The Evolution of Language: Proceedings of the 6th International Conference (EVOLANG6), rome, Italy, 12-15 April 2006

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EVOLUTION LANGUAGE

Editors

Angelo Cangelosi Andrew D M Smith Kenny Smith

EV0LUJION LANGUAGE

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EVOLUTION LANGUAGE Proceedings of the 6th International Conference (EVOLANG6) Rome, Italy

1 2 - 1 5 April 2006

Editors

Angelo Cangelosi University of Plymouth, UK

Andrew D M Smith & Kenny Smith University of Edinburgh, UK

Y||? World Scientific NEW JERSEY

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THE EVOLUTION OF LANGUAGE Proceedings of the 6th International Conference (EVOLANG6) Copyright © 2006 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the Publisher.

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Preface This volume collects the refereed papers and abstracts of the 6th International Conference on the Evolution of Language (EVOLANG 6), held in Rome on 1215 April 2006. Although EVOLANG has been running biennially since 1996, this is the first time that a full published proceedings has been produced. Submissions were solicited in two forms, papers and abstracts, and this is reflected in the structure of this volume. The EVOLANG conference focuses on the origins and evolution of human language, and brings together researchers from many disciplines including anthropology, archaeology, artificial life, biology, cognitive science, computer science, ethology, genetics, linguistics, neuroscience, palaeontology, primatology, and psychology. The multi-disciplinary nature of the field makes the refereeing process very challenging, and we would like to thank all 85 reviewers for their conscientious and valuable efforts. Special thanks go to Jim Hurford for his encyclopaedic knowledge of researchers in the relevant fields. Further thanks are due to: •

•

• •

•

The EVOLANG committee: Bernard Comrie, Jean-Louis Dessalles, Tecumseh Fitch, Jim Hurford, Chris Knight, Domenico Parisi, Maggie Tallerman and Alison Wray. The local organising committee: Davide Marocco, Alberto Acerbi, Massimiliano Caretti, Allegra Cattani, Eduardo Coutinho, Andrea di Ferdinando, Onofrio Gigliotta, Isabella La Rovere, Gianluca Massera, Mariagiovanna Mazzapioda, Orazio Miglino, Stefano Nolfi, Angelo Rega and Vadim Tikhanoff. The Fondazione Rosselli, the Italian Association for Cognitive Science and the University of Rome La Sapienza, for their financial support. The invited plenary speakers: Tecumseh Fitch, Vittorio Gallese, Steven Mithen, Domenico Parisi, Alberto Piazza & Luigi Cavalli Sforza, Robert Seyfarth & Dorothy Cheney, Luc Steels, Leonard Talmy and Michael Tomasello. Finally, and most importantly, the authors of all the contributions collected here. Angelo Cangelosi, Andrew Smith & Kenny Smith January 2006 V

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Contents Preface Part I: Papers The Mirror System Hypothesis: From a Macaque-like Mirror System to Imitation Michael A. Arbib, James Bonaiuto & Edina Rosta Bootstrapping Communication in Language Games: Strategy, Topology and All That Andrea Baronchelli, Vittorio Loreto, Luca Dall'Asta & Alain Barrat Language Learning, Power Laws, and Sexual Selection Ted Briscoe The Baldwin Effect Works for Functional, but not Arbitrary, Features of Language Morten H. Christiansen, Florencia Reali & Nick Chater On the Emergence of Compositionality Joachim de Beule & Benjamin K. Bergen Towards a Fixed Word Order in a Society of Agents: A Data-driven Baseline Perspective Guy de Pauw

3

11 19

27 35

43

Simulation Model for the Evolution of Language with Spatial Topology Cecilia Di Chio & Paolo Di Chio

51

Mostly Out of Africa, but What Did the Others Have to Say? Dan Dediu

59

A Comparison of the Articulatory Parameters Involved in the Production of Sound of Bonobos and Modern Humans Didier Demolin & Veronique Delvaux Generalised Signalling: A Possible Solution to the Paradox of Language Jean-Louis Dessalles vn

67 75

Vlll

Innateness and Culture in the Evolution of Language Mike Dowman, Simon Kirby & Thomas L. Griffiths

83

Early Human Language was Isolating-Monocategorial-Associational David Gil

91

Computational Simulation on the Coevolution of Compositionality and Regularity Tao Gong, James W. Minett & William S.-Y. Wang

99

An Epistemological Inquiry into the "What is Language" Question and the "What Did Language Evolve For" Question Nathalie Gontier

107

Minimalist Foundations of Language Evolution: On the Question of Why Language Is the Way It Is Wolfram Hinzen

115

Why Has Ambiguous Syntax Emerged? Stefan Hoefler

123

Proto-propositions James R. Hurford

131

Convex Meanings and Evolutionary Stability Gerhard Jdger

139

Natural-language "Cheap Talk" Enables Coordination on A Social-dilemma Game in a Culturally Homogeneous Population Mark Jeffreys

145

Constraining the Time When Language Evolved Sverker Johansson

152

Working Backwards from Modern Language to Proto-grammar Sverker Johansson

160

Language Co-evolved with the Rule of Law Chris Knight

168

A Saltationist Approach for the Evolution of Human Cognition and Language Susan J. Lanyon

176

Interaction of Developmental and Evolutionary Processes in the Emergence of Spoken Language John L. Locke

184

Labels Facilitate Learning of Novel Categories Gary Lupyan

190

IX

Emergence of Communication in Teams of Embodied and Situated Agents Davide Morocco & Stefano Nolfi

198

A Language Emergence Model Predicts Word Order Bias James W. Minett, Tao Gong & William S-Y. Wang

206

Talking to Oneself as a Selective Pressure for the Emergence of Language Marco Mirolli & Domenico Parisi

214

Learning Models for Language Acquisition Shashi Mittal & Harish Karnick

222

Simulating the Evolutionary Emergence of Language: A Research Agenda Domenico Parisi

230

Evolving the Narrow Language Faculty: Was Recursion the Pivotal Step? Anna R. Parker

239

From Mouth to Hand Dennis Philps

247

Diffusion of Genes and Languages in Human Evolution Alberto Piazza & Luigi Cavalli Sforza

255

Differences and Similarities between the Natural Gestural Communication of the Great Apes and Human Children Simone Pika & Katja Liebal The Evolution of Language as a Precursor to the Evolution of Morality Joseph Poulshock Modelling the Transition to Learned Communication: An Initial Investigation into the Ecological Conditions Favouring Cultural Transmission Graham Ritchie & Simon Kirby

267 275

283

Towards A Spatial Language for Mobile Robots Ruth Schulz, Paul Stockwell, Mark Wakabayashi & Janet Wiles

291

Why Talk? Speaking as Selfish Behaviour Thorn Scott-Phillips

299

Semantic Reconstructibility and the Complexification of Language Andrew D. M. Smith

307

The Protolanguage Debate: Bridging the Gap? Kenny Smith

315

How to do Experiments in Artificial Language Evolution and Why Luc Steels

323

X

The Implications of Bilingualism and Multilingualism for Potential Evolved Language Mechanisms Daniel A. Sternberg & Morten H. Christiansen

333

Selection Dynamics in Language Form and Language Meaning Monica Tamariz

341

A Statistical Analysis of Language Evolution Marco Turchi & Nello Cristianini

348

Evolutionary Games and Semantic Universals Robert van Rooij

356

Overextensions and the Emergence of Compositionality Paul Vogt

364

Grammaticalisation and Evolution Henk Zeevat

372

Stages in the Evolution and Development of Sign Use (SEDSU) Jordan Zlatev & The SEDSU Project

379

Part II: Abstracts Alarm Calls and Organised Imperatives in Male Putty-nosed Monkeys Kate Arnold & Klaus Zuberbuhler Perception Acquisition as the Causes for Transition Patterns in Phonological Evolution Ching-Pong Au The Evolution of Syntactic Capacity from Navigational Ability Mark Bartlett & Dimitar Kazakov The Subtle Interplay between Language and Category Acquisition and How it Explains the Universality of Colour Categories Tony Belpaeme & Joris Bleys

389

391 393

395

The Evolution of Meaningful Combinatoriality Jill Bowie

397

The Adaptive Advantages of Knowledge Transmission Joanna J. Bryson

399

Determining Signaler Intentions; Use of Multiple Gestures in Captive Bornean Orangutans (Pongo pygmaeus) Erica Cartmill & Richard Byrne Nuclear Schizophrenic Symptoms as the Key to the Origins of Language Timothy J. Crow

401 403

Articulator Constraints and the Descended Larynx Bart de Boer Evolutionary Support for a Procedural Semantics for Generalised Quantifiers Samson Tikitu de Jager

405

407

The Evolution of Spoken Language: A Comparative Approach W. Tecumseh Fitch

409

Allee Effect on Language Evolution Jose F. Fontanari & Leonid I. Perlovksy

411

Rapidity of Fading and the Emergence of Duality of Patterning Bruno Galantucci, Theo Rhodes & Christian Kroos

413

Reconsidering Kirby's Compositionality Model Towards Modelling Grammaticalisation Takashi Hashimoto & Masaya Nakatsuka The Interrelated Evolutions of Colour Vision, Colour and Colour Terms David J. C. Hawkey A Little Bit More, A Lot Better: Language Emergence from Quantitative to Qualitative Change Jinyun Ke, Christophe Coupe & Tao Gong

415 417

419

Major Transitions in the Evolution of Language Simon Kir by

421

Modelling Unidirectionality in Semantic Change Frank Landsbergen

423

The Origin of Music and Its Linguistic Significance for Modern Humans Steven Mithen

425

Co-evolution of Language and Behaviour in Autonomous Robots Sara Mitri & Paul Vogt

428

Iconic versus Arbitrary Mappings and the Cultural Transmission ofLanguage Padraic Monaghan & Morten H. Christiansen

430

Mother Tongue: Concominant Replacement of Language and MtDNA in South Caspian Populations of Iran Ivan Nasidze & Mark Stoneking

432

What can Grammaticalization Tell Us about the Origins of Language? Frederick J. Newmeyer

434

Xll

Bootstrapping Shared Combinatorial Speech Codes from Basic Imitation: The Role of Self-organization Pierre-Yves Oudeyer

436

How Language Can Guide Intelligence Leonid I. Perlovsky & Jose F. Fontanari

438

The Roles of Segmentation Ability in Language Evolution Kazutoshi Sasahara, Bjorn Merker & Kazuo Okanoya

440

Primate Social Cognition and the Cognitive Precursors of Language Robert Seyfarth & Dorothy Cheney

442

Agonistic Screams in Wild Chimpanzees: Candidates for Functionally Referential Signals Katie Slocombe & Klaus Zuberbilhler

443

An Individual-based Mechanism for Adaptive Semantic Change Daniel W. Smith

445

A Holistic Protolanguage Cannot Be Stored, Cannot Be Retrieved Maggie Tallerman

447

Recombinance in the Evolution of Language Leonard Talmy

449

Ape Gestures and Human Language Michael Tomasello

452

Prehistoric Handedness: Some Hard Evidence Natalie Uomini

453

Lateralization of Intentional Gestures in Non Human Primates: Baboons Communicate with Their Right Hand Jacques Vauclair & Adrien Meguerditchian

455

Emergence of Grammar as Revealed by Visual Imprinting in Newly-hatched Chicks Elisabetta Versace, Lucia Regolin & Giorgio Vallortigara

457

Beyond the Argument from Design Willem Zuidema and Timothy O'Donnell

459

Author Index

461

Papers

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THE MIRROR SYSTEM HYPOTHESIS: FROM A MACAQUE-LIKE MIRROR SYSTEM TO IMITATION MICHAEL A. ARBIB, 2 ' 3 - 4 JAMES BONAIUTO 3 - 4 & EDINA ROSTA 1 ^Chemistry, ^-Computer Science, ^Neuroscience

and 4USCBrain

University of Southern California, Los Angeles, CA

Project

90089-2520

The Minor System Hypothesis (MSH) of the evolution of brain mechanisms supporting language distinguishes a monkey-like mirror neuron system from a chimpanzee-like mirror system that supports simple imitation and a human-like mirror system that supports complex imitation and language. This paper briefly reviews the seven evolutionary stages posited by MSH and then focuses on the early stages which precede but are claimed to ground language. It introduces MNS2, a new model of action recognition learning by mirror neurons of the macaque brain to address data on audiovisual mirror neurons. In addition, the paper offers an explicit hypothesis on how to embed a macaque-like mirror system in a larger human-like circuit which has the capacity for imitation by both direct and indirect routes. Implications for the study of speech are briefly noted.

1. The Mirror System Hypothesis Both premotor area F5 and parietal area PF of the macaque monkey brain contain mirror neurons each of which fires vigorously both when the monkey executes a certain limited set of actions and when the monkey observes some other perform a similar action. Imaging data show that the human brain contains mirror regions in both frontal and parietal lobes, namely regions that show high activation both when a human performs a manual action and when the human observes a manual action, but not when the human simply observes an object. It is widely assumed that such mirror regions contain mirror neurons, based on similarities between the human and macaque brain. The Mirror System Hypothesis (MSH; Rizzolatti and Arbib, 1998) asserts that the parity requirement for language in humans - that what counts for the speaker must count approximately the same for the hearer - is met because Broca's area (often associated with speech production) evolved atop the mirror system for grasping with its capacity to generate and recognize a set of actions. However (Hurford, 2004), one must distinguish the mirror system for the signifier (phonological form) from the neural schema for the signified, and note the need for linkage of the two. On this view, Broca's area becomes the meeting place for phonological perception and production, but other areas are required to link phonological form to semantic form. 3

4

The crucial point is that humans have capacities denied to monkeys. Mirror regions in a human can be activated when the subject imitates an action, or even just imagines it, but there is a consensus that monkeys cannot imitate save in the most rudimentary sense. By contrast, chimpanzees exhibit "simple imitation", the ability to approximate an action after observing and attempting its repetition many times; while humans alone among the primates have the capacity for "complex imitation", being able to recognize another's performance as a combination of more-or-less familiar actions and to use this recognition to approximate the action, with increasing practice yielding increasing skill. Thus research on MSH requires not only a fuller understanding of the mirror system of the macaque, but also an understanding of how the mirror system and the circuitry with which it interacts must have changed in the course of evolution. Arbib (2002, 2005a) modified and developed MSH by hypothesizing seven stages in the evolution of language. The first three stages are pre-hominid: SI: Grasping. S2: A mirror system for grasping, shared with the common ancestor of human and monkey. S3: A system for simple imitation of grasping shared with the common ancestor of human and chimpanzee. The next 3 stages distinguish the hominid line from that of the great apes: S4: A complex imitation system for grasping. S5: Protosign, a manual-based communication system that involves the breakthrough from employing manual actions for praxis to using them for pantomime (not just of manual actions), and then going beyond pantomime to add conventionalized gestures that can disambiguate pantomimes. S6: Protospeech, resulting from linking the mechanisms for mediating the semantics of protosign to a vocal apparatus of increasing flexibility. Arbib (2005b) argues that protosign and protospeech evolved together in an expanding spiral. The final stage is then: S7: Language: the change from action-object frames to verb-argument structures to syntax and semantics. Arbib (2005) provides arguments and counter-arguments for these various claims. The present article focuses on the earlier, rather than the later, stages in this progression. It contributes to this argument by (a) introducing a new model of action recognition learning by macaque mirror neurons which addresses data on auditory input; (b) outlining how to embed a macaque-like mirror system in a larger human-like circuit which has direct and indirect paths for "complex imitation"; and (c) noting implications for the study of speech.

5

2. MNS2: Recognizing Audible Actions

Figure 1: System diagram for the MNS2 model, updating the MNS model of Oztop & Arbib, 2002. The dashed outline shows the system for generating the reach to and grasp of an observed object. The remaining circuitry defines the mirror system and the subsystems which feed it. The encoding of the grasp motor program (F5 canonical) provides the training signal for a recurrent network which models the areas 7b and F5 mirror, shown here in the gray parallelogram, by the activity of its hidden and external output layers, respectively. The dotted arrows denote the connections unique to the MNS2 model. Auditory information about actions reaches the F5 mirror neurons via the auditory cortex. Visual data on hand-object spatial relations is input into the Object AffordanceHand State Association schema and into working memory. When this information is not available externally, the dynamically remapped working memory trace serves in its place.

The MNS model (Oztop & Arbib, 2002) of the monkey mirror system was designed to associate activity in canonical neurons providing a premotor encoding of the type of a grasp with visual input encoding the trajectory of a hand relative to an observed object. The learning mechanism was a feed-forward backpropagation network of units with one hidden layer which required an unnatural recoding of its input. Bonaiuto et al. (2005) developed a model, MNS2, that could process the time series of hand-object relationships without such recoding, using an adaptive recurrent network to learn to classify grasps based on the temporal sequence of hand-object relations. Umilta et al. (2001) have shown that mirror neurons in the macaque monkey can recognize a grasp if the monkey has seen the target object which was then hidden, but cannot recognize the action lacking current or recent input on the affordances and location of the object. MNS2 incorporates working memory and dynamic remapping components (Figure 1) which allow the model to recognize

6 grasps even when the final stage of object contact is hidden and must be inferred. Before being hidden, the object position and its affordance information are stored in working memory. Once the hand is no longer visible, the working memory of wrist position is updated using the still-visible forearm position. If the model observes an object which is then hidden by a screen, and then observes a grasp that disappears behind that screen, the wrist trajectory will be extrapolated and the grasp will be classified accordingly. However, the more important contribution of MNS2 within the context of MSH is that it addresses data on "audiovisual" mirror neurons which associate sounds with manual actions. Kohler et al. (2002 -see Figure 2 right) found that some of the mirror neurons in area F5 of the macaque premotor cortex responsive to the sight of actions associated with characteristic noises (such as peanut breaking) are just as responsive for the sounds of these actions. 100-

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Figure 2: Left: Activation of the model's external output layer when presented with a precision grasp sequence containing (from top to bottom) visual and congruent audio, visual only, audio only, and visual and incongruent audio information. The black vertical lines indicate the time step at which the hand made contact with the object. The unit encoding the precision grasp shows the greatest level of activation, while the unit corresponding to power grasps shows a small level of transient activity. At the bottom is an oscillogram of the sound associated with the precision grasp. Right: Activation from Kohler et al. (2002) of an audiovisual mirror neuron responding to (from top to bottom) the visual and audio components, visual component alone, and audio component alone of a peanut-breaking action.

Bonaiuto et al. (2005) associate each sound with a distinct pattern of activity which is applied to audio input units which are fully connected to the output layer of the recurrent neural network, corresponding to a direct connection from auditory cortex to F5. These connection weights are modified using Hebbian learning. In this way, any sound that is consistently perceived during multiple occurrences of an executed action becomes associated with that action and incorporated into its representation. This type of audio information is inherently

7

actor-invariant and this allows the monkey to recognize that another individual is performing that action when the associated sound is heard. 3. A Dual Route Model of Imitation Gated by Attention It is often suggested that mirror neurons are the substrate for imitation, matching observed actions onto motor programs producing similar or equivalent actions. However, as we saw earlier, only humans have "complex imitation", the ability to imitate sequences of behaviors and approximate novel actions as variants of known actions after one or just a few viewings of this novel behavior. As backdrop for our own work, we draw some important lessons from apraxia. DeRenzi (1989) reports that some apraxics exhibit a semantic deficit having difficulty both in classifying gestures and in performing familiar gestures on command - yet may be able copy the pattern of a movement of such a gesture without "getting the meaning" of the action of which it is part. We call this residual ability low-level imitation to distinguish it from imitation based on recognition and "replay" of a goal-directed action. With Rothi, Ochipa, and Heilman (1991), we thus propose a dual route imitation learning model to serve as a platform for studying apraxia. The direct route for imitation of meaningless and intransitive gestures converts a visual representation of limb motion into a set of intermediate limb postures or motions for subsequent execution. The indirect route for imitation of known transitive gestures recognizes and then reconstructs known object-directed actions. The distinction between the direct and indirect routes in praxis may be related to the well-known distinction between the dorsal and ventral streams in vision (Ungerleider & Mishkin, 1982) which also plays a crucial role in our model of the visual control of hand movements (Fagg & Arbib, 1998) and may in turn have implications for the study of language. We suggest that the interaction of these two routes underlies the human capacity for complex imitation. We hypothesize that, during sequential or complex actions, contributions from each route are encoded in a competitive queuing mechanism (Rhodes et al., 2004). The focus of attention (whether directed toward the object and limb, limb posture, or movement) determines the relative competitive weight of the movement segment encoded by each route. A modification to the competitive choice layer implements a sort of selective, n-winners-take-all mechanism, allowing non-interfering movement segments with similar weights to be executed simultaneously. In this way novel movements can be recognized as combining known actions (indirect route) with intransitive limb adjustments (direct route).

8 4. Complex/Goal-Directed Imitation We have argued that humans have "complex imitation", the capacity for recognizing novel actions as combinations of (variants of) known object-directed actions, with joint adjustments to meld them together. These novel actions can then be acquired as skills through successive approximation. In addition, humans have the ability to imitate complex "meaningless" movements which are not directed towards objects - as we saw in defining the "direct route". In their theory of goal-directed imitation, Wohlschlager et al. (2003) present the hypothesis that imitation is the result of the decomposition of the aspects of a movement and the hierarchical structuring of these goal aspects. Each of these goal aspects triggers the associated motor program for reproducing that aspect of the movement. Wohlschlager et al. (2003) attribute differences in imitative abilities across species to differences in working memory capacity. However, this is not evident from the current data, and differences in imitative ability could very well be due to differences in the mechanism(s) of hierarchical movement aspect decomposition. The fact that humans can imitate intransitive movements does not seem to be due to an increased working memory capacity, but rather the ability to decompose aspects of intransitive movements such as relative limb postures and via points. Through this process of successive approximation, complex movements can be reproduced with increasing accuracy by increased attention being paid to its subparts. This increased attention may result in a finerscaled decomposition of the observed movement, resulting in execution of a more congruent movement. 5. Discussion The audio properties of mirror neurons are of major interest because they may have been crucial in the transition from gesture to vocal articulation in the evolution of language. These multi-modal mirror neurons may have allowed arbitrary vocalizations to become associated with communicative gestures, facilitating the emergence of a speech-based language from a system of manual gestures. If this is indeed the case, the development of audio-visual mirror neurons may have implications for the recognition of communicative actions and ground the multi-modality of language (Fogassi & Ferrari, 2004; Arbib, 2005b). The possible relation of the direct and indirect routes in praxis to the dorsal and ventral streams in vision may in turn have implications for the study of language. Hickok & Poeppel (2004) observe that early cortical stages of speech perception involve auditory fields in the superior temporal gyrus bilaterally

9 (although asymmetrically) but offer evidence that this cortical processing system then diverges into two streams: A dorsal stream maps sound onto articulatory-based representations which projects dorso-posteriorly. It involves a region in the posterior Sylvian fissure at the parietal-temporal boundary, and ultimately projects to frontal regions. This network provides a mechanism for the development and maintenance of "parity" between auditory and motor representations of speech; and A ventral stream maps sound onto meaning which projects ventro-laterally toward inferior posterior temporal cortex (posterior middle temporal gyrus) which serves as an interface between sound-based representations of speech in the superior temporal gyrus (again bilaterally) and widely distributed conceptual representations. The distinction between the direct and indirect routes in praxis may also be relevant to the distinction made by Levelt (e.g., Levelt et al., 1999) between overt and internal speech. Using our normal perceptual system, we can monitor our own vocal output and discover errors, dysfluencies, or other problems of delivery in our own overt speech. However, Levelt further claims that we can monitor some internal representation - Wheeldon and Levelt (1995) offer evidence that this takes the form of a somewhat abstract phonological representation - as it is produced during speech encoding and use this internal self-monitoring ability to trace the process of phonological encoding itself. As noted by one of the reviewers, a fruitful topic for future research is to pursue the development of this dual-feedback architecture on an evolutionary scale as part of the task of elaborating the Mirror System Hypothesis. References Arbib, M.A. (2005a). From Monkey-like Action Recognition to Human Language: An Evolutionary Framework for Neurolinguistics, Behavioral and Brain Sciences, 28, 105-167. (Supplemental commentaries and the author's "electronic response" are at Behavioral and Brain Sciences, http://www.bbsonline.org/Preprints/Arbib05012002/Supplemental/Arbib. E-Response_Supplemental.pdf.) Arbib, M.A. (2005b). Interweaving Protosign and Protospeech: Further Developments Beyond the Mirror, Interaction Studies: Social Behaviour and Communication in Biological and Artificial Systems 6, 145-171. Bonaiuto, B., Rosta, E., and Arbib, M.A. (2005). Recognizing Invisible Actions, Workshop on Modeling Natural Action Selection, Edinburgh, July, 2005. (An expanded version has been submitted for publication under the title "Extending the Mirror Neuron System Model, I: Audible Actions and Invisible Grasps".)

10 DeRenzi, E. (1989). Apraxia. In F. Boiler & J. Grafman (Eds.), Handbook of neuropsychology, Amsterdam: Elsevier. Vol. 2., pp. 245-263. Fagg, A.H., Arbib, M.A. (1998). Modeling Parietal-Premotor Interactions in Primate Control of Grasping, Neural Networks 11, 1277-1303. Fogassi, L., Ferrari, P.F. (2004). Mirror neurons, gestures and language evolution, Interaction Studies: Social Behaviour and Communication in Biological and Artificial Systems 5,345-363. Hickok, G., and Poeppel, D., 2004, Dorsal and ventral streams: a framework for understanding aspects of the functional anatomy of language, Cognition, 92, 67-99. Hurford, J.R. (2004). Language beyond our grasp: what mirror neurons can, and cannot, do for language evolution. In D.K. Oiler and U. Griebel (Eds.), Evolution of Communication Systems: A Comparative Approach. Cambridge, MA: The MIT Press, pp. 297-313. Kohler, E., Keysers, C , Umilta, M.A., Fogassi, L., Gallese, V., Rizzolatti, G. (2002). Hearing Sounds, Understanding Actions: Action Representation in Mirror Neurons. Science, 297, 846-848. Levelt, W.J.M., Roelofs, A., Meyer, A.S. (1999). A theory of lexical access in speech production. Behavioral and Brain Sciences, 22, 1-38. Oztop, E., Arbib, M.A. (2002). Schema design and implementation of the grasp-related mirror neuron system. Biological Cybernetics, 87, 116-140. Rizzolatti, G., Arbib, M.A. (1998). Language Within Our Grasp. Trends in Neuroscience, 21, 188-194. Rothi, L.J.G., Ochipa, C , and Heilman, K.M. (1991). A cognitive neuropsychological model of limb praxis. Cogn. Neuropsychol. 8, 443-458. Umilta, M.A., Kohler, E., Gallese, V., Fogassi, L., Fadiga, L., Keysers, C , Rizzolatti, G. (2001). I know what you are doing: a neurophysiological study. Neuron, 31, 155-65. Ungerleider, L.G., Mishkin, M. (1982) Two cortical visual systems. In D.J. Ingle, M.A. Goodale, and R.J.W. Mansfield (Eds.), Analysis of Visual Behavior. Cambridge, MA: The MIT Press, pp.549-586. Wheeldon, L.R., Levelt, W.J.M. (1995) Monitoring the time course of phonological encoding. Journal of Memory and Language 34, 311-34. Wohlschlager, A., Gattis, M., Bekkering, H. (2003). Action generation and action perception in imitation: an instance of the ideomotor principle. Phil. Trans. R. Soc. Lond., 355,501-515.

BOOTSTRAPPING COMMUNICATION IN LANGUAGE GAMES. STRATEGY, TOPOLOGY AND ALL THAT

ANDREA BARONCHELLI, VITTORIO LORETO Dipartimento di Fisica and SMC center, Universitd "la Sapienza", P.le Aldo Mow 2, Roma, 00187, Italy andrea. baronchelli @ romal. infn. it, vittorio. loreto @ romal. infn. it

LUC A DALL'ASTA, ALAIN BARRAT Laboratoire de Physique Theorique (UMR du CNRS 8627), Universite de Paris-Sud, Batiment 210, Orsay Cedex, 91405, France luca.dallasta @ th. u-psud.fr, Alain.Barrat@th. u-psud.fr Semiotic dynamics is a fast growingfieldaccording to which language can be seen as an evolving and self-organizing system. In this paper we present a simple multi-agent framework able to account for the emergence of shared conventions in a population. Agents perform pairwise games andfinalconsensus is reached without any outside control nor any global knowledge of the system. In particular we discuss how embedding the population in a non trivial interaction topology affects the behavior of the system and forces to carefully consider agents selection strategies. These results cast an interesting framework to address and study more complex issues in semiotic dynamics.

1. The Naming Game In recent times, the view of language as a complex dynamical system that evolves and self-organizes has gained ground in the scientific community (Steels, 2000). In this new perspective, complex systems science turns out to be a natural allied in the quest for the general mechanisms underlying the emergence of a shared set of conventions in a population of individuals. The issue is of the outmost topicality since, for the first time, the web allows for the spreading and the study of global bottom up created semiotic systems. Recently, for instance, new web tools (such as del.icio.us or www.flickr.com) enable users to self organize systems of tags and in that way build up and maintain social networks and share information. On the other hand, many technological systems are nowadays composed of single communicating entities. The capability of developing ontologies or proto languages without any intervention from the outside would be of great importance for instance in those cases in which teams of ar11

12 tificial embodied agents should explore highly unknown environments, such as distant planets or deep seas. A possible approach to the understanding of language self-organization is that of modeling artificial population of agents and studying their evolution. The choice is then between endowing agents with simple properties, so that one can hope to fully understand what happens in simulations, or with more complicated and realistic structures that yet risk to confuse experiments outputs. We choose to follow the first possibility since we are more interested in the global behavior of the population. In this perspective we do not seek answers to specific issues in the evolution of language, but rather we aim at analyzing deeply basic models that can constitute valuable starting points for more sophisticated investigations. Nevertheless, as we shall see, also extremely transparent agents and interaction rules can give rise to very complex and rich global behaviors and the study of simple models can help to shed light on general properties - a well known lesson in statistical physics. We discuss here a recently introduced Naming Name model (Baronchelli, Felici, Caglioti, Loreto, & Steels, 2005), inspired to the one proposed by Steels (1995), in which agents play pairwise interactions in order to negotiate conventions, i.e. associations between forms and meanings. The population reaches a final convergence state without any external or global control. This is a central point, since, of course, no such control has been present in the development of natural language, and, as mentioned above, its absence is becoming a desirable feature also for many technological systems. Also, it is worth noting that this model accounts for the emergence of a shared set of conventions (a vocabulary, in our case) from the point of view of cultural transmission (Hutchins & Hazlehurst, 1995; Steels, 1995), without resorting to any evolutionary issue (Hurford, 1989; Nowak, Plotkin, & Krakauer, 1999). The game is played by a population of AT agents. Each agent is characterized by its inventory, i.e. a list of form-meaning associations that evolve dynamically during the process. For the sake of simplicity we do not take into account the possibility of homonymy, so that all meanings are independent and we can work with only one of them, without loss of generality. Agents aim to converge to a unique shared form (or word) to associate with the meaning (or object). Agents have empty inventories at time t = 0 and at each time step (t = 1,2,..) two players are picked at random to play an interaction: one of them plays as speaker and the other as hearer. Their interaction obeys the following rules: • The speaker randomly extracts a word from its inventory, or, if its inventory is empty, invents a new word. • If the hearer has the word selected by the speaker in its inventory, the interaction is a success and both players maintain in their inventories only the winning word, deleting all the others.

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30000 .20000 10000 0 1200 800 400

3 1, the exponent, defines the slope of the plot, frequency c(w) is the token count of word type w in text, and rank r(w) is the position of word type w in the list of word types sorted in descending order of frequency, c(w). Guiraud's (1954) related law states that the number of word types V in a text is proportional to the length of that text TV: V oc NA

(2)

Although the models of power law distributions of which I am aware have a dynamical component, they have received little attention from evolutionary linguists. 19

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I know of only one argument, due to an evolutionary psychologist (Miller, 2000), which utilises Zipf's observation about word frequencies in attempting to explain large, redundant vocabularies in terms of sexual selection. I argue against this explanation in §4, but, before doing so, I discuss the ubiquity of power law distributions in §2, some relevant models of them in §3, return to Miller's argument in §4, and then discuss some issues power law distributions raise for evolutionary models of iterated language learning in §5. 2. Manifestations of Power Law Distributions Power law distributions are very different from normally distributed phenomena, such as height, which yield the characteristic 'Bell Curve'. The factors that influence a person's height, such as nutrition and genetic inheritance, combine in a more linear manner so that (relatively minor) variation in height is normally distributed around a mean that can be accurately estimated from a representative sample of the population. Zipf (1949) noted that Pareto's observations about the distribution of wealth in the population could also be modelled using a version of his 'law'. What makes wealth different from height intuitively is that the factors that influence the amount of money we have combine non-linearly and there are strong (positive) feedback effects (i.e. 'the rich get richer'). We now know that power law distributions are good approximations of many other non-linguistic phenomena, such as the distribution of people within cities, citations amongst scientists, accesses of web pages, species within habitats, authors amongst scientific articles, actors within films, links between web pages, activation of genes, size of earthquakes, number of sexual partners, and many more (e.g. Albert & Barabasi,2002). There are similar results regarding extrinsic properties of languages: for instance, the distribution of languages within language families approximates a power law (Wichmann, 2005). In terms of inherent properties of language, Zipf also showed that plotting the length or the number of meanings of word types against their frequency also yields similar distributions. With the increasing availability of annotated electronic corpora, these observations have been extended to many other areas of language organisation, such as the frequency of contiguous sequences of words (bigrams, digrams, and more generally ngrams), of grammatical rules, of construction types, of lexical relations between word types, as well as the length of constituents, and the association of verbs with constructions (e.g. Sharman, 1989; Manning & Schutze, 1999; Korhonen 2002; Yook et al, 2001). 3. Models of Power Law Distributions So far I have used the term 'distribution' ambiguously between the linguistic and probabilistic sense. The most important insight about such distributions with large numbers of rare events (e.g. Baayen, 2001) is that converting a frequency-rank plot into a probability-rank plot via maximum likelihood (i.e. relative frequency)

21 estimation, and treating the result as a probability distribution is unwise. Since the counts of the tail are very low, statistical estimation theory tells us that they will be unreliable. A rare word, for instance, may suddenly become fashionable (e.g. eggregious, serendipity) and thus increase in relative frequency over a given time period. Since, we always see a long tail of rare events no matter how much (more) text we sample, and the number of types grows in proportion to the size of this sample (Guiraud's law), power law distributions are often described as 'scale-free'. In statistical terms, power law distributions which remain invariant over different sample sizes are a strong indication that we may be sampling from a statistically unrepresentive non-stationary (i.e. dynamical) system. Baayen (1991), following in the tradition of Mandelbrot (1953) and Simon (1955), develops a stochastic Markovian model of phonotactically legal Dutch word strings and relates it to empirical data on similarities between words by phonological form and by relative frequency. He finds that to model these effects accurately, it is necessary to add a second 'dynamical' stochastic model which introduces or removes word types with probability proportional to their token frequency. This has the effect of increasing overall frequency-based and decreasing form-based similarity. For present purposes, it is indicative that the second dynamical word 'birth-death' processs is required even though it says nothing directly about the relationships between word types. Albert & Barabasi (2002) provide a recent survey of work on 'small world' networks in which most nodes of a network can be reached by any other in a small number of (node) steps, though the overall number of nodes can be arbitrarily high. They define a dynamical algorithm for generating such networks, by continuously adding new nodes and attaching them to old nodes with probability proportional to their number of existing links. They prove that such networks evolve to a scale-free organisation obeying a power law distribution in which there is a long tail of nodes with low numbers of links and a small number of 'popular' nodes with many links. They also prove that both 'growth', the dynamical component, and 'preferential attachment' are necessary for this pattern to emerge. Such networks have been applied to models like that of Baayen (1991), described above (e.g. Bornholdt and Ebel, 2001), and to lexical semantic organisation (e.g. Yooke/a/.,2001). 4. Power Laws and Sexual Selection Miller (2000:369f), in the context of a more general argument that human language evolved by sexual selection, argues that large vocabulary size, in comparison with those of other (artificial and natural) animal communication systems, evolved through sexual selection. Women preferred men with large active vocabularies but needed to acquire large passive vocabularies themselves to assess the trait. Miller offers, as evidence for the non-functional nature of much of this vocabulary, Zipf's observation that vocabulary distributes like a power law and

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contains many near synonyms: ...any of the words we know is likely to be used on average about once in every million words we speak... Why do we bother to learn so many rare words that have practically the same meanings as common words, if language evolved to be practical? (Miller, 2000:370) He argues that human variation in vocabulary acquisition correlates with intelligence and has a heritable component, and thus is an (indirect) fitness indicator, triggering an 'arms race' in which advertising excessive vocabulary size is a 'display' of fitness akin to the peacock's tail, precisely because it does not contribute usefully to communication. In §2 we saw that power law distributions manifest themselves in many areas of linguistic organisation. For instance, there is a tail of rare long constituents in text samples (Sharman, 1989). However, there is no evidence that 'display' of such forms is a particular feature of courtship, nor that such forms are nonfunctional. As we saw in §3 models predicting such distributions need only a dynamical component and no element of natural or sexual selection whatever. Evidence of power law distributions in both idiolects and language forces us to conclude that both are best modelled as dynamical systems - rather than wellformed sets, as in generative linguistics (e.g. Sampson, 2001:165f) - but nothing more. If vocabulary size were non-functional, we might expect there to be many truly synonymous words. What we find in the organisation of vocabulary is that partially synonymous words have different distributions in terms of specificity of reference, syntactic potential, or genre and register. There is, in fact, considerable evidence that children avoid hypothesising synonyms in language acquisition (e.g. Clark, 2003) and that language users adhere to the convention of preemption by synonymy, except where discourse or syntactic context triggers a nonsynonymous reading (e.g. Briscoe et ai, 1995; Copestake & Briscoe, 1995). For instance, cow, unlike chicken, is not generally used to refer to the meat because of the existence of beef. However, in an appropriate context cow can be used this way and triggers an implicature of 'disgust': There were five thousand extremely loud people on the floor eager to tear into roast cow with both hands and wash it down with bourbon whiskey. (Tom Wolfe, 1979. The Right Stuff, Farrar, Straus and Giroux, New York (p. 298, Picador edition, 1991)) Similarly, the word stealer, formed by the fairly productive derivational rule of agentive +er nominalisation, is blocked by thief, except in syntactic contexts where the specificity of reference is narrowed: He is an inveterate * stealer / thief / stealer of Porsche 911 s

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These and many similar observations suggest that partial synonymy is communicatively useful and actively exploited to convey meaning. To understand why we have so many words and how the cognitive ability to cope with them (co-)evolved, consider the likely environment of adaptation for language. In a foraging, scavenging or hunter society, the ability to discriminate - and thus name more and more species, according to nutritional value, location, method of capture or harvesting, and so forth - would be of value for survival because it would allow efficent transmission of these skills to kin as well as survival over larger and more varied habitats. Modern hunter-gatherers are known to have large vocabularies specialised in this way (Diamond, 1997). This may not have been the sole driver for increasing vocabulary size, but it has the advantage that it predicts that vocabulary will be to a large extent organised by specificity of reference. It is useful not only to be able to talk about plants in general but also species and subgroups (e.g. by location or edible part) in order to discriminate the edible, find the source, and harvest effectively. Once we accept such a pressure to name in an increasingly complex and multifaceted environment, then the tendency for there to be smaller numbers of high frequency words of generic reference and a larger number of rarer words with highly specific denotations is just a case of the structure of vocabulary mirroring (our perception of) this environment. 5. The Real Challenge - Iterated Learning One achievement of recent evolutionary models of language is the demonstration that treating languages as complex adaptive systems responding to conflicting selection pressures (e.g. Briscoe, 2000) leads to insightful acccounts of typological and other linguistic universals without the need to invoke innateness. These accounts rely heavily on the iterated learning model (ILM, e.g. Kirby, 2001) in which linguistic traits must undergo repeated relearning by successive generations of language learners acquiring their language from that of the previous generation. For instance, Kirby (2001) demonstrates that languages in the ILM evolve to have compositional structure in which only high frequency irregular form-meaning mappings are stable, given the following assumptions: 1. an invention strategy for form-meaning pairs, 2. a production bias to express meanings using short forms, 3. an inductive bias to learn small grammars and lexicons, 4. a learning period in which not all form-meaning pairs appear 5. and environmental structure which favours some meanings In the simulation, initial (proto)languages are holistic and non-compositional but chance regularities which emerge in form-meaning mappings are acquired by

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learners, who then reliably exemplify them for the next generation of learners, because regularities are, by definition, more frequent in data. Thus, over time the language evolves to be mostly compositional and regular. However, (short) irregular mappings can survive provided they are associated with meanings which are expressed frequently and, therefore, also occur reliably during the learning period. This instantiation of the ILM neatly explains the observation that irregularity correlates with high frequency in attested languages: children would continue to say goed into adulthood if went were not a high frequency form. The corollary, however, is that rare unpredictable properties of language which do not follow from some regularity manifest during the learning period should be unstable and, therefore, rarely observed. Rare word-meaning associations are unpredictable and may also influence lexico-grammatical behaviour. For example, the verb obsess is a stable lexeme of English, but does not appear in any of the 40 or so case studies of child-directed speech in CHILDES". It is transitive but usually appears in the passive in adult speech accompanied by a PP headed by by, with or over. However, vocabulary acquisition continues through adulthood, so the ILM (and other models) simply predict that such vocabulary will be acquired later (and less universally). Marked but predictable constructions, such as multiple centre-embeddings, which Sampson (2001:21) estimates occur once in every 250K words on average, are also not counter-examples if one believes that they are a consequence of learners acquiring, in the basis of more frequent constructions, grammatical rules which correctly predict the appropriate form-meaning mapping for these constructions. A more challenging case for the ILM is diathesis alternation, in which verbs of certain semantic classes semi-predictably occur in alternant constructions often with predictable meaning changes. For instance, eat can appear in intransitive and transitive constructions but when it occurs intransitively the theme of the action is 'understood'. However, verbs with similar senses, such as devour or consume do not undergo this alternation. There is evidence that children learn at least some of these alternation rules by 3 years because they produce errors, such as Don 'tfall my dolly down - the causative-inchoative alternation. However, the rate at which such errors occur also suggest that alternation rules are learnt conservatively and only rarely overapplied. There are on the order of 100 such alternation rule types in English, when productive meaning change is taken into account. Figure 1 shows log-log plots of the unconditional probability of over 150 verbheaded constructions against their inverse rank on the left and of the conditional probability of these constructions when headed by any form of the verb believe on the right, calculated from 30M words of automatically parsed text along with the closest fit straight line derived using (1) above with B set appropriately. Both distributions loosely approximate power laws with long tails of rare events, but 'http://childes.psy.cmu.edu/data/

25

Figure 1. AUVerb/Believe Constructions

critically the correlation between the unconditional distribution and conditional ones for individual verbs is low (0.47 Spearman-Rank Coefficient for 14 verbs). This means that it is not possible to predict the individual association of verbs and constructions on the basis of the unconditional distribution. For instance, sentential complements are rare overall but the most common construction with believe, accounting for over 90% of occurrences. This lack of correlation, taken together with the fact that analysis of CHILDES shows that child-directed speech only exemplifies common verb-construction associations (e.g. Buttery & Korhonen, 2005), suggests that children do not have reliable evidence for the existence of most alternation rules - assuming that evidence would be several exemplars of the same alternation involving several different verbs. It may be that such semi-productive alternations are also acquired later in life, despite the occasional errors in children's speech. This is a general strategy that proponents of ILM-style explanations can take. But on the other hand, there must also be some learning 'bottleneck', caused by limited exposure to data during the learning period, for ILM accounts of linguistic evolution to work. Cases like this pose interesting challenges for the approach because they suggest that linguistic data is distributed in such a fashion that there may still be a 'poverty of stimulus' issue during the sensitive period for acquisition. More empirical work on language acquisition is needed to determine whether the ILM's predictions hold up for such specific cases. References Albert, R. & A. Barabasi (2002) 'Statistical machanics of complex networks', Reviews of Modern Physics, vol.74, 47-97. Baayen, H. (1991) 'A stochastic process for word frequency distributions', Proceedings of the Assoc, for Computational Linguistics, Morgan Kaufmann, Menlo Park, CA, pp. 271-278. Baayen, H. (2001) Word Frequency Distributions, Kluwer, Dordrecht.

26 Bornholdt, S. & Ebel.H. (2001) 'World Wide Web scaling exponent from Simon's 1955 model', Physical Review, vol.64, 035104. Briscoe, E.J. (2000) 'Evolutionary perspectives on diachronic syntax' in (eds) Pintzuk, S., Tsoulas, G. and Warner, A. (eds.), Diachronic Syntax: Models and Mechanisms, Oxford University Press, Oxford, pp. 75-108. Copestake, A.A. and E.J. Briscoe (1995) 'Regular polysemy and semi-productive sense extension', Journal of Semantics, vol.12, 15-67. Briscoe, E.J., A.A Copestake and A. Lascarides (1995) 'Blocking' in St. Dizier, P. and Viegas, E. (eds.), Computational Lexical Semantics, Cambridge University Press, Cambridge, pp. 273-302. Buttery, P. & A. Korhonen (2005) 'Large-scale analysis of verb subcategorization differences between child directed speech and adult speech', Proceedings of the Interdisciplinary Workshop on the Identification and Representation of Verb Features and Verb Classes, Saarland University. Clark, E. (2003) First Language Acquisition, Cambridge University Press, Cambridge. Diamond, J. (1997) Guns, Germs and Steel: The Fate of Human Societies, Random House, New York. Guiraud, H. (1954) Les Charact'eres Statistiques du Vocabulaire, Press Universitaires de France, Paris. Kirby, S. (2001) 'Spontaneous evolution of linguistic structure: an iterated learning model of the emergence of regularity and irregularity', IEEE Transactions on Evolutionary Computation, vol.5(2), 102-110. Korhonen, A. (Computer Laboratory, University of Cambridge) Subcategorization Acquisition, Techical Report UCAM-CL-TR-530. 2002 Mandelbrot, B. (1953) 'An informational theory of the statistical structure of language' in W. Jackson (eds.), Communication Theory, Butterworths, London. Manning, C. & H. Schutze (1999) Foundations of Statistical Natural Language Processing, MIT Press, Cambridge MA. Miller, G. (2000) The Mating Mind: How Sexual Choice Shaped the Evolution of Human Nature, William Heinemann, London. Sampson, G. (2001) Empirical Linguistics, Continuum, London. Sharman, R. (1989) Observational Evidence for a Statistical Model of Language, IBM UKSC Report 205. Simon, H. (1955) 'On a class of skew distribution functions', Biometrika, vol.42, 435-440. Wichmann, S. (2005) 'On the power law distribution of language family sizes', Journal of Linguistics, vol.41, 117-131. Yook S., Jeong, H., Barabasi, A-L & Tu, Y (2001) 'Weighted evolving networks', Physical Review Letters, vol.86, 5835-5838. Zipf, G. (1935) The Psycho-Biology of Language: An Introduction to Dynamic Philology, Houghton-Miflin, New York. Zipf, G. (1949) Human Behavior and the Principle of Least Effort, Addison-Wesley, Cambridge, MA.

THE BALDWIN EFFECT WORKS FOR FUNCTIONAL, BUT NOT ARBITRARY, FEATURES OF LANGUAGE MORTEN H. CHRISTIANSEN & FLORENCIA REALI Department of Psychology, Cornell University, Uris Hall, Ithaca, NY 14853, USA NICKCHATER Department of Psychology, University College London, 26 Bedford Way London, WC1H0AP, UK Human languages are characterized by a number of universal patterns of structure and use. Theories differ on whether such linguistic universals are best understood as arbitrary features of an innate language acquisition device or functional features deriving from cognitive and communicative constraints. From the viewpoint of language evolution, it is important to explain how such features may have originated. We use computational simulations to investigate the circumstances under which universal linguistic constraints might get genetically fixed in a population of language learning agents. Specifically, we focus on the Baldwin effect as an evolutionary mechanism by which previously learned linguistic features might become innate through natural selection across many generations of language learners. The results indicate that under assumptions of linguistic change, only functional, but not arbitrary, features of language can become genetically fixed.

1.

Introduction

Although the world's languages differ considerably from one another, they nonetheless share many systematic constraints on how they are structured and used. Explaining how such universal linguistic constraints evolved in the hominid lineage is the focus of much debate in language evolution research. One view suggests that linguistic universals are best viewed as arbitrary features of language with no functional explanation, but instead deriving from an innate Universal Grammar (UG; Chomsky, 1965). This abstract body of linguistic knowledge is proposed, by some theorists, to have evolved gradually through biological adaptations complex grammars (e.g., Briscoe, 2003; Pinker & Bloom, 1990). An alternative view seeks to explain linguistic universals as functional features of language, emerging due to communicative and cognitive factors outside of grammatical knowledge (e.g., Bybee, 1998). These features are seen as by-products of linguistic adaptation, in which language itself has been adapted through cultural transmission across many generations of language learners (e.g., Tomasello, 2003). 27

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The Baldwin effect (1896) is the primary evolutionary mechanism by which the arbitrary features of UG are envisioned to have been genetically fixed in the human population. Although a Darwinian mechanism, the Baldwin effect resembles Lamarckian inheritance of acquired characteristics in that traits that are learned or developed over the life span of an individual become gradually encoded in the genome over many generations (see Weber & Depew, 2003). That is, if a trait increases fitness, then individuals that, due to random genetic variation, require less exposure to the environment to develop that trait will have a selective advantage. Over generations, the amount of environmental exposure needed to develop this trait decreases, as individuals evolve increasingly better initial conditions for its rapid development. Eventually, no environmental exposure may be needed; the trait has become genetically encoded. A frequently cited example of the Baldwin effect (e.g., Briscoe, 2003) is the ability to develop hard skin on certain areas of the body with relatively little environmental exposure. Over time, natural selection would have favored individuals that could develop hard skin more rapidly (because it aids in mobility, prevents infection, etc.) until it became fixed in the genome, requiring little environmental stimulation to develop. Similarly, it has been suggested that arbitrary linguistic features, which would originally have had to be learned, gradually became genetically fixed in UG via the Baldwin Effect (Pinker & Bloom, 1990). In this paper, we use computer simulations'1 to investigate the circumstances under which the Baldwin effect may operate, for arbitrary and functional features of language. Building on previous work (Chater, Christiansen & Reali, 2004), Simulation 1 indicates that arbitrary linguistic features cannot be genetically fixed via the Baldwin effect when linguistic change is incorporated — even when this change is driven in part by the genes themselves. In Simulation 2, we show how functional features of language can come to be genetically fixed in the population when they promote better communicative abilities. Finally, we discuss the implications of the simulations for theories of language evolution. 2.

Simulation 1: Arbitrary Language Features

Following recent work on the possible evolution of UG (e.g., Briscoe, 2003; Nowak, Komarova & Nyogi, 2001), we model language and learners as a set of binary vectors. Specifically, we adopt the framework of the pioneering All simulations were replicated several times due to their stochastic nature.

29 simulations of Hinton & Nowlan (1987), used by Pinker & Bloom (1990) to support their suggestion that the Baldwin effect underlies the gradual genetic fixing of arbitrary grammatical features in UG. Our previous work indicated that although the Baldwin effect can occur within this framework in the context of arbitrary linguistic features, the effect disappears when language is allowed to change (Chater et al., 2004). However, these simulations were limited in scope; we therefore conducted a new series of simulations to determine whether our original results would replicate after addressing the limitations. In our earlier simulations, a language was defined as a set of arbitrary binary features, Fi...F„, taking the values 0 or 1. The n "genes" of the learners correspond to each of the n features of the language. The genes can take three values, representing an innate bias (0, 1) for a feature being 0 or 1 in the language; or neutrality (represented as '?'). For example, if n = 3 the language may correspond to [0, 1, 1] and the genes of a random agent to [?, 1, 0]. At the beginning of each generation, an initial language (phenotype) is expressed for each agent based on its genes (genotype). The innate bias toward a particular feature value will in most cases result in that value being expressed in the phenotype (in most of the simulations the 'stickiness' of the bias is 95% in the direction of the designated value), but on occasion it will be expressed in the opposite direction. For the neutral (learning) genes there is a 50% change of either setting (1 or 0). Thus, in our previous example, the initial language of the agent could be [1, 1, 1]. If the initial language does not match the target language, the agent begins a process of trial and error learning, in which learners randomly sample features using the biases in their genes. Once a feature is 'guessed' correctly, it is not changed. The learner keeps guessing until all the features in its language match those of the target language, with the fastest learners being selected to form the basis for the next generation. Some mutations would occur across generations, with an equal probability of randomly reassigning a gene to 0, 1, or ? (mutation rate varied between simulations). Although the neutral bits initially speeds learning, agents that are genetically biased toward a feature F, will guess it faster. Thus the Baldwin effect should gradually ensure that all the arbitrary features of the language become genetically encoded. Chater et al. (2004) found a Baldwin effect for arbitrary linguistic features, for the case where the language is fixed. In these simulations, reproduction was implemented as simple duplications of the top 50% of the learners subject to a 1% mutation rate. Does the same result hold, given a more realistic model of genetic transmission? To better approximate hominid evolutionary dynamics,

30 Table 1. Number of generations needed to reach the success criterion for the Baldwin effect (parameter value : number of generations) Genome Size 10:25 20:51 50:201 80 : 1045

Population Size 24 : 369 100:51 250 : 47

% Initial Neutral Bits 0:23 25:69 75 : 137 100 : 147

Stickiness of Innate Bias 100 : 152 95:51 90:85 80:88

% Survivors 26:52 50:51 74 : 195

% Mutation Rate 0.1:232 1:51 2.5 : 104

the current simulations use a simple model of sexual reproduction, instantiated as random cross-over between two sets of learner genes. We first replicated our original results in which the language/genome size was set to 20, the population size to 100, the number of initially neutral bits to 50%, the 'stickiness' of the innate genetic bias to 95%, the number of surviving agents to the top 50%, and a 1% mutation rate. Using a success criterion that more than 95% of the initial bits in the top 50% of the learners' genomes should correctly match the target language, we found that a robust Baldwin effect occurred after 51 generations. We then varied the simulation parameters and found that a robust Baldwin effect occurred in all circumstances, with parameter variations only affecting the speed with which it emerged (see Table 1). These results show that our earlier results generalize to sexual reproduction, and show that the Baldwin effect is highly robust, with a fixed language. If such a robust effect disappears under when the language is allowed to change, this cannot easily be dismissed. An jmportant limitation of our original simulations is that language change was completely independent of the genes. It seems reasonable to assume that if the genes control language learnability then they should also influence the direction of language change in a process similar to Baldwinian niche construction (e.g., Odling-Smee, Laland & Feldman, 2003). To explore this, we carried out a set of simulations in which language at time t+1 was determined by a combination of genes and language at time t. Specifically, p percent of the change would be determined by the most frequent gene values in the previous population and the remaining 1-p percent of change by the previous language. Given that other pressures than learnability also affects language change (such as cognitive/communicative constraints, parsability, language contact, linguistic drift, etc.), we also incorporated random language change at a rate of ten times faster than the mutation rate (i.e., 10%). The faster rate of linguistic change reflects the fact that cultural evolution is much faster than biological evolution (Dawkins, 1976). Whereas linguistic change is measured in thousands of years, biological evolution is measured in hundreds of thousands of years. Other

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0

100

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300

400

500

600

700

800

900

1000

Generations Figure 1. The effect of population influence on the emergence of the Baldwin effect.

simulation parameters were the same as in our initial replication above. The results of these simulations (Figure 1) show that only when there is a very high degree of population influence does the Baldwin effect emerge. Only when the direction of linguistic change is at least 50% determined by the previous generations genes do we observe a robust Baldwin effect after 835 generations. This suggests that arbitrary features of language would have to be predetermined strongly by the genes from the very beginning, thus leaving little room for subsequent evolution of the kind envisioned by Pinker & Bloom (1990). This corroborates our previous findings that under reasonable assumptions about language change, the Baldwin effect does not occur for arbitrary linguistic features. Unlike the example of hard skin, where the environment provides a stable target for the Baldwin effect, language change is too fast for genetic commitments to arbitrary features to be worthwhile. However, it is possible that non-arbitrary features of language could become genetically fixed in the population if they facilitated communication in some manner; e.g., improved abilities for word learning, increased working memory capacity for language, vocal apparatus optimizations for speech, and so on. 3.

Simulation 2: Functional Language Features

Because the arbitrary features of language by definition do not affect communicative function (e.g., Pinker & Bloom, 1990), Simulation 1 did not need to incorporate communication between agents. However, to explore the degree to which functional features of language could have become genetically

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fixed via the Baldwin effect, it is necessary to take communication into account to provide a context within which the non-arbitrary features can be functional. We used the same representation of language and genes as before, with the initial language expressed in the same way. However, learning was implemented differently, now mediated by communicative interactions. Communication was only possible between agents who had a majority of the same kinds on language features (either 0 or 1). Thus, an agent, ah whose language is [0, 0, 0, 0, 1], would be able to communicate with an agent, a2, with a [0, 0, 0, 0, 0] language but not with agent aj that has a [0, 1, 1, 1, 0] language. Agents benefit mutually from successful communication in proportion to the overlap in their features. The successful two-way interaction between ai and a2 would result in an increase in both agents' communication scores by 9 (the combined number of 0s in their two languages). The simulations also integrate the developmental trend that comprehension precedes production: even though cti can only "produce" four 0s, it can "comprehend" a2s five 0s. However, if the difference between the productive abilities of two agents is more than one unit, then lesser competent "speaker" will not be able to understand its more proficient communication partner, resulting in a one-way interaction. In this case, the proficient speaker received the combined communication score (as before), whereas the less competent agent would only receive its own contribution to that score. Hence, if a; interacted with a4, whose language is [0, 1, 0, 1, 0], a2 would increase its communication score by 8 while a / s score would only increase by 3. In this framework, less competent agents are able to learn from more competent agents (with stronger bias towards 0s or 1 s); this is meant to reflect the tendency for children to learn much of their language from others with greater language skills than themselves (e.g., adults or older children). Learning can only happen when two-way communication is possible (as described above), and consists in a process in which the less competent agent, based on the biases in its genome, re-samples the first bit in its language that differs from the more competent agent's language. For example, in a communicative interaction between as and a4, the latter would resample its second language bit. If a / s genome encoded an innate bias (0 or 1), then there would be a 95% chance of getting this bit expressed; but if the genome encoded a neutral bit, the chance of either value would be 50%. Thus, genes constrain learning as in Simulation 1. To further mirror the learning conditions from the previous simulations, we introduced noise into the learning process at a rate ten times higher than the mutation rate. During 10% of the learning opportunities a random bit in the

33 100 00

(_ o U I.

300 400 Generations

500

Figure 2. The influence of variations in the number of initial learnable bits on the Baldwin effect for different mutation rates (mr) and noises rates (nr).

learner's language would be chosen for potential reassignment (given the learner's genetic bias for that bit) instead of the first bit that deviated from the competent speaker's language. This paralleled the 10% random change in the target language in Simulation 1. From each generation of 100 learners, pairs of agents were randomly picked for 500 interactions. The 50 agents with the highest communication scores were selected, and cross-over sexual reproduction used to create the next generation (combined with a 1% mutation rate). The results (Figure 2) show that a robust Baldwin effect emerges across several different variations in mutation rate and number of neutral bits in the first generation. Even when the first generation has all neutral (learnable) bits, a robust Baldwin effect emerges after 33-269 generations. Thus, functional features that improve communication abilities may become genetically fixed in the population. For example, vocabulary learning is likely to rely on innate domain-general abilities for establishing reliable mappings between forms and meanings (e.g., Bloom, 2002). As such, the ability to acquire a large vocabulary may have become gradually innate by way of the Baldwin effect because it would have increased communicative abilities. 4.

General Discussion

These results indicate that the Baldwin effect may not provide a suitable evolutionary mechanism for explaining the emergence of arbitrary features of language. Rather, the results suggest that functional features that facilitate communication may be a better candidate for aspects of language that have come

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to be genetically fixed over evolutionary time. For a trait to be amenable to the Baldwin effect, it needs to be stable over a period of many generations. Functional features are stable in that they facilitate communication on a continuous basis and thus are likely to become 'Baldwinized' when communicative abilities affect selective fitness in a population. In contrast, abstract linguistic features are free to change randomly exactly because they are non-functional and not subject to direct selective pressures. More generally, the simulations raise doubts about the gradual evolutionary emergence of a UG, as proposed by Pinker & Bloom (1990), and instead support a cultural transmission model of language evolution in which the Baldwin effect has enabled certain cognitive/functional features to become genetically encoded. References Baldwin, J.M. (1896). A new factor in evolution. American Naturalist, 30, 441-451. Bloom, P. (2002). How children learn the meanings of words. New York: OUP. Briscoe, T. (2003). Grammatical assimilation. In M.H. Christiansen & S. Kirby (Eds.), Language evolution (pp. 295-316). New York: OUP. Chater, N., Christiansen, M.H. & Reali, F. (2004). Is coevolution of language and language genes possible? Paper presented at the Fifth International Conference on the Evolution of Language, Leipzig, Germany. Chomsky, N. (1965). Aspects of the theory of syntax. Cambridge, MA: MIT Press. Bybee, Joan (1998). A functionalist approach to grammar and its evolution. Evolution of Communication 2. 249-278. Dawkins, R. (1976). The selfish gene. New York: Oxford University Press. Hinton, G.E. & Nowlan, S.J. (1987). How learning can guide evolution. Complex Systems, 1, 495-502. Nowak, M.A., Komarova, N.L. & Nyogi, P. (2001). Evolution of universal grammar. Science, 291, 114-118. Odling-Smee, F.J., Laland, K.N. & Feldman, M.W. (2003). Niche construction: The neglected process in evolution. Princeton, NJ: Princeton University Press. Pinker, S., & Bloom, P. (1990). Natural language and natural selection. Behavioral and Brain Sciences, 13, 707-784. Tomasello, M. (2003). On the different origin of symbols and grammar. In M.H. Christiansen and S. Kirby (Eds.), Language evolution (pp. 94-110). New York: OUP. Weber, B.H. & Depew, D.J. (Eds.) (2003). Evolution and learning: The Baldwin effect reconsidered. Cambridge, MA: MIT Press.

ON THE EMERGENCE OF COMPOSITIONALITY

JOACHIM DE BEULE ARTI, Free University of Brussels, Pleinlaan 2, Brussel, 1050, Belgium joachim @ arti. vub.ac.be BENJAMIN K. BERGEN Linguistics Department,

University of Hawaii, Honolulu, HI 96822, U.S.A. bergen @ hawaii. edu

Compositionality is a hallmark of human language - words and morphemes can be factorially combined to produce a seemingly limitless number of viable strings. This contrasts with nonhuman communication systems, which for the most part are holistic - encoding a whole message through a single, gestalt form. We show that compositional language can arise automatically through grounded communication among populations of communicators. The proposed mechanism is the following: if a holistic and a compositional approach are in competition and if both structured (compositional) and atomic meanings need to be communicated, the holistic strategy becomes less successful as it does not recruit already acquired bits of language. We demonstrate the viability of this explanation through computer simulations in which artificial agents perform a communicative task. It is shown that simple reinforcement mechanisms applied during communicative interactions can account for the emergence of linguistic compositionality.

1. Introduction Compositionality is a universal feature of human language. There are varied accounts of where this feature comes from. On the one hand, it could be a requisite characteristic of human language, dictated by an innate universal language capacity endowed upon us by evolution (Chomsky, 1975; Pinker & Bloom, 1990). Alternatively, it could be a cultural innovation, which different language communities composed of generally intelligent humans have consistently converged on because of its tremendous utility. It is the latter explanation that we explore below. We investigate the conditions under which a language community driven only by the success of individual communicative interactions will come to preferentially adopt a globally compositional, rather than a holistic language strategy. The literature on cultural causes for the emergence of compositional language (Nowak & Krakauer, 1999; Kirby, 2000; Brighton, 2002; Smith, Brighton, & Kirby, 2003) proposes a variety of factors, including iterated learning (IL), learning bottlenecks, expressibility, and the presence of noise in transmission. While all these explanations provide important insights into potential sources of language 35

36 emergence and change, they leave out three critical considerations. First, they often address only the emergence of the form and not the meaning facet of compositionality. When linguistic units are brought together to form a composite structure, their forms are assembled (one word might precede the other, for example), but so are their meanings. The Enlgish phrase "Jack kisses Mary" not only describes a kissing event involving Jack and Mary, but additionally specifies what roles Jack and Mary play relative to the event. The compositional grammars of Nowak and Krakauer (1999), Brighton (2002) and Smith et al. (2003) are unable to distinguish between "Jack kisses Mary" and "Mary kisses Jack" unless e.g. separate words are used for Jack depending on whether he fulfills the agent or patient role in the kissing event, and the same holds for Mary. Without semantic compositionality, formal compositionality is of questionable utility to language users, and in any case does not approach the expressive power of compositionality in human languages.2 Second, in previous work, compositionality emerges at least partially on the basis of experimenter-imposed principles, like an implemented drive to search for generalizations in the learning data and reduce language inventory size (i.e. hypothesize compositional rules). The concern we have with such solutions is that while a trend for decreased inventory size over time (in language learners or populations) might correlate with increased compositionality, implementing this as a causal mechanism effectively causes compositionality to evolve in conformity with the experimenters beliefs about preferred properties of a language system. A more causally satisfying solution would be one in which the language evolves through fundamental principles of communicative interaction, and where changes in inventory size and learnability are byproducts of lower-level causal factors. Third, IL models typically require many hundreds or thousands of generations for a compositional language to evolve. Moreover, they do not explain how the resulting language could become shared among the members of a larger community, as most IL studies consider only one teacher and one learner. We take issue with such models because of the wealth of evidence suggesting that a population of communicators can arrive at a successful, compositional communication system within one or two generations, as evidenced by the emergence of Pidgins and Creole languages and new signed languages, among others. These properties of previous models all appear to derive from the assumption that the function of language is irrelevant to its form. Because their focus is on how IL can account for the structure and evolution of language, they (often explicitly) a This criticism does not apply to Kirby (2000) who simulates a population of agents negotiating about how to express 5 'even((?ev,?agent,?patient)' type events.(We adopt a logic-based representation of meaning. Symbols starting with a question mark are variables.) He shows that if agents hypothesize generalizations (rules of grammar), then because more general rales have a better chance of being replicated to the next generation, a shared and compositional language emerges. Despite this quite interesting result his model still suffers from two of the concerns mentioned in the text.

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disregard linguistic function in their causal models. We present an alternative view on why and how compositional language might emerge, which accounts for both formal and semantic compositionality without relying on endstate-oriented learning mechanisms, and demonstrates convergence on compositional language strategies within a single generation. This view starts from the assumption that the primary function of language is communication. In the experiments we report on, a population of agents is iteratively faced with a communication task in which they talk about a set of observed scenes. The next section spells out the nature of the task and the world in which it is performed. In the following section, a simulation shows how low-level interactive mechanisms - communicative success and a variant of classical conditioning - lead to the emergence of a compositional language when the world obeys certain characteristics. Following (Elman, 1993), we then show how the same effect can be achieved with a wider range of world configurations if instead the agents go through developmental stages. 2. Experimental Setup Negotiation Model. In our experiments, a speaker and hearer are randomly selected from a population of agents to perform a communication task. They observe a set of scenes, which may differ in terms of their entities or events, the roles the entities play in the events, or combinations of these. The speaker is given one out of the set of events to verbalize. He will use existing language if appropriate, or propose new elements of language if needed, as described below. The hearer decodes the speaker's utterance and tries to identify the topic. If he does so correctly, the game is a success; otherwise it is a failure and the speaker points to the topic. In both cases the agents learn from the outcome. Other negotiation models could of course be envisioned, and the one used is a radical simplification of the human equivalent. But it is a not unreasonable facsimile of something humans do very frequently both as language learners and language users - namely, observing the world, picking a subset of observations to talk about, and describing that subset using whatever linguistic tools are available. This negotiation framework also importantly allows us to test our main thesis that compositional language can arise automatically in communicative interactions and it does not bias agents towards a holistic or compositional type of solution, only towards a solution that yields communicative success. The same will hold for the language and learning models as explained below. World Model. Similar to Kirby (2000), the set of potential topics to be verbalized contain partial instantiations of events involving agents or patients of the form 'evenf(?ev,?agent,?patient), person(lagmi)' or levert?(?ev,?agent,?patient), perj0rt(?patient)'. Crucially the fact that some entity is playing a particular role in the event, that is, the link (equality) between the ?agent (or ?patient) variable in the event predicate and the corresponding one in the person predicate, is part of the meaning that has to be expressed. If there are Ne different event predicates

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(e.g. kiss, kick, etc.) and Np different person predicates (e.g. John, Mary, etc. ), this yields 2NeNp structured topics. The atomic topics are also included, i.e. one of the Ne + Np atomic events or people. The fraction of structured topics with which the agents are presented is called the task complexity. When it is 0, agents only have to verbalize the atomic topics, and when it is 1, they verbalize only the structured ones. With intermediate values, they eventually get to see them all. Language Model. The agents are implemented using the Fluid Construction Grammar (FCG) formalism (De Beule & Steels, 2005; Steels, De Beule, & Neubauer, 2005) which is a general unification-based inference engine, designed to support experiments in the self-organization of language. For the current purposes it suffices to say that in the experiments reported on, an agent's language inventory consists of a lexicon and a set of linking constructions which pair word-order with agent and patient role bindings. Agents start with an empty inventory. Whenever a speaker's lexicon does not cover some meaning to be expressed, he creates a new entry, associating a new form with the uncovered meaning. Crucially, the uncovered meaning can be the complete meaning to be expressed or only part of it. For example, if the meaning is '&w.y(?ev,?agent,?patient), Mary(?agent)', and the speaker does not have a word for kiss or for Mary, he introduces a new word for the entire meaning, which will be holistic. If, however, he already knows a word for kiss but not for Mary, he will only introduce a new word for Mary, which might lead to compositional language. In this case he also needs a linking construction to express the fact that Mary plays the agent role in the kissing event. The speaker has two choices: he can put the predicate either before or after the agent. For successful communication a different word order needs to be used for encoding the agent and patient roles, but whether this solution is found is left to the learning dynamics, based on communicative success. So it may be that a speaker initially uses the same order to encode both the agent and patient cases, or that different agents use different conventions. Newly introduced words or constructions need to be adopted by the hearer. In our model, after pointing a hearer has all the necessary ingredients to adopt any newly introduced words or constructions if at most one word in the utterance is unknown to him. A strength (a number between 0 and 1) is associated with all lexical and constructional entries. Because all agents introduce new elements of language, an agent's inventory will quickly contain competing entries, e.g. (partial) synonyms and incompatible word order conventions. When verbalizing a topic, a speaker chooses those entries that have the greatest combined strength. Learning Model. Agents learn by increasing the likelihood that they will reuse successful language strategies, and decreasing the likelihood of reusing unsuccessful ones. Whenever a hearer successfully understands an utterance, he increases the strength su of the linguistic elements used according to su ah'ak) the function of d we will use to weight the payoffs. The fitness