–103) Barlow and his colleagues (Mitchell et al., 1998) took a more experimental approach to demonstrating how music can influence physiological aspects of sexual motivation. The researchers exposed men to what was described as happy or sad music prior to watching a pornographic video. The subjects then self-reported their sexual arousal, and their penis tumescence was measured. Both were more elevated in men who were exposed to the happy music compared to the men who were exposed to the sad music, even though both groups of men were viewing the same video. The effect of music can reach far beyond the sex organs deep into the recesses of the brain. We know that music elicits responses from various nuclei in the mesolimbic reward system, as does the male song in zebra finches (Maney, 2013; Spool & Riters, 2017, see the following). Blood and Zatorre (2001) showed that PET scans detected increased blood flow to various regions of the reward system when the subject reported the sensation of “shivers down the spine” or “chills” when listening to certain musical pieces. These studies were confirmed and extended by Menon and Levitin (2005) with fMRIs, which provide more resolution than PET scans (see also Chapters 7 and 15). Investigating the neural underpinnings of hedonic pleasures of music is now a major venture in cognitive neuroscience that is revealing a detailed understanding as to how various parts of the brain respond to music. For example, Kim et al. (2019), also using fMRI studies, documented the interaction between spectral and temporal properties of music throughout neural circuits in the frontal cortex. It seems clear that there will be even more fascinating details of the neural architecture underlying music appreciation as neuroscientists continue to dig even deeper.

Cognitive biases and sexual beauty It is somewhat arbitrary to draw a distinction between an animal’s sensory system and its cognitive system. In cognitive ecology and cognitive ethology, the two fields that apply notions of cognition to animals, the definition of cognition is usually the acquisition and analysis of information to inform decision-making (Shettleworth, 1998). Sensory ecology in general, and sensory investigations into the basis of sexual attraction specifically, have usually grown out of an interaction between neuroethology and behavioural/evolutionary ecology. But much of these fields also draw inspiration from studies of human cognition (Vasconcelos et al., 2015). For example, many animal studies are interested in nonlinearities and are modelled on studies of human psychophysics (e.g., Shepard, 1987). Since mate choice often prefers sexual traits that are greater in magnitude, the relationship between magnitude and preference has important implications for how evolution might proceed.

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Comparisons in assessments of sexual beauty Just as sensory systems show nonlinear responses to changes in stimulus magnitude, cognitive evaluations of difference in magnitudes between stimuli can also be nonlinear. An example of this is Weber’s law. We know in humans that the magnitude of difference between stimuli to allow a just noticeable difference ( JND) follows a power law such that the greater the magnitude of the stimuli, the greater the difference between them is needed for a JND. Animal studies have adopted the notion of a JND into a more naturalistic concept of the JMD, the just meaningful difference (Nelson & Marler, 1990). We know that in a variety of tasks, such as foraging decisions, the JMD follows a Weber function (Akre & Johnsen, 2014). If the same were true for traits involved in sexual attraction, this would have important consequences for the tempo of evolution of these traits. As noted previously, túngara frogs can add chucks to the basic whine component of their mating call to increase their attractiveness. Although a single chuck increases the attraction of the call fivefold, additional chucks further increase call attractiveness. Akre et al. (2011) asked if the sexual attraction of these complex calls increased linearly with additional chucks or if, alternatively, they followed a Weber function. The results were quite clear. Additional chucks did increase attractiveness, but the relationship between attractiveness and chuck number followed a Weber function. The difference in attractiveness of calls with two versus one chuck was much greater than the difference in attractiveness between calls with five versus four chucks, for example. Also, as noted previously, frog-eating bats are attracted to the túngara frog call and are more attracted to calls with chucks than without chucks. Interestingly, this animal with a very different auditory system and a much more complicated brain than the túngara frog followed a statistically identical function in their attraction to calls with more chucks. Thus whether it is frogs searching for a mate or bats searching for a meal, the attraction of more complex calls followed similar power functions. We know that in sexual selection, choosers can exert strong directional selection on traits to become more attractive; usually the traits respond to sexual selection by evolving an increase in some component of magnitude, conspicuousness, or complexity. But sexual selection is opposed by natural selection since these traits also have higher costs, whether they be metabolic, developmental, or due to predation risk. The study of the túngara frog shows that given the power function underlying female attraction to these calls, there might also be a cognitive brake on the evolution of more attractive traits. As the peacock tail gets longer, it requires an even greater increment in size to become more attractive to its competitors. Studies of animal decision-making also draw inspiration from what economists sometimes call irrational behaviours and have been especially influenced by prospect theory as developed by Tversky and Kahneman (e.g., Tversky & Kahneman, 1989, 1992; Kahneman & Tversky, 2013). This latter cross-fertilization comes about because both behavioural economics and evolutionary biology predict that individuals should behave in a manner that maximizes some utility, monetary or happiness gains in humans, and Darwinian fitness gains in animals. Irrational behaviours are therefore interesting either as test of the fundamental assumptions in both of these fields or, more commonly, examples of the exceptions that might prove the rules (Vasconcelos et al., 2015). One of the major assumptions of economic rationality is that utilities assigned to different objects are not influenced by irrelevant alternatives. For instance, an individual is given the following choice: a trip to Paris versus a trip to Rome, both with a free breakfast included. She may not show a strong preference between the two. If a third option is added, a trip to Rome without a free breakfast, then the trip to Rome with a free breakfast quickly becomes the preferred option (Ariely, 2009). This is considered a violation of economic rationality because the relative preference between two options should not change in the presence of an irrelevant third option. Numerous studies have shown animals are susceptible to the influence of irrelevant alternatives in the context of foraging, but only recently have these ideas been applied to female decisions based on a male’s

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sexual attraction (Bateson & Healy, 2005). The túngara frog again was a useful model for addressing socially induced variation in perceived attractiveness. Lea and Ryan (2015) identified three natural mating calls of males that differed in static attractiveness, A, B, and C. They experimentally manipulated the call rate of each of these calls (females are usually more attracted to calls produced at a more rapid rate). Thus, the three calls varied over two dimensions of attractiveness: static attractiveness and call rate. Preliminary tests showed that call C was the least attractive. In one set of phonotaxis experiments, there was no significant preference between calls A and B in a binary choice test, but when C was added in a trinary choice tests, there was a significant preference for call A. In another set of experiments, there was a preference for call A versus B, but when call C was added in a trinary choice test, the preference for A vanished. It is an interesting paradox that what one finds attractive can be influenced by what one finds unattractive. Weber’s law and departures from regularity add an interesting twist to understanding what goes on in an animal’s head when it is making decisions about sexual beauty. It seems the further we get from the sense organs, the more complex the interpretations.

Perceptual fluency and processing biases Revisiting von Uexküll, we must remember not only that different animals can have different perceptions of their environment but their environments can also have important effects on how they perceive and extract meaning from the world around them. Inevitably, some objects will be perceptually processed more easily than others. Recent studies suggest that ease of perceptual processing may deliver hedonic rewards, potentially revealing the biochemical basis of appreciation of some types of beauty (for further discussions of this literature, see also Chapters 6 and 26). Reber at al. (2004) argued that in humans, ease of processing, or perceptual fluency, can result in aesthetic pleasures. They make several points. First, they argue that objects will vary in the fluency with which they are processed. Interestingly, features that are highlighted by objectivist theories of beauty, such as symmetry and contrasts with background, also enhance perceptual fluency. The second point the authors make is that processing fluency is, in their words, “hedonically marked”; that is, interacting with such object results in aesthetic pleasure, as has been illustrated in psychophysiological studies (Reber et al., 2004). Renoult and Mendelson (2019) extend this idea of perceptual fluency to mate attraction. They suggest that preference for certain sexual traits can be emotionally rewarding if those traits exploit a processing bias due to efficient information processing. This idea of a processing bias is an important extension to theories of sensory and perceptual biases in mate choice (e.g., Ryan & Cummings, 2013). The first test of this general idea that perceptual fluency is related to attractiveness was addressed in a study of human facial attractiveness by Renoult et al. (2016). The type of perceptual fluency studied by these authors was efficient neural coding, specifically sparse coding. Sparse coding is the representation of an object by the activation of a relatively small number of neurons—the more sparse the coding, the more efficient it is. Renoult et al. modelled the activity of simple cells in V1, the primary visual cortex, of humans, as it processed images of women’s faces. They found that the sparseness of coding in the model was significantly and positively correlated with men’s evaluations of the attractiveness of these same faces. The sparseness of the code predicted 17% of the variation in the attractiveness scores given by men. The authors also point out that sparse coding in the primary visual cortex is not an adaptation for spatial facial recognition but probably evolved to solve the more general problem of extracting patterns from complex natural scenes. If sparse coding in visual systems evolved to analyse natural scenes, then the correlation between sparse coding and facial attraction suggests that some aspects of facial phenotypes might have evolved to exploit sparse coding that was already in place for its more general domain function. This hypothesis fits well with studies of the composition of natural scenes and the patterns of letters utilized in different languages.

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Changizi and his colleagues (2006; Changizi, 2010) posit that the shapes most commonly used for letters across languages should be drawn from patterns that are most abundant in natural scenes. They analysed 93 speechwriting systems and found that the average number of strokes per letter was three. This was a fairly close match to the average in natural visual scenes. They also showed, quite convincingly, that there is an almost perfect correlation between the use of 19 visual patterns in these alphabets and in natural visual scenes. This study highlights an interesting interaction between the environment and culture. The environment selected for processing biases that could extract patterns from natural scenes; later in human evolution, culture resulted in different groups using these patterns in their writing systems. This scenario has striking similarities to sexual selection by sensory exploitation (Ryan, 1990; Verpooten & Nelissen, 2012; Renoult & Mendelson, 2019). Returning to beauty, there seems to be an interesting relationship between patterns in natural scenes and artists’ portraits of human faces. Natural scenes are sometimes characterized as scale invariant. Redies et al. (2007) showed that artists often enhance the attractiveness of human faces, which are not scale invariant, with patterns that approximate the scale-invariant properties of natural scenes. Again, this might be another example of a cultural expression being shaped by a domain general processing bias. Mendelson and her colleagues (Hulse et al., 2020) apply a similar rationale to the evolution of patterns of male courtship traits in a group of fish called darters. As with other studies, they use Fourier analysis to decompose spatial patterns in natural scenes where the fish reside to the spatial patterns across the bodies of both males and females. The slope of the log-log plot (power as a function of frequency) of the Fourier spectrum was used to characterize this pattern (Hulse et al., 2020). They showed that there was strong concordance between the habitat statistics and the male patterning statistics but not the female patterning statistics. They argued quite sensibly that camouflage was an unlikely explanation for these results; if that were the case, one would expect both males and females to be equally advantaged by camouflage. Instead, they argue that by matching the habitat statistics these male patterns were more attractive to females because of the hedonic pleasure of perceptual fluency. The previous studies suggest that our visual processing systems have evolved to extract information from natural visual scenes and that letters in various alphabets, portrait paintings, and patterns of courtship traits in some fish can all be explained to some degree by their match to natural scenes. Can the same argument be applied to natural acoustic scenes? Such an approach was adopted by Purves and his colleagues (Schwartz et al., 2003; Bowling & Purves, 2015). Much as Changizi showed how natural visual scenes influenced the details of a cultural adaptation, letters in alphabets, Purves et al. asked how natural acoustic scenes influenced universals in another cultural trait, music (Mehr et al., 2019). These authors pointed out that the dominant acoustic scene of humans was human speech. The frequency spectrum of human speech sounds results from an interaction of the spectrum at the source, which generate triangle waves which are then filtered by the resonance of the vocal tract. The researchers normalized the amplitude of all the frequencies to that frequency with the greatest amplitude. The resulting Fourier transform illustrates frequencies as ratios relative to this loudest frequency (Figure 11.3). The resulting spectra were almost identical for speech in the four languages that they examined. Sound frequency is a continuous variable, but in musical scales, these frequencies are categorized into discrete tones. In Western music terminology, the chromatic scale has 12 tones within an octave (Figure 11.3). Each of these tones can be assigned a frequency ratio such that the unison tone by definition is 1.0, while the fifth is 1.5 and the octave is 2.0. When one superimposes the ratios of these notes onto the power spectrum of human speech, there is remarkable concordance (Figure 11.3). Thus the tones used in the chromatic scale, as well as other scales, were chosen to match the power output of human speech sounds. The acoustic structure of speech sounds not only predicts musical tones but also our preference for such tones. Studies of the consonance of two-tone notes have been conducted over more than a century and show 230

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231 Figure 11.3 (A) A power spectrum of human speech normalized to the frequency with the greatest amplitude (B) A portion of a piano keyboard indicating the chromatic scale tones over one octave, their names, and their frequency ratios with respect to the tonic in one of the major tuning systems, “Just Intonation,” used in Western music. (C) The majority of the musical intervals of the chromatic scale (arrows) correspond to the mean amplitude peaks in the normalized spectrum of human speech sounds, shown here over a single octave. (D) Consonance rankings of musical dyads as a function of the normalized spectrum of speech sounds (from Schwartz et al., 2003).

Michael J. Ryan

repeatable results. For example, the octave, the fifth, and the fourth are quite pleasing to our ears, while the major second, the major seventh, and the minor second are less so. When one compares the normalized amplitude of the higher of the two tones in the musical dyad to its consonance ranking, there is also a strong correlation (Figure 11.3). It is not clear how this finding applies to non-Western music cultures. This concept of perceptual fluency is a new one in addressing animal sexual beauty. It loans further credence to Darwin’s idea of animal aesthetics by suggesting that choosers might be attracted to the traits of some courters for the mere pleasure it delivers to them, perhaps similar to why we might be attracted to certain pieces of art or music.

Incentive salience in mate choice decision-making Rosenthal (2018) correctly pointed out that sensory, perceptual, and cognitive biases can be important components of the chooser’s preference for attractive sexual traits, but these are not sufficient to account for an animal’s sexual aesthetics. A bright red cap on a courting male bird might be sexually attractive to a female, but a bright red band on a coral snake would elicit aversion from the same bird even if the stimulation of long wavelength cones were identical in both contexts. He points out that there are a variety of evaluating mechanisms that can result in positive, neutral, or negative hedonic value to a stimulus. Further, these evaluative mechanisms can vary among and within individuals at any point in time, and these differences can be modified by genetic, environmental, or internal physiological mechanisms. One of these evaluative mechanisms that is quite important in hedonic evaluation is the mesolimbic reward system (see Chapter 2). Lynch and Ryan (2020) recently discussed the potential role of incentive salience in sexual selection by mate choice per se, not merely its more general involvement in reproductive behaviour. This section borrows heavily from that review. In the context of an animal’s sexual aesthetics, we are interested in the role of dopamine in modulating the animal’s “wanting” rather than “liking” or “learning” of a stimulus. This distinction was illustrated by studies of rats in which Berridge and colleagues showed that dopamine antagonists did not influence the animal’s liking of a food reward but did influence the degree that the animal would work to obtain such a reward, that is, the degree to which the animal wanted the food (reviewed in Kringelbach & Berridge, 2012). Berridge (2007, 2019; see also Chapter 3) reviews evidence for the roles of dopamine in liking, learning, and wanting and suggests that the evidence strongly supports a role in modulating wanting. Dopamine assigns incentive salience to an object or action. If we apply this role of dopamine to the context of mate choice, it can result in reward-seeking behaviour toward sexual signals of some males over others. Dopamine alone, however, is likely not sufficient to stimulate reward seeking toward sexual signals, as a sexually motivational state must first be initiated by reproductive steroids such as oestrogen. In this case, once oestrogen (and other reproductive steroids) heightens a female’s interest in sexual stimuli, dopamine may act to “mark” salience of signals and stimulate pursuit of those signals. It is doubtful that dopamine alone, without elevated reproductive steroids, would play this role in sexual behaviour. For example, in the few studies that examine dopamine’s role in incentive salience during sexual decision-making, the animals tested were either treated with exogenous steroid hormones or peptides that increase steroid hormones, or the animals were in a natural breeding condition, indicating they had elevated reproductive hormones. Lynch and Ryan (2020) reviewed three types of studies that speak to the potential role of dopamine in mate choice: (1) dopamine agonist or antagonist administered to females during mate choice, (2) measures of neural activity in dopaminergic neural circuits during mate choice, and (3) social regulation of dopamine in females when exposed to mate signals. One avenue for exploring the role of dopamine in mate choice is to treat females with dopamine agonists or antagonists immediately prior to mate choice. Riters and her colleagues (Pawlisch & Riters, 2010) took

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this approach to investigate responses, courtship solicitation displays, to male courtship song in reproductively primed female European starlings (Sternus vulgaris). They stimulated dopamine receptors with peripheral injections of a non-selective dopamine reuptake inhibitor (GBR-12909, an indirect dopamine receptor agonist). Dopamine eliminated selective female responses to male song so much that it induced female responses to heterospecific as well as conspecific songs. These results suggest that dopamine transforms a previously unattractive stimulus into one that initiates an appetitive sexual response from the female. In a recent preliminary study of female tùngara frogs dopamine also similarly expanded the female’s preference landscape. These researchers administered a non-selective dopamine agonist (apomorphine) to reproductively active females just prior to exposure to a synthetic nonconspecific mating call known to be unattractive from a previous study (Ryan & Rand, 2003). None of the control females responded to this typically unattractive call, which was also true in the earlier study, while 20% of the females treated with apomorphine showed a positive phonotaxis response to those same calls. This is another case in which dopamine plays a role in expanding the females’ preference landscape (Ryan et al., unpublished data), but it is not known whether this occurs via a learning, liking, or wanting mechanism. Nonetheless, this result raises the interesting possibility that dopamine might play a creative role in the evolution of the amazing diversity of courtship traits that we see in nature. Another approach to documenting the role of dopamine in sexual aesthetics is to measure proxies of neural activity in dopaminergic neural circuits when individuals are presented with members of the opposite sex. In humans, the presentation of a picture of one’s sexual partner increased activation of dopaminergic circuitry in an fMRI study (Fisher et al., 2005). In a related study, homosexual and heterosexual men and women rated the attractiveness of faces, and fMRI results showed similar amounts of activation in areas of the brain involved in face recognition among the four groups. But when reward areas of the brain were measured, there was variation among subjects and sexual orientation; heterosexual women and homosexual men both showed enhanced activation of the reward circuit when they viewed men’s faces, while homosexual women and heterosexual men showed enhanced activation of the same circuit when they viewed women’s faces (Kranz & Ishai, 2006). These results indicate the reward system exhibits sexual preference–based rather than gender-based responses. It also highlights the difference between liking and wanting. In a sense, men and women, heterosexuals and homosexuals, exhibited similar liking of faces, but wanting of faces was a function of sexual preference. In nonhuman animals, studies that have probed reward circuits often utilize activity-dependent genes such as immediate early genes (IEG) as a proxy for neural excitation. Such studies have shown that female whitethroated sparrows (Zonotrichia albicollis) and túngara frogs exhibit a correlation between neural activity in the mesolimbic reward system after exposure to conspecific mate signals (Maney, 2013; Chakraborty et al., 2010; Hoke et al., 2010). While we do not know if these IEG responses are specific to dopaminergic cells within the mesolimbic reward system, these studies do implicate the reward system in assessment of mating signals. These studies, and some others, are noteworthy because they specifically examine how these reward circuits respond to different sexual signals (Lynch & Ryan, 2020). Not only does dopamine seem to regulate the female’s response to the male’s sexual signals, there is a reciprocal interaction of the male’s sexual signals socially regulating the female’s dopamine levels. Alger et al. (2011) showed in female zebra finches that tyrosine hydroxylase (TH) labelling density, indicative of dopamine-containing cells, in two dopamine-dense nuclei of the reward circuit, the ventromedial nucleus of the hypothalamus and the rostral ventral tegmental area (VTA), were positively related to the amount of courtship received from their partners. While elevated TH within reward circuitry of female zebra finches does not respond with increased IEG expression to simple playback of songs (Svec et al., 2009; Lynch et al., 2012), other regions containing TH do respond during simple song playback experiments, particularly the locus coeruleus, a region that is predominately noradrenergic in nature (Lynch et al., 2012).

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Finally, in a study of plainfin midshipmen, Sisneros and co-workers (Forlano et al., 2017) showed that female responses to advertisement calls are correlated with the activation of a specific subset of catecholaminergic (CA) and social decision-making network (SDM) nuclei underlying auditory-driven sexual motivation. The dopaminergic reward system has been intensively studied and is known to stamp in incentive salience in a number of domains. Moreover, there has been intensive study of the role of dopamine in consummatory sexual behaviour. There has been surprisingly little consideration, however, of how dopamine, particularly within the mesolimbic reward system, can assign incentive salience to courtship signals. In other words, our understanding of how dopamine acts during the decision-making or appetitive phases of sex is lagging. The data that are available show that it plays a similar role in assessing sexual signals as it does in some other systems such as human appreciation for loved ones and music. Some studies also suggest that dopamine might play a creative role in the evolution of sexual displays by expanding the stimuli that females find desirable. This attraction of previously unencountered stimuli might have some analogies to the involvement of dopamine in obsessive and addictive behaviour of humans such as drug use (Volkow et al., 2011), gambling (Comings et al., 1996), overeating (Stice et al., 2010), and compulsive viewing of pornography (Hilton, 2013).

Major challenges, goals, and suggestions Darwin presented his theory of sexual selection, with its emphasis on sexual aesthetics, 150 years ago. But it was not until 50 years ago that there was a renewed interest in Darwin’s theory in general, specifically the potency of mate choice in influencing the evolution of sexual beauty. Initially, studies concentrated on what type of information sexual signals contain about their bearer. More recently, there is a new interest in the mechanisms that form the bases of sexual aesthetics. Detailed and controlled manipulations of sexual traits started to give some insights as to why these traits are sexually attractive. These interests led to studies of the brain using both electrophysiological and gene expression techniques to give us some understanding of how sexual traits are processed and why they are so appreciated. More recently, however, this research area has been strongly influenced by studies of humans in fields such as psychophysics (e.g., Weber’s law), behavioural economics (e.g., competitive decoys), and neuroaesthetics (e.g., perceptual fluency). Many of the questions are similar; for example, why is it animals find that certain visual and acoustic patterns enhance the sexual attractiveness of a mate, and to what extent do these traits themselves induce hedonic pleasure? Researchers in sexual selection, most of whom have a firm grounding in naturalistic studies of animals in the wild, can benefit greatly by immersing themselves in these parallel disciplines. The elephant in the room, or at least the elephant wandering through this review, is whether non-human animals and humans share the same aesthetics. There is no doubt that sexual beauty and appreciation for sexual beauty are similar to the manifestations of liking and wanting in humans who are exposed to potential sexual partners, music, and art. Are these systems homologous, shared through a common ancestor, or do they represent convergent similarities? Certainly the colours of bird plumage and fish scales have no shared history with one another, let alone with the paints that artists put to canvas. But all of these animals have some similarities in how they sense the world around them and how their brains begin to process information. Importantly, we should keep in mind that all vertebrates, including us, have homologous reward and decision-making systems (O’Connell & Hofmann, 2012). Is sexual aesthetics a phenomenon that exhibits continuity between other animals and us, or are these common solutions to similar problems? This question is still open for debate (Skov, unpublished; White, 2020).

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Q., & Purves, D. (2003). The statistical structure of human speech sounds predicts musical universals. Journal of Neuroscience, 23(18), 7160–7168. https://doi.org/10.1523/JNEUROSCI.23-18-07160.2003 Searcy, W. A. (1992). Song repertoire and mate choice in birds. American Zoologist, 32(1), 71–80. https://doi.org/10.1093/ icb/32.1.71 Seehausen, O., Terai, Y., Magalhaes, I. S., Carleton, K. L., Mrosso, H. D., Miyagi, R., van der Sluijs, I., Schneider, M. V., Maan, M. E., Tachida, H., Imai, H., & Okada, N. (2008). Speciation through sensory drive in cichlid fish. Nature, 455(7213), 620–626. https://doi.org/10.1038/nature07285 Shepard, R. N. (1987). Toward a universal law of generalization for psychological science. Science, 237(4820), 1317– 1323. https://doi.org/10.1126/science.3629243 Shettleworth, S. (1998). Cognition, evolution, and behaviour. Oxford University Press. Skov, M. (unpublished). 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12 AESTHETIC SENSITIVITY Origin and development Ana Clemente

To provide general explanations for the influence of object features on people’s liking, preference, or appreciation is one of the central goals of empirical aesthetics and neuroaesthetics (Berlyne, 1971; Fechner, 1876; Martindale, 2001; Skov, 2019, 2020). Such explanations often invoke general perceptual, cognitive, and affective processes to account for regular and predictable ways in which people value object features, such as symmetry and complexity in the visual domain (Gartus & Leder, 2017) or uncertainty and surprise in the musical domain (Cheung et al., 2019). For instance, people generally prefer complex and symmetric visual designs (Westphal-Fitch et al., 2013; Nadal et al., 2010; Friedenberg, 2018; Jacobsen & Höfel, 2001) and high surprise and low uncertainty or low surprise and high uncertainty in music (Cheung et al., 2019; Gold et al., 2019). The reason for broadly shared preferences for sensory features is that sensory valuation relies on common perceptual, cognitive, and affective processes. General preference patterns, however, do not necessarily imply uniformity of preferences. In fact, there is substantial evidence for differences in how people assess the value of visual and musical features (Corradi et al., 2019, 2020; Clemente, Pearce, & Nadal, 2021; Clemente, Pearce, Skov et al., 2021). Such differences have been attributed to the effects of personality (Chamorro-Premuzic et al., 2009; Mastandrea et al., 2009; McManus  & Furnham, 2006), intelligence (Chamorro-Premuzic  & Furnham, 2004; Furnham  & Chamorro-Premuzic, 2004), expertise (Belke et al., 2010; Pang et al., 2013; Silvia & Barona, 2009), and other personal traits. The study of these individual differences in the appreciation of art and aesthetics has a long history, beginning in the 1920s with the search for efficient measures of artistic and aesthetic abilities in school children that could be used to test achievement and for vocational guidance (Burt, 1927, 1933; Meier, 1927, 1928; Thorndike, 1916, 1917). Among such measures, aesthetic sensitivity stood out for its prognostic value, suitability for laboratory research, and crucial role in several influential paradigms. Although the concept of aesthetic sensitivity had been used occasionally in the late 19th century, it developed during the central decades of the 20th century, reaching its heyday between 1960 and 1980 (Figure 12.1). Aesthetic sensitivity became a central concept in the thinking of Norman C. Meier (1927), Cyril L. Burt (1933), Hans J. Eysenck (1940), and Irving L. Child (1962) and a conceptual tool for defining and explaining variance in aesthetic appreciation that was inextricably linked to different views on aesthetics, beauty, and human cognition. In this chapter, I discuss three different approaches to aesthetic sensitivity: The first views sensitivity as subordinate to a normative standard of aesthetic taste, according to which a person can be more or less sensitive to an object’s true aesthetic value and people are born with unalterable degrees of good or bad taste. The 240

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241 Figure 12.1

Frequency of books mentioning aesthetic sensitivity (blue), esthetic sensitivity (red), aesthetic sensitiveness (g reen), and esthetic sensitiveness (orange) from 1800 to 2019. Source: Google Ng ram Viewer (Michel et al., 2011).

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second also assumes an ideal of good taste that people deviate from or approach but considers aesthetic sensitivity dependent on culture and environment and improvable through learning. Finally, the third disputes the existence of norms of objectively good taste, regarding aesthetic sensitivity as differences in the extent to which stimulus features are factored in aesthetic valuation.

Historical background The earliest conceptions of aesthetic sensitivity emerged from attempts to identify efficient ways of measuring artistic talent in the context of psychological testing in schools for the purpose of measuring aptitude and offering vocational guidance (Burt, 1933; Meier, 1926, 1927, 1928; Thorndike, 1916, 1917). Thorndike, for instance, developed a measure of the merit of children’s drawings that was based on psychophysical scaling rules (1913, 1924) and a measure of aesthetic merit and appreciation ability that was based on agreement and disagreement with majority norms (1916, 1917). He also compared the “good taste” of different communities (Powel et al., 1942; Thorndike & Woodyard, 1943), concluding that it seemed “positively associated with differences in the intelligence, morality, and competence of their residents” (Thorndike & Woodyard, 1943, p. 59). Likewise, McAdory (1929) developed a measure of artistic taste that was based on rankings derived from academic grades (Siceloff & Woodyard, 1933), just as Binet and Simon’s (1916) scale of intelligence included pairs of prettier/uglier drawings of faces. Meier (1927, 1928, 1939) argued that aesthetic sensitivity, understood as “the ability to recognize compositional excellence in representative art-situations, or the ability to ‘sense’ quality (beauty?) in an aesthetic organization” (Meier, 1928, p. 185; also 1939), was the most efficient and predictive measure of artistic talent. To measure aesthetic sensitivity, Meier and Seashore (1929; also Meier, 1942) developed the Art Judgment Test. Later, Meier (1940) published the Meier Art Tests: I. Art Judgment, premised upon the belief that the aesthetic character of art resides in the organization of parts according to universal principles of goodness and that aesthetic judgment involves detecting such principles. The Meier Art Tests: II. Aesthetic Perception (1963) was designed to assess the perceptual-facility factor of artistic talent, that is, the ability to detect subtle aspects of aesthetic significance. For Meier, aesthetic sensitivity was a measure of agreement with norms of artistic value determined by the original artworks versus their distortions—following Abbott and Trabue’s method (1921), or the so-called controlled-alteration process. Crucially, Meier (1934, 1939) regarded aesthetic sensitivity and perceptual facility largely acquired components of artistic aptitude and thus fruit of experience and training. These studies led to two approaches to individual differences in aesthetic appreciation and their underlying factors. Both considered objective factors to form a common foundation for aesthetic appreciation and developed into the determinist and educational normative views of aesthetic sensitivity, understood as degrees of convergence with, or deviation from, some external reference. Alternative approaches treated objective and subjective factors as constituents of aesthetic appreciation, intertwined and with different relevance for different people. This ultimately crystallized as the subjective responsiveness view of aesthetic sensitivity.

The determinist view of aesthetic sensitivity Cyril L. Burt was the leading figure and pioneer of the determinist view of aesthetic sensitivity. He was strongly drawn to Galton’s ideas on statistics, individual differences, mental tests, and eugenics. In 1913, Burt was appointed part-time chief psychologist at the London County Council (Arnold, 2013), where he worked on developing measures of aptitude and achievement. He defended the view that intelligence and mental abilities were genetically based and the main determinants of social position and that these abilities could be objectively and accurately measured using mental tests (Norton, 1981). Burt’s goal was to introduce such tests to the educational system and use measures of children’s performance to determine the vocational

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path that best fitted the individual child’s natural aptitude. Amongst his test battery were tasks targeted at measuring children’s aptitude for literary, musical, and visual appreciative and creative abilities. Burt found two factors driving aesthetic judgment. What he called the general factor of artistic ability was thought to account for good taste: the ability to appreciate relations among elements in art, music, and poetry (Burt, 1933, 1949, 1960). For him, this general factor of artistic ability was unitary, inherited, unalterable, and measurable through simple tests (Bulley & Burt, 1933; Burt, 1960). Burt (1939) believed that aesthetic appreciation should be measured against expert judgments, which established the true rank of aesthetic values. A second bipolar factor distinguished between objective or classical and subjective or romantic types (Burt, 1915, 1933)—resembling Bullough’s (1908, 1910) types. This second factor was more pronounced when controlling for the first factor and for younger or less artistically sophisticated people, for which “irrelevant factors become more obvious” (Burt, 1915; Dewar, 1938; Stephenson, 1936). This twofold factor was deemed analogous to those in Binet’s (1903) intelligence tests and Burt’s (1912) temperamental differences and close to Jungian extravert/introvert types (Dewar, 1938). Burt’s theories exerted a broad, profound, and long-lasting influence on education, society, and politics. Burt’s doctoral student Hans J. Eysenck continued his psychometric approach to aesthetics. Eysenck’s (1940) factor analysis uncovered what he deemed a general objective factor of aesthetic appreciation. Eysenck asserted that this factor explained performance on virtually any aesthetic appreciation test and claimed that his factor was universal, biologically determined, and innate (Eysenck, 1941b, 1941c, 1942, 1981). He equated this factor, t (for taste), with an ability to appreciate objective beauty (Eysenck, 1941c, 1942, 1981) and described it as distinct—because “[this ability], independently of intelligence and personality, determines the degree of good or bad taste” (Eysenck, 1983, p. 213)—general—for “it covers a large number of, probably all, pictorial tests” (Eysenck, 1940, p. 100)—stable—as “[it] presumably [has] a genetic foundation in the structure of the nervous system” (Götz et al., 1979, p. 801)—and insensitive to experience —“[it] is independent of teaching, tradition, and other irrelevant associations” (Eysenck, 1940, p.  102)—and culture—given the “comparative absence of cultural factors determining aesthetic judgments” (Eysenck & Iwawaki, 1971, p. 817; Eysenck & Iwawaki, 1975; Soueif & Eysenck, 1971). He later identified a second bipolar factor, k (Eysenck, 1941a; Frois & Eysenck, 1995), characterized by “brightness or intensity as opposed to darkness or lack of intensity” (Eysenck, 1983, p. 91). According to Eysenck (1941a, 1942), aesthetic sensitivity scaled with the degree to which liking approximated true aesthetic value, as determined by group agreement or expert opinion (Eysenck, 1972a, 1981; Eysenck & Iwawaki, 1971). In this way, someone’s aesthetic sensitivity could easily be calculated by comparing their average liking ratings with group averages or with expert judgments. Eysenck first examined ratings of artworks (Eysenck, 1940). Later, he used simple geometric designs (Eysenck, 1972b; Eysenck & Castle, 1971), borrowed from Birkhoff (1933), and the Figure Preference Test (Barron & Welsh, 1952; Welsh & Barron, 1949). Finally, he developed the Visual Aesthetic Sensitivity Test (VAST; Chan et al., 1980; Götz et al., 1979; Iwawaki et al., 1979). However, the VAST exhibited low internal consistency and structural validity, and its scores were explained by intelligence, figural creativity, and personality traits (Chamorro-Premuzic & Furnham, 2004; Furnham & Chamorro-Premuzic, 2004; Myszkowski et al., 2018; Myszkowski et al., 2014; Payne, 1967). Thus, contrary to Eysenck’s (1941a, 1942) claims, aesthetic sensitivity appeared not to be a distinct ability but to draw upon general cognitive processes, learning, and experience. To overcome this issue, Myszkowski and colleagues have recently suggested two mutually compatible amends: First, Myszkowski and Zenasni (2016) proposed a composite measure of aesthetic aptitude that brings together measures of aesthetic exploration, art expertise, sensitivity to complexity, aesthetic empathy, and aesthetic sensitivity as aesthetic balance recognition. Second, Myszkowski and Storme (2017) introduced a revised version of the VAST with improved internal consistency and structural validity.

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Regardless of the instrument used to measure it, normative conceptions of aesthetic sensitivity assume that aesthetic appreciation adheres to an objective standard of taste: that aesthetic sensitivity, or good taste, can be defined as an ability that approximates a true order of taste (Eysenck, 1972a) or an absolute beauty (Eysenck, 1972a; Eysenck & Iwawaki, 1971) with some objects objectively superior to others. Furthermore, this deterministic view maintains that aesthetic taste is innate and impervious to experience, despite the scarce and contradictory (e.g., Eysenck, 1972a, 1983) empirical support for these claims—particularly if considering expert judgments norms, given that expertise is acquired by definition.

The educative view of aesthetic sensitivity The educative view of aesthetic sensitivity agrees with the determinist view that objective aesthetic values and standards of taste exist. However, it does not agree that aesthetic taste is innate and immutable. Instead, the educative view suggests that aesthetic tastes are acquired through learning and, therefore, culturally and socially moulded. Especially dominant in the 20th century, the educative view of aesthetic sensitivity has been espoused by a wide range of academics, including social scientists (e.g., Dai & Shader, 2001), political scientists (Suga, 2003), philosophers (Mitchells, 1966), and artists (Fehl, 1953; Smets & Knops, 1976), as well as psychologists and educators (Adler, 1929; Anderson, 1972, 1975; Bullock, 1971; Day, 1976; Gernet, 1940; Hahn, 1954; Hevner, 1930; Kwalwasser & Dykema, 1930; Kyme, 1967; Reimer, 1965, 1968a, 1968b; Taunton, 1982; Trabue, 1923; Vernon, 1930; Webster, 1988a, 1988b). The educative view of aesthetic sensitivity emphasizes contextual, situational, and personal factors. Accordingly, Hevner (1937; Hevner & Mueller, 1939) showed that information about an object was able to modulate its aesthetic appreciation. Similarly, Voss (1936) observed notable improvements in aesthetic analysis and judgment in children aware of criteria for aesthetic merit, and Clair (1939) overtly refuted the condition of disinterestedness because “critical and appraising analysis of works of art . . . intensifies . . . [their] appreciation” (p. 67). Remarkably, Carroll (1932) showed that the relationship between the ability to appreciate art, literature, and music was very slight. This finding not only discredited the determinist claim of generality but pointed to a modality-specific basis of aesthetic appreciation, even if it still accorded to standards. Among other determinants of creativity, Barron and Welsh investigated aesthetic judgments defined as the ability “to discriminate the good from the poor (as judged by experts)” (Barron, 1952, p. 387, 1963, 1969; Barron  & Welsh, 1952). In Welsh’s (1949) study, a group of artists—ostensiably representatives of good taste—exhibited a preference for complexity and asymmetry, dissident personality, and unconventional political views (Barron & Welsh, 1952). Barron (1952) confirmed that artists preferred complex and asymmetric figures and were rebellious against authority and tradition (1953). The paintings artists liked at that time were “ ‘modern’ art movements as Primitivism, Expressionism, Impressionism, and Cubism” (Barron, 1952, p. 391), known for rebelling against traditional art traditions—thus evincing the mutable character of expert opinion and, consequently, of aesthetic sensitivity conceived as agreement with expert judgment. Irving L. Child was the central proponent of this educative conception of aesthetic sensitivity in empirical aesthetics. Child (1962) was sceptical about Eysenck’s assumption that average rankings represented true aesthetic value and that the extent to which individual preferences agreed with the average constituted a valid measure of aesthetic sensitivity. He therefore tested preference versus aesthetic value as determined by experts (Child, 1962). He developed a measure of aesthetic value as the average rating of expert judges weighted by agreement with the other judges and compared it to non-experts’ averaged preferences. He found that “the degree to which preferences are related to aesthetic value is a very stable characteristic of the individual . . . [and that the] degree of agreement with an aesthetic standard is an even more consistent characteristic than [the] degree of agreement with group preferences” (Child, 1962, p. 504). Finally, he found a negative or absent correlation between individual measures of preferences defined as the extent to which they resemble one kind of standard or the other. He concluded that “the degree of agreement between one’s preferences 244

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and aesthetic value is an index of aesthetic sensitivity . . . [whereas the] degree of agreement with group preferences does not correspond to an external criterion of aesthetic sensitivity” (p. 506). Child (1964, 1965) understood aesthetic sensitivity as “the extent to which, when a person judges the esthetic value of works of art, his judgments agree with an appropriate external standard of their esthetic value . . . provided by the judgment of experts” (p. 476). Child believed that “aesthetic sensitivity is expressed in a tendency to prefer the aesthetically good” (p. 508) and that “[i]f one set out to measure aesthetic sensitivity, he would ordinarily not ask people to express personal preferences, he would generally do better to ask them to make aesthetic judgments, as in the Bulley Test” (p. 510). Thus, like Eysenck, Child thought of aesthetic sensitivity as a measure of the ability to make aesthetic judgments according to standards of value. However, in contrast to Eysenck, Child (1962, 1965) believed that aesthetic sensitivity was cultivated with practice and resulted not from a specific ability but from a general cognitive style or personality (Child, 1964, 1965; Iwao & Child, 1966). For him, high aesthetic sensitivity was the manifestation of an “actively inquiring mind, seeking out experience that may be challenging because of complexity or novelty, even alert to the potential experience offered by stimuli not already in the focus of attention” (Child, 1965, p. 508). Thus, a highly sensitive person would be “interested in understanding each experience thoroughly and for its own sake rather than contemplating it superficially and promptly filing it away in a category, and able to do all this with respect to the world inside himself as well as the world outside” (p. 508)—emphasizing the relevance of motivation, theoretical interest, and personal and contextual factors. In the 1980s, this educative view of aesthetic sensitivity was revised and updated. Bamossy and colleagues (1985) criticized previous work for inappropriately distinguishing between aesthetic responses, preferences, and judgments. Only the latter are about the object, and thus only those “can be relevant or irrelevant,” meaning susceptible to value judgments; whereas responses “cannot validly be categorized as ‘appropriate or inappropriate’, or ‘relevant or irrelevant’,” and one “cannot be better or worse at having preferences” (Bamossy et al., 1985, pp. 64–65). As noted by Bamossy and colleagues (1985), instruments such as Welsh’s (1959; Barron & Welsh, 1952; Welsh & Barron, 1949), Graves’s (1939, 1948), and Thorndike’s (1916) “have not observed this distinction and consequently seem to lack validity” (p. 65). They also observed that manipulating masterpieces was a standard technique in artistic movements like Pop Art, so they questioned the utility of tests based on comparisons between originals and alterations and emphasized the mutable character of expert judgments. They criticized the direct use of expert judgment in Child’s (1962), Graves’s (1939, 1948), Meier’s (1940), Welsh’s (1959; also Barron  & Welsh, 1952; Welsh  & Barron, 1949), Thorndike’s (1916), Bottorf ’s (1946), and Williams and Hattwick’s (1932) tests, as it is “easier to get agreement among experts on reasons for judgments than on judgments themselves” (Bamossy et al., 1985, p. 67). For these reasons, Bamossy and colleagues (1985) rejected the prevailing notion of aesthetic sensitivity while advocating for the existence of objective value and the utility of assessing aesthetic ability. Their Aesthetic Judgment Ability Test (Bamossy et al., 1985), based on the theory of cognitive development of aesthetic judgment (Parsons & Durham, 1979; Parsons et al., 1978), measured judgments’ sophistication, with expert criteria representing higher-stage reasons for aesthetic judgments. Another aspect of aesthetic sensitivity that was revised in this decade was the idea that it represented a single factor or ability. Winner et al. (1986) studied the development of aesthetic sensitivity in children, finding that it was art form specific—that is, not a single factor—and property specific—that is, multiple. In this line, Elliot (1995) asserted that “it is highly doubtful that there is any such general capacity as aesthetic sensitivity. Multiple intelligence theories and contemporary studies of creativity argue against such possibility” (p. 249).

The responsiveness view of aesthetic sensitivity The two normative conceptions of aesthetic sensitivity reviewed in the preceding two sections were dominant during the 20th century. However, an alternative notion emerged during the early decades of the 20th 245

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century. As part of her research on factors modulating aesthetic experience, Washburn introduced the concept of affective sensitiveness to distinguish between people with a strong tendency to like and dislike materials of different sorts—for example, tones, colours, and speech sounds—from people who are relatively indifferent to them (Babbitt et al., 1915; Clark et al., 1913). Affective sensitiveness was calculated as “the ratio of the sum of the number of judgments of extreme pleasantness and extreme unpleasantness to the number of judgments of indifference” (Washburn et al., 1923, p. 105; Clark et al., 1913). Washburn’s experiments demonstrated that affective sensitiveness depended to certain degree on different circumstances and conditions. They showed, for instance, that fatigue reduced aesthetic sensitivity (Robbins et al., 1915), experience and expertise in art and aesthetics increased aesthetic sensitivity (Washburn et al., 1923), and art interest led people to approximate expert’s rankings (Cattell et al., 1918). A century later, Corradi and colleagues (2019, 2020) developed another responsiveness approach to aesthetic sensitivity. They defined aesthetic sensitivity as the extent to which someone’s liking is determined by a specific stimulus feature, such as symmetry or complexity. In this sense, someone is aesthetically sensitive to complexity when their liking is influenced by complexity: for example, they prefer complex to simple ones or simple to complex designs. Conversely, someone is aesthetically insensitive to complexity when their liking is uninfluenced by complexity: that is, their preference has little to do with the complexity or simplicity of designs. The idea resembles Washburn’s but with one fundamental difference: whereas Washburn’s notion of affective sensitiveness reflected the magnitude of individual responsiveness to visual and auditory stimuli, it did not reflect responsiveness to variations in specific stimulus features. In contrast, Corradi and colleagues’ (2019, 2020) notion is closer to Fechner’s (1876) psychophysical conception of empirical aesthetics in that it captures the extent to which variations in stimulation (e.g., complexity or symmetry) translate into variations in valuation (e.g., liking or preference). Corradi and colleagues (2019, 2020) showed that people differ in the extent to which their aesthetic valuation takes into account the balance, contour, symmetry, and complexity of visual designs. Clemente and colleagues (Clemente, Pearce, & Nadal, 2021; Clemente, Pearce, Skov et al., 2021) reported similar differences in the extent to which people’s liking for melodies depends on their balance, contour, symmetry, and complexity. These studies found little co-variation among sensitivities to different features. People can be highly sensitive to some features but not to others: someone’s liking for visual designs might depend more on their symmetry than their complexity, while someone else’s liking might depend more on their complexity than their symmetry (Corradi et al., 2020). Likewise, some people are more aesthetically sensitive to musical balance than contour, while others are more sensitive to musical contour than balance (Clemente, Pearce, & Nadal, 2021). This approach to aesthetic sensitivity has allowed asking new questions, such as whether sensitivity to one feature entails sensitivity to an analogous feature in a different sensory modality. Is someone highly sensitive to visual complexity also highly sensitive to musical complexity; that is to say, does complexity influence this person’s liking regardless of sensory modality? Clemente, Pearce, Skov, and colleagues (2021) showed that this is not the case, but aesthetic sensitivity is modality specific: for example, symmetry might influence someone’s liking for visual designs but not for musical motifs, and complexity might influence someone’s liking for musical motifs but not for visual designs. The exception seems to be contour: people who disliked jagged visual contours also tended to dislike jagged melodies (Clemente, Pearce, Skov et al., 2021). Although these patterns of aesthetic sensitivity for visual and musical features vary between people, they seem to be relatively stable in time within people, as suggested by the similarity of liking ratings for the same stimuli weeks apart (Corradi et al., 2020; Clemente, Pearce, & Nadal, 2021). However, aesthetic sensitivities appear to be unrelated to other individual traits such as personality, intelligence, and interest in art (Clemente, Pearce, Skov et al., 2021; Corradi et al., 2020).

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Conclusions Empirical aesthetics and neuroaesthetics strive to explain how cognitive, affective, and neural systems allow us to enjoy perceiving objects around us, to be pleased by music and art. Like other domains of the behavioural and brain sciences, they aim to provide general principles across cases or circumstances while accounting for individual differences. The need to find a different explanation for each person or object of appreciation would entail no accounting for taste, preference, or pleasure. One of the main goals of the foundational text of empirical aesthetics (Fechner, 1876) was to elaborate on several principles of aesthetic pleasure. The principle of union of diverse elements, for instance, states that objects that produce aesthetic pleasure are often composite and manifold. The quest for general principles of aesthetic appreciation continues today. For instance, fluency theory holds that ease of processing leads to a pleasant feeling of fluency, which can translate into assessments of beauty, likeability, and attractiveness: “The more fluently perceivers can process an object, the more positive their aesthetic response” (Reber et al., 2004, p. 364). There is extensive evidence supporting these general trends. For example, people usually find objects composed of arranged elements aesthetically pleasing, and ease of processing often enhances this experience. Nevertheless, extensive evidence suggests that aesthetic appreciation is not the result of only these sorts of aesthetic principles. If this were the case, we would all share the same aesthetic preferences. The obvious fact that people are aesthetically pleased by different painting and musical styles, literary and cinema genres, or clothing fashions begs an explanation. If certain principles influence aesthetic appreciation, why do people differ so much in what they find aesthetically pleasing? This question has fascinated philosophers for centuries and psychologists and neuroscientists for decades. It is also the question that work on aesthetic sensitivity seeks to answer. Aesthetic sensitivity has a central place in empirical aesthetics. It was initially conceived as an ability to recognize objective beauty in arrangements and compositions, easy to measure and predictive of artistic abilities. For almost a century, it has continued to be associated with individual differences in aesthetic valuation. Such differences, however, have been understood in substantially different ways, reflecting fundamental ideas or views about art, aesthetics, and human cognition. From the perspective that I have referred to as the determinist view, there is such a thing as objective beauty or true aesthetic value. Aesthetics and art are consubstantial, in the sense that the greatness of art depends on the extent to which it embodies aesthetic values such as beauty or sublimity in unique ways. The human mind apprehends aesthetic value through an aesthetic sense, a sense of beauty, or taste. As with other mental capacities, some people have better taste than others, and these differences are conditioned mainly by biological factors, relatively insensitive to learning and experience. From this perspective, aesthetic sensitivity is a specific fixed ability to value—with greater or lesser accuracy—the beauty of objects, most notably art, as determined by group average or expert opinion. From the perspective that I have referred to as the educative view, cultures and societies arrive at a consensus about what is considered beautiful or aesthetically valuable. Art is a material embodiment of cultural and aesthetic values that vary from culture to culture and must be learned and cultivated. Experts are able to recognize the beautiful and the good art because of their extensive experience and knowledge about that particular culture or society’s values. People vary in the extent to which their appreciation agrees with experts because they differ in the experiences, personality traits, and interests that foster attention to aesthetics and art. From this perspective, aesthetic sensitivity is a general and learned capacity to recognize—with greater or lesser accuracy—the sort of qualities experts and connoisseurs rely on to recognize in an artwork a great embodiment of aesthetic and cultural values. From the perspective that I have referred to as the responsiveness view, beauty is not a quality of objects but a quality of our experience of objects. Beauty and other feelings result from general sensory valuation

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processes that involve assigning hedonic value (pleasure–displeasure) to sensory information in the context of judgments, decisions, and choices. Art is one of the many classes of objects susceptible to valuation. People vary in the extent to which their valuation relies on different object properties or features. From this perspective, aesthetic sensitivity to a given object feature is the extent to which people take that feature into account when assessing the hedonic value of that object. From this perspective, aesthetic sensitivity is not measured against any external standard or norm but is a measure of pleasurable responsiveness to variations in a particular kind of sensory information. There is little hope for reconciliation between these notions of aesthetic sensitivity: they emerge from diverging ideas about beauty, art, and human cognition. Nevertheless, these disagreements continue to stimulate important advances in the study of aesthetic sensitivity in recent years. They have led to the clarification and refinement of arguments and concepts and new experiments yielding decisive data. Perhaps most critically, they emphasize the importance of continuing to study the reasons people differ in what they like or find beautiful, because any comprehensive explanation of aesthetic appreciation needs to account for such differences.

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13 THE EVOLUTION OF SENSORY VALUATION SYSTEMS Esther Ureña and Marcos Nadal

Ernst Mayr (1961), one of the architects of the modern evolutionary synthesis, distinguished two sorts of questions that can be asked about biological systems. Functional questions, on the one hand, ask about the way structural elements operate and interact to make the system work. Evolutionary questions, on the other hand, ask about the historical events that brought those elements and their features into existence. The focus of most chapters in this volume is on functional questions about aesthetics: how do different brain regions work together to attach hedonic value to objects and events (Chapter 2)? How do they generate the experiences of liking and disliking (Chapters 3 and 4)? How do feelings of pleasure and appreciation arise in different sensory domains and for different classes of objects (Chapters 6 to 10)? The focus of this chapter, unlike the preceding ones, is on evolutionary questions about the biological systems underlying aesthetic valuation. Its aim is to summarize what is known about the evolution of sensory valuation systems and their features. The possibility of answering evolutionary questions about aesthetics has roused a good deal of scepticism, even among evolutionary biologists. This scepticism is grounded on the view that aesthetic valuation is a uniquely human trait, unparalleled in other animals: Aesthetics and art are usually regarded as exclusively human possessions. Sensitivity to beauty and making or doing things that are perceived as ‘beautiful’ are among the traits that elevate man above the brutes. This renders the problem of the origin and biological meaning of art and aesthetics in human evolution particularly challenging. (Dobzhansky, 1962, p. 214) If aesthetic valuation is a uniquely human trait, it follows that it must have appeared along the human evolutionary lineage, after it diverged from the lineage of chimpanzees: “the aesthetic sense evolved in response to important new selection pressures long after the human lineage had separated from that of the apes” (Washburn, 1970, p. 824). To complicate matters further, there seems to be little hope of finding material evidence for the evolution of this peculiarly human trait: “It does not seem possible to determine the phylogenetic origin of the human capacity for appreciating beauty. Neither fossil nor archaeological records provide convincing evidence to ascertain the appearance of this competence” (Ayala, 2017, p. 613). The view that aesthetic liking and pleasure are somehow different to other forms of liking and pleasure is not uncommon (Nadal & Skov, 2018; Skov & Nadal, 2018, 2020). There is a long history of efforts directed toward identifying what makes aesthetic liking and pleasure special, from the earliest days of psychology to 254

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the present (Bain, 1883; Christensen, 2017; Fingerhut  & Prinz, 2020; James, 1884, 1890; Menninghaus et al., 2017, 2019; Sully, 1892). The historical reasons behind this view have been explained in Chapter 1 of this volume and have to do with assumptions about aesthetics and the mind that filtered from 18th-century philosophy to 19th-century psychology and to 20th-century neuroscience.

Aesthetic valuation is rooted in common sensory valuation systems As presented in detail in the preceding chapters of this volume, neuroaesthetics has produced abundant evidence showing that aesthetic valuation is rooted in general sensory valuation systems. Sensory valuation is a process by which sensory information is conveyed to the reward circuit, where it is imbued with a hedonic value that depends on its relevance to the internal state, needs and goals, and the external situation and context. The reward circuit is a distributed system of brain regions that include the nucleus accumbens, caudate nucleus, pallidum, amygdala, orbitofrontal cortex, anterior cingulate cortex, and insula (Bartra et al., 2013; Brown et  al., 2011; Haber  & Knutson, 2010; Sescousse et  al., 2013). Reward signals computed in these regions attach a hedonic value to information relayed from sensory cortices about the perceptual attributes of objects (Becker et al., 2019; Berridge & Kringelbach, 2015; Skov, 2019). Let us illustrate this with two studies. Salimpoor and colleagues (2013) measured blood oxygenation level–dependent activity while participants listened to excerpts of unfamiliar music and placed economic bids to listen to them again. Their results showed that activity in the nucleus accumbens was the best predictor of the amount participants were willing to bid and that functional connectivity between the nucleus accumbens and the primary and surrounding auditory cortices increased significantly when participants listened to the excerpts they found most desirable. In another study, Cheung and colleagues (2019) showed that musical pleasure arises from combinations of the uncertainty of perceivers’ musical expectations and their surprise when musical events deviate from those expectations: musical pleasure is greatest when events are highly surprising in a low-uncertainty context or when events are not very surprising in a high uncertainty context. Moreover, the interaction between uncertainty and surprise was related to brain activity in the amygdala, hippocampus, and auditory cortex. Sensory valuation, therefore, involves integrating information about perceptual attributes, such as tonal patterns in the auditory domain or contour and symmetry in the visual domain, with information about hedonic attributes, such as reward prediction or reward value. The integration of sensory and hedonic processes is crucial for two reasons. First, sensory information that is not relayed to the reward circuit does not acquire hedonic value. This is the case with specific musical anhedonia (SMA), the inability to experience pleasure from music. People with SMA have reduced white matter connectivity between auditory brain regions and the ventral striatum, a key region of the brain’s reward circuit (Sachs et al., 2016). Even in people without SMA, individual differences in experienced musical pleasure correlates with differences in connectivity between the auditory cortex and the reward circuit (Loui et al., 2017; Martínez-Molina et al., 2016). Second, the integration of sensory and hedonic information marks the distinction between different sorts of hedonic values. The same reward circuit is involved in the pleasurable experiences we get from many sources, including music, painting, food, sex, and drugs (Levy & Glimcher, 2012; Mallik et al., 2017; Nadal & Skov, 2018). What distinguishes those pleasures from each other is the sort of sensory information that is relayed to the reward circuit and the path it is relayed along (Mas-Herrero et al., 2021; Sescousse et al., 2013). There are good reasons, therefore, to doubt that anything like a distinct aesthetic faculty appeared at some point in human evolution after our lineage split from the chimpanzee lineage, giving us a taste for beauty and art. The neuroscientific evidence shows that aesthetic liking and aesthetic pleasure rely on brain systems we share with other animals and that evolved to signal relevant objects and situations by making them pleasurable (Nadal & Skov, 2018; Skov & Nadal, 2018). But not only is it the case that we share common reward systems with other animals; there is abundant evidence that preference for sensory features and configurations is widespread in the animal kingdom. 255

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Preferences are ubiquitous among animals Empirical aesthetics and neuroaesthetics have devoted much effort to understanding the psychological and neurobiological mechanisms underlying people’s liking and preference for object attributes (Berlyne, 1972; Meier, 1942; Nadal & Vartanian, 2022; Valentine, 1962). Some of these attributes, including colour hue, colour brightness, regularity and symmetry, proportion, and curvature, have received much attention, because they are often regarded as the basic formal or design elements of plastic artworks (Feldman, 1971; Lopes, 2005). We might wonder why humans evolved a preference for symmetry or curvature or what the adaptive advantage conferred to early humans by such preferences was, as if these were aspects of a uniquely human aesthetic sense. As it happens, however, humans are not unique in having preferences for colours, shapes, and patterns. Many classes of animals, such as birds, rodents, and primates, also have preferences for colours, shapes, arrangements, and movements.

Colour preferences Studies on human preferences for colours go back to the earliest days of empirical aesthetics (Nadal & Ureña, 2021). Some of the first studies suggested there was no accounting for colour preferences, but these were fraught with methodological problems (Eysenck, 1941). Later studies improved experimental control of materials and procedures, and showed that people generally agree in their colour preferences: People generally prefer cooler colour hues, like green and blue, to warmer colour hues, like red, orange, and yellow, and they prefer bright to dim colours (Palmer et al., 2013; Vartanian, 2022). Humans are far from the only animals that have colour preferences. Newly hatched chicks show colour preferences during imprinting. These young birds prefer to follow blue objects rather than red objects, and any of these rather than green objects. The object colours they least prefer to follow are orange, grey, yellow, and white (Schaefer & Hess, 1959). Young chicks, herring gulls, and pigeons also exhibit colour preferences when pecking on objects: they preferentially peck on objects in the blue and orange regions of the spectrum (Delius & Thompson, 1970; Hess, 1956; Sahgal & Iversen, 1975). These preferences seem to be innate, as they are observed even when the chicks hatched in total darkness (Fischer et al., 1975; Salzen et al., 1971), as they are not modified by pre-hatch stimulation with different coloured lights (Fischer et al., 1975), and as chicks learn faster in colour discrimination tasks when they are reinforced for pecking on preferred colours than when they are reinforced for pecking on nonpreferred colours (Kovach & Hickox, 1971). However, early exposure to colours interacts with these innate preference tendencies (Kovach, 1971; Salzen et  al., 1971): Chicks reared for three days with red or blue stimuli show strong preferences for the colours they were reared with, but chicks reared for 3 days with green stimuli prefer red and green equally (Salzen et al., 1971). Chicks also show brightness preferences. They prefer bright-coloured stimuli to dim-coloured stimuli (Zolman & Lattin, 1972), and this preference is not eliminated by incubating, hatching, or rearing for 3 days in darkness (Zolman & Lattin, 1972), though it becomes stronger with exposure to light during the first 3 days of life (Zolman & Lattin, 1972). Finally, colour preferences have also been observed in nonhuman primates. Several studies coincide in finding that the preferred colour of macaques and squirrel monkeys is blue, followed by green, yellow, orange, and red (Green et al., 1966; Humphrey, 1971, 1972; Sahgal et al., 1975). Thus, well-controlled studies show that animal and human colour preferences are quite similar (McManus et al., 1981). What role might preference for colour hue and brightness play in chicks and monkeys? It has been suggested that they are the result of adaptive perceptual templates that draw attention to, help discriminate, and elicit approach toward informative features of the environment during development (Kovach & Wilson, 1981). At certain stages, these preferences seem to be quite fixed, while at other stages learning experiences through interaction with the environment can lead to modifications of some of these preferences.

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Preference for curvature People generally prefer curvature to angularity. They prefer objects (Bar & Neta, 2006, 2007), designs (Westerman et al., 2012), geometric figures (Bertamini et al., 2016; Palumbo & Bertamini, 2016; Palumbo et al., 2015; Silvia & Barona, 2009), rooms (Vartanian et al., 2013), and artworks (Ruta et al., 2021) with curved contours to those with sharp-angled contours. Preference for curvature has been observed using several different experimental paradigms (Gómez-Puerto et al., 2016; Palumbo & Bertamini, 2016) and even in young infants ( Jadva et al., 2010) and newborns (Fantz & Miranda, 1975). Preference for curvature might be common, but it by no means fixed. Studies have shown that is modulated by the semantic content of the objects, presentation time, experience, expertise, and familiarity (Chuquichambi et al., 2021; Corradi et al., 2019; Silvia & Barona, 2009; Vartanian et al., 2018). Preference for curvature has also been observed in birds, rodents, and nonhuman primates. Very young chicks approach, and peck on, curved forms, such as circles and ellipsoids, more than angular forms, such as triangles and stars (Fantz, 1957; Goodwin & Hess, 1969). Preference for curvature seems to be innate, as it has been observed in the absence of any prior visual experience, it persists for weeks even when unrewarded, and it takes chicks longer to learn not to respond to curved forms than to learn not to respond to angular forms (Fantz, 1957; Zolman, 1969; Zolman et al., 1975). Rodents have also shown preference for curvature: Rats (Rattus norvergicus) prefer to explore rounded objects over cylindrical ones (Winne et al., 2015). Nonhuman primates, including orangutans, gorillas, and chimpanzees, also prefer to look at or choose objects with curved contours over objects with angular contours (Ebel et al., 2020; Munar et al., 2015).

Preference for order, regularity, and symmetry Humans generally prefer regular, ordered, and symmetrical arrangements to irregular, disordered, and asymmetrical ones (Bode et al., 2017; Nadal et al., 2010; Van Geert & Wagemans, 2019; Westphal-Fitch et al., 2012). This, of course, does not mean that everyone prefers order and regular designs on every occasion. These preferences seem to be modulated by a multitude of factors. Even the general preference for symmetry can be modulated by affect, personality, familiarization, and expertise (Gollwitzer & Clark, 2019; Gollwitzer et al., 2020; Leder et al., 2019; Pecchinenda et al., 2014; Tinio & Leder, 2009; Weichselbaum et al., 2018) Again, far from being a uniquely human attribute, preference for regularity is widespread among different classes of animals. Birds (Coloeus monedula, Corvus corone) show a preferential selection for regular and symmetrical geometrical patterns (Rensch, 1958; Tigges, 1963). Rensch (1957) showed that a capuchin monkey (Cebus apella) and a vervet monkey (Chlorocebus aethiops) preferred to interact with geometric patterns that contained lines with good continuation, radial and bilateral symmetry, regularity, and repetition of patterns than with similar patterns that lacked these features and were thus irregular. Anderson and colleagues (2005) replicated and extended Rensch’s (1957) original observations: When presented with pairs of geometric patterns differing in regularity and symmetry, primates—capuchin monkeys (Cebus apella) and squirrel monkeys (Saimiri sciureus)—systematically approached the regular and symmetrical ones first.

Preference for biological motion When given a choice, people usually prefer to look at patterns of biological motion than other motion patterns. This is true even for human newborns in the first days of life, suggesting that we are born with a predisposition to latch onto biological motion patterns (Sifre et al., 2018; Simion et al., 2008), regardless of whether the movement corresponds to another person or an animal (Bardi et al., 2011).

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Humans share with other animals this preference for biological motion. Other animals seem to discriminate and prefer autonomously moving objects to objects that move because they have been pushed: Newly hatched, visually naïve domestic chicks prefer moving objects that are self-propelled to moving objects caused by physical contact (Mascalzoni et al., 2010). Moreover, newly hatched chicks that were reared and hatched in darkness, and thus have no visual experience whatsoever, prefer to approach biological motion patterns over other kinds of motion patterns (Sifre et al., 2018). This preference is not specific to the motion of hens but also potential predators, such as cats (Vallortigara & Regolin, 2006; Vallortigara et al., 2005). It has been suggested that preference for biological motion in chicks is crucial for imprinting during the early stages of development (Miura & Matsushima, 2016). Preference for biological motion has also been observed in fish and seems to serve the recognition of animal species, sex, and group members and be crucial in social engagement and in promoting shoaling behaviour (Larsch  & Baier, 2018; Nakayasu  & Watanabe, 2014; Nunes et al., 2020).

Looking at sensory valuation through the lens of evolution The evidence presented previously demonstrates that humans are not alone in having preferences for perceptual attributes. We have seen that, like humans, many kinds of animals, from fish to primates, have preferences for colours, curved contours, order and regularity, and biological motion. Although we are far from understanding exactly what their function is, how they evolved, and how the neural systems underlying them differ from one class of animals to another, these are widespread and robust preferences, which suggest that they play a crucial role in development and survival (Bardi et al., 2011; Sifre et al., 2018). They seem to be the result of basic perceptual mechanisms that bias animals’ attention towards individually and socially relevant environmental cues. Colours, shapes, and movements, together with a plethora of information from other sensory domains we have not examined here, serve as signals to attend to and engage with objects and conspecifics during key developmental stages, triggering the relevant consummatory (e.g., pecking) and affiliative behaviours (e.g., following and shoaling). Because some of these preferences require no previous stimulation, they seem to be innate predispositions. But that does not mean they are fixed and unchanging. As noted previously, some of these innate predispositions are malleable to a certain extent by learning experiences. It would seem, therefore, that human preferences for colour, curvature, order and regularity, and biological motion—often referred to as aesthetic preferences—did not appear anew along the human lineage after it split from the chimpanzee lineage. They evolved upon the foundations of animal preferences that served to direct attention to relevant aspects of the environment, like conspecifics and potential food, and to create opportunities for learning about the environment and others. A thorough explanation of aesthetic liking requires understanding the evolution of the underlying neural systems: What were the circumstances that led to their appearance and subsequent modifications? Why did they take the form they did? What was their function? What was their adaptive value? Many of the details of the evolution of sensory valuation systems remain obscure and might remain so forever. But there is much to be learnt about the features of sensory valuation systems if we look at them through the lens of evolution. And the first thing we learn is that they originated in some of the basic functions of deceptively simple single-celled organisms.

From single cells to the first nervous systems No living cell is isolated from its environment: All cells respond to different sorts of stimuli and process the signals they carry (Lichtneckert  & Reichert, 2009). Complex networks of biochemical reactions enable single celled protozoans to organize their behaviour based on inner and outer conditions (Bray, 1995; Fitch, 2021). Single celled organisms, such as paramecia, amoebae, bacteria, and ciliates, respond to variations in 258

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their environments, compute and store changes in chemical gradients, locate prey and potential mates, avoid predators and aversive stimuli, learn through habituation and repeated exposure to the same conditions, and anticipate periodic stimulation (Coyle et al., 2019; Dexter et al., 2019; Dussutour, 2021; Lan & Tu, 2016; Tang & Marshall, 2018). These elemental forms of behaviour are the product of basic mechanisms of information processing and storage carried out by cellular ion channels, receptors, vesicular transporters, signalling molecules, and genetic regulatory circuits (Fitch, 2021; Göhde et al., 2021). Thus, the molecular substrates of the computations underlying sensory valuation predate the appearance of metazoans (Figure 13.1): most of the basic elements of cognition were already present and functional before the nervous system evolved. The ability to selectively perceive specific stimuli, the discrimination between favourable and unfavourable, the assessment of the overall valence of a situation, the retention of memory, and the integration of information for decision-making—all of this was in place in one form or another in unicellular organisms and early metazoans that did not (yet) possess a nervous system. (Arendt, 2021, p. 1)

Figure 13.1 Simplified representation of the evolutionary relations among the major groups of organisms mentioned in the main text. The groups highlighted in bold correspond to the major transitions in the evolution of sensory valuation systems.

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Metazoans are multicellular eukaryotic organisms that evolved from colonies of protists about 900 million years ago. Except for sponges and placozoans, all metazoans possess a network of excitable cells with projections that connect to each other and to other cells that conform different sorts of nervous system (Burkhardt  & Jékely, 2021). Neurons evolved over 700  million years ago from epithelial excitable secretory cells that became able to transduce external mechanical, electromagnetic, or chemical stimulation and transmit these signals to adjoining cells (Cisek, 2022; Göhde et al., 2021; Varoqueaux & Fasshauer, 2017) (Figure 13.2). Neurons show many of the excitatory, regulatory, and secretory features of single-celled organisms, such as choanoflagellates: they receive information from the interior and exterior of the organisms, process and store it, and use it to organize behaviour and are also capable of generating activity endogenously (Göhde et al., 2021). What sets neurons apart is their unique morphological and functional specialization for electrical and chemical transmission of signals, secretion of biologically active substances, and endogenous generation of pace-making activity through rhythmically oscillating potentials (Lichtneckert  & Reichert, 2009; Liebeskind et al., 2017; Schneider, 2014). Networks of neurons endowed early multicellular organisms with the capacity to coordinate whole-body movements during feeding and locomotion and the capacity to integrate different sorts of information from the environment and the body’s own movement (Arendt, 2021; Cisek, 2022). These networks produced a system of intercellular communication that allowed integration, that was faster and more targeted, and that included a new kind of memory in the form of synapses (Ginsburg & Jablonka, 2021). The Ctenophora and Cnidaria are the oldest extant animal lineages with nervous systems (Burkhardt & Jékely, 2021; Schneider, 2014). Ctenophora, or comb jellies, are planktonic gelatinous animals that generally have two tentacles for capturing prey and eight rows of swimming ciliary combs (Figure 13.3). Their nervous system is a diffuse network of neurons that from tracts below the combs and around the mouth and pharynx. These organisms have sensory nerve cells interspersed among the epithelial cells and concentrated at the apical organ, consisting of nerve cells and a statocyst, the balance sensory receptor. The apical organ directs the comb jellies’ movement through pace-making activity transmitted through the comb plate rows, and excitatory and inhibitory paths coordiante the activity of comb plate cells and tentacle movements that enable capturing and ingesting prey. Cnidarians have evolved a broad array of nervous systems, including diffuse two-dimensional nets formed by different types of neurons and with different concentrations; intermediate neurons between sensory neurons and motor neurons; ring-shaped nerve tracts that serve as integrative centres where peripheral paths converge and behaviour is generated; and specialized sensory organs around the bell margin, such as statocysts and ocelli and even lens eyes (Lichtneckert & Reichert, 2009; Schneider, 2014).

The origin of the central nervous system in early bilaterians The nervous system of bilaterians evolved, about 540–560  million years ago (Burkhardt  & Jékely, 2021; Marshall, 2006), from the ectodermal nerve net in a cnidarian ancestor (Figure 13.1). Several similarities in the expression of regional patterning genes in cnidarians and bilaterians, as well as in genes that confer neural identity, suggest that the blastopore of a cnidarian-like ancestor of bilaterians became elongated and formed a slit. This gastric slit subsequently closed partially, creating a separate mouth and anus (Nielsen et al., 2018). The mouth evolved at one end and the anus at the other, and a nerve cord appeared from the fused tissue in between, with an anterior concentration of neurons (Holland, 2017). The appearance of an anteroposterior axis also brought about the differentiation of body segments aligned along this axis and the development of specialized body parts. Similarities in the expression of genes that mediate anteroposterior and mediolateral patterning of the central nervous system (CNS) in lampreys, amphibians, mammals, annelids, and arthropods suggest that early bilaterians had an anteriorly placed brain with a sensory-neurosecretory apical system capable of orienting the organism in relation to light, gravity, and chemical gradients, followed by several sections and a single or a pair of nerve cords, forming a mechanosensory griddle around the ventral midline. This 260

Figure 13.2 Proposal for the evolution of neurons and synapses. Choanoflagellates might have had a polarized vesicle transport system. Initially, there was no chemical signal transduction at soma or filopodial plasma membrane contact sites (1). In a hypothetical colonial ancestor of metazoans, the apical–basal directed vesicle transport translocated to soma and/or filopodial plasma membrane contact sites. This resulted in one cell becoming a signal donor (2) and another a signal receiver (3). This relationship was stabilized in an epithelialized multicellular animal ancestor. From this condition, more stable presynaptic (4) and postsynaptic (5) relationship evolved in different groups of early branching animals. Reproduced with permission from Göhde and colleagues (2021), originally published by the Royal Society under the terms of the Creative Commons Attribution License.

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Figure 13.3  Mertensia ovum, or Arctic comb jelly, is a species of ctenophore. Visible in this photograph is the apical organ, at the top right, the rows of swimming ciliary combs running down the body, and the two tentacles. Photograph by Alexander Smenov, reproduced with permission.

would later evolve into the preoral and postoral chain of ganglia in invertebrates and the brain and spinal cord in vertebrates (Arendt et al., 2021; Cisek, 2022; Formery et al., 2019; Fritzsch & Glover, 2009; Tosches & Arendt, 2013). The evolution of this basic CNS in early bilaterians involved determining the disposition and size of the CNS within the ectoderm during embryonic development and the specification of morphologically and functionally diverse types of neurons. Both are interdependent processes, in that the developmental patterning produces migratory pathways that lead specific neuron types to their correct places; dendritic and axonal growth patterns that connect neurons to each other, creating tracts and nerves; and the regulation of neural proliferation and survival (Fritzsch & Glover, 2009). The radical change in the bilaterian body plan and the appearance of the CNS was associated not only with specific genes that regulate axis specification and the induction of mesoderm and endoderm. It was also associated with the appearance of genes that orchestrate the development of the CNS and regulate its function. Among these bilaterian-specific genes are those that code neurotrophin receptors, crucial elements of the signalling system that regulates aspects of neural development and plasticity, including neuron survival, synapse formation, and axon guidance; those that code monoamine neurotransmitter receptors for serotonin, adrenaline, and dopamine; and those that code hormone receptors involved in the regulation of feeding, homeostasis, and stress (Heger et al., 2020). Bilaterians thus represent not only a new body plan with different and complex parts that result from regional patterning and specification (Peterson & Davidson, 2000). They represent a new way of engaging with the environment, one which entailed a centralized nervous system linked to physiological regulation of the organism through diverse signalling molecules, such as dopamine or serotonin (Barron et al., 2010; Moroz et al., 2021), that enabled associative learning (Ginsburg & Jablonka, 2021) and modulated approach and avoidance movements in response to appetitive or threatening stimuli, exploratory movements, reward seeking, learning, memory, and reproductive and activity cycles, among others. 262

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Evolution of the vertebrate brain The early worm-like bilaterians eventually gave rise to deuterostomes and protostomes (Figure 13.1). At some point during the evolution of deuterostomes, there was a dorsal–ventral inversion, with the ventral nerve cord becoming placed dorsally (Butler & Hodos, 2005). The nervous system of the cephalochordate amphioxus is considered a good approximation of that of the last common ancestor of cephalochordates and vertebrates (Figure 13.1). Similarities in the patterns of neural connectivity in adults and embryos, of neurochemical circuits, and of gene expression (the genes mediating anteroposterior and dorsoventral patterning are expressed similarly) of amphioxus and lampreys ( jawless fish) indicate that chordates evolved from an ancestor with a CNS with differentiated diencephalic forebrain, midbrain, hindbrain, and spinal cord (Figure 13.4) (Cisek, 2022; Fritzsch & Glover, 2009; Holland, 2017; Pombal & Megías, 2017). The early vertebrates evolved from an ancestral chordate. They became larger and powerful swimmers. They had pharyngeal muscles, which allowed them to increase the rate of food ingestion, and vascularized gills, which facilitated the exchange of gases. These changes came with innovations in the sense organs, including a pair of image-forming eyes, a complex vestibular system, mechanosensory and electrosensory lateral lines, taste receptors, and an olfactory system (Striedter & Northcutt, 2020). The CNS of vertebrates evolved through the progressive elaboration of the chordate basic scheme, with the addition and refinement of systems for sensory processing, motor control, motivational and homeostatic control, memory, anticipation, and planning (Butler, 2000; Butler & Hodos, 2005; Lacalli, 2022; Schneider, 2014). Forward locomotion led to innovations in sensory processing and the motor control of orienting towards or fleeing from objects. The sensory inputs were conveyed from the diencephalon to the rhombencephalon, where chemosensory processing evolved into a gustatory system that processed not only food selection and rejection, but also the motivation to search based on stored information about past encounters with suitable or unsuitable foods, where vestibular processing evolved into a system that kept track of the orientation and position of body and head, where many reflexes and fixed action patterns, such as fleeing, became organized and where the control of vital functions, such as breathing, wakefulness, and arousal, became centralized. These systems linked to centres for sensorimotor integration in the mesencephalon that received inputs from multiple sensory modalities, coordinated orienting, feeding, escape, aggression, mating, parenting, and other action patterns triggered by sensory input and exerted motivation control over these behaviours (Cisek, 2022; Schneider, 2014). This elaboration of rhombencephalon and mesencephalon neural circuits occurred concurrently with the elaboration of embryological proliferative zones that develop into prosencephalon circuits. The ventral aspect of the prosencephalon evolved into the striatum and amygdala, the dorsomedial aspect evolved into the medial pallium and eventually into the hippocampus, the dorsal aspect evolved into the dorsal pallium and

Figure 13.4 Schematic representation of the central nervous system of the vertebrate ancestor with differentiated diencephalon, mesencephalon, rhombencephalon, and nerve cord. Adapted from Butler (2000; Butler & Hodos, 2005).

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eventually into the cortex, and the lateral aspect remained the olfactory cortex. This prosencephalic expansion seems to have initially been driven by the adaptive value of elaborating two links between the olfactory system and systems for motor control, object identification, and spatial memory. One of these links was the precursor of the ventral striatum. By connecting systems that processed olfactory stimuli, systems for motion control, and motivational systems in the diencephalon and mesencephalon, the ventral striatum acquired a fundamental role in disinhibiting approach and avoidance behaviours (Cisek, 2022). As a link between the olfactory bulbs and the hypothalamus, the ventral striatum modulated endocrine and autonomic nervous control. As a link to the midbrain, the ventral striatum became involved in the control of orienting movements and approach and avoidance behaviours. A fundamental feature of this early ventral striatum link was its capacity to establish associations between sensory information and actions and their outcomes by relying on phasic dopamine releases as reinforcement signals. This extension of the role of dopamine from a tonic signal of average food intake to a phasic signal of momentary increases or decreases in intake boosted learning and reinforcing state-action associations (Cisek, 2022). The capacity to learn responses and create habits conferred by this plasticity probably led to the subsequent projection of other sensory modalities from the thalamus and subthalamic regions to the striatum. Initially, these new projections would have overlapped with olfactory projections, but eventually they segregated, leading to the differentiation of the dorsal and ventral striatum (Schneider, 2014). The second link was the incipient medial pallium. With its projections to the ventral striatum, hypothalamus, and centres for long-range locomotion, it became specialized in storing associations between gradients of cues in the environment and the positive or negative consequences of being in, or approaching, those locations, and enabled places to be remembered as safe and favourable or as threatening or unfavourable (Cisek, 2022; Manns & Eichenbaum, 2009; Schneider, 2014). These innovations must have occurred very early on in the history of the vertebrate lineage, as the CNS of lampreys, the oldest group of living vertebrates that diverged from the lineage that leads to mammals close to 560 million years ago (Figure 13.1), shares many features with the CNS of jawed vertebrates (Butler, 2000; Grillner, 2021; Grillner & Robertson, 2017; Suryanarayana et al., 2021). They share, for instance, the same rostrocaudal organization: telencephalon, diencephalon, mesencephalon, rhombencephalon, and spinal cord. Lampreys share with other vertebrate lineages the general pattern of connectivity, chemical markers, development, and expression of homeobox genes in the basal ganglia (Smeets et al., 2000): in mammals and lampreys, the striatum functions as the input structure to the basal ganglia and receives excitatory signals from the thalamus and pallium/cortex and modulatory signals from the dopamine, serotonin, and histamine systems. Lampreys and mammals also share general projection patterns from pallium to downstream structures mediated by glutamatergic projection neurons. They also share the topography, connectivity, and histochemistry of the hippocampus (Bingman et al., 2017). They also have neural nuclei that resemble in development, connectivity, and function, the homologue of the amygdala. The vertebrate amygdala emerged as a neural centre that emotionally tagged odours by associating them with appetitive or aversive stimuli detected through the olfactory or vomeronasal systems. Given the volatile nature of odours, this would have conferred early vertebrates a significant advantage in detecting the appetitive or aversive significance of potentially beneficial or pernicious objects and guiding behaviour accordingly (Martínez-García et al., 2009; Medina et al., 2017). These similarities in overall brain partitioning, connectivity, topographical specialization, and neural signalling suggest that some of the fundamental features of the CNS of vertebrates appeared very early on (Grillner, 2021; Grillner & Robertson, 2017; Schneider, 2014; Smith, 2021; Striedter, 2005). The basal ganglia are a good example of this structural and functional conservation, especially among tetrapods (Marín et al., 1998; Stephenson-Jones et al., 2011). All tetrapods (Figure 13.1) have a striatum that receives dopaminergic projections from the posterior diencephalon and the midbrain tegmentum, a nucleus accumbens, a pallidum, similar pathways connecting these structures, and a direct output pathway related to the release of specific behavioural patterns and an indirect output pathway related to the inhibition of competing behavioural patterns (Grillner & Robertson, 2016; Reiner, 2009; Smeets et al., 2000; Stephenson-Jones et al., 2011). This is 264

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not to say that there was little evolution of the brain along the vertebrate lineage. This basic vertebrate CNS plan was elaborated in different ways and to different extents along the vertebrate lineage, which is especially clear in the cortex, the amygdala, and the basal ganglia.

Elaboration of the vertebrate dorsal pallium/cortex The telencephalon of amphibians, which diverged from tetrapods around 350  million years ago (Iyer  & Briggman, 2021), is relatively simple (Figure  13.5, top). It possesses a medial pallium, which is probably homologous to the mammalian hippocampus; a lateral pallium, which receives projections from the olfactory bulb and is probably homologous to the mammalian olfactory cortex; and a dorsal pallium, which receives projections from the dorsal thalamus and is probably homologous to the mammalian neocortex (Manns & Eichenbaum, 2009; Striedter, 2005). Nevertheless, the amphibian dorsal pallium differs from the mammalian cortex in that it has only two layers, its neurons lack basal dendrites, it is not the main target of thalamic projections (the amphibious thalamus projects mainly to the medial and lateral pallia), its thalamic inputs are mainly multimodal, it has dominant bidirectional connections with the olfactory bulb, and it does not project out of the telencephalon (Butler & Hodos, 2005; Striedter, 2005). The amphibian dorsal pallium is so unclearly defined that it has been considered more of a transition area between the medial and lateral pallia, implying that the dorsal pallium appeared with the first amniotes (Striedter & Northcutt, 2020). Together with birds, reptiles are the living representatives of sauropsids, which appeared about 340 million years ago. The telencephalon of reptiles evolved to be larger and more complex than that of amphibian (Figure 13.5, middle). Their dorsal cortex receives stronger inputs from the dorsal thalamus, lacks reciprocal connections with the olfactory bulb, contains neurons that are more like mammalian pyramidal neurons, is organized forming three better-defined layers (one layer of cells between two layers of axons and dendrites that extend radially and tangentially), and projects to the medial cortex—the homologue of the hippocampus—and various targets outside of the telencephalon. Unlike mammals, however, the dorsal cortex of reptiles does not project to the spinal cord, it lacks reciprocal connections with the contralateral dorsal cortex, and it does not receive strong projections from the sensory nuclei of the thalamus (which project mostly to the ventral pallium in sauropsids) (Striedter, 2005; Striedter & Northcutt, 2020).

Elaboration of the vertebrate amygdala Like sauropsids and mammals, amphibians possess an amygdaloid formation, with the main pallial and subpallial components involved in orchestrating and regulating visceral and behavioural responses to stimuli and locations. However, the internal organization of the central and medial extended amygdala of amphibians is much simpler, which would indicate a less sophisticated and flexible organization of fear and anxiety responses to threatening stimuli and of responses to sexual and social stimuli. Also, the amphibian amygdala lacks differentiated cortical and basolateral nuclei, and it is mainly dedicated to processing olfactory and vomeronasal information. This is related to the fact that the cerebral hemispheres of amphibians lack pallial visual, auditory, and somatosensory centres: thalamic sensory inputs reach only the striatum (MartínezGarcía et al., 2009; Medina et al., 2017). The reptilian and avian amygdala homologues are also divided into pallial and subpallial components. However, in contrast to amphibians, reptiles and birds have pallial regions that receive sensory information from the thalamus, and project to the basolateral pallial amygdala. Thus, the appearance of sensory pallial regions in amniotes—vertebrates that evolved membranes that covered their eggs, allowing them to survive on land (Figure 13.1)—occurred in tandem with a differentiation within the pallial amygdala of superficial cortical nuclei that continued to receive direct olfactory inputs and a basolateral amygdala connected with the pallial areas that processed other sensory modalities (Martínez-García et al., 2009; Medina et al., 2017). Moreover, the general connectivity pattern of the reptilian and avian amygdala is 265

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Figure 13.5 Representation of the telencephalon of an amphibian (salamander, top row), reptilian (turtle, middle row), and mammal (hedgehog, bottom row). On the left is a representation of regional homologies across the three vertebrate groups. On the right, low-magnification views showing pyramidal or pyramidal-like neurons in black and ascending thalamic inputs in red. Modified from Striedter (2005).

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very similar to that of mammals. This suggests that the ancestral amniote already possessed a pallial amygdala with lateropallial and centropallial components and a subpallial extended amygdala with medial and central components that were involved integrating multiple signals, learning, and organizing responses to affectively and socially relevant stimuli in complex and changing ecological niches and that these aspects have been conserved throughout evolution (Martínez-García et al., 2009; Medina et al., 2017; Pessoa et al., 2022).

Elaboration of the vertebrate basal ganglia The elaboration of the different pathways through the basal ganglia varies considerably among groups of vertebrates (Smeets et  al., 2000). In amphibians, the basal ganglia are cell sparse and receive little tegmental dopaminergic input; the striatum receives its main sensory input from sensory nuclei in the dorsal thalamus, and projects mainly to the pallidum, which projects directly and indirectly to the optic tectum (Reiner et  al., 1998). In comparison to amphibians, the basal ganglia of amniotes are larger, have more interneurons (Reiner, 2009) and are enriched with tegmental dopaminergic projections and cortical input, which gives basal ganglia neurons their reward-dependent plasticity (Hikosaka et al., 2014). The connectivity scheme of basal ganglia in reptiles resembles that of amphibians, except that the sensory inputs arrive to the striatum from the dorsal ventricular ridge, suggesting that the striatum receives more highly processed sensory information in reptiles than in amphibians. In mammals, basal ganglia interneurons are even more abundant than in reptiles, and the connectivity pattern has also changed: most of the sensory inputs to the striatum arrive from the neocortex, from where almost all areas project to the striatum, and most of the outputs project to the dorsal thalamus and from there to the neocortex, suggesting a further elaboration of the sensory information arriving to the basal ganglia (Reiner et al., 1984, 1998). Thus, the appearance of amniotes and, subsequently, of mammals was accompanied by increases in the number and complexity of connections within the basal ganglia and of pallio-/cortico-striatal projections, at each step increasing the involvement of the cortex, or cortical homologues, in the processing of sensory information conveyed to the basal ganglia and the detail of the functional cortical map at the striatal level (Reiner, 2009; Schneider, 2014; Smeets et al., 2000). This gradual elaboration of the basal ganglia structure and connectivity enabled an increase in the flexibility of reward-based learning, the creation of habits based on repeated choices, and the capacity to direct gaze in anticipation of rewards. Together, these changes increased the possibility of using different kinds of sensory information to detect stimuli in the environment that motivated approach and avoidance behaviours and of executing a larger and more sophisticated repertoire of value-based and experience-dependent behavioural patterns, which were crucial for the adaptation to a fully terrestrial way of life (Cisek, 2022; Grillner & Robertson, 2016; Hikosaka et al., 2014; MacIver & Finlay, 2022; Reiner et al., 1998; Stephenson-Jones et al., 2011).

Evolution of the mammalian brain About 315 million years ago, early amniotes diverged into the synapsid and sauropsid lineages (Figure 13.1). Sauropsids evolved into the lineages that led to current-day reptiles and birds, and close to 200 or 250 million years ago, synapsids led to the early mammals. These had a small body; they were endothermal and nocturnal, fed on small invertebrates and vertebrates, and cared for their offspring. Being nocturnal, they had well-developed olfactory and auditory systems but poor vision. Their brains were small, with a differentiated but modest neocortex (Kaas, 2017; Rowe, 2017). These primitive mammals evolved into a lineage that led to present-day monotremes (platypuses and echidnas) and the lineage of therian mammals. About 150 million years ago, therian mammals branched into the marsupial and placental mammals (Murphy et al., 2004). The early evolution of the CNS in mammals was deeply influenced by changes in mastication, head movement, ventilation, and sensory systems related to olfaction, dentition, and integument. These changes led to 267

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some of the particular features of mammals’ CNS, physiology, and behaviour (Kaas, 2017; Schneider, 2014). Mammals retained the primitive tetrapod orthonasal smell, in which sniffing draws airborne molecules into the nose, where they activate the olfactory epithelium. Early mammals evolved a flexible cranio-vertebral articulation, which enabled unique modes of head movement; specialized thoracic and lumbar vertebrae and ribs, which enabled diaphragmatic ventilation; and a huge olfactory receptor genome. Together, these innovations conferred unique features on mammals’ orthonasal smell, such as the ability to track scents. In addition, they evolved a secondary palate, which changed the way they chewed and swallowed, with the tongue becoming the major guide of food around the mouth and toward the oesophagus (Rowe & Shepherd, 2016). These transformations in olfaction, mastication, head movement and ventilation paved the way for retronasal smell, in which odour molecules produced by chewing are carried by exhaled air from the mouth across the olfactory epithelium. In retronasal smell, olfaction combines with taste, vision, and hearing to produce flavour (see also Chapter 8 of this volume). Information from the orthonasal and retronasal stimulation of the olfactory epithelium converges with somatosensory information from the lips, tongue, cheeks, and teeth in the orbitofrontal cortex. This system evolved as a multimodal map that allowed the integration of different kinds and combinations of information in new and unique ways (Kaas, 2017; Rowe, 2017; Rowe & Shepherd, 2016). Improvements in mammalian audition were also related to changes in mastication that led to the structural and functional uncoupling of chewing and hearing: the jaw bones became specialized in chewing, and the bones that had previously joined the lower jaw to the skull turned into the chain of middle ear bones specialized in conveying vibrations. Together with this, the appearance of eardrums, long coiled cochleas, and hair cells that amplify intracochlear vibrations greatly improved mammals’ ability to detect sounds and expanded the range of (especially high) frequencies that could be heard. This new capability was exploited by the appearance of the specifically mammalian auditory cortex (Kaas, 2017; Striedter, 2005), organized as tonotopic maps, regions that respond to specific temporal patterns, and regions that respond to specific natural sounds, such as conspecific vocalizations. Processing in the auditory cortex enabled mammals an unprecedented capacity to identify acoustic patterns informative not only of the location in space of the source, but also of its identity (Schneider, 2014). One of the most crucial innovations in mammals was the evolution of a six-layered cortex that received thalamic input radially (Figure 13.5, bottom) and could process, integrate, and store sensory information with great detail and organize movements with great precision (Kaas, 2017; Striedter, 2005). Mammalian cortical neurons are packed together, forming functional units called minicolumns, densely interconnected neurons aligned in vertical rows. This microcircuitry of the mammalian neocortex is more intricate and elaborate than reptiles’ and is richer in intracortical connections than in thalamic input (Barsotti et al., 2021; Kaas, 2017). The cortex is subdivided across its surface, forming specialized areas. The neocortex of early mammals probably had around 20 cortical areas and included at least a primary visual area and two secondary visual areas, one auditory area, cingulate, retrosplenial, prefrontal, and perirhinal areas, and a primary somatosensory area, and up to five secondary somatosensory areas (Figure 13.6). The primary motor and premotor areas appeared, by partially taking over the motor functions of the somatosensory areas, only in placental mammals (Kaas, 2017; Krubitzer, 2007). The addition of cortical primary and secondary sensory and motor areas enabled a more accurate and detailed perception of the world, a greater precision of movement control, and a greater ability to anticipate stimuli and plan actions in advance (Nudo & Frost, 2009; Schneider, 2014). As the neocortex of mammals evolved, it established connections with all levels of the CNS. For instance, unlike amphibians or reptiles, and unlike the output of other telencephalic components, the neocortex of mammals sends projections that bypass the midbrain and target the hindbrain and spinal cord (Cisek, 2022; Nudo  & Frost, 2009; Schneider, 2014). As the neocortex enlarged and specialized into novel functional areas, its outputs began reaching the striatum, thalamus, hypothalamus, midbrain, and cerebellum, exerting increasing influence on behaviour and its regulation, enabling, among other things, the transition from the control of whole-body locomotion to the control of forelimbs for grasping and manipulating objects. The 268

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Figure 13.6 Phylogenetic tree showing the relations among major groups of mammals and illustrating the extension of primary and secondary visual, auditory, and somatosensory cortical areas. Reproduced with permission from Krubitzer (2007).

evolution of the neocortex was coupled with a substantial increase in the connectivity of the circuits that linked the cortex and basal ganglia, as noted previously, with the cortex becoming increasingly involved in the processing of the thalamic sensory information projected to the basal ganglia and with the basal ganglia informing cortical sensory and motor circuits, creating integrative corticostriatal loops that play a fundamental role in action selection, associative learning, and goal-directed behaviours (Cisek, 2022; Kaas, 2017; Schneider, 2014; Smeets et al., 2000; Striedter & Northcutt, 2020). The neocortex also established interconnections with the amygdala, and these became critical for the timely and contextually appropriate engagement of behaviours driven by motivationally and socially relevant goals, such as approach-avoidance 269

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learning, foraging, defence against predators, and social signalling (Dixon & Dweck, 2021; Murray & Fellows, 2021; Pessoa et al., 2019). In mammals, the amygdala reached its greatest complexity and structural differentiation. It became a true multisensory hub that contextualizes sensory information and uses it, together with learnt associations between stimuli and between stimuli and outcomes, to determine the emotional, motivational, and social relevance of sensory information and to initiate the neuroendocrine, autonomic and behavioural aspects of approach and avoidance responses (Martínez-García et al., 2009; Medina et al., 2017; Nieuwenhuys et al., 2008; Pessoa et al., 2022). It conserves the same two main subdivisions that appeared early in the vertebrate lineage, which differ in the principal neurotransmitter they rely on, their primary connections, and function. The cortex-like pallial amygdala of mammals includes the cortical (which processes mostly olfactory information) and basolateral (which processes information from different modalities) divisions. The striatum-like subpallial amygdala includes the central and medial nuclei, which are continuous with the bed nucleus of the stria terminalis and together form the centromedial extended amygdala. All mammals, including monotremes, marsupials, and placentals, share the same basic subdivisions of the amygdala, although the relative sizes of these vary among the groups (Medina et  al., 2017). The mammalian amygdala receives sensory information from the olfactory, vomeronasal, gustatory, viscerosensory, and nocioceptive systems; from the thalamus; and from different areas of the neocortex. It also receives numerous projections that modulate its functioning: cholinergic projections from the nucleus basalis-substantia innominate complex, dopaminergic projections from the ventral tegmental area and substantia nigra, serotoninergic projections from the raphe complex and locus coeruleus, and projections from several nuclei in the hypothalamus. The amygdala has three main output pathways that modulate behaviour and physiology. The pathway descending from the central extended amygdala to centres in the hypothalamus and brainstem is involved in the orchestration of the motor, vegetative, and endocrine aspects of fear and anxiety. Projections from the medial extended amygdala and portions of the pallial amygdala to centres in the hypothalamus mediate the behavioural and neuroendocrine responses to conspecific chemical signals related to reproductive and agonistic behaviours. Projections from the basolateral amygdala to the ventral striatum are related to the processing of reward, the generation of positive emotions, appetitive behaviours, reward expectation, and learning through stimulusreward associations (Martínez-García et al., 2009).

Evolution of the primate brain Primates diverged from other orders of placental mammals around 80–90 million years ago. Early primates were small-bodied and small-brained nocturnal predators that foraged for insects and small vertebrates in the fine branches of trees and shrubs (Fleagle & Seiffert, 2017; Ho et al., 2021; Kaas et al., 2022; Preuss, 2009; Ross & Martin, 2009; Sussman, 1991; Wise, 2017). As primates evolved, their brains became larger in relation to body mass than the brains of other mammals and acquired a number of specializations, the most conspicuous of which is their enlarged neocortex (Preuss, 2009; Striedter, 2005). Among the earliest impetus for this increase in brain and neocortex size were somatosensory and motor innovations related to new ways of feeding and moving in the terminal branches of angiosperm trees and to an expanded and elaborated visual system (Cartmill, 1992; DeCasien & Higham, 2019; Ho et al., 2021; Ross & Martin, 2009). Grasping extremities appeared very early on in primate evolution, linked to the early primate form of feeding and locomotion, consisting of leaping with the hindlimbs and grasping with the forelimbs. The development of primate grasping for feeding and locomotion was accompanied by the elaboration of cerebral motor systems. Whereas most mammal species have few premotor areas, primates have nine or more. They are special in having a ventral premotor area specialized in hand and mouth movements that projects directly to the spinal cord, conferring more dexterity in hand and arm movements involved in reaching, grasping, and manipulating food items and bringing them to the mouth. In addition, the evolution of 270

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touch-sensitive finger and toe tips was accompanied by an enlargement of the somatosensory cortex, increasing the amount of somatosensory information available, especially related to hand haptics and the sensory control of grasping (Preuss, 2009; Striedter, 2005). These early primates evolved forward-facing eyes, which increased the scope of stereoscopic vision, enabling better depth perception (Preuss, 2009; Striedter, 2005; Wise, 2017). The frontal-facing eyes were later complemented by modifications to the retinal receptors, neural circuitry underlying colour vision, and changes in retinal projections to the mesencephalon: in most other orders, the projection is almost completely crossed, but in primates, each retina projects roughly in equal proportions to both superior colliculi. The lateral geniculate and the superior colliculus became larger and more differentiated, as did the cortical regions devoted to visual processing (Kaas et al., 2022; Striedter, 2005). In most primates, the visual system encompasses almost half of the cortex. It includes an enlarged cortical primary visual area V1 and several extrastraite retinotopically organized visual areas that are not present in other mammals. The projections from V1 and related visual areas to the parietal and temporal lobes, which had depended more on information relayed on information from the superior colliculus in the primate ancestors, became the primary source for the cortical processing of information. The dominance of cortical visual processing areas led to two functionally specialized paths: a ventral stream, toward the inferior temporal cortex and related mainly to the visual identification of objects, and the dorsal stream, toward the posterior parietal cortex, a multimodal area with major visual inputs devoted mainly to organizing specific eye and hand movements towards objects in nearby space, such as reaching and grasping, taking hand to mouth, or protecting face or body (Goodale & Milner, 1992; Goodale & Westwood, 2004; Kaas et al., 2022; Kaas & Stepniewska, 2016; Preuss, 2009). Some of the distinctive features of primate brains are found in the prefrontal cortex. Primates are unique among mammals in that they have granular areas in the prefrontal cortex (Preuss, 2009; Preuss & Wise, 2021; Rudebeck & Izquierdo, 2021; Wise, 2017). The distinction between agranular and granular cortex has to do with the number and density of cell bodies in layer 4, the internal granular layer: Granular cortical areas have a conspicuous layer 4, whereas agranular cortical areas have fewer cell bodies located in layer 4. The appearance of primates was associated with the development of granular areas in the orbitofrontal and caudal prefrontal cortex, including the frontal eye fields. Together with the new premotor and dorsal visual stream areas noted previously, these new prefrontal areas improved early primates’ capacity to search and attend to valuable objects and to compare and update the values of those objects based on their visual features, current biological needs, and previous choices (Cisek, 2022; Passingham & Wise, 2012; Wise, 2017). Early primates eventually gave rise to anthropoids, which became larger, diurnal, arboreal quadrupeds that foraged over larger territories, relying less on olfaction and more on visual cues. In anthropoids, the dorsal and ventral processing streams were elaborated further, with the posterior parietal cortex representing spatial references and several different metrics, including relative numerosity, duration, and distance, and the temporal cortex representing the identity of objects based on visual and auditory cues. These innovations were coupled with the evolution of new granular areas in the dorsolateral, dorsomedial, ventral, and polar prefrontal cortex, forming part of a larger system of association areas. These new areas enabled anthropoid primates to extend their foraging range; to anticipate seasonal changes and to reduce their foraging errors and the risk of predation by representing abstract goals, learning and using abstract rules, representing events extended in time, keeping goals and rules active in memory until they are needed, planning ahead, and anticipating the appetitive or aversive outcomes of their actions (Cisek, 2022; Passingham & Wise, 2012; Preuss & Wise, 2021; Rudebeck & Izquierdo, 2021; Wise, 2017).

Evolution of the human brain The most striking feature of the human brain is its disproportionately large size in relation to the human body. As illustrated in Figure 13.7, humans are between three and four times as encephalized as African great 271

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Figure 13.7

Illustration of the evolutionary relations among hominoid species, with macaque monkeys as outgroup, with a comparison of adult brain sizes. Reproduced with permission from Sousa and colleagues (2017).

apes (Barton, 2006; Preuss, 2017b; Rilling & Insel, 1999). This increase in brain size is clearly recognizable in the fossil record with the origin of the genus Homo, about 2.5 million years ago, and especially in Homo erectus, which appeared about 1.8 million years ago (Brunet et al., 2002; Falk, 2015; Holloway, 2015; Preuss, 2017b). Studies comparing human and nonhuman brains show that the evolution of the human brain did not involve just an increase in overall size. Most of this increase occurred in the neocortex (Finlay & Darlington, 1995; Rilling, 2006; Rilling & Insel, 1999; Verendeev & Sherwood, 2017) and came along with an extended and delayed process of brain maturation (Hublin et al., 2015; Somel et al., 2009) and increased developmental plasticity (Gómez-Robles et al., 2015, 2013). Studies on the evolution of the human brain have focused on four main issues: the absolute and relative expansion of association areas, new patterns of long-distance cortical connections, changes in the cellular structure of cortical areas, and changes in the expression of numerous genes related to neural development and metabolism (Preuss, 2011; Verendeev & Sherwood, 2017).

Expansion of association areas There is evidence of differential expansion of the inferior prefrontal, posterior parietal, and occipital cortex of Homo erectus by 1.7–1.5  million years ago, which achieved its maximum in our own species between 272

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Figure 13.8

Distr ibuted association zones are disproportionately expanded in humans. The estimated cortical expansion in humans compared to macaques is shown on the left and in humans compared to chimpanzees on the right. The colours indicate the scaling value required to achieve the size in the human brain. The primary visual area (V1) is displayed in the inset. The figure shows that human cortical motor and primary sensory areas have expanded very little (blue hues), whereas cortical association areas have expanded substantially (orange-yellow hues). Reproduced with permission from Buckner and Krienen (2013).

100,000 and 35,000 years ago (Aldridge, 2011; Bruner, 2004; Neubauer et al., 2018; Ponce de León et al., 2021). Given that cortical areas devoted to primary motor and sensory processing are about the same relative size in humans and apes, it seems that most of the neocortical expansion in humans involved the association cortex and secondary sensory cortex in the frontal, parietal, and temporal lobes, as can be seen in Figure 13.8 (Buckner & Krienen, 2013; Mars et al., 2017; Passingham et al., 2017; Preuss, 2017a). This means that the human neocortex has more resources dedicated to processing nonprimary information than other primates. For instance, throughout evolution, there were several functional segregations in the human parietal cortex that enabled a finer-grained analysis of form and motion: whereas the intraparietal sulcus of monkeys has two shape-sensitive regions, one representation of central vision, and one motion-sensitive area that is not very sensitive to 3D motion, the intraparietal sulcus of humans has four regions sensitive to shape, three representations of central space, and four motion-sensitive regions that are sensitive to 3D motion (Orban et al., 2006). In the course of human evolution, there was a substantial increase in the granular areas of the prefrontal cortex (Preuss & Wise, 2021). The proportion of grey and white matter volumes in the prefrontal association cortex is substantially larger in humans than in nonhuman primates. The proportion of prefrontal grey matter volume is 1.9 times greater in humans than in macaques and 1.2 times greater than in chimpanzees. The proportion of prefrontal subcortical white matter volume is 2.4 times greater in humans than in macaques and 1.7 times greater than in chimpanzees (Donahue et al., 2018). These results show that during the evolution of apes, and especially during human evolution, there was a disproportionate increase in the number of neurons in the frontal association cortex, accompanied by an even greater disproportionate increase in the connectivity of those neurons (Smaers et al., 2017).

Cortical connectivity Although the structural connectivity and resting state functional connectivity of the human and nonhuman primate neocortex are very similar, there appear to be human-specific structural and functional connectivity patterns linking cortical association areas involved in language, social learning, and tool use (Hecht et al., 2013; Rilling & Van Den Heuvel, 2018; van den Heuvel et al., 2016). The neocortex of humans is more modularly structured than the cortex of other primates, such as macaques or chimpanzees, and the human 273

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temporal, lateral parietal, and inferior frontal multimodal association areas are more profusely interconnected (Ardesch et al., 2019; Passingham et al., 2017). These results suggest that the evolution of the human brain entailed a shift toward a greater modularization, which would have facilitated local functional specialization and probably led to increased asymmetries in cortical area and thickness and an enhanced multimodal connectivity, which would have improved the integration of higher-order multimodal information (Ardesch et al., 2019; Mars et al., 2017; Xiang et al., 2020). Several studies have documented evolutionary changes in the white matter tracts that connect the frontal, temporal, and parietal association cortices that awarded a greater role to the inferior frontal gyrus in social learning, imitation, and tool use (Hecht et al., 2015, 2013). There is also evidence of a substantial reorganization of the cortical terminations of the arcuate fasciculus, which connects Broca and Wernicke’s areas, during human evolution. In humans, unlike in chimpanzees and monkeys, the left hemisphere acuate fasciculus establishes strong connections between the frontal cortex and a region in the middle and inferior temporal giri. This region is part of the extrastriate cortex in monkeys, but in humans, it has enlarged substantially and represents word meaning (Preuss, 2017b; Rilling et al., 2008; Rilling et al., 2012; Sousa et al., 2017).

Microstructure of the neocortex The increase in connectivity throughout human evolution noted previously is reflected in changes in the microstructure of the neocortex. For instance, Bianchi and colleagues (2013) found that, throughout the cortex, the dendritic arbors of human pyramidal neurons are longer, are more branched, and have a higher density of spines than those of chimpanzees and macaques. Changes in the cytoarchitecture of the neocortex are clearest in the parietal, temporal, and frontal association areas. For instance, there have been changes in the arrangements of minicolumns in the planum temporale, a transition area between the auditory association cortex and the inferior parietal lobe that forms part of Wernicke’s area. Minicolumns in this region are larger, contain more neuropil space, and pack more cells in humans than in other primates (Buxhoeveden & Casanova, 2002; Buxhoeveden et  al., 2001), suggesting that neurons in this region are more extensively interconnected in humans than in other primates (Buxhoeveden et al., 2001; Sherwood et al., 2017). In the temporal lobe, the amount of white matter in humans is greater than predicted by primate allometric trends, suggesting that temporal lobe connectivity patterns have undergone a certain amount of reorganization since the appearance of the human lineage (Schenker et al., 2005). In the human frontopolar cortex and Broca’s area, there is more neuropil, and neurons are more spaced than in other primates (Schenker et al., 2008; Semendeferi et al., 2011), indicating that this region contains a greater proportion of dendrites, axons, synapses, and glial cell processes, all contributing to an enhanced connectivity within the areas and with other higher-order association areas. These results suggest that throughout human evolution, there was reorganization in the cytoarchitecture of certain cortical regions that involved a greater within- and between-area connectivity and a greater proliferation of glial cells to meet the energy costs of the denser dendritic arbors and increased long-range axons (Sherwood et al., 2017, 2006; Sousa, Meyer et al., 2017).

Gene expression Recent studies have identified several uniquely human patterns of gene expression in different brain regions. Such studies have found that most of the differences in gene expression patterns between humans and nonhuman primates involve upregulation, leading to an increased expression of genes related to higher levels of neural activity and metabolism, a pattern that is not observed in other tissues (Cáceres et  al., 2003). For instance, throughout human evolution, there has been a substantial upregulation of genes related to aerobic energy metabolism (Uddin et al., 2004) and to synaptogenesis and dendrite outgrowth in neurons throughout the neocortex (Cáceres et al., 2007), to dopamine biosynthesis and signalling in populations of 274

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interneurons in the neocortex and striatum (Sousa, Zhu et al., 2017), to synaptic function in neurons in the prefrontal cortex (Berto et al., 2019), plus an increase in the complexity of gene coexpression networks in frontopolar neurons (Konopka et al., 2012) and an accelerated evolution of gene expression in prefrontal oligodendrocytes (Berto et al., 2019). The fact that most of the changes in gene expression in cortical tissue have to do with facilitating axonal function, promoting synaptic transmission, plasticity, and energy metabolism supports the notion that the evolution of the human brain entailed changes that significantly increased neural and synaptic activity (Preuss, 2017b; Sousa, Meyer et al., 2017; Verendeev & Sherwood, 2017). It is becoming increasingly clear that it is not only changes in coding regions of the genome that led to the evolution of the specific features of the human brain but that gene regulation is also a driving force in brain evolution (Florio et al., 2017; Tilot et al., 2021; Wilsch-Bräuninger et al., 2016; Won et al., 2019). Many regulatory human accelerated regions—regions of the genome that show an extremely high rate of mutations in humans and are indicative of fast evolution—are close to genes involved in the development of brain cells and are, therefore, believed to affect proliferation of neural progenitor cells and their differentiation and axogenesis (Liu et al., 2021). There is evidence that human-specific gene regulatory relations promoted the evolution of the human brain by organizing various molecular programs at different stages in development and in different cell types that led to cortical expansion, structural changes in the frontal cortex, and increases in the connectivity within and between areas (Tilot et al., 2021; Won et al., 2019).

Evolution of the human amygdala and basal ganglia Subcortical brain regions have traditionally been regarded as relatively conserved among mammals and unchanged throughout human evolution and thus less subjected to evolutionary pressures (Lew & Semendeferi, 2017). However, a more recent understanding of subcortical structures as part of integrated systems that include cortical-subcortical circuits (Dixon & Dweck, 2021; Pessoa et al., 2022) has sparked an interest in examining how they differ in humans. It is now known that, although regions such as the amygdala and basal ganglia have not undergone such spectacular transformations in human evolution as the neocortex, there have been changes in the size and connectivity of their nuclei that might be relevant to cognitive function and dysfunction (Hardman et al., 2002; Raghanti et al., 2016; Stephenson et al., 2017). There is evidence for structural specializations in the human amygdala. The human lateral nucleus is substantially larger and contains almost 60% more neurons than expected for a human-sized ape brain, whereas the basal and central nuclei are much smaller and contain fewer neurons than expected (Barger et al., 2014, 2012, 2007). These patterns differ from what is observed in great apes, whose basal nucleus contains the greatest number of neurons. The functional implications of these changes are unclear. However, given the connectivity of the lateral nucleus with the temporal lobe, the increase in size and number of neurons in the lateral nucleus of the amygdala might reflect the concerted expansion of the human temporal association areas and the enhancement of evaluating multimodal information projected to the amygdala. Conversely, given the connectivity of the central nucleus with the brainstem and the olfactory system, its reduction in size might also be related to the decreasing reliance on olfaction along human evolution (Barger et al., 2012, 2007). There is also mounting evidence of structural and functional reorganization of the basal ganglia and their connections. The human striatum is smaller—though not significantly—than expected for a human-sized ape brain, suggesting that throughout human evolution, the striatum became somewhat smaller in relative terms (Barger et  al., 2014). Despite this slight reduction in size, there are reasons to believe that human evolution brought changes in the input, internal processing, and output of information to the basal ganglia. Hardman and colleagues (2002) showed that humans have relatively more neurons than other primates in two internal relay nuclei: the subthalamic nucleus and external globus pallidus. In addition, they found that humans have relatively fewer dopaminergic substantia nigra neurons than other primates. Raghanti and 275

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Figure 13.9

Differences between humans and macaques in cortico-striatal resting-state functional connectivity of the left and right dorsal caudate. The cortical regions with positive values (warm hues) have stronger functional connectivity with the human dorsal caudate, whereas cortical regions with negative values (cool hues) have stronger functional connectivity between macaque dorsal caudate and homologous cortical regions. Reproduced from Liu and colleagues (2021), originally published and distributed under the terms of the Creative Commons CC-BY license.

colleagues (2016) showed that humans have higher dopaminergic innervation within the medial caudate nucleus, a convergence region for inputs from the dorsolateral, ventromedial, orbitofrontal, and dorsal anterior cingulate regions of the prefrontal cortex, which might be related to speech production. Balsters and colleagues (2020) showed that some regions of the human striatum have stronger functional connectivity to the lateral prefrontal and frontopolar cortex than other mammals and primates. Finally, Liu and colleagues’ (2021) recent resting-state functional connectivity study found that the cortico-striatal connectivity profiles of different regions of the human caudate and putamen are more complex than in other primates. Furthermore, they found that whereas the cortico-striatal resting-state functional connectivity of the rostral caudate was similar in humans and other primates, there were differences in the cortico-striatal resting-state functional connectivity of the dorsal caudate, a region involved in the prediction of action-outcome contingency and learning of complex skills. In macaques, the dorsal caudate is most strongly connected to sensory and motor regions, whereas in humans, it is most strongly connected to prefrontal regions (Figure 13.9). Together, these results suggest that although human evolution produced no new structures in the basal ganglia, there were changes in the intrinsic and extrinsic connectivity, which altered the degree of convergence of prefrontal inputs, the extent to which substantia nigra dopamine regulates their activity, the intricacy of internal processing, and the functional coupling with prefrontal association regions. The functional implications of these changes are far from clear, but they seem to be related to changes in the prefrontal association cortex linked to reward prediction, speech, and the acquisition of complex skills (Balsters et al., 2020; Hardman et al., 2002; X. Liu et al., 2021; Raghanti et al., 2016).

Summary and conclusions Neuroscience abounds with concepts born from novel observations. For instance, in 1889, Wilhelm His coined dendrite to refer to the branching sort of processes that he saw emanating from the cellular bodies of neurons, and in 1897, Charles Sherrington coined synapse to refer to the functional junction between neurons he believed explained the one-way direction of neural transmission and his observation of the localized 276

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nature of neural degeneration (Finger, 2000; Wickens, 2015). Concepts like dendrite and synapse were introduced to refer to directly observable entities, such as parts of the neuron, or hypothetical entities that could explain other observations, like the way neural impulses travelled or the way neural damage did not spread as if throughout a network. But other concepts that are still commonly used in neuroscience, such as emotion, motivation, and attention, were introduced centuries ago as part of philosophical or psychological discourses based on deduction and introspection, not empirical evidence. In time, these concepts were accepted as the names for mental processes and mental states, the constituting elements of the human mind (Danziger, 1997). However, with the accumulation of psychological and neuroscientific evidence, it has become clear recently that many of these terms, including motivation, emotion, and cognition, do not map onto the architecture of the neural systems that make up the brain. It has become equally clear that continuing to treat them as distinct entities is holding back psychology and neuroscience (Brick et al., 2021; Buzsáki, 2019; Pessoa et al., 2022, 2019). Concepts with such a long history and an established position within psychology and neuroscience as attention and emotion are currently being re-evaluated from this perspective (Anderson, 2011; Barrett & Satpute, 2019; Fiske, 2020; Hommel et al., 2019). Advances in neuroaesthetics during the last two decades have led to a similar re-evaluation of such fundamental concepts as aesthetic emotion, aesthetic pleasure, and aesthetic experience. There is little evidence for special forms of perception, pleasure, or emotion that would lend support to the idea of a distinct kind of aesthetic experience, aesthetic mode of processing, or aesthetic sense (Nadal & Skov, 2018; Skov & Nadal, 2018). What the evidence does show is that aesthetic valuation is rooted in neural systems for sensory valuation. These systems are composed of nuclei distributed throughout the brain that work together to assess the hedonic value of sensory information in the light of internal and external circumstances and of current and anticipated states (Becker et al., 2019; Berridge & Kringelbach, 2015; Skov, 2019). Humans share with many other classes of animals the basic constituents of these sensory valuation systems, which explains how widespread animal preferences are. Like humans, birds, rodents, and nonhuman primates have preferences for colours, curved contours, ordered and regular arrangements, and biological motion. These preferences serve crucial functions during development: drawing attention to individually and socially relevant cues in the environment, eliciting appropriate behaviours, and affording learning experiences (Kovach & Wilson, 1981; Miura & Matsushima, 2016; Nunes et al., 2020). There is no need to explain how and when colour and form preferences appeared in human evolution, because colour and form preferences existed long before humans. What we call “aesthetic preferences” and “aesthetic valuation” evolved from the preference sensory valuation systems possessed by our primate, mammalian, and earlier ancestors. Many details of how this evolution took place are unknown. But there is enough evidence to sketch the evolution of human sensory valuation systems as a series of stages whereby innovations were added to pre-existing, often simpler systems.

Stage 1: Sensory valuation in single cells and simple neural networks Sensory valuation began with complex molecular systems in unicellular organisms. These systems involve receptors, ion channels, vesicular transporters, signalling molecules, and genetic regulatory circuits that enable these organisms to detect relevant substances in the environment, to trigger approach and avoidance, to modify their behaviour depending on experience and internal states, and even to anticipate events. Close to 900 million years ago, colonies of such single-celled organisms evolved into multicellular organisms when they had become so specialized that they needed each other to live. About 700 million years ago, some of these specialized cells became able to transduce external stimulation into chemical and electrical signals they conveyed to adjoining cells. These early neurons connected to light-, chemical-, and gravity-sensitive cells and to each other, forming networks that provided animals with a targeted and fast system of intercellular communication that could integrate exterior and interior information and orchestrate feeding and locomotion movements. In the earliest animals with nervous systems, thus, sensory valuation tasks were divided up 277

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and consigned to specialized cells: some cells detected events and created signals, other cells conveyed these signals, and others coordinated movements.

Stage 2: Sensory valuation in early bilaterians About 580 million years ago, the body plan of some of those animals experienced a radical transformation, giving rise to the anteroposterior axis of bilaterians. This change came with a reorganization of the nervous system that placed a sensory-neurosecretory system capable of orienting in relation to light, gravity, and chemical gradients at the front, followed by nerve cords forming a mechanosensory griddle, and a whole new suite of neural signalling systems. Some of these systems regulated neural development and plasticity, while others were involved in neurotransmission and homeostatic regulation. With bilaterians, sensory valuation systems became centralized and inextricably linked to physiological regulation through several signalling pathways that relied on specialized neurotransmitters, such as dopamine and serotonin, and hormones, such as adrenaline. These pathways enabled the targeted and general modulation of associative learning, approach and avoidance movements in response to appetitive or aversive stimulation, exploratory movements and reward seeking in response to homeostatic signals, and reproductive and activity cycles.

Stage 3: Sensory valuation in early vertebrates The brain of early chordates, which appeared close to 560 million years ago, was segmented into diencephalic forebrain, midbrain, hindbrain, hindbrain, and spinal cord. The brain of vertebrates, which appeared about 525 million years ago, evolved by elaborating this basic chordate scheme. Vertebrates developed true sensory systems that processed visual, vestibular, mechanical, and electric stimuli and specialized, yet interconnected, systems devoted to motor, motivational, and homeostatic control. The sensory valuation system of vertebrates included a differentiated ventral striatum and amygdala. The ventral striatum evolved as a link between the olfactory system and the motor system and hypothalamus, acquiring a fundamental role in releasing approach and avoidance movements and regulating the activity of the endocrine and the autonomous nervous systems. The capacity of the ventral striatum to establish associations between sensory information and actions and their outcomes by relying on dopamine as reinforcement signals greatly expanded the capacity to learn and create habits, turning it into a critical element of the sensory valuation system. The amygdala emerged as a centre specialized in the affective tagging of odours by associating them with appetitive or aversive stimuli, becoming crucial to the detection of the survival significance of perceived objects and events. In the course of vertebrate evolution, the basal ganglia started to receive processed sensory information from the pallium and developed a richer internal connectivity, which increased the flexibility of reward-based learning and the repertory of value-based and experience-dependent behaviours. At the same time, more nuclei were added to the amygdala, including some that connected directly to pallial regions that processed sensory information other than olfaction, which enabled the integration of different kinds of information to organize responses to affectively and socially relevant stimuli.

Stage 4: Sensory valuation in early mammals Mammals appeared about 250  million years ago. They evolved new ways of moving their heads and of breathing, smelling, chewing, and hearing, linked to important changes in their brains. The most striking innovation was the appearance of the neocortex, which increased the accuracy of perceptual processing and the precision of movement control while also creating convergence zones, such as the orbitofrontal cortex, where olfactory, gustatory, and somatosensory information from the mouth came together. As the neocortex evolved, it established stronger connections with the basal ganglia, creating integrative corticostriatal loops 278

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involved in action selection, associative learning, and goal-directed behaviours. The neocortex also established stronger connections with the amygdala, which became a centre for multisensory integration that signalled the affective and social relevance of perceived objects and initiated the neuroendocrine, autonomic, and behavioural components of approach-avoidance responses. The cortical-amygdala circuits were crucial for the contextually dependent engagement of behaviours directed towards motivationally and socially relevant goals, such as approach-avoidance learning, foraging, defence, and social signalling.

Stage 5: Sensory valuation in primates Primates diverged from other mammals close to 85 million years ago. Their nocturnal and terminal branchdwelling lifestyle was made possible by changes in the motor, somatosensory, and visual cortex. As primates evolved, their prefrontal cortex increased in size, enabling them to assess, compare, and update the value of objects based on their attributes, current needs, and past experience and eventually to represent abstract goals, learn and use abstract rules, and anticipate and plan into the future. In primates, the links between the neocortex and the basal ganglia and the amygdala became stronger and, because of the abstract nature of the information projected from association areas, these sensory valuation systems became uncoupled from the here and now. They became able to assess and compare the reward value and affective and social significance of objects that were anticipated, events that occurred in different contexts, and actions that were planned.

Stage 6: Sensory valuation in humans When humans appeared, about 7 million years ago, their brains were not much larger than chimpanzees’. But about 2 million years ago, the size of the human brain began to grow disproportionally in relation to the human body. This was due to the unprecedented increase in extension, specialization, and within and between connectivity of neocortical association regions, which led to improvements in representation of abstract goals and rules, planning, executive functions, social learning, imitation, tool use, and language. The evolution of the neocortex occurred in tandem with the evolution of the structure and connectivity of the amygdala and basal ganglia. The human amygdala became more involved in processing multimodal information at the expense of olfactory information. The human basal ganglia became more elaborate in their intrinsic and extrinsic connectivity and functionally more coupled to areas in the prefrontal cortex and less coupled to sensory and motor areas. These changes could have led to the representation, assessment, pleasurable experience, and anticipation of highly abstract rewards; improvements in reward prediction; and the acquisition of language and complex skills.

Aesthetic valuation in the light of evolution Neuroaesthetics has shown that aesthetic valuation arises from systems in our brain that assess the hedonic value of sensory stimuli. The fact that other animals show similar preferences for perceptual attributes that run on comparable systems suggests a common evolutionary history. This chapter has sketched what is known about the origin and evolution of these sensory valuation systems. They originated in the complex molecular interactions that enabled unicellular organisms to use internal and external information to execute approach and avoidance behaviours and even to learn from their consequences. When multicellular organisms appeared, sensory valuation tasks were divided and assigned to specialized cells. With the evolution of the CNS came a new way of regulating the internal milieu, one that relied on neurotransmitters and hormones that modulated approach-avoidance behaviours depending on internal and external states and allowed greater speed and flexibility of learning. In vertebrates, sensory valuation systems grew and 279

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diversified substantially, becoming crucial associative relays between sensation and behaviour, responsible for learning and anticipating the consequences of actions, for signalling the affective and social relevance of stimuli, and for setting in motion appropriate responses. In mammals, and later in primates, sensory valuation systems became intertwined with the neocortex, most crucially, and with association areas and began receiving more abstract information, and they became more elaborate and specialized, capable of subtle regulation of learning and of contextually dependent organization of approach-avoidance behaviours. The human sensory valuation system evolved upon these foundations: it continued to diversify, specialize, and interact with cortical association areas, creating abstract rewards and pleasures. We like things and find them beautiful because of how our sensory valuation systems became wired throughout evolution. We have colour, form, rhythmic, and melodic preferences because those systems evolved to assess the hedonic value of certain perceptual attributes that signalled important objects in the environment. We are pleased by liking and beauty because those systems evolved to use pleasure as an incentive to approach things that were life promoting. Beauty can be an emotional experience because those systems evolved to mobilize the endocrine and autonomous nervous systems for action. We tend to like music that combines a certain degree of predictability and surprise because the systems that create that enjoyment evolved to anticipate events based on regular patterns. Music can bring back memories of events and places, because those systems evolved to link affectively valenced objects and events to places. Familiarity, exposure, and expertise influence what we like and find beautiful, because those systems evolved to depend on learning and experience. We are able to enjoy the elegance of mathematical formulas because our sensory valuation systems can assess the hedonic value of abstract concepts that are relayed from cortical association areas that represent those formulas. Paraphrasing Dobzhansky (1973), everything in aesthetics makes sense in the light of evolution. The brain systems that allow us to feel pleasure and displeasure, and to like and dislike, did not evolve because of the adaptive value of enjoying or creating opera, sculpture, dance, or fine calligraphy, just as noses and ears did not evolve to support eyeglasses. Quite the other way around: the shape of eyeglasses was designed to fit around ears and noses, just as cultural artefacts, such as music, dance, buildings, and gardens, were designed to fit around the biological systems that generate pleasure and displeasure, liking and disliking. These systems evolved because of the selective advantage conferred by existing capacities to detect and approach life-favouring substances, objects, and places; to detect and withdraw from life-threatening substances, objects, and places; and to modify behaviour based on such experiences. Thus, the attributes of the biological systems underlying aesthetic liking, and of the cultural artefacts humans have invented to tickle these systems, were shaped by their primordial function of organizing animal behaviour in adaptive ways: approaching what’s good, avoiding what’s bad, and learning from this.

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In M. Nadal & O. Vartanian (Eds.), Oxford handbook of empirical aesthetics. Oxford University Press. Vartanian, O., Navarrete, G., Chatterjee, A., Fich, L. B., Leder, H., Modroño, C., Nadal, M., Rostrup, N., & Skov, M. (2013). Impact of contour on aesthetic judgments and approach-avoidance decisions in architecture. Proceedings of the National Academy of Sciences of the United States of America, 110(Suppl. 2), 10446–10453. https://doi.org/10.1073/ pnas.1301227110 Vartanian, O., Navarrete, G., Chatterjee, A., Fich, L. B., Leder, H., Modroño, C., Rostrup, N., Skov, M., Corradi, G., & Nadal, M. (2018). Preference for curvilinear contour in interior architectural spaces: Evidence from experts and nonexperts. Psychology of Aesthetics, Creativity, and the Arts, 13(1), 110–116. https://doi.org/10.1037/aca0000150 Verendeev, A., & Sherwood, C. C. (2017). Human brain evolution. Current Opinion in Behavioral Sciences, 16, 41–45. https://doi.org/10.1016/j.cobeha.2017.02.003 Washburn, S. L. (1970). Comment on “A possible evolutionary basis for aesthetic appreciation in men and apes”. Evolution and Human Behavior, 24(4), 824–825. https://doi.org/10.1111/j.1558-5646.1970.tb01818.x Weichselbaum, H., Leder, H., & Ansorge, U. (2018). Implicit and explicit evaluation of visual symmetry as a function of art expertise. i-Perception, 9(2), 2041669518761464. https://doi.org/10.1177/2041669518761464 Westerman, S. J., Gardner, P. H., Sutherland, E. J., White, T., Jordan, K., Watts, S.,  & Wells, S. (2012). Product design: Preference for rounded versus angular design elements. Psychology and Marketing, 29, 595–605. https://doi. org/10.1002/mar Westphal-Fitch, G., Huber, L., Gómez, J. C., & Fitch, W. T. (2012). Production and perception rules underlying visual patterns: Effects of symmetry and hierarchy. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 367(1598), 2007–2022. https://doi.org/10.1098/rstb.2012.0098 Wickens, A. P. (2015). A history of the brain: From stone age surgery to modern neuroscience. Psychology Press. Wilsch-Bräuninger, M., Florio, M., & Huttner, W. B. (2016). Neocortex expansion in development and evolution— From cell biology to single genes. Current Opinion in Neurobiology, 39, 122–132. https://doi.org/10.1016/J. CONB.2016.05.004 Winne, J., Teixeira, L., de Andrade Pessoa, J., Gavioli, E. C., Soares-Rachetti, V., André, E., & Lobão-Soares, B. (2015). There is more to the picture than meets the rat: A study on rodent geometric shape and proportion preferences. Behavioural Brain Research, 284, 187–195. https://doi.org/10.1016/j.bbr.2015.02.018 Wise, S. P. (2017). The evolution of the prefrontal cortex in early primates and anthropoids. In J. H. Kaas (Ed.), Evolution of nervous systems (Vol. 3, 2nd ed., pp. 387–422). Academic Press. Won, H., Huang, J., Opland, C. K., Hartl, C. L., & Geschwind, D. H. (2019). Human evolved regulatory elements modulate genes involved in cortical expansion and neurodevelopmental disease susceptibility. Nature Communications, 10(1), 1–11, 2396. https://doi.org/10.1038/s41467-019-10248-3 Xiang, L., Crow, T. J., Hopkins, W. D., & Roberts, N. (2020). Comparison of surface area and cortical thickness asymmetry in the human and chimpanzee brain. Cerebral Cortex. https://doi.org/10.1093/CERCOR/BHAA202 Zolman, J. F. (1969). Stimulus preferences and form discrimination learning in young chicks. Psychological Record, 19(3), 407–416. https://doi.org/10.1007/BF03393867

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PART II

Art

14 PERCEPTION AND COGNITION IN VISUAL ART EXPERIENCE Rebecca Chamberlain

Experiences with visual art are complex, multi-faceted, contextually dependent phenomena. They integrate a variety of personal and situational factors, as well as object and event-related attributes. Given the complex behavioural nature of visual art experiences, it is with good reason that the neural basis of such phenomena has been challenging to characterize and explain. Efforts to provide an understanding of the role neural systems play in visual art experiences remain in their infancy. Nevertheless, research in the field of visual neuroaesthetics has gathered apace in recent years. This chapter’s purpose is to outline the state of the knowledge of the field at present. It will serve as a foundation for more in-depth discussions on the behavioural and neural basis for individual components of visual art experience and responses to other visual stimuli in other chapters in this volume. To provide a firm foundation to this topic, it will be necessary to first provide some brief historical background with respect to psychological and neuroscientific research on experiences with visual art. I will then outline some of the key psychological models that have been proposed to explain some or all components of visual art experience. Following this, I will present more recent theoretical models that attempt to ground these psychological mechanisms in the workings of the brain. A helpful roadmap is provided by one of the leading frameworks, the Aesthetic Triad (Chatterjee & Vartanian, 2014), which characterizes visual art experiences as an integration of three interacting neural systems associated with: emotion-valuation, sensory-motor and meaning-knowledge components of aesthetic experience. I will use the structure of the Aesthetic Triad to delineate the various lines of empirical research that attempt to pinpoint and explain the neural basis of aspects of visual art experience. Finally, I will highlight areas of research that are likely to be the focus of future investigations in the field and might serve to address some of the most compelling gaps in our knowledge that remain.

Historical origins of empirical approaches to visual art experience Empirical approaches to visual art experience can trace their origins back to Gustav Fechner’s (1876) seminal work on aesthetic psychophysics, Vorschule der Aesthetik. Fechner’s aesthetics “from below” positioned objective stimulus properties at the heart of the empirical aesthetic project, providing the foundation for later efforts to establish lawful relationships between aspects of visual art and aesthetic preferences (Birkhoff, 1933; Eysenck, 1940). As such, early research into the nature of the visual art experience placed much of their focus on the role of artwork properties such as colour, symmetry, proportion, contrast and contour, a tradition which continues in empirical work today (e.g., Dijkstra & van Dongen, 2017; Ruta et al., 2021; DOI: 10.4324/9781003008675-16

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Specker et al., 2020). Fechner not only provided a theory of how certain visual stimuli come to be appreciated, but he also pioneered methodological approaches for studying observers’ responses to artworks that also remain in contemporary use including the method of production, the method of use and the method of choice (Westphal-Fitch, 2019). Much as Fechner’s theory and methodological approach informs contemporary research in the study of experiences of art, the Gestalt movement of the early 20th century also holds influence over current theories of visual art experience. Rudolf Arnheim (1965) put forward a psychological framework embedding Gestalt principles in the study of art. Arnheim’s underlying theory was that Gestalt grouping principles (e.g., good continuation, proximity, similarity, closure) can be formally analysed within works of art and drive an observer’s aesthetic response to an artwork. Specifically, Arnheim linked aesthetic pleasure to Prägnanz (Koffka, 1935), a law that specifies that elements are perceptually organized according to the most parsimonious structure, such that violations of Prägnanz result in unattractive stimuli. Furthermore, Arnheim argued that expressive aspects of artworks could be derived quickly and spontaneously by the observer, as they are embedded in the surface structure of an artwork. Arnheim’s theory therefore linked the visual structure of an artwork (particularly the formation of groupings of artwork elements) to their aesthetic and expressive properties. Later, Gestalt theory fell out of favour, due to a lack of empirical support for the posited mapping between holistic activity in the brain and the structure of the visual world (Cupchik, 2007). However, the notion that certain configurations of artwork elements give rise to different kinds of aesthetic experience and that more harmonious groupings elicit aesthetic pleasure still prevails in contemporary thinking around visual art and also design (Hekkert, 2006; Muth et al., 2013; Muth & Carbon, 2013; Van Geert & Wagemans, 2020). In the 1970s Daniel Berlyne attempted to position a theoretical understanding of empirical aesthetics firmly within biological mechanisms. Berlyne’s psychobiological aesthetics posited that the collative features of a stimulus (e.g., novelty, complexity, uncertainty, etc.) influenced arousal levels of an organism, motivating behaviour and generating emotion via systems of reward and aversion (Berlyne, 1974). The relationship between collative features and dependent variables such as liking were predicted to form an inverted U-shape, a function of the interaction between reward and aversion systems as a particular collative property increased. While the biological basis for Berlyne’s theory failed to gain empirical support and predicted inverted-U relationships proved difficult to isolate in experimental conditions, Berlyne’s work represented a crucial step toward establishing empirical aesthetics in the mainstream of experimental psychology. Additionally, much like Fechner and Arnheim’s work, the theory acknowledged the role of stimulus properties in driving observers’ aesthetic responses but additionally accounted for the role of observer-specific factors on the reception of a stimulus, such as prior experience. Later theory and research in the field also focused on sensory and cognitive processing dynamics, modelling how observers respond to salient properties of the stimulus through processing fluency mechanisms (Flavell et al., 2020; Reber et al., 2004) while at the same time incorporating the sensory and cognitive history of the observer through effects of mere exposure and preference for prototypical stimuli (Cutting, 2003; Winkielman et al., 2006; Zajonc, 1968). Contemporary models of visual art experience attempt to capture many of the phenomena and perspectives set forth by these early theories by integrating them into a series of information processing stages or modules (Chatterjee & Vartanian, 2014; Leder et al., 2004; Pelowski et al., 2017). While Gestalt inspired accounts of visual art (Arnheim, 1965) and theories based in psychobiology (Berlyne, 1974) put forward tentative predictions for the way in which aesthetic experience relies on the workings of the brain, the ability to empirically validate such theories was limited until the wide availability of functional neuroimaging technologies such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) in the latter stages of the 20th century. The first neuroaesthetic theories emerged in the wake of these advances (see Chapter 1), with neurobiologist Semir Zeki and neuroscientist V. S. Ramachandran positing connections between the functioning of the brain and art experience. The approaches of Zeki 296

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and Ramachandran have been classed as “descriptive neuroasthetics” (Chatterjee & Vartanian, 2014), as they do not employ experimental techniques to test the role and function of brain regions in our experience of visual art but instead make observations of how the structure and function of art maps onto the structure and function of the brain. Their thesis is that there is a deep structure underlying and unifying our experience of art. They posit that “all visual art must obey the laws of the visual system” (Zeki & Lamb, 1994, p. 607) and that therefore artists have evolved specific pictorial techniques to “titillate the visual areas of the brain” (p. 17, Ramachandran & Hirstein, 1999). Both Ramachandran and Hirstein (1999) and Zeki (1999) highlight the artist’s ability to capture essential characteristics of a visual stimulus, in much the same way as the visual system itself does. In this way artists are conceived as visual neuroscientists; they unconsciously reveal the function and organization of the visual system (Zeki & Lamb, 1994). Ramachandran and Hirstein (1999) go further and suggest that artists amplify particular visual features (such as form, colour, motion, contrast) in order to produce a super stimulus, functioning via the peak shift principle to maximize motivational and affective responses of the observer. Since Semir Zeki popularized the term neuroaesthetics in 1999, the neuroscientific study of visual art experience has proceeded apace. Before digging into the emergent findings from this discipline, it will be helpful to outline prevailing psychological models of visual art experiences, which can provide a framework for understanding how neural systems are involved in the experience of art.

Psychological models of the experience of visual art Psychological models of visual art experience tend to place focus on different elements of the aesthetic process, in turn reflecting relative focus on stimulus-based or observer-based features. Bottom-up approaches tend to focus on early-stage processing of low- and mid-level sensory features, while top-down accounts place emphasis on subjective factors that may shape incoming information. Different models also address intervening mechanisms (i.e., how sensory input is translated into reward-based output) to varying degrees. A relatively simple model that includes both early and late-stage processing is the mirror model (Tinio, 2013; Figure 14.1). This model captures the relationship between art creation and appreciation. The first stage of aesthetic experience constitutes the final stage of artwork creation. While the observer engages in early, automatic sensory processing at this stage, what is actually represented is the finalizing stage of artwork creation, in which the surface-level information is added by the artist. The intermediate stage of the model is one which engages memory-based processing in the observer, while the artist engages in expansion and adaptation of earlier derived artistic themes. The final stage for the observer involves meaning-making and the experience of aesthetic judgments and emotions, corresponding with initialization of the artistic ideas contained within the artwork (Tinio, 2013). Tinio’s mirror model is the only model to acknowledge the role of the artistic creative process in the experience of the observer. However, other models have provided a much more in-depth description and explanation of the various stages of aesthetic processing undertaken by the observer during an experience with visual art. Locher et al.’s (2007, 2010) model focuses on earlier stage processing, arguing that viewing an artwork involves translating an original gist-like representation into a focused stage of attention on elements forming compositional units, which can then give rise to impressions of expressive and stylistic aspects (Locher, 2015). The model serves to explain empirical effects of eye movements when viewing artworks, as well as qualitative data based on observation of museum behaviour and observer descriptions of artwork viewing. In their model, Locher and colleagues emphasize the role of both properties of the artwork but also the context of the observer (Locher et al., 2010). By contrast, Silvia (2005) focuses on late-stage mechanisms for translating an experience of an artwork into an emotional response. Noting the deficiency of processing fluency accounts of artwork experience (Reber et al., 2004) in capturing the range of emotional responses to art, Silvia harnessed appraisal theory (Scherer, 2001) to explain why certain observers have certain emotional reactions to different artworks. Silvia (2005) emphasizes the role of the evaluative process itself in generating 297

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Figure 14.1 Tinio’s (2013) Mirror model of art. Reprinted with permission from Psychology of Aesthetics, Creativity, and the Arts.

an emotional response to an artwork rather than any formal features of the artwork. From Silvia’s perspective, emotional responses to artworks result from a novelty check (to what extent the artwork coheres with the observer’s existing schemas), an estimate of coping potential (the observer’s sense of their control over the artwork viewing situation) and finally the importance of the artwork viewing situation to the observer’s sense of self. More recently, the distancing-embracing model (Menninghaus et al., 2017) further elaborates on the experience of negative emotions during art experiences (including but not limited to visual art). It posits that distancing mechanisms activated by an artistic schema and implying safety and control interact with embracing mechanisms afforded by an artistic experience (meaning-making, aesthetic attributes of stimuli, mixed emotions), which convert negative emotions into enjoyable aesthetic experiences. The models of Locher et al. (2007, 2010), Silvia (2005) and Meninghaus et al. (2017) enable research in visual art experience to encompass both low-level emotional processing reflected by fluency mechanisms as well as more complex emotional responses to works of art. The most comprehensive and influential model of experience with artworks is that of Leder et al. (2004; Figure  14.2). Designed to model experiences with modern art, it encompasses both early and late-stage processing, as well as elaborating on intervening mechanisms. It is a linear model which conceives of artwork viewing as a series stages of information-processing, consisting of a pre-classification stage, followed by five processing stages (perceptual analysis, implicit memory integration, explicit classification, cognitive mastery, evaluation), resulting in both an aesthetic judgment and an aesthetic emotion. In a similar manner to the model of Locher et al. (2010), Leder et al.’s (2004) model takes into account formal aspects of the artwork (the focus of the perceptual analysis and explicit classification stages), as well as aspects that the observer brings to the experience, such as meaning-making and integration with expertise and existing schema (the focus of the implicit memory integration and cognitive mastery stages). It also distinguishes between two potential outcomes of the artwork viewing process: aesthetic judgments and emotions, which arise from the final evaluative stage. The model has been highly influential in the field, arguably due to its modular design, 298

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299 Figure 14.2 Leder et al.’s (2004) model of aesthetic appreciation and aesthetic judgements. Reprinted with permission from British Journal of Psychology.

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which made the isolation of different aspects of the art viewing process possible to study empirically, as well as having a level of complexity that allowed for the integration of processes that had previously been considered in a unitary manner (Leder & Nadal, 2014). Pelowski et al. (2017) integrated Leder et al.’s (2004) model with the appraisal theory approach of Silvia (2005) and an earlier iteration of their own model (Pelowski & Akiba, 2011) to create the Vienna integrated model of top-down and bottom-up processes in art perception (VIMAP). The VIMAP represents the most comprehensive model of artwork viewing to date, adding several behavioural outcomes to the various information processing stages of the Leder et al. (2004) model. As a result, the VIMAP describes a much wider range of artwork viewing phenomena than had been accounted for by previous models and theories. In addition, the VIMAP is one of the first models to elaborate extensively on the potential neural substrates of the various processing stages and resulting outcomes. I will briefly outline some of the brain regions and functions implicated in this model in the following sections before going into more detail on the empirical basis for these neural correlates later in this chapter.

The VIMAP and neural substrates of art viewing Mapping on to Leder et al.’s (2004) model, the VIMAP’s (Pelowski et  al., 2017) processing stages begin with a pre-classification stage (the observer’s state prior to their artwork viewing experience) which serves to frame the observer’s experience through contextual factors (e.g., being in an art gallery context) and personal factors (e.g., their expertise, personality, pre-existing emotional state, etc.). The neural substrates implicated in this stage are largely based on empirical studies on framing effects, which point to the role of the medial region of the orbitofrontal cortex (OFC) when observers view artworks within specific contexts (e.g., Huang et al., 2011; Kirk et al., 2009). The perceptual analysis stage (which takes place immediately following artwork onset) encompasses early visual analysis (