Self-Organization in the Evolution of Speech

S T U D I E S I N T H E E V O L U T I O N O F L A N G U A G E

General EditorsJames R. Hurford, University of Edinburgh

Frederick J. Newmeyer, University of Washington

PU B L I S H E D

1The Origins of Vowel Systems

Bart de Boer

2The Transition to Language

Edited by Alison Wray

3Language Evolution

Edited by Morten H. Christiansen and Simon Kirby

4Language Origins

Evolutionary PerspectivesEdited by Maggie Tallerman

5The Talking Ape

How Language EvolvedRobbins Burling

6Self-Organization in the Evolution of Speech

Pierre-Yves Oudeyertranslated by James R. Hurford

IN PR E P A R A T I O N

The Origins of LanguageJean-Louis Dessalles

translated by James Grieve

PU B L I S H E D I N AS S O C I A T I O N W I T H T H E SE R I E S

Language DiversityDaniel Nettle

Function, Selection, and InnatenessThe Emergence of Language Universals

Simon Kirby

The Origins of Complex LanguageAn Inquiry into the Evolutionary Beginnings of Sentences, Syllables, and Truth

Andrew Carstairs McCarthy

Self-Organization in theEvolution of Speech

Pierre-Yves Oudeyer

Translated by James R. Hurford

3

3Great Clarendon Street, Oxford OX2 6DP

Oxford University Press is a department of the University of Oxford.It furthers the University’s objective of excellence in research, scholarship,and education by publishing worldwide in

Oxford New York

Auckland Cape Town Dar es Salaam Hong Kong KarachiKuala Lumpur Madrid Melbourne Mexico City NairobiNew Delhi Shanghai Taipei Toronto

With offices in

Argentina Austria Brazil Chile Czech Republic France GreeceGuatemala Hungary Italy Japan Poland Portugal SingaporeSouth Korea Switzerland Thailand Turkey Ukraine Vietnam

Oxford is a registered trade mark of Oxford University Pressin the UK and in certain other countries

Published in the United Statesby Oxford University Press Inc., New York

c© Pierre-Yves Oudeyer 2006English translation c© James R. Hurford 2006

The moral rights of the author have been assertedDatabase right Oxford University Press (maker)

First published 2006

All rights reserved. No part of this publication may be reproduced,stored in a retrieval system, or transmitted, in any form or by any means,without the prior permission in writing of Oxford University Press,or as expressly permitted by law, or under terms agreed with the appropriatereprographics rights organization. Enquiries concerning reproductionoutside the scope of the above should be sent to the Rights Department,Oxford University Press, at the address above

You must not circulate this book in any other binding or coverand you must impose this same condition on any acquirer

British Library Cataloguing in Publication Data

Data available

Library of Congress Cataloguing in Publication Data

Data available

Typeset by SPI Publisher Services, Pondicherry, IndiaPrinted in Great Britainon acid-free paper byBiddles Ltd., King’s Lynn, Norfolk

ISBN 0–19–928914–X 978–0–19–928914–1

ISBN 0–19–928915–8 978–0–19–928915–8

To Cecile and Arthur

Contents

Preface XList of Figures xii

1. The Self-Organization Revolution in Science 1

1.1 Self-organization: a new light on nature 1

1.2 Language origins 6

1.2.1 Interdisciplinarity 7

1.2.2 Computer modelling 92. The Human Speech Code 14

2.1 The instruments of speech 14

2.2 Articulatory phonology 16

2.3 The organization of the speech code: universals 21

2.3.1 The speech code is discrete and combinatorial 22

2.3.2 The speech code is a classification system shared bythe whole linguistic community 24

2.3.3 Statistical regularities in the phoneme inventories ofhuman languages 25

2.4 The diversity of speech codes 28

2.5 Origins, development, and form 293. Self-Organization and Evolution 32

3.1 Self-organization 32

3.1.1 Rayleigh–Benard convection 32

3.1.2 Ferro-magnetization 35

3.2 Self-organization and natural selection 38

3.2.1 Classic neo-Darwinism 39

3.2.2 Self-organization: constraining the search space 40

3.2.3 Evolutionary explanations: function is not enough 42

3.2.4 Exaptation 48

3.3 Explaining the origin of living forms 51

viii Contents

4. Existing Theories 53

4.1 The reductionist approach 53

4.2 The functionalist approach 56

4.3 Operational scenarios 58

4.4 Going further 655. Artificial Systems as Research Tools 68

5.1 What is the scientific logic? 68

5.2 What is the point of constructing artificial systems? 706. The Artificial System 75

6.1 Mechanism 75

6.1.1 Assumption 1: neural units 76

6.1.2 Assumption 2: perceptuo-motor correspondences 77

6.1.3 Assumption 3: perception and plasticity 80

6.1.4 Assumption 4: production 83

6.1.5 Assumption 5: initial distribution of preferred vectors 84

6.1.6 Assumption 6: no coordinated interactions 86

6.1.7 What is not assumed 86

6.2 Dynamics 86

6.2.1 The case of uniform initial distribution 86

6.2.2 The case where the initial distribution is non-uniform 96

6.3 Categorization and acoustic illusions 977. Learning Perceptuo-motor Correspondences 106

7.1 The articulatory synthesizer and a model of vowel perception 111

7.2 Dynamics: predicting human vowel systems 1138. Strong Combinatoriality and Phonotactics 123

8.1 Temporal neurons and their self-organized death 124

8.2 The dynamic formation of phonotactics and patterns ofcombinations 126

8.3 The impact of articulatory and energetic constraints 1339. New Scenarios 139

9.1 Compatibility with neuroscience 140

9.2 Contribution to scenarios of the origins of speech 143

9.2.1 An adaptationist scenario: an origin linked to theevolutionary advantage of linguistic communicationsystems 143

Contents ix

9.2.2 Another adaptationist scenario, with the exaptationof discreteness, shared categorization, andcombinatoriality 146

9.2.3 An exaptationist scenario in which the origin ofthe whole speech system results from architecturalside effects 147

10. Constructing for Understanding 150

Bibliography 155Index 163

Preface

The extraordinary capacities of the human brain have fascinated me for a longtime. It is without doubt the most complex system we know. Reading theworks of the founding fathers of artificial intelligence (von Neumann, Tur-ing, Minsky among others) made me aware that computers could be a crucialinstrument in our quest to understand the brain. These machines have the po-tential to play the same role for the cognitive sciences as particle acceleratorsplay in physics: they make it possible to re-create in a controlled environmentsimpler versions of the brain, while keeping to an interesting level of com-plexity. The use of the computer, a calculating machine, to simulate and studynatural phenomena is not new: Pascal used his little calculator to simulate thebehaviour of mathematical series, Lorenz used the first computers to study thebehaviour of climatic models, Fermi to study the interactions between mag-netized particles, Turing to imagine how processes of morphogenesis could beself-organized, von Neumann to study self-replication.

Later, my meeting with Luc Steels, who invited me to work in his researchteam, made me realize that there is one subject whose study could show itselfparticularly useful for understanding cognition, namely language, and espe-cially the origins of language. In addition, after several years, research into theorigins of language underwent spectacular development and mobilized theenergies of researchers from very different scientific cultures: linguists, biolo-gists, philosophers, anthropologists, ethnologists, primatologists, neuroscien-tists, and researchers in artificial intelligence.

It was therefore quite natural for me to undertake research in this area, ob-viously with the idea that using a computer would be the touchstone. Just asquickly, I chose to focus on studying the origin of speech, the physical support-ing medium of language, which seemed to me an ideal compromise betweencomplexity and generality. By way of example, Jakobson’s work in phoneticsin the first half of the twentieth century established the bases of structuralismwhich were, and still are, highly influential in all domains of Western thought.

Preface xi

This book is a synthesis of the results of these years of research. It is boundtogether by the concept of self-organization, a property by which complex sys-tems spontaneously generate organized structures, and by the role which self-organization could have played in the evolution of speech. From a theoreticalpoint of view, it participates in the revolution in the sciences of complexitywhich took place in the second half of the twentieth century, and which hasalready made possible the reconceptualizing of whole sections of the physicaland biological sciences. An example of this is the new understanding of thearchitectural and social structures of insect societies. My aim is to contributeto this momentum, showing how the concept of self-organization makes pos-sible a better understanding of a fundamental phenomenon of human culture,the origin of speech. In this book, however, the reader will not find a definitiveanswer to this question. We are still a long way from resolving it. The booktries, rather, to explore and structure the space of hypotheses, and to developconceptions and intuitions about the complex dynamics of speech. In short, itshould be read as a tool for thinking about the origin of speech, which I hopewill find a place in the construction of theories about the origin of language.

I thank Luc Steels for welcoming me into his team at the Sony ComputerScience Laboratory in Paris, as well as for the confidence and support whichhe has extended to me in this research work. His visionary papers on theorigin of language are among the main sources of inspiration for my work.I also thank Frederic Kaplan for stimulating discussions which enabled me toexplore many aspects of my work, as well as for his critical commentary onthe text. I have learned a lot from him on how to present my ideas. Thanksto Nicole Bastien for the time she took to reread this text and the articleswhich preceded it. I am also grateful to Michael Studdert-Kennedy, Bart deBoer, Louis Goldstein, and Jim Hurford, who helped with the elaboration ofmy arguments by the constructive rejoinders they made on careful reading ofmy work. The encouragements, kindness, and open-mindedness of MichaelStuddert-Kennedy have been a precious help. I am indebted to Jim Hurfordfor his editorial work, which improved the quality of the text, as well as for hisfaithful and precise translation, and to John Davey for his amiable comport-ment. Finally, I give special thanks to my wife, Cecile, for the energy she givesme each day, and which has motivated me during the writing of this book.

List Of Figures

1.1. The forms and patterns in inorganic nature 21.2. The self-organization of ice crystals 31.3. The self-organization of termite nests. 42.1. The vocal tract 152.2. The cochlea 162.3. The basilar membrane 172.4. The constriction variables in articulatory phonology 182.5. The places of constriction 192.6. An example of a gestural score: the word ‘pan’ 202.7. The relation between gestures and phonemes 202.8. The three representational spaces of speech 212.9. Distribution of counts of vowel inventories 23

2.10. Distribution of counts of consonant inventories 232.11. The perceptual magnet effect 262.12. Distribution of consonants in UPSID 272.13. Distribution of vowel systems in UPSID 273.1. Self-organization of parallel stripes in Benard convection 333.2. Convection currents in Benard liquids for different temperatures 343.3. Self-organization of hexagons in Benard Convection 353.4. The edge of chaos in ferromagnetic structures 373.5. Magnetization dynamics in a 2D ferromagnetic model 373.6. The growth and form of shell shapes 453.7. The growth and form of fishes’ morphology 463.8. Hexagonal cells in beehives and in packed water bubbles 473.9. Non-adaptive stripes in molluscs 495.1. Cellular automaton with growth of crystal-like structures 726.1. Neurons have a Gaussian activation function 786.2. General architecture of a full system 796.3. A simplified architecture 806.4. The perception of dynamic vocalizations 81

List of Figures xiii

6.5. Updating of activation function when a stimulus is perceived 826.6. Producing a vocalization 846.7. Initial distribution of preferred vectors in two agents 876.8. The crystallized distribution of preferred vectors 886.9. Examples of crystallized distributions of preferred vectors 89

6.10. Evolution of the entropy of preferred vectors distributions 906.11. Evolution of the kl-distance of preferred vectors distribution 906.12. Varying σ 2 936.13. The phase space 946.14. Examples of systems generated for different values of σ 2 956.15. Initial biased distribution of preferred vectors in two agents 976.16. Crystallized distributions with biases 976.17. Examples of crystallized biased distributions 986.18. Initial distribution of preferred vectors in two dimensions 1016.19. Perceptual warping at the beginning of the simulation 1026.20. Corresponding neural maps and basins of attraction after

crystallization 1036.21. The categorization mechanism with the coding–decoding cycle 1046.22. Examples of landscapes of basins of attraction after crystallization 1057.1. Agents’ architecture when learning the perceptuo-motor

correspondences. 1077.2. The perceptual neural maps of two agents shortly after

the beginning of the simulation 1147.3. Crystallization of neural maps on a common five-vowels system 1157.4. Another example of a system that can be obtained. This is

“an eight vowel system” 1167.5. Another example “of a vowel system containing five vowels.” 1167.6. Another example “of a vowel system containing six vowels.” 1177.7. Evolution of entropy and kl-distance of preferred vectors

distributions 1177.8. Further examples of vowel systems 1187.9. The UPSID database 119

7.10. Identifying patterns in the artificial vowel systems 1197.11. Distributions of sizes of vowel systems obtained in the

simulations and in the UPSID database 1217.12. Distribution of vowel inventories obtained in the simulations

and in the UPSID database 121

xiv List of Figures

8.1. The spatial and temporal maps of two agents 1278.2. Crystallized temporal map if the pruning mechanism is not used 1288.3. Crystallized temporal map using the pruning mechanism 1298.4. Another example of a crystallized temporal map 1308.5. Evolution of the number of surviving temporal neurons 1318.6. Another example of evolution of the number of surviving

temporal neurons 1328.7. Initial biased distributions of preferred vectors in the spatial map 1348.8. Representation of the initial biases due to non-linearity and

energetic cost 1368.9. Distribution of surviving temporal neurons in 500 simulations. 137

Index

Figures and notes are indexed as f and n in bold. Where two figures appearon one page, they are indexed consecutively, e.g. 37f(a) or 37f(b).

σ 77, 78f, 86–87, 91–94, 94f, 95f,96

Abbott, L. F. 100, 140abduction 69, 154Abry, C. 25, 57, 117, 119acoustics 10, 21, 24, 54, 65, 111,

146, 148, 149continuum 22forms 14illusions 25, 28, 97–105modifications 54representations 21f, 76, 107,

147signals 77–8space 65, 77, 118–19, 127trajectories 24, 30, 77–8, 107,

108, 112, 124activations 79–80, 84f, 99–101,

102f, 104, 104f, 107, 108,109–10, 114, 124, 125–6,128, 129, 131–4, 136, 141,147, 149;

see also muscles, activationsfunction 76–7, 78f, 79, 82, 83,

100, 104, 106, 107, 108,113, 124, 125, 140

adaptationism 11, 11n, 13, 56,56n, 91, 100, 109, 111,140–1, 142–7, 149, 151,153

Alden, E. 51allophones 24, 25, 115–16alphabets 22Ameisen, J.-C. 126, 142American 26fAndry, P. 72apoptosis 126, 142Arbib, M. 140, 141architecture 106, 147–9, 151

constraints 48–9, 50–2, 67, 139,147–8, 149, 153

articulations 10, 12, 16–21, 22, 24,25, 30, 55, 56, 64, 75, 85,87, 109, 110–11, 113–14;

see also speechbias 110, 114f, 133–4configurations 53–4, 85–6, 96,

97f(a) 106, 110–12, 135,141, 147, 148

constraints 124, 133–8continuum 22, 30, 63–4costs 57ease 63

164 Index

articulations (continued)organs 54–5, 142phonology 16–17, 20–1, 21f,

75, 84fspace 54, 59–60, 111synthesizer 110, 111–12,

113–14, 114f, 115targets 17, 24, 30, 65, 75, 76n,

78, 78f, 81, 82, 83, 84, 84f,84–5, 87, 87f, 88, 88f, 97,97f(b), 98, 99, 107, 111,123, 124, 125, 127, 128,133, 135, 136f, 137f, 142

trajectories 30, 53, 80, 107, 135articulatory/perceptual

non-linearities 137–8artificial systems 9–10, 11–12, 30,

36, 59, 66, 67, 68, 69–70,73n, 74, 75–9f, 80–105,120–1, 121f(b), 122, 139,143, 144–6, 148, 150,151–154

construction 67–74Ashby, W. R. 1assumptions 9, 10, 11, 12, 54,

62–3, 65, 72–4, 76, 83,86–7, 106, 126, 139, 140,143, 144, 148, 152, 153

atoms 35–6, 37f(a)attractors 32, 35, 104, 142

landscape 41, 102, 120attunement game 63–4auditory neural systems 143, 153

babbling 79, 108, 109, 148–9,153

Bachelard, G. 68Badin, P. 25

Bailly, G. 76, 78n, 79Ball, P. 3, 5, 33, 48Baldwin, J. 48n, 73Baldwin effect 48n, 73–4n, 153nBall, P. 38, 44Bamquet, J. P. 72Barks 112, 113basilar membrane 15, 15f, 17fbasins of attraction 41, 42, 102,

103f, 104, 105f, 114f, 118fBatali, J. 10, 68beehives 45–7, 47f, 52, 67, 152–3Beloutzov–Zabotinsky reaction

48, 49fBenard cells 32, 32n, 40–2, 44, 94

temperature 35, 35f, 40–1Benard Convection 32Benard liquids 34f, 149Bernard, C. 68biased distribution 85, 97f(a)Bickford, J. A. 19bifurcation 37f(b), 38biology 3, 5, 38, 50, 68, 73–4, 145

components 143, 153forms 40, 43–4, 153

bipedalism 29, 42–3birds 50–1, 146Blake, R. 16Bloom, P. 53, 62, 145Boe, L.-J. 25, 57, 117, 119Bonabeau, E. 3Bowles, S. 51brains 5, 6, 7, 8, 20, 21, 21f, 57,

62, 65, 99, 103, 112,126, 137–8, 139,140, 141–3, 145,147, 148–9, 153

Index 165

bridges 50Browman, C. 16, 55, 56, 63, 64,

65, 66, 75, 144, 153

Camazine, S. 3, 38Cangelosi, A. 10n, 68, 86Carlson, R. 112Carre, R. 54categorical perception 100categorization 10, 97–105, 102,

103, 104, 104f, 105, 110,113, 115f, 120, 138, 142,146–7

causal explanations 55cautionary tale 73cells 41–2, 44, 47, 71, 149, 153;

see also hexagonal cells; squarecells; stem cells

division 48, 49fChalmers, A. 68Changeux, J.-P. 126, 141, 142, 148Chauvet, G. 5Chen, C. C. 57, 134Chinese 29Christophe, A. 106classification system 24–5clusters 87–8, 88f, 89f, 90, 90f(a),

91, 96, 97(b), 98, 99, 100,102, 104, 113, 115f, 117(b),120, 124, 128, 129, 130–3,137

see also modescochlea 14, 15, 16f, 21, 53, 63, 78,

79, 81, 96, 107–8, 112codes see speech, codescoding 44, 78, 79, 106, 109, 134,

135, 151, 153nsee also decoding

coding/decoding 103f, 113, 120

cognitive nativism 53, 145–6

combinatoriality 10, 12–13, 22,24, 30, 54, 55, 63, 66,85–6, 88, 92, 98–9, 146–7,150;

see also speech, codes

strong 123–38

commands see gestures

communication 14, 29, 43, 51, 56,58–9, 61, 64–7, 86, 122,138, 139, 143, 145, 150,151–3;

see also linguisticcommunication

complexity 1, 9, 30, 30n, 36, 42n,44, 52, 60, 130, 139, 145,150–2;

see also self-organization

evolutionary 66, 66nsciences of 1–2, 5, 9

systems 3, 144, 150

theory 5, 6

computational theories 71–2

computers;

see also simulations

modelling 9–13

programs 75

connection weights 108–9

consonants 18–19, 22, 23f(b), 25,26f, 27f(a), 28–9, 58, 124,130

constrictions 17, 19, 19f, 20–1, 22,28, 30, 55, 63–4

variables 17, 18, 18fconstructivism 68, 70

control systems 83, 84f, 103, 106

166 Index

convection currents 33, 33f, 34fcoordinated interactions 64, 86;

see also interactionsCoppens, Y. 148coupling:

perceptual and motormodalities 150

production and perception 92,110, 138

Crothers, J. 118crystallization 1, 2f, 3f, 87, 88f,

89f, 90, 91, 113culture 5–6, 10, 100, 144, 150, 152

evolution 9, 122selection 62, 66

Danchin, A. 126, 141, 142Darwin, C. (1809–1882) 6, 73Darwinian reasons 48, 143,

146–7Dawkins, R. 1, 46n, 51Dayan, P. 140de Boer, B. 10, 29, 59, 60, 61, 62,

63, 64, 66, 66n, 68, 77, 86,111, 112, 120, 122, 138,144, 145, 151, 152, 153

decoding 8, 99, 100–2, 102f, 103,103f,

see also coding; populationvector

decomposition 15–16, 16f, 17fDemiris, J. 142Deneubourg, J. L. 3, 38development 29, 44disciplines 8discreteness 22, 54, 55, 63–4,

85–6, 92, 96, 98–9,146–7;

see also speech, codessystems 10, 22, 63, 92, 145, 146,

150vocalizations 66, 88, 150

Dispersion-Focalization theory 57dissipative systems 32, 34, 92Distinctive Region Model 54distinctive speech sounds 152;

see also speech, soundsdistributions 87, 88, 88f, 88n, 89f,

89n, 90, 90f(a), 90f(b), 91,93, 94f, 97f(b), 101, 110,113, 114f, 117f(b), 120,121f(a), 121f(b), 133, 134,134f

see also uniform distributionsdiversity 30, 88, 96, 114, 121, 122DNA 41–2Duda, R. 62, 88, 88n, 89ndynamic systems 9–10, 32, 35, 38,

41–2, 44, 68, 92, 99, 104,106, 142, 154

dynamic vocalizations 81;see also vocalizations

dynamics 86–97, 113–22,137–8, 139, 140–2,142–3, 147, 148, 151,153–4

ears 8, 14, 15, 106, 110, 112, 143ecosystem 5Edelman, G. 103, 141, 142, 148edge of chaos 36, 94Einstein, A. 70, 71Eldredge, N. 41energetic constraints 124, 133–8energy 56, 61, 111

costs 57–8, 134–5, 137–8

Index 167

flow 32, 34, 47vowel systems 57

English 25, 28, 29, 32n, 54, 123entropy 88, 89n, 90, 90f(a), 93,

94f, 117f(b)epigenesis see neural epigenesistequilibrium states 33, 34, 35, 36,

41, 87, 91, 92, 110, 113Escudier, P. 22, 27, 119evolution 7, 145, 146–7, 148, 151,

153explanations 42–8, 51, 74theory 7, 41, 42–8

exaptations 11, 11n, 48–51, 67,139, 146, 147–9,153–4

Fadiga, L. 141Fant, G. 112feedback loops 100, 103, 103f;

see also positive feedback loopsferro-magnetization 35–7f(a),

37f(b), 38, 41, 87, 93–4,149

Feyerabend, P. 68fishes 44, 46fixed point see attractorsFogassi, L. 141formants 57, 57n, 59–60, 78, 107,

108, 111–13, 116f(a),116f(b), 117f(a), 119f(b)

formation 29, 48, 49, 51, 55, 59,60, 122, 124, 131, 133, 134,137, 144, 147, 152–3

languages 61, 65–66, 145, 152forms 32, 36, 42, 44, 48, 53, 58,

144, 151, 152biology 40, 43–4, 153

creation 12, 39, 149living 12, 31, 51–2, 149organisms 40, 41, 42phenotypes 40, 42repertoires 1, 10, 14, 61

Fourier analysis 15, 16fFranks, N. R. 3, 38Freeman, W. 142French 123French, R. M. 72function mapping 85–6, 97(a),

106, 111–12, 133functionalism 42, 50, 55, 139, 145,

146, 147, 151, 154speech sounds 56–8

fuzzy binning technique 88n

Gallese, V. 141Galvan, A. 76, 78n, 79Gaussier, P. 72, 82Gaussian functions 77, 78f, 88n,

91, 93, 93f, 94, 100, 104,113, 140

genes 39–40, 41, 44, 48, 51, 67, 73,147

genetics 7, 9, 73genomes 7, 8–9, 40, 41, 42, 44, 52,

53, 73, 145, 151, 153genotypes 73–4

space 40, 43–4, 47Georgopoulos, A. P. 99, 100gestures 16–17, 18, 18f, 19, 20, 21,

22, 24, 25, 28, 30, 55, 63,64, 65, 75, 76, 83, 84f, 85,92

see also discreteness; relationsbetween organs

commands 65

168 Index

gestures (Continued)continuum 24forms 14scores 19, 20, 20f(a), 20f(b), 21,

22space 24representations 21, 21f, 24,

106tracks 54, 54f, 55–6

Ghosh, A. 126, 135Gintis, H. 51Gjaja, M. N. 100, 141Glasersfeld, E. 68, 70glottis 14, 15, 15f, 19GO signal 80, 83, 106, 125Gold, E. 62Goldstein, L. 15, 16, 18, 20, 21, 54,

55, 63, 64, 65, 66, 75, 144,153

Gould, S. J. 41, 48, 49, 50, 149Granstrom, B. 112Guenther, F. 100, 141Guerin, B. 54Guillaume, P. 72

Harnad, S. 100harmonics 15, 16f, 17fHart, P. 62, 88, 88n, 89nHebbian rule 109, 140, 141,

145–7hexagonal cells 45–7, 48, 67, 151,

152–3hexagonal shapes 47, 52hexagonal tessellation 47, 47fHinton, G. 73, 73nhumans 3, 9, 65, 137, 138, 140,

141, 148–9, 150, 151,153;

see also brains; speech, codeslanguages 10–11, 12, 28, 53, 59,

96–7, 115, 120, 121f(b),130, 134, 137, 139, 140, 150

systems 11, 12, 74, 113, 120–3

imitation 55, 59–60, 72, 84, 146–7,150, 152, 153

games 59, 61, 63, 64, 66, 86,138, 152, 144, 152

implementations 53, 69Infeld, L. 71information 14, 43, 51, 54, 60, 86,

99, 103, 112, 113, 144, 151insect societies 3–4, 38, 51;

see also self-organization,termite nests

interactions 72f, 86, 93, 113,129–30, 131f, 132f, 133,142, 144–5, 149, 150, 152,153

interdisciplinarity 7–9interpolations 83, 111, 135;

see also polynomialinterpolations

iron plates 35–6, 41

Japanese 25, 26f, 28, 54, 124, 130jaw 17, 123Jessell, T. 75, 83n, 84, 140, 142

Kandel, E. 75, 83, 84, 140, 142Kaneko, K. 142Kaplan, F. 10, 29, 59, 61, 68Kauffman, S. 1, 3, 94Keefe, A. 40Kirby, S. 10, 59, 61, 68, 145Kitano, H. 5

Index 169

Kohonen, T. 140Kolb, B. 17Konishi, M. 146Kuhl, P. K. 25, 26f, 100, 102Kuhn, T. S. 68, 70Kullback–Leibler distance 88, 89nKupiec, J.-J. 5

Laboissiere, R. 76, 78n, 79Lacerda, F. 25, 26f, 100, 102Lamarckism 73landscape see attractors,

landscapeLangton, C. 68Language Acquisition Device 53languages 5–7, 9, 14, 21, 53, 54,

62, 68, 122, 123, 124, 138,139, 144, 145;

see also formation, language;humans, languages

evolution 6, 7, 10, 11, 29, 62faculty 7–8families 115, 119fgames 59–60, 61, 66, 86,

144–5origins 6–7, 9, 10, 11, 61, 64, 67,

68, 145, 152larynx 8, 14–15, 18, 19f, 22learnability 61learning 61, 62, 72, 73, 85, 87, 109Liberman, A. M. 21Liljencrants, L. 57Lindblom, B. 25, 26f, 43, 57, 58,

59, 61, 63, 100, 102, 134,151, 153

linear mapping 40, 96linguistic communication 11, 13,

24–5, 52, 67, 139,

143–6, 150, 151, 152,153;

see also communicationlinguistics 68, 123lips 14, 16–18, 19, 19f, 22, 54, 75,

87, 111liquids 42

heating 32–3, 33ftemperature 33, 33f, 34, 34f,

40–1living forms 12, 31, 51–2living organisms 31, 39–40, 41, 44

MacNeilage, P. 123Maddieson, I. 22, 54, 115magnetization 37f(b)Mataric, M. 142mathematical evaluations 8, 9Mattingly, I. G. 21mechanisms 32, 38–9, 42n, 47, 51,

59, 66, 69, 70, 71, 74, 75–6,84, 87, 91, 97–9, 104, 106,107, 125, 133, 144, 149,151 ;

see also neural mechanismsconstant 44, 45fpruning 130f

Mehler, J. 106Messinger, A. 73methodology of the artificial 68Miikkulainen, R. 57, 134mirror neurons 141;

see also neuronsmodalities 55, 141, 147–8, 150modes 69, 88, 88f, 90f(a), 90f(b),

91–2, 93, 96;see also clusters

170 Index

Moga, S. 72Mohan, A. 142molluscs 44, 46f, 48, 49fMorgan, C. L. 73morpho-perceptual nativism 53morpho-perceptual organs 62morphology 54

constraints 12, 66correlates 55

motor commands 24–5, 83, 142,143

motor control 142, 143, 147motor maps 17, 109, 110motor networks 106, 111, 113,

142–3, 145, 146, 148,149

motor neurons 106, 107, 108, 109,110, 111, 147;

see also neurons; perceptualneurons

motor spaces 113–14, 141, 153motor systems 143, 145Motor Theory of Speech

Perception 21Mrayati, M. 54muscles 18, 18f, 21, 21f, 24, 30, 76,

77, 106, 134activations 78, 79, 80f, 106, 127

Nadel, J. 72Nakanish, A. 22narratives 69, 70, 71–2nativist cognition 62natural sciences 7, 68–74natural selection 1, 3, 12, 13, 30,

32, 38–9, 39n, 40, 42, 42n,43, 43n, 44, 47, 51–2, 56.

66, 67, 139, 143, 145, 151,153

see also self-organization

natural systems 3, 32, 35

nature 8, 9, 31, 39

neo-Darwinism 7, 11, 39, 39n, 40,43, 47, 51, 52, 56, 67, 149,151;

see also Darwin, C.

networks 143

neural architecture 12, 78, 146

neural epigenesis 126, 141–2,153

neural maps;

see also temporal neural maps76, 79, 85, 87, 88f, 88n,89n, 92n, 99, 100–101,101f, 102, 102f, 103, 103f,104, 104f, 106, 107, 100,110–11, 113, 115f, 124,127f

neural nets 65, 76, 141

neural networks 78–9, 81f, 83,92, 109–10, 123, 129, 141,142

neural structures 13, 53, 143,145–7, 149

neural systems 113

neural units 76, 80–1, 81f, 82, 82f,83–7, 89f, 90f(a), 90f(b),97f(a), 99–101, 104

neurons 76, 76n, 77, 78f, 79,81–2, 82f, 84–5, 91, 92,96, 98, 99, 100, 101, 102f,103, 106, 107, 109, 111,113, 120, 124, 125–7, 129,

Index 171

131, 132f, 133–4, 140–3,148

neuroscience 7, 8, 66n, 99, 139–43neurotrophins 126, 134–6, 136f,

142Newton, Sir I. (1642–1727) 1, 3Newtonian physics 70Nicolis, G. 92noise 40, 63, 64, 65, 77, 104, 122non-linearities 43n, 53, 54, 64,

85–6, 91, 96, 97f(a), 110,111, 112, 133, 137, 149

transition 38non-uniform distributions 96,

113, 114fsee also distributions; uniform

distributionsnorms 29, 59, 66Nowlan, S. 73, 73n

On Growth and Form(Thompson) 44, 45n

ontogenetics 9, 30ontogenesis 44, 147, 148operational mechanisms 139operational models 9, 107operational scenarios 58optimality 52, 56–7, 58order and chaos 94organisms 40–4, 46n, 48–50, 51,

91, 149see also living organisms

organizational properties 1, 32organs 14, 16–18, 20, 24, 55, 75,

143;see also relations between

organs

relation space 77n, 78–80, 80f,81, 81f, 83, 85, 86–7, 87f,96, 97f(a), 97f(b), 101,101f, 106, 107, 127, 127f,128, 130f, 128f, 133–4,134f

speech 21, 54–5, 79trajectories 21, 24

origins;see also languages, originsof forms 43, 48

living 52of speech 12, 13, 29, 70, 74, 101,

139, 143–9, 153–4Oudeyer, P.–Y. 10, 61, 62, 63, 64,

66, 68, 76, 79, 86, 138, 153

panda 49Parisi, D. 10n, 86parallel stripes 33f, 34, 34f, 41parameters 41, 44, 64, 91, 92–3,

94–5 120, 122, 125, 126Parisi, D. 10n, 68patterns, 1, 34, 34f, 38, 44, 48, 49f,

92, 118, 119f(b), 120,123–4, 129, 129f, 130, 131,153;

see also speech, soundshexagonal 41of combinations 126, 130organized 32sounds 11, 62temporal 125

Peirce, C. S. 69, 154perception 54, 60, 75, 80–2, 83–4,

99perception/production coupling

83

172 Index

perceptual categories see attractors

perceptual distinctiveness 56, 57,58, 63, 138, 151

perceptual magnet effect 25, 26f,100–2

perceptual maps 106–7, 110, 113,118–19, 120

perceptual motor mapping 109perceptual networks 111, 113,

142–53

perceptual neural maps 110, 114fperceptual neural networks 78, 79,

85, 113

perceptual neurons 107–8, 147perceptual non-linearities 54;

see also non-linearities

perceptual representations 106,110, 112

perceptual salience 56, 57

perceptual spaces 77, 80f, 106, 141

perceptual systems 143, 145perceptual trajectories 108

perceptual warping 25, 102, 102fperceptual/articulatory mapping

109

perceptuo-motorcorrespondences 77–80,106, 107f, 108–22

phases 35, 41, 93–4, 94fphenotypes 73–4

forms 40, 44

space 40, 42, 43–4, 51, 52

phonatory motor structures 143

phonatory networks 146phonatory neural systems 143

phonemes 19, 20f(b), 22, 24, 25–8,30, 54, 56, 57–8, 61, 63, 66,

98, 114, 115–16, 120,123–4, 128–9, 129, 129f,130, 133, 135, 138, 147, 152

inventories 26, 28, 55, 58, 89f,96–7, 123–4, 142

phonemics 22, 25, 113

coding 54, 55–6, 59, 96, 110,146, 149, 151

phonetics 19, 145

phonology 22, 28, 130, 133, 145

attunement 64, 84, 143

phonotactics 28, 58, 63, 66,123–30, 130f, 131–3, 137,138, 147, 150

Picq, P. 148

Pinker, S. 53, 62, 145

plasticity 77, 80–2, 125–6

polynomial interpolations 83, 107,111, 142;

see also interpolations

Popper, K. 68

populations of agents 11, 12, 29,91, 92n, 106, 114, 130f,139, 143, 144, 145, 146,150, 151–2

population vectors 99–102, 102,102f, 103, 104f, 106,109–10, 113, 120

see also decoding

positive feedback loops 26, 32,113, 114, 132;

see also feedback loops

poverty of the stimulus 62

preferred vectors 76, 79, 80, 81–2,82f, 83, 84, 84f, 85–6, 87,87f, 88, 88n, 89f, 90f(a),90f(b), 91, 94, 94f, 96,

Index 173

97f(a), 97f(b), 99–101,101f, 104, 106, 107–11,113, 114f, 117f(b), 120,127–8, 133–4, 134f, 140–1,142–3, 145

Prigogine, I. 1, 92

production 56, 83–4, 137, 142

vowels 137

programmed cell death 126

programmed neuron death 142

pronunciation 117–18, 134

property 32, 41

psychology 55, 58, 71

punctuated equilibrium 41

quantal theory of speech 53–4

Ramus, F. 106

random mutations 39

randomness 38, 86, 107, 109, 110,123, 125, 126, 130, 132,141, 142, 144–6, 147, 148,151

Rayleigh–Benard convection 32,32n, 33–5

re-entrance systems 103

reality 70–2

Redford, M. A. 57, 58, 61, 134

reductionism 1, 3, 53–6, 59

regularities 25, 30, 114–15, 117,118, 150, 151, 152

relation space 20, 76, 78

relations between organs 65, 75,78–9, 80f, 83, 84f, 85, 106,107, 111, 124;

see also organstrajectories 76, 76n, 77–8,

78freplication 40, 51, 149representations 7, 70, 72, 75–6,

78, 79f, 81, 83, 101, 102–3,106, 112, 113, 137–8,147

reproduction 43–4, 51, 145, 151Rizzolati, G. 141robots 10, 59, 72–3, 75Rotokas 29

salient sounds 58Salinas, E. 100scalar products 100scenarios 139–49Schwartz, J. 75, 83n, 84, 140, 142Schwartz, J.-L. 23, 25, 27, 57, 112,

117, 119science 68–72;

see also natural sciencessearch space 40–2Sejnowsky, T. 109Sekuler, R. 16self-organization 38, 40, 41, 42,

123, 137, 138, 139, 140,145–7, 149, 150–1, 153,154

see also natural selectionbubbles 1, 32constraints 138dunes 1, 2fice crystals 1, 2f, 3f, 4, 32, 71,

72f, 71, 72fmountains 1, 2fshells 39–40, 49f, 50–1, 149termite nests 3–4, 4f

174 Index

sensitization 81f

sentences 7–8

sequence of gestures see relationsbetween organs

Shannon, C. 54

shapes 38, 39, 43, 43n, 45, 47, 52,62, 71, 72f, 104, 153

organisms 40, 42, 48

side effects 48–51, 52, 67, 100,147–9, 153

sigma see σ

sign languages 8, 10–11, 14, 29,29n

signals 144, 150, 152

simulations;

see also computers 59, 61, 62,63–6, 71–3, 75, 83, 86, 87,88, 89f, 90, 90f(b), 91–3,101, 101f, 102, 102f, 107,110–11, 113, 114, 115f,118–19, 120, 121, 121f(a),122, 125, 126, 127f, 128,129, 129f, 130, 131f, 132,133–4, 136, 137f, 139, 141,142, 143–4, 145–7, 149,150

Sneyd, J. 3, 38

Sonigo, P. 5

Sonority Hierarchy 28, 61

sounds 15, 21f, 24–5, 30, 43, 53,54, 59, 60, 62, 64–5, 66, 76,77–9, 80, 84, 85–6, 87, 91,96, 97, 99, 100–2, 102f,104–6, 109–12, 113–14,141, 143, 144–5, 146, 147,150, 152

see also patterns, speech;speech, sounds

classification 28, 29

continuum 26finventories 59, 144

patterns 11, 62

perception 15, 20, 21, 30, 75,142–3

production 60, 75

salient 58

space 25, 146

systems 30, 59, 101–2, 123, 134,145, 149

waves 14, 15, 15f, 17

spatial maps 124, 125, 127–8, 130,131, 134, 134f

neural 124–5

spatial networks 131

spatial neurons 124–6, 131, 133,137, 140

spatial organization 1, 32

spectrum of amplitude 25

speech 6–8, 10–11, 11n, 14, 14n,24, 28, 30, 53–6, 65, 67, 68,69, 106, 108, 141, 147, 149,150–1, 153;

see also origins, of speech;patterns, sound; speech,organs; vocal tracts

codes 10–11, 12, 13, 14, 24, 29,44n, 52, 58–9, 67, 88, 88f,92n, 122, 123, 139, 143,144–5, 147, 149, 150–4

combinatorial 22–4, 88f, 106,143, 147; discrete 12, 22–4,30, 64–5, 106, 143, 145,146, 147; diversity 28–9;

Index 175

human 14–31, 137, 143;origins 30–1, 30n, 55,56–7; universals 21–8

instruments 14–16

inventories 25

production 20–1, 21f, 58, 75

properties 24, 55

quantal theory 53–4

regularities 25

shared 24

signals 137–8

sounds 4, 7–8, 10, 14, 16, 54,55, 96, 152

properties 53, 56–8; syntax10, 14, 123

systems 28, 67, 104–5, 122, 139,147–149

universals 21

square cells 41;

see also cells; hexagonal cells;stem cells

static vowels 61;

see also vowels

statistical phonotactic

preferences 133, 137

Steels, L. 10, 29, 59, 61, 68, 86,144, 145

stem cells 40–1, 44;

see also cells; hexagonal cells;square cells

Stevens, K. N. 25, 26f, 53, 54, 100,102

stochasticity 61, 87, 88, 89f, 91,104, 132, 132f

stops 18

Stork, D. 62, 88, 88n, 89n

strong combinatoriality seecombinatoriality, strong

structures 40, 43, 43n, 44–5, 48,49–50, 51, 52, 56, 66–7, 92,94, 96, 106, 113, 120, 123,134, 139, 145–6, 149,150–3

Studdert-Kennedy, M. 54, 55, 56,64, 151, 153

syllabaries 22syllables 22, 24, 28, 30, 57–8, 61–3,

68, 123–4, 130, 134, 138symbolic representations 11symmetry 33, 36, 87, 110, 149

breaking 32, 35, 36, 94liquids 33–4

syntactic rules 30systemic biology see biology,

integrativesystematic re-use see

combinatorialitySzostak, J. 40

talk see speechTashliyt Berber 28, 123temporal evolution 90f(a), 124n,temporal filter 81, 99, 105, 108

resolution 112temporal maps 127, 127f, 128,

129, 129f, 131, 132, 135temporal networks 126temporal neural maps 125–6, 131,

136ftemporal neurons 124–7, 127f,

128, 129, 129f, 130, 131,131f, 132, 132f, 133, 135–6,136f, 137f, 140, 141, 148

temporal resolutions 108

176 Index

thawing 1, 32, 139theories 69, 70, 71–2, 74, 141–2,

148, 153–4theory of mind 72–3Theraulez, G. 3, 38Thom, R. 1Thompson, D. 44, 45, 45f, 45n,

46, 47, 48n, 67, 151, 152tones 29tongues 15, 16–17, 18, 19, 54, 111trachea 14traits 40, 42, 43, 51, 73

organisms 48, 50trajectories 18, 21Tritton, D. J. 34Tsuda, I. 142Tuggy, D. 19tundra 1, 32turn-taking 60, 152

uniform distribution 86–8, 89n,90–1, 94, 100, 104, 107,110–11

universal rules 145universals 21–8UPSID 22, 25, 27f(a), 27f(b), 28,

115–16, 119f, 120–1,121f(a), 122

utility 52utterances 20, 54–6, 60, 115

Vallee, N. 25, 57, 111, 112, 117,119

valences 130Varela, F. 1variation 40, 51vectors see population vectors;

preferred vectors

velum 14, 15, 18, 19, 54

verbal theories 68–9, 71–2

Vihman, M. 84, 143

virtual worlds 75

vocal neural nets 153

vocal tracts 8, 12, 14, 15, 15f, 16,17, 18, 21, 24, 53, 54–5, 63,85, 96, 106, 107, 110, 111,143, 148

vocalizations 12, 14, 19, 21, 22, 65,66, 75, 79–80; 80f, 81, 83,83n, 84, 84f, 85–6, 87, 87f,88, 88f, 92, 93, 96, 97f(b),98f, 99, 101–2, 103f, 105,107, 107f, 108, 109, 110,111–13, 114f, 115f, 122,123, 124–5, 126, 127n, 128,129, 132–8, 142, 143, 144,148, 150

see also sounds

Von Foerster, H. 1

Von Frisch, K. 48n, 153n

vowels 12, 19, 22, 23f(a), 25, 26–726f, 28–9, 54, 58–62,100–1, 111–12, 114, 120,124, 130

constriction 18–19

inventories 114–15, 120,121f(b), 151

perception 111–12, 137, 141

prototypes 117–18, 120

repertoires 59

systems 27, 27f(b), 43, 58, 59,60–1, 63, 66, 115, 115f,116f(a), 116f(b), 117f(a),118, 118f, 119f(a), 119f(b),

Index 177

120, 121f(a), 121f(b), 122,140, 152

energy 57; human 113–22;shared 60, 68

triangle 113, 114f, 120Vrba, E. S. 50

water molecules 1, 2f, 4, 32, 71Waldrop, M. 38whirlpools 1, 4, 32

wild cards 73

Williams, K. A. 25, 26f, 100, 102

Williamson, M. 142

Wishaw, I. Q. 17

Wolfram, S. 71

words 7, 19, 20f, 21, 28, 57–8

writing systems 14, 22

zones of stability 54