Language Evolution and Computation Bibliography

Language change is considered as a transition of population among languages. The language dynamics equation represents such a transition of population. Our purpose in this paper is to develop a new formalism of language dynamics for a real situation of language contact. We assume a situation that memoryless learners are exposed to a number of languages. We show experimental results, in which contact with other language speakers during acquisition period deteriorates the learning accuracy and prevents the emergence of a dominant language. If we suppose a communicative language, when learners are frequently exposed to a variety of languages, the language earns relatively higher rate of population. We discuss the communicative language from the viewpoint of the language bioprogram hypothesis.

In this chapter, we show that from a minimal neural kit for vocal replication, a shared combinatorial speech code with structural regularities and diversity spontaneously self-organizes in a population of agents. This allows to understand that the evolutionary step from vocal replication systems to modern human speech systems might have been rather small.

One of the most distinctive characteristics of human language is the extent to which it relies on learned vocal signals. Communication systems are ubiquitous in the natural world but vocal learning is a comparatively rare evolutionary development (Jarvis, 2004). In this paper we take one example of this phenomena, bird song, which displays some remarkable parallels with human language (Doupe \& Kuhl, 1999), and we focus on one particular case study, that of the Bengalese finch (Lonchura striata var. domestica), a domesticated species whose song behaviour differs strikingly from its feral ancestor in that it has complex syntax and is heavily influenced by early learning (Okanoya, 2002). We present a computational model of the evolutionary history of the Bengalese finch which demonstrates how an increase in song complexity and increased influence from early learning could evolve spontaneously as a result of domestication. We argue that this may provide an insight into how increased reliance on vocal learning could evolve in other communication systems, including human language.

Learning to communicate is an emerging challenge in AI research. It is known that agents interacting in decentralized, stochastic environments can benefit from exchanging information. Multiagent planning generally assumes that agents share a common means of communication; however, in building robust distributed systems it is important to address potential mis-coordination resulting from misinterpretation of messages exchanged. This paper lays foundations for studying this problem, examining its properties analytically and empirically in a decision-theoretic context. Solving the problem optimally is often intractable, but our approach enables agents using different languages to converge upon coordination over time.

The problem of heterogeneous case representation poses a major obstacle to realising real-life multi-case-base reasoning (MCBR) systems. The knowledge overhead in developing and maintaining translation protocols between distributed case bases poses a serious challenge to CBR developers. In this paper, we situate CBR as a flexible problem-solving strategy that relies on several heterogeneous knowledge containers. We introduce a technique called language games to solve the interoperability issue. Our technique has two phases. The first is an eager learning phase where case bases communicate to build a shared indexing lexicon of similar cases in the distributed network. The second is the problem-solving phase where, using the distributed index, a case base can quickly consult external case bases if the local solution is insufficient. We provide a detailed description of our approach and demonstrate its effectiveness using an evaluation on a real data set from the tourism domain.

The interoperability among distributed and autonomous systems is the ultimate challenge facing the semantic web. Heterogeneity of data representation is the main source of problems. This paper proposes an innovative solution that combines lexical approaches and language games. The benefits for distributed annotation systems on the web are twofold: firstly, it will reduce the complexity of the semantic problem by moving the focus from the full-featured ontology level to the simpler lexicon level; secondly, it will avoid the drawback of a centralized third party mediator that may become a single point of failure. The main contributions of this work are concerned with (1) providing a proof of concept that language games can be an effective solution to creating and managing a distributed process of agreement on a shared lexicon, (2) describing a fully distributed service oriented architecture for language games, (3) providing empirical evidence on a real world case study in the domain of ski mountaineering.

The blogosphere refers to the distributed network of user opinions published on the WWW. Whereas centralized review sites such Amazon.com previously allowed users to post opinions on goods such as books and CDs, blogging software allows users to publish opinions on any topic without constraints on predefined schema. However, centralized review sites such as Amazon.com have one significant advantage: reviews pertaining to a single topic are collected together in one place, allowing readers to peruse a diverse range of opinions quickly. In this paper we examine how such a topic-centric view of the Blogosphere can be created. We characterise the problems in aligning similar concepts created by a set of distributed, autonomous users and describe current initiatives to solve the problem. Finally, we introduce the Tagsocratic project, a novel initiative to solve the concept alignment problem using techniques derived from research in language acquisition among distributed, autonomous agents.

The 'Talking Robots' experiment, inspired by the 'Talking Heads' experiment from Sony, explores possibilities on how to ground symbols into perception. We present here the first results of this experiment and outline a possible extension to social behaviors grounding: the purpose is to have the robots develop not only a lexicon but also the interaction protocol, or language game, that they use to create the lexicon. This raises several complex problems that we review here.

We describe the reconstruction of a phylogeny for a set of taxa, with a character-based cladistics approach, in a declarative knowledge representation formalism, and show how to use computational methods of answer set programming to generate conjectures about the evolution of the given taxa. We have applied this computational method in two domains: to historical analysis of languages, and to historical analysis of parasite-host systems. In particular, using this method, we have computed some plausible phylogenies for Chinese dialects, for Indo-European language groups, and for Alcataenia species. Some of these plausible phylogenies are different from the ones computed by other software. Using this method, we can easily describe domain specific information (e.g. temporal and geographical constraints), and thus prevent the reconstruction of some phylogenies that are not plausible.

We show how a population of simulated robots developed their communication capabilities in order to solve a collective navigation problem. The self-organized emergent vocabulary includes four different signals that influence both the motor and signalling behaviour of other robots. The analysis of the evolved behaviours also indicates: (a) the emergence of a simple form of communication protocol that allows individuals to switch signalling on and off, (b) the emergence of tightly co-adapted communicative and non-communicative behaviours, and (c) the exploitation of properties resulting from the dynamical interactions between motor and signalling behaviours produced by interacting robots.

The paper describes a neural network model of early language acquisition with an emphasis on how language positively influences the categories with which the child categorizes reality. Language begins when the two separate networks that are responsible for nonlinguistic sensory-motor mappings and for recognizing and repeating linguistic sounds become connected together at 1 year of age. Language makes more similar the internal representations of different inputs that must be responded to with the same action and more different the internal representations of inputs that must be responded to with different actions.

In this paper it is argued that the way the world is conceptualized for language is language dependent and the result of negotiation between language users. This is investigated in a computer experiment in which a population of artificial agents constructs a shared language to talk about a world that can be conceptualized in multiple and possibly conflicting ways. It is argued that the establishment of a successful communication system requires that feedback about the communicative success is propagated to the ontological level, and thus that language shapes the way we conceptualize the world for communication.

Models of language evolution have demonstrated how aspects of human language, such as compositionality, can arise in populations of interacting agents. This paper analyzes how languages change as the result of a particular form of interaction: agents learning from one another. We show that, when the learners are rational Bayesian agents, this process of iterated learning converges to the prior distribution over languages assumed by those learners. The rate of convergence is set by the amount of information conveyed by the data seen by each generation; the less informative the data, the faster the process converges to the prior.

This paper investigates the interaction between cultural evolution and biological evolution in the emergence of phonemic coding in speech. It is observed that our nearest relatives, the primates, use holistic utterances, whereas humans use phonemic utterances. It can therefore be argued that our last common ancestor used holistic utterances and that these must have evolved into phonemic utterances. This involves co-evolution between a repertoire of speech sounds and adaptations to using phonemic speech. The culturally transmitted system of speech sounds influences the fitness of the agents and could conceivably block the transition from holistic to phonemic speech. This paper investigates this transition using a computer model in which agents that can either use holistic or phonemic utterances co-evolve with a lexicon of words. The lexicon is adapted by the speakers to conform to their preferences. It is shown that although the dynamics of the transition are changed, the population still ends up of agents that use phonemic speech.

Typically, multi-agent models for studying the evolution of perceptually grounded lexicons assume that agents perceive the same set of objects, and that there is either joint attention, corrective feedback or cross-situational learning. In this paper we address these two assumptions, by introducing a new multi-agent model for the evolution of perceptually grounded lexicons, where agents do not perceive the same set of objects, and where agents receive a cue to focus their attention to objects, thus simulating a Theory of Mind. In addition, we vary the amount of corrective feedback provided to guide learning word-meanings. Results of simulations show that the proposed model is quite robust to the strength of these cues and the amount of feedback received.

The complexity, variation, and change of languages make evident the importance of representation and learning in the acquisition and evolution of language. For example, analytic studies of simple language in unstructured populations have shown complex dynamics, depending on the fidelity of language transmission. In this study we extend these analysis of evolutionary dynamics to include grammars inspired by the principles and parameters paradigm. In particular, the space of languages is structured so that some pairs of languages are more similar than others, and mutations tend to change languages to nearby variants. We found that coherence emerges with lower learning fidelity than predicted by earlier work with an unstructured language space.

This paper presents a computational framework for studying the influence of learning on the evolution of avian communication. We conducted computer simulations for exploring the effects of different learning strategies on the evolution of bird song. Experimental results show the genetic assimilation of song repertoires as a consequence of interactions between learning and evolution.

The effect of adding noise to an expression-induction model of language evolution was investigated. The model consisted of a number of artificial people who were able to infer the denotation of basic colour terms from examples of colours which the words had been used to identify, using a Bayesian inference procedure. The artificial people would express colours to one-another, so producing data from which other people could learn. Occasionally they would be creative, which allowed new words to enter the language. When certain points in the colour space were made especially salient, so that the artificial people were more likely to remember colours at these points, the languages emerging over a number of generations in evolutionary simulations replicated the typological patterns seen in the 110 languages of the world colour survey. It was found that if random noise was added to the data from which the artificial people learned, this had no major effect on the emergent languages, demonstrating that the Bayesian inference procedure is able to learn effectively despite the presence of random noise, even when placed in an evolutionary context.

Evolutionary computation is used to explore the emergence of language, focusing particularly on the intrinsic relationship between the lexicon and syntax, and the exogenous relationship between language use and cultural development. A multi-agent model traces a coevolution of the lexicon and syntax, and demonstrates that linguistic and some distance constraint on communications can trigger and maintain cultural heterogeneity. This model also traces an optimization process using evolutionary mechanisms based on local information. Certain mechanisms in this model, such as recurrent pattern extraction, strength-based competition and indirect feedback, can be generalized to study robot learning, optimization and other evolutionary phenomena.

Early agent research recognised that co-operating agents require access to unambiguous, semantic description of the same concept, entity or object. In fact, agent-based research on this problem anticipates many of the current initiatives of the Semantic Web project. The proposed solution involves developing a domain-specific ontology that can be mapped to other ontologies as required. In this paper we describe an alternative approach which allows autonomous agents to index shared objects without requiring ex-ante agreement on an ontology. Using a process of distributed negotiation, each agent builds a lexicon of the problem-solving competences of other agents. We present an overview of our work using this approach in three domains: a web services scenario, a multi-case-based agent approach and finally, Tagsocratic, a blog-indexing service. We then describe our future work on several open issues related to this research.

This paper introduces a game which, when used in conjunction with a language learning algorithm, exhibits features of natural language production found to occur in young children. The game enables a rich and complex set of training data to be generated and acts as a quantifiable measure of linguistic ability, both for human and simulated players.

This paper reports further progress into a computational implementation of a new formalism for construction grammar, known as Fluid Construction Grammar (FCG). We focus in particular on how hierarchy can be implemented. The paper analyses the requirements for a proper treatment of hierarchy in emergent grammar and then proposes a particular solution based on a new operator, called the J-operator. The J-operator constructs a new unit as a side effect of the matching process.

The question of language evolution is of interest to linguistics, biology and recently, engineering communicating networks. Previous work on these problems has focused mostly on a fully-connected population. We are extending this study to structured populations, which are generally more realistic and offer rich opportunities for linguistic diversification. Our work focuses on the convergence properties of a spatially structured population of learners acquiring a language from one another. We investigate several metrics, including mean language coherence and the critical learning fidelity threshold.

We present here a digital scenario to simulate the emergence of self-organized symbol-based communication among artificial creatures inhabiting a virtual world of predatory events. In order to design the environment and creatures, we seek theoretical and empirical constraints from C.S.Peirce Semiotics and an ethological case study of communication among animals. Our results show that the creatures, assuming the role of sign users and learners, behave collectively as a complex system, where self-organization of communicative interactions plays a major role in the emergence of symbol-based communication. We also strive for a careful use of the theoretical concepts involved, including the concepts of symbol, communication, and emergence, and we use a multi-level model as a basis for the interpretation of inter-level relationships in the semiotic processes we are studying.

We examine evidence for the hypothesis that language could have passed through a stage when words were combined in structured linear segments and these linear segments could later have become the building blocks for a full hierarchical grammar. Experiments were carried out on the British National Corpus, consisting of about 100 million words of text from different domains and transcribed speech. This work extends and supports the results of our previous work based on a smaller corpus reported previously. Measuring the entropy of the texts we find that entropy declines as words are taken in groups of 2, 3 and 4, indicating that it is easier to decode words taken in short sequences rather than individually. Entropy further declines when punctuation is represented, showing that appropriate segmentation captures some of the language structure. Further support for the hypothesis that local sequential processing underlies the production and perception of speech comes from neurobiological evidence. The observation that homophones are apparently ubiquitous and used without confusion also suggests that language processing may be largely based on local context.

Both Middle English and Old French had a syntactic property called verb-second or V2 that disappeared. This paper describes a simulation being developed to shed light on the question of why V2 is stable in some languages, but not others. The simulation, based on a Markov chain, uses fuzzy grammars where speakers can use an arbitrary mixture of idealized grammars. Thus, it can mimic the variable syntax observed in Middle English manuscripts. The simulation supports the hypotheses that children use the topic of a sentence for word order acquisition, that acquisition takes into account the ambiguity of grammatical information available from sample sentences, and that speakers prefer to speak with more regularity than they observe in the primary linguistic data.

One of the key problems in any language processing system is to establish an adequate syntax/semantics interface, and one of the major requirements of such an interface is that partial meanings contributed by individual words are properly linked with each other based on grammatical constructions. This paper reports how we deal with this problem within the context of Fluid Construction Grammars ({\sc fcg}). {\sc Fcg} is a general unification-based inference engine which has been designed to support experiments in the self-organisation of language in a population of interacting situated embodied agents. The paper focuses on technical details pertaining to the linking problem.

This paper investigates the effect of predefining semantics in modelling the evolution of compositional languages versus allowing agents to develop these semant ics in parallel with the development of language. The study is done using a mult i-agent model of language evolution that is based on the Talking Heads experimen t. The experiments show that when allowing a co-evolution of semantics with lang uage, compositional languages develop faster than when the semantics are predef ined, but compositionality appears more stable in the latter case. The paper con cludes that conclusions drawn from simulations with predefined meanings, which m ost studies use, may need revision.

In this paper we discuss issues relating to modelling language evolution in large populations of autonomous agents that are situated in a realistic environment where they have to evolve and learn means to survive for extended periods of time. As we intend to build such a model in relation to the recently started New Ties project, we identify three major problems that are expected for such a model. The paper proposes some solutions and discusses future directions.

This paper investigates the effect of scaling the population size in a simulation studying the emergence and evolution of compositionality in languages. The simulations are based on multi-agent systems that play language games in order to communicate, invent and learn language. The language games are integrated with the iterated learning model that simulates a population turnover, where the population contains adults and children. Experiments show that when the population size is increased, after an initial decrease in performance, the results show an important improvement when the population size is increased further. These results are explained by a hypothesised trade off between increasing difficulties in achieving a conventionalised system and an increased likelihood of finding structures that emerge by chance when the population size increases.

The contribution of language history to the study of the early dispersals of modern humans throughout the Old World has been limited by the shallow time depth (about 8000 {+/-} 2000 years) of current linguistic methods. Here it is shown that the application of biological cladistic methods, not to vocabulary (as has been previously tried) but to language structure (sound systems and grammar), may extend the time depths at which language data can be used. The method was tested against well-understood families of Oceanic Austronesian languages, then applied to the Papuan languages of Island Melanesia, a group of hitherto unrelatable isolates. Papuan languages show an archipelago-based phylogenetic signal that is consistent with the current geographical distribution of languages. The most plausible hypothesis to explain this result is the divergence of the Papuan languages from a common ancestral stock, as part of late Pleistocene dispersals.

The challenge of tracing the history of the world's languages faces a serious problem--words change far too rapidly to reveal deep historical links. In his Perspective, Gray discusses language analyses by Dunn et al. in which a database of structural linguistic features was created and computational methods derived from evolutionary biology were applied. The approach offers new hope for uncovering these ancient connections.

Language acquisition is one of the most fundamental human traits, and it is obviously the brain that undergoes the developmental changes. During the years of language acquisition, the brain not only stores linguistic information but also adapts to the grammatical regularities of language. Recent advances in functional neuroimaging have substantially contributed to systems-level analyses of brain development. In this Viewpoint, I review the current understanding of how the 'final state' of language acquisition is represented in the mature brain and summarize new findings on cortical plasticity for second language acquisition, focusing particularly on the function of the grammar center.

Cultural and linguistic groups are often expected to represent genetic populations. In this article, we tested the hypothesis that the hierarchical classification of languages proposed by J. Greenberg [(1987) Language in the Americas (Stanford Univ. Press, Stanford, CA)] also represents the genetic structure of Native North American populations. The genetic data are mtDNA sequences for 17 populations gleaned from literature sources and public databases. The hypothesis was rejected. Further analysis showed that departure of the genetic structure from the linguistic classification was pervasive and not due to an outlier population or a problematic language group. Therefore, Greenberg's language groups are at best an imperfect approximation to the genetic structure of these populations. Moreover, we show that the genetic structure among these Native North American populations departs significantly from the best-fitting hierarchical models. Analysis of median joining networks for mtDNA haplotypes provides strong evidence for gene flow across linguistic boundaries. In principle, the language of a population can be replaced more rapidly than its genes because language can be transmitted both vertically from parents to children and horizontally between unrelated people. However, languages are part of a cultural complex, and there may be strong pressure to maintain a language in place whereas genes are free to flow.

We address the problem, fundamental to linguistics, bioinformatics, and certain other disciplines, of using corpora of raw symbolic sequential data to infer underlying rules that govern their production. Given a corpus of strings (such as text, transcribed speech, chromosome or protein sequence data, sheet music, etc.), our unsupervised algorithm recursively distills from it hierarchically structured patterns. The ADIOS (automatic distillation of structure) algorithm relies on a statistical method for pattern extraction and on structured generalization, two processes that have been implicated in language acquisition. It has been evaluated on artificial context-free grammars with thousands of rules, on natural languages as diverse as English and Chinese, and on protein data correlating sequence with function. This unsupervised algorithm is capable of learning complex syntax, generating grammatical novel sentences, and proving useful in other fields that call for structure discovery from raw data, such as bioinformatics.

This report contains a linguistic description of a language created spontaneously without any apparent external influence in a stable existing community. We describe the syntactic structure of Al-Sayyid Bedouin Sign Language, a language that has arisen in the last 70 years in an isolated endogamous community with a high incidence of nonsyndromic, genetically recessive, profound prelingual neurosensory deafness. In the space of one generation from its inception, systematic grammatical structure has emerged in the language. Going beyond a conventionalized list of words for actions, objects, people, characteristics, and so on, a systematic way of marking the grammatical relations among those elements has appeared in the form of highly regular word order. These systematic structures cannot be attributed to influence from other languages, because the particular word orders that appear in Al-Sayyid Bedouin Sign Language differ from those found both in the ambient spoken languages in the community and in the other sign language found predominantly in the surrounding area. Therefore, the emerging grammatical structures should be regarded as an independent development within the language.

Understanding developmental and evolutionary aspects of the language faculty requires comparing adult languages users' abilities with those of non-verbal subjects, such as babies and non-human animals. Classically, comparative work in this area has relied on the rich theoretical frameworks developed by linguists in the generative grammar tradition. However, the great variety of generative theories and the fact that they are models of language specifically makes it difficult to know what to test in animals and children lacking the expressive abilities of normal, mature adults. We suggest that this problem can be mitigated by tapping equally rich, but more formal mathematical approaches to language.

We use words to communicate about things and kinds of things, their properties, relations and actions. Researchers are now creating robotic and simulated systems that ground language in machine perception and action, mirroring human abilities. A new kind of computational model is emerging from this work that bridges the symbolic realm of language with the physical realm of real-world referents. It explains aspects of context-dependent shifts of word meaning that cannot easily be explained by purely symbolic models. An exciting implication for cognitive modeling is the use of grounded systems to `step into the shoes' of humans by directly processing first-person-perspective sensory data, providing a new methodology for testing various hypotheses of situated communication and learning.

Several important recent advances in various sciences (particularly biology and physics) are based on complex network analysis, which provides tools for characterizing statistical properties of networks and explaining how they may arise. This article examines the relevance of this trend for the study of human languages. We review some early efforts to build up language networks, characterize their properties, and show in which direction models are being developed to explain them. These insights are relevant, both for studying fundamental unsolved puzzles in cognitive science, in particular the origins and evolution of language, but also for recent data-driven statistical approaches to natural language.

Evolutionary game dynamics have been proposed as a mathematical framework for the cultural evolution of language and more specifically the evolution of vocabulary. This article discusses a model that is mutually exclusive in its underlying principals with some previously suggested models. The model describes how individuals in a population culturally acquire a vocabulary by actively participating in the acquisition process instead of passively observing and communicate through peer-to-peer interactions instead of vertical parent-offspring relations. Concretely, a notion of social/cultural learning called the naming game is first abstracted using learning theory. This abstraction defines the required cultural transmission mechanism for an evolutionary process. Second, the derived transmission system is expressed in terms of the well-known selection-mutation model defined in the context of evolutionary dynamics. In this way, the analogy between social learning and evolution at the level of meaning-word associations is made explicit. Although only horizontal and oblique transmission structures will be considered, extensions to vertical structures over different genetic generations can easily be incorporated. We provide a number of simplified experiments to clarify our reasoning.

The speech code is a vehicle of language: it defines a set of forms used by a community to carry information. Such a code is necessary to support the linguistic interactions that allow humans to communicate. How then may a speech code be formed prior to the existence of linguistic interactions? Moreover, the human speech code is discrete and compositional, shared by all the individuals of a community but different across communities, and phoneme inventories are characterized by statistical regularities. How can a speech code with these properties form? We try to approach these questions in the paper, using the `methodology of the artificial'. We build a society of artificial agents, and detail a mechanism that shows the formation of a discrete speech code without pre-supposing the existence of linguistic capacities or of coordinated interactions. The mechanism is based on a low-level model of sensory-motor interactions. We show that the integration of certain very simple and non language-specific neural devices leads to the formation of a speech code that has properties similar to the human speech code. This result relies on the self-organizing properties of a generic coupling between perception and production within agents, and on the interactions between agents. The artificial system helps us to develop better intuitions on how speech might have appeared, by showing how self-organization might have helped natural selection to find speech.

John Maynard Smith and EoSzathma argued that human language signified the eighth major transition in evolution: human language marked a new form of information transmission from one generation to another [Maynard Smith J, Szathma E. The major transitions in evolution. Oxford: Oxford Univ. Press; 1995]. According to this view language codes cultural information and as such forms the basis for the evolution of complexity in human culture. In this article we develop the theory that language also codes information in another sense: languages code information on their own structure. As a result, languages themselves provide information that influences their own survival. To understand the consequences of this theory we discuss recent computational models of linguistic evolution. Linguistic evolution is the process by which languages themselves evolve. This article draws together this recent work on linguistic evolution and highlights the significance of this process in understanding the evolution of linguistic complexity. Our conclusions are that: (1) the process of linguistic transmission constitutes the basis for an evolutionary system, and (2), that this evolutionary system is only superficially comparable to the process of biological evolution.

The similarity of the evolution of human languages (or alphabets, bird songs, ...) to biological evolution of species is utilized to study with up to $10^9$ people the rise and fall of languages either by macroscopic differential equations similar to biological Lotka-Volterra equation, or by microscopic Monte Carlo simulations of bit-strings incorporating the birth, maturity, and death of every individual. For our bit-string model, depending on parameters either one language comprises the majority of speakers (dominance), or the population splits into many languages having in order of magnitude the same number of speakers (fragmentation); in the latter case the size distribution is log-normal, with upward deviations for small sizes, just as in reality for human languages. On a lattice two different dominating languages can coexist in neighbouring regions, without being favoured or disfavoured by different status. We deal with modifications and competition for existing languages, not with the evolution or learning of one language.

Language is a symbolic, culturally transmitted system of communication, which is learnt through the inference of meaning. In this paper, I describe the importance of meaning inference, not only in language acquisition, but also in developing a unified explanation for language change and evolution. Using an agent-based computational model of meaning creation and communication, I show how the meanings of words can be inferred through disambiguation across multiple contexts, using cross-situational statistical learning. I demonstrate that the uncertainty inherent in the process of meaning inference, moreover, leads to stable variation in both conceptual and lexical structure, providing evidence which helps to explain how language changes rapidly without losing communicability. Finally, I describe how an inferential model of communication may provide important theoretical insights into plausible explanations of the bootstrapping of, and the subsequent progressive complexification of, cultural communication systems.

Color categories enjoy a special status among human perceptual categories as they exhibit a remarkable cross-cultural similarity. Many scholars have explained this universal character as being the result of an innate representation or an innate developmental program which all humans share. We will critically assess the available evidence, which is at best controversial, and we will suggest an alternative account for the universality of color categories based on linguistic transmission constrained by universal biases. We introduce a computational model to test our hypothesis and present results. These show that indeed the cultural acquisition of color categories together with mild constraints on the perception and categorical representation result in categories that have a distribution similar to human color categories.

Much is known about the evolution of speech. Fossil evidence points to modern adaptations for speech appearing between 1.5 million and 500,000 years ago. Studies of vocal behavior in apes show the ability to use combinatorial vocalizations in some species (but not chimpanzees) and some cultural influence on vocalizations, but little ability for vocal imitation. For modern speech, the comparison of many languages shows that speech can become extremely complex, but that it can also be astonishingly simple. Finally, the way in which infants acquire speech is becoming increasingly clear. We therefore know about the starting point of the evolution of speech, its end point and some steps in between.

Much less is known about the exact scenario and the dynamics of the evolution of the acquisition of speech. As it involves co-evolution between culturally transmitted sounds and genetic evolution, the dynamics are complex. Computer models are therefore ideal for studying these dynamics. This paper presents a computer model of the co-evolution between a repertoire of speech sounds and a population of learners that can either represent a lexicon holistically or combinatorially. It shows that cultural influences can change the dynamics of the transition between a population of holistic and combinatorial learners.

The current research describes a functional trajectory from sensorimotor sequence learning to the learning of grammatical constructions in language. A brief review of the functional neurophysiology of the cortex and basal ganglia will be provided as background for a neural network model of this system in sensorimotor sequence learning. Sequential behavior is then defined in terms of serial, temporal and abstract structure. The resulting neuro-computational framework is demonstrated to account for observed sequence learning behavior. More interestingly, this framework naturally extends to grammatical constructions as form-to-meaning mappings. Predictions from the neuro-computational model concerning parallels in language and cognitive sequence processing are tested against behavioral and neurophysiological observations in humans, resulting in a refinement of the allocation of model functions to subdivisions of Broca's area. From a functional perspective this analysis will provide insight into the relation between the coding structure in human languages, and constraints derived from the underlying neurophysiological computational mechanisms.

This paper shows how phonological structures can be culturally selected so as to become learnable and adapted to the ecological niche formed by the brains and bodies of speakers. A computational model of the cultural formation of syllable systems illustrates how general learning and physical biases can influence the evolution of the structure of vocalization systems. We use the artificial life methodology of building a society of artificial agents, equipped with motor, perceptual and cognitive systems that are generic and have a realistic complexity. We demonstrate that agents, playing the ''imitation game,'' build shared syllable systems and show how these syllable systems relate to existing human syllable systems. Detailed experiments study the learnability of the self-organized syllable systems. In particular, we reproduce the critical period effect and the artificial language learning effect without the need for innate biases which specify explicitly in advance the form of possible phonological structures. The ability of children agents to learn syllable systems is explained by the cultural evolutionary history of these syllable systems, which were selected for learnability.

We present a novel connectionist model for acquiring the semantics of a simple language through the behavioral experiences of a real robot. We focus on the ``compositionality'' of semantics and examine how it can be generated through experiments. Our experimental results showed that the essential structures for situated semantics can self-organize themselves through dense interactions between linguistic and behavioral processes whereby a certain generalization in learning is achieved. Our analysis of the acquired dynamical structures indicates that an equivalence of compositionality appears in the combinatorial mechanics self-organized in the neuronal nonlinear dynamics. The manner in which this mechanism of compositionality, based on dynamical systems, differs from that considered in conventional linguistics and other synthetic computational models, is discussed in this paper.

This paper investigates the productive creativity of children in a computational model on the emergence and evolution of compositional structures in language. In previous models it was shown that compositional structures can emerge in language when the language is transmitted from one generation to the next through a transmission bottleneck. Due to the fact that in these models language is transmitted only in a vertical direction where adults only speak to children and children only listen, this bottleneck needs to be imposed by the experimenter. In the current study, this bottleneck is removed and instead of having a vertical transmission of language, the language is -- in most simulations -- transmitted horizontally (i.e. any agent can speak to any other agent). It is shown that such a horizontal transmission scenario does not need an externally imposed bottleneck, because the children face an implicit bottleneck when they start speaking early in life. The model is compared with the recent development of Nicaraguan Sign Language, where it is observed that children are a driving force for inventing grammatical (or compositional) structures, possibly due to a sparseness of input (i.e. an implicit bottleneck). The results show that in the studied model children are indeed the creative driving force for the emergence and stable evolution of compositional languages, thus suggesting that this implicit bottleneck may -- in part -- explain why children are so typically good at acquiring language and, moreover, why they may have been the driving force for the emergence of grammar in language.

This article suggests that the parser underlying human syntax may have originally evolved to assist navigation, a claim supported by computational simulations as well as evidence from neuroscience and psychology. We discuss two independent conjectures about the way in which navigation could have supported the emergence of this aspect of the human language faculty: firstly, by promoting the development of a parser; and secondly, by possibly providing a topic of discussion to which this parser could have been applied with minimum effort. The paper summarizes our previously published experiments and provides original results in support of the evolutionary advantages this type of communication can provide, compared with other foraging strategies. Another aspect studied in the experiments is the combination and range of environmental factors that make communication beneficial, focusing on the availability and volatility of resources. We suggest that the parser evolved for navigation might initially have been limited to handling regular languages, and describe a mechanism that may have created selective pressure for a context-free parser.

This research takes grammatical constructions (sentence form-to-meaning mappings) as an alternative to abstract generative grammars in the context of understanding the emergence of language. A model of sentence processing based on this construction grammar approach is presented, and then a series of neuropsychological and neurophysiological studies are reviewed that attempt to validate the model and to establish its neurophysiological underpinnings. The resulting model is demonstrated to provide insight into a developmental and evolutionary passage from unitary idiom-like holophrases to progressively more abstract grammatical constructions. The model is then functionally validated by its insertion into a perceptually grounded system that allows spoken language interaction with a human interlocutor. The potential utility of this emergence approach in understanding language is discussed.

Distributed co-ordination is the result of dynamical processes enabling independent agents to co-ordinate their actions without the need of a central co-ordinator. In the past few years, several computational models have illustrated the role played by such dynamics for self-organizing communication systems. In particular, it has been shown that agents could bootstrap shared convention systems based on simple local adaptation rules. Such models have played a pivotal role for our understanding of emergent language processes. However, only few formal or theoretical results have been published about such systems. Deliberately simple computational models are discussed in this paper in order to make progress in understanding the underlying dynamics responsible for distributed co-ordination and the scaling laws of such systems. In particular, the paper focuses on explaining the convergence speed of those models, a largely under-investigated issue. Conjectures obtained through empirical and qualitative studies of these simple models are compared with results of more complex simulations and discussed in relation to theoretical models formalized using Markov chains, game theory and Polya processes.

What enables an organism to perform behaviour we would call cognitive and adaptive, like language? Here, it is argued that an essential prerequisite is the ability to build up mental representations of external situations to uncouple the behaviour from direct environmental control. Such representations can be realized by building up cell assemblies. The recurrent neural network presented to cope with this task has been used for generation of action but can also be utilized as a basis for mental representations due to its attractor characteristics. In this context, a new learning algorithm (Dynamic Delta Rule) is proposed, which leads to a self-organized weight distribution yielding stable states on the one hand and which, on the other hand, only activates subpopulations of larger networks that code for the respective situation. In a second step, ways are shown of how the static information of these internal models can be transformed into time-dependent behavioural sequences.

Linguistic forms are shaped by forces operating on vastly different time scales. Some of these forces operate directly at the moment of speaking, whereas others accumulate over time in personal and social memory. Our challenge is to understand how forces with very different time scales mesh together in the current moment to determine the emergence of linguistic form.

In this paper, we explore various adaptive factors that can influence the emergence of a communication system that benefits the receiver of signals (the hearer) but not the emitter (the speaker). Using computer simulations of a population of interacting agents whose behaviour is determined by a neural network, we show that a stable communication system does not emerge in groups of unrelated individuals because of its altruistic character. None the less, another set of simulations shows that the emergence of a language that confers an advantage only to hearers, not to speakers, is possible under at least three conditions: (1) if the hearer and the speaker tend to share the same genes, as predicted by kin selection theory; (2) if the population is `docile' and the communication system is culturally transmitted together with other adaptive behaviours, as predicted by Simon's docility theory; and (3) if the linguistic system is used not only for social communication, but also for talking to oneself, in particular as an aid to memory.

In this paper, I discuss in which conditions a population of embodied and situated agents that have to solve problems that require co-operation might develop forms of ritualized interaction and communication. After reviewing the most relevant literature, I shall try to identify the main open research problems and the most promising research directions. More specifically, I shall discuss: (a) the type of problems, the agents' characteristics and the environmental/social conditions that might facilitate the emergence of an ability to interact and communicate; and (b) the behavioural and cognitive capabilities that are crucial for the development of forms of communication of different complexity.

This paper shows how a society of agents can self-organize a shared vocalization system that is discrete, combinatorial and has a form of primitive phonotactics, starting from holistic inarticulate vocalizations. The originality of the system is that: (1) it does not include any explicit pressure for communication; (2) agents do not possess capabilities of coordinated interactions, in particular they do not play language games; (3) agents possess no specific linguistic capacities; and (4) initially there exists no convention that agents can use. As a consequence, the system shows how a primitive speech code may bootstrap in the absence of a communication system between agents, i.e. before the appearance of language.

In this paper, efforts to understand the self-organization and evolution of language from a cognitive modelling point of view are discussed. In particular, the paper focuses on efforts that use connectionist components to synthesize some of the major stages in the emergence of language and possible transitions between stages. New technical results are not introduced, but some dimensions for mapping out the research landscape are discussed.

A description is given of a `back-tracking' approach that could be used to model neural language development and language evolution. This approach aims to develop a neural model of human language capacity that incorporates important constraints on language structure, language comprehension and performance, and brain structure. The model is then to be used as a target for development or evolution. To this end, the target model would have to be simplified, so that the target model can be derived from the simplified version and learning algorithms (or structural changes). The benefit of this approach is that the development of important constraints such as the combinatorial productivity of human language is ensured. The paper illustrates the importance of including this constraint in models of language development and evolution. It then describes a neural model in which this constraint is satisfied. Finally, the paper describes how such a model could be used to investigate language development and/or evolution.

Language is about symbols, and those symbols must be grounded in the physical world. Children learn to associate language with sensorimotor experiences during their development. In light of this, we first provide a computational account of how words are mapped to their perceptually grounded meanings. Moreover, the main part of this work proposes and implements a computational model of how word learning influences the formation of object categories to which those words refer. This model simulates the bi-directional relationship between word and object category learning: (1) object categorization provides mental representations of meanings that are mapped to words to form lexical items; (2) linguistic labels help object categorization by providing additional teaching signals; and (3) these two learning processes interplay with each other and form a developmental feedback loop. Compared with the method that performs these two tasks separately, our model shows promising improvements in both word-to-world mapping and perceptual categorization, suggesting a unified view of lexical and category learning in an integrative framework. Most importantly, this work provides a cognitively plausible explanation of the mechanistic nature of early word learning and object learning from co-occurring multisensory data.

We study a model for language evolution which was introduced by Nowak and Krakauer ([M.A. Nowak and D.C. Krakauer, The evolution of language, PNAS 96 (14) (1999) 8028-8033]). We analyze discrete distance spaces and prove a conjecture of Nowak for all metrics with a positive semidefinite associated matrix. This natural class of metrics includes all metrics studied by different authors in this connection. In particular it includes all ultra-metric spaces. Furthermore, the role of feedback is explored and multi-user scenarios are studied. In all models we give lower and upper bounds for the fitness.

The article analyzes the neural and functional grounding of language skills as well as their emergence in hominid evolution, hypothesizing stages leading from abilities known to exist in monkeys and apes and presumed to exist in our hominid ancestors right through to modern spoken and signed languages. The starting point is the observation that both premotor area F5 in monkeys and Broca's area in humans contain a 'mirror system' active for both execution and observation of manual actions, and that F5 and Broca's area are homologous brain regions. This grounded the Mirror System Hypothesis of Rizzolatti & Arbib (1998) which offers the mirror system for grasping as a key neural 'missing link' between the abilities of our non-human ancestors of 20 million years ago and modern human language, with manual gestures rather than a system for vocal communication providing the initial seed for this evolutionary process. The present article, however, goes 'beyond the mirror' to offer hypotheses on evolutionary changes within and outside the mirror systems which may have occurred to equip Homo sapiens with a language-ready brain. Crucial to the early stages of this progression is the mirror system for grasping and its extension to permit imitation. Imitation is seen as evolving via a so-called 'simple' system such as that found in chimpanzees (which allows imitation of complex 'objectoriented' sequences but only as the result of extensive practice) to a so-called 'complex' system found in humans (which allows rapid imitation even of complex sequences, under appropriate conditions) which supports pantomime. This is hypothesized to provide the substrate for the development of protosign, a combinatorially open repertoire of manual gestures, which then provides the scaffolding for the emergence of protospeech (which thus owes little to non-human vocalizations), with protosign and protospeech then developing in an expanding spiral. It is argued that these stages involve biological evolution of both brain and body. By contrast, it is argued that the progression from protosign and protospeech to languages with full-blown syntax and compositional semantics was a historical phenomenon in the development of Homo sapiens, involving few if any further biological changes.

This article proposes a number of models to examine through which mechanisms a population of autonomous agents could arrive at a repertoire of perceptually grounded categories that is sufficiently shared to allow successful communication. The models are inspired by the main approaches to human categorisation being discussed in the literature: nativism, empiricism, and culturalism. Colour is taken as a case study. Although we take no stance on which position is to be accepted as final truth with respect to human categorisation and naming, we do point to theoretical constraints that make each position more or less likely and we make clear suggestions on what the best engineering solution would be. Specifically, we argue that the collective choice of a shared repertoire must integrate multiple constraints, including constraints coming from communication.

We propose that the crucial difference between human cognition and that of other species is the ability to participate with others in collaborative activities with shared goals and intentions: shared intentionality. Participation in such activities requires not only especially powerful forms of intention reading and cultural learning, but also a unique motivation to share psychological states with others and unique forms of cognitive representation for doing so. The result of participating in these activities is species-unique forms of cultural cognition and evolution, enabling everything from the creation and use of linguistic symbols to the construction of social norms and individual beliefs to the establishment of social institutions. In support of this proposal we argue and present evidence that great apes (and some children with autism) understand the basics of intentional action, but they still do not participate in activities involving joint intentions and attention (shared intentionality). Human children's skills of shared intentionality develop gradually during the first 14 months of life as two ontogenetic pathways intertwine: (1) the general ape line of understanding others as animate, goal-directed, and intentional agents; and (2) a species-unique motivation to share emotions, experience, and activities with other persons. The developmental outcome is children's ability to construct dialogic cognitive representations, which enable them to participate in earnest in the collectivity that is human cognition.

We analyze how data on the mirror system for grasping in macaque and human ground the mirror system hypothesis for the evolution of the language-ready human brain, and then focus on this putative relation between hand movements and speech to contribute to the understanding of how it may be that a schizophrenic patient generates an action (whether manual or verbal) but does not attribute the generation of that action to himself. We make a crucial discussion between self-monitoring and attribution of agency. We suggest that vebal hallucinations occur when an utterance progresses through verbal creation pathways and returns as a vocalization observed, only to be dismissed as external since no record of its being created has been kept. Schizophrenic patients on this theory then confabulate the agent.

Gray & Atkinson's (2003) application of quantitative phylogenetic methods to Dyen, Kruskal & Black's (1992) Indo-European database produced controversial divergence time estimates. Here we test the robustness of these results using an alternative data set of ancient Indo-European languages. We employ two very different stochastic models of lexical evolution - Gray & Atkinson's (2003) finite-sites model and a stochastic-Dollo model of word evolution introduced by Nicholls & Gray (in press). Results of this analysis support the findings of Gray & Atkinson (2003). We also tested the ability of both methods to reconstruct phylogeny and divergence times accurately from synthetic data. The methods performed well under a range of scenarios, including widespread and localized borrowing.

Researchers interested in the history of the Indo-European family of languages have used a variety of methods to estimate the phylogeny of the family, and have obtained widely differing results. In this paper we explore the reconstructions of the Indo- European phylogeny obtained by using the major phylogeny estimation procedures on an existing database of 336 characters (including lexical, phonological, and morpho- logical characters) for 24 Indo-European languages. Our study finds that the different methods agree in part, but that there are also several striking differences. We dis- cuss the reasons for these differences, and make proposals with respect to phylogenetic reconstruction in historical linguistics.

It has been observed that borrowing within a group of genetically related languages often causes the lexical similarities among them to be skewed. Consequently, it has been proposed that borrowing can sometimes be inferred from such skewing. However, heterogeneity in the rate of lexical replacement, as well as borrowing from other languages, can also give rise to skewed lexical similarities. It is important, therefore, to determine to what degree skewing is a statistically significant indicator of borrowing. Here, we describe a statistical hypothesis test for detecting language contact based on skewing of linguistic characters of arbitrary type. Significant probabilities of correct detection of contact are maintained for various contact scenarios, with low false alarm probability. Our experiments show that the test is fairly robust to substantial heterogeneity in the retention rate, both across characters and across lineages, suggesting that the method can provide an objective criterion against which claims of significant skewing due to contact can be tested, pointing the way for more detailed analysis.

We investigate how very large populations are able to reach a global consensus, out of local ``microscopic'' interaction rules, in the framework of a recently introduced class of models of semiotic dynamics, the so-called Naming Game. We compare in particular the convergence mechanism for interacting agents embedded in a low-dimensional lattice with respect to the mean-field case. We highlight that in low-dimensions consensus is reached through a coarsening process which requires less cognitive effort of the agents, with respect to the mean-field case, but takes longer to complete. In 1-d the dynamics of the boundaries is mapped onto a truncated Markov process from which we analytically computed the diffusion coefficient. More generally we show that the convergence process requires a memory per agent scaling as N and lasts a time N^{1+2/d} in dimension d<5 (d=4 being the upper critical dimension), while in mean-field both memory and time scale as N^{3/2}, for a population of N agents. We present analytical and numerical evidences supporting this picture.

The main contribution of this paper is threefold. First, it presents a new software system for empirical investigations of evolving agent societies in SugarScape like environments. Second, it introduces a conceptual framework for modeling cooperation in an artificial society. In this framework the environmental pressure to cooperate is controllable by a single parameter, thus allowing systematic investigations of system behavior under varying circumstances. Third, it reports upon results from experiments that implemented and tested environments based upon this new model of cooperation. The results show that the pressure to cooperate leads to the evolution of communication skills facilitating cooperation. Furthermore, higher levels of cooperation pressure lead to the emergence of increased communication.

If we accept the view that language first evolved from the conceptual structure of our pre-linguistic ancestors, several questions arise, including: What kind of structure? Concepts about what? Here we review research on the vocal communication and cognition of nonhuman primates, focusing on results that may be relevant to the earliest stages of language evolution. From these data we conclude, first, that nonhuman primates' inability to represent the mental states of others makes their communication fundamentally different from human language. Second, while nonhuman primates' production of vocalizations is highly constrained, their ability to extract complex information from sounds is not. Upon hearing vocalizations, listeners acquire information about their social companions that is referential, discretely coded, hierarchically structured, rule-governed, and propositional. We therefore suggest that, in the earliest stages of language evolution, communication had a formal structure that grew out of its speakers' knowledge of social relations.

The major ''contribution'' of generative grammar to cognitive science is negative. The hermetic disjuncture of linguistic research from biological principles and facts has influenced cognitive science. Linguists have followed the pied piper taking a different path from that pointed out by Charles Darwin. As Dobzhansky (1973) noted, ''Nothing in biology makes sense except in the light of evolution.'' The hermetic nature of much linguistic research is apparent even in phonology which must reflect biological facts concerning speech production. For example, studies dating back to 1928 show that tongue ''features'' do not specify vowel distinctions. However, the irrefutable findings of these cineradiographic and MRI studies are generally ignored by linguists. Chomsky's central premise, that syntactic ability derives from an innate ''Universal Grammar'' common to all human beings constitutes a strong biological claim. But if a UG genetically similar for all ''normal'' individuals existed, one of the central premises of Darwinian evolutionary biology, genetic variation would be false. Concepts and processes borrowed from linguistics such as ''modularity'' have impeded our understanding of brain-behavior relations. Some aspects of behavior are regulated in specific localized ''modules'' in the brain, but current research demonstrates that the neural architecture regulating human language is also implicated in motor control, cognition, and other aspects of behavior. The neural bases of enhanced human language are not separable from cognition and motor ability. The supposed unique aspect of syntax, its ''reiterative'' productivity, appears to derive from subcortical structures that play a part in neural circuits regulating motor control. Natural selection aimed at enhancing adaptive motor control ultimately yielded a basal ganglia ''sequencing engine'' that can produce a potentially infinite number of novel actions, thoughts , or ''sentences'' from a finite number of basic elements. Recent studies suggest that the human FOXP2 gene, which differs from similar regulatory genes in chimpanzees and other mammals, acts on the basal ganglia and other subcortical structures to confer enhanced human reiterative ability in domains as different as syntax and dancing. The probable date of the critical mutations on FOXP2 is coincident with the appearance of anatomically modern human beings about 150,000 to 200,000 years ago. Humans thus can create more complex sentences than chimpanzees, but has anyone ever seen an ape dancing?

The objective of this research is to develop a system for language learning based on a ``minimum'' of pre-wired language-specific functionality, that is compatible with observations of perceptual and language capabilities in the human developmental trajectory. In the proposed system, meaning (in terms of descriptions of events and spatial relations) is extracted from video images based on detection of position, motion, physical contact and their parameters. Meaning extraction requires attentional mechanisms that are implemented from low-level perceptual primitives. Mapping of sentence form to meaning is performed by learning grammatical constructions, i.e., sentence to meaning mappings as defined by Goldberg [Goldberg, A. (1995). Constructions. Chicago and London: Univ. of Chicago Press]. These are stored and retrieved from a ``construction inventory'' based on the constellation of grammatical function words uniquely identifying the target sentence structure. The resulting system displays robust acquisition behavior that reproduces certain observations from developmental studies, with very modest ``innate'' language specificity.

The concept of a temporal phylogenetic network is a mathematical model of evolution of a family of natural languages. It takes into account the fact that languages can trade their characteristics with each other when linguistic communities are in contact, and also that a contact is only possible when the languages are spoken at the same time. We show how computational methods of answer set programming and constraint logic programming can be used to generate plausible conjectures about contacts between prehistoric linguistic communities, and illustrate our approach by applying it to the evolutionary history of Indo-European languages.

Here, assuming a general communication model where objects map to signals, a power function for the distribution of signal frequencies is derived. The model relies on the satisfaction of the receiver (hearer) communicative needs when the entropy of the number of objects per signal is maximized. Evidence of power distributions in a linguistic context (some of them with exponents clearly different from the typical \beta \approximate 2 of Zipf's law) is reviewed and expanded. We support the view that Zipf's law reflects some sort of optimization but following a novel realistic approach where signals (e.g. words) are used according to the objects (e.g. meanings) they are linked to. Our results strongly suggest that many systems in nature use non-trivial strategies for easing the interpretation of a signal. Interestingly, constraining just the number of interpretations of signals does not lead to scaling.

We use Monte Carlo simulations and assumptions from evolutionary game theory in order to study the evolution of words and the population dynamics of a system made of two interacting species which initially speak two different languages. The species are characterized by their identity, vocabulary, and have different initial fitness, i.e. reproduction capability. We investigate how different initial fitness affects the vocabulary of the species or the population dynamics by leading to a permanent populational advantage. We further find that the spatial distributions of the species may cause the system to exhibit pattern formation or segregation. We show that an initial fitness advantage, even though very quickly balanced, leads to better spatial arrangement and enhances survival probabilities of the species. In most cases the system will arrive at a final state where both languages coexist. However, in cases where one species greatly outnumbers the other in population and fitness, then only one species survives with its 'final' language having a slightly richer vocabulary than its initial language. Thus, our results offer an explanation for the existence and origin of synonyms in spoken languages.

We develop a network using the syllables of the Portuguese language. In this language the syllables are close to the basic phonetic unities. The nodes of the network are the syllables. The links are established each time two syllables form part of the same word. We use two different data sets to perform the numerics: a Portuguese dictionary and the complete work of the most important Brazilian writer--Machado de Assis. The syllabic network shows a low distance and a high clustering coefficient when compared with an associated Erdos-Renyi graph and with an associated random network with the same distribution of connectivity. The distribution of connectivity of the syllabic network follows a power law with exponent y=~1.4 indicating complex behavior.

Although many species possess rudimentary communication systems, humans seem to be unique with regard to making use of syntax and symbolic reference. Recent approaches to the evolution of language formalize why syntax is selectively advantageous compared with isolated signal communication systems, but do not explain how signals naturally combine. Even more recent work has shown that if a communication system maximizes communicative efficiency while minimizing the cost of communication, or if a communication system constrains ambiguity in a non-trivial way while a certain entropy is maximized, signal frequencies will be distributed according to Zipf's law. Here we show that such communication principles give rise not only to signals that have many traits in common with the linking words in real human languages, but also to a rudimentary sort of syntax and symbolic reference.

Here we focus on communication systems following Zipf's law. We study the relationship between the properties of those communication systems and the exponent of the law. We describe the properties of communication systems using quantitative measures of the semantic vagueness and the cost of word use. We try to reduce the precision and the economy of a communication system to a func tion of the exponent of Zipf's law and the size of the communication system. Taking the exponent of the frequency spectrum, we show that semantic precision grows with the exponent whereas the cost of word use reaches a global minimum between 1.5 and 2 if the size of the communication system re mains constant. We show that the exponent of Zipf's law is a key aspect for knowing about the num ber of stimuli handled by a communication system and determining which of two systems is less vague or less expensive. We argue that the ideal exponent of Zipf's law should be very slightly above 2.

H. Simon proposed a simple stochastic process for explaining Zipf's law for word frequencies. Here we introduce two similar generalizations of Simon's model that cover the same range of exponents as the standard Simon model. The mathematical approach followed minimizes the amount of mathematical background needed for deriving the exponent, compared to previous approaches to the standard Simon's model. Reviewing what is known from other simple explanations of Zipf's law, we conclude there is no single radically simple explanation covering the whole range of variation of the exponent of Zipf's law in humans. The meaningfulness of Zipf's law for word frequencies remains an open question.

Here we present a new model for Zipf's law in human word frequencies. The model defines the goal and the cost of communication using information theory. The model shows a continuous phase transition from a no communication to a perfect communication phase. Scaling consistent with Zipf's law is found in the boundary between phases. The exponents are consistent with minimizing the entropy of words. The model differs from a previous model [Ferrer i Cancho, SoleProc. Natl. Acad. Sci. USA 100, 788-791 (2003)] in two aspects. First, it assumes that the probability of experiencing a certain stimulus is controlled by the internal structure of the communication system rather than by the probability of experiencing it in the `outside' world, which makes it specially suitable for the speech of schizophrenics. Second, the exponent ? predicted for the frequency versus rank distribution is in a range where ?>1, which may explain that of some schizophrenics and some children, with ?=1.5-1.6. Among the many models for Zipf's law, none explains Zipf's law for that particular range of exponents. In particular, two simplistic models fail to explain that particular range of exponents: intermittent silence and Simon's model. We support that Zipf's law in a communication system may maximize the information transfer under constraints.

Words in humans follow the so-called Zipf's law. More precisely, the word frequency spectrum follows a power function, whose typical exponent is $\beta \approx 2$, but significant variations are found. We hypothesize that the full range of variation reflects our ability to balance the goal of communication, i.e. maximizing the information transfer and the cost of communication, imposed by the limitations of the human brain. We show that the higher the importance of satisfying the goal of communication, the higher the exponent. Here, assuming that words are used according to their meaning we explain why variation in $\beta$ should be limited to a particular domain. From the one hand, we explain a non-trivial lower bound at about $\beta=1.6$ for communication systems neglecting the goal of the communication. From the other hand, we find a sudden divergence of $\beta$ if a certain critical balance is crossed. At the same time a sharp transition to maximum information transfer and unfortunately, maximum communication cost, is found. Consistently with the upper bound of real exponents, the maximum finite value predicted is about $\beta=2.4$. It is convenient for human language not to cross the transition and remain in a domain where maximum information transfer is high but at a reasonable cost. Therefore, only a particular range of exponents should be found in human speakers. The exponent $\beta$ contains information about the balance between cost and communicative efficiency.

For many years the evolution of language has been seen as a disreputable topic, mired in fanciful ``just so stories'' about language origins. However, in the last decade a new synthesis of modern linguistics, cognitive neuroscience and neo-Darwinian evolutionary theory has begun to make important contributions to our understanding of the biology and evolution of language. I review some of this recent progress, focusing on the value of the comparative method, which uses data from animal species to draw inferences about language evolution. Discussing speech first, I show how data concerning a wide variety of species, from monkeys to birds, can increase our understanding of the anatomical and neural mechanisms underlying human spoken language, and how bird and whale song provide insights into the ultimate evolutionary function of language. I discuss the ``descended larynx'' of humans, a peculiar adaptation for speech that has received much attention in the past, which despite earlier claims is not uniquely human. Then I will turn to the neural mechanisms underlying spoken language, pointing out the difficulties animals apparently experience in perceiving hierarchical structure in sounds, and stressing the importance of vocal imitation in the evolution of a spoken language. Turning to ultimate function, I suggest that communication among kin (especially between parents and offspring) played a crucial but neglected role in driving language evolution. Finally, I briefly discuss phylogeny, discussing hypotheses that offer plausible routes to human language from a non-linguistic chimp-like ancestor. I conclude that comparative data from living animals will be key to developing a richer, more interdisciplinary understanding of our most distinctively human trait: language.

In this response to Pinker and Jackendoff's critique, we extend our previous framework for discussion of language evolution, clarifying certain distinctions and elaborating on a number of points. In the first half of the paper, we reiterate that profitable research into the biology and evolution of language requires fractionation of ``language'' into component mechanisms and interfaces, a non-trivial endeavor whose results are unlikely to map onto traditional disciplinary boundaries. Our terminological distinction between FLN and FLB is intended to help clarify misunderstandings and aid interdisciplinary rapprochement. By blurring this distinction, Pinker and Jackendoff mischaracterize our hypothesis 3 which concerns only FLN, not ``language'' as a whole. Many of their arguments and examples are thus irrelevant to this hypothesis. Their critique of the minimalist program is for the most part equally irrelevant, because very few of the arguments in our original paper were tied to this program; in an online appendix we detail the deep inaccuracies in their characterization of this program. Concerning evolution, we believe that Pinker and Jackendoff's emphasis on the past adaptive history of the language faculty is misplaced. Such questions are unlikely to be resolved empirically due to a lack of relevant data, and invite speculation rather than research. Preoccupation with the issue has retarded progress in the field by diverting research away from empirical questions, many of which can be addressed with comparative data. Moreover, offering an adaptive hypothesis as an alternative to our hypothesis concerning mechanisms is a logical error, as questions of function are independent of those concerning mechanism. The second half of our paper consists of a detailed response to the specific data discussed by Pinker and Jackendoff. Although many of their examples are irrelevant to our original paper and arguments, we find several areas of substantive disagreement that could be resolved by future empirical research. We conclude that progress in understanding the evolution of language will require much more empirical research, grounded in modern comparative biology, more interdisciplinary collaboration, and much less of the adaptive storytelling and phylogenetic speculation that has traditionally characterized the field.

In a continuation of the conversation with Fitch, Chomsky, and Hauser on the evolution of language, we examine their defense of the claim that the uniquely human, language-specific part of the language faculty (the ``narrow language faculty'') consists only of recursion, and that this part cannot be considered an adaptation to communication. We argue that their characterization of the narrow language faculty is problematic for many reasons, including its dichotomization of cognitive capacities into those that are utterly unique and those that are identical to nonlinguistic or nonhuman capacities, omitting capacities that may have been substantially modified during human evolution. We also question their dichotomy of the current utility versus original function of a trait, which omits traits that are adaptations for current use, and their dichotomy of humans and animals, which conflates similarity due to common function and similarity due to inheritance from a recent common ancestor. We show that recursion, though absent from other animals' communications systems, is found in visual cognition, hence cannot be the sole evolutionary development that granted language to humans. Finally, we note that despite Fitch et al.'s denial, their view of language evolution is tied to Chomsky's conception of language itself, which identifies combinatorial productivity with a core of ``narrow syntax.'' An alternative conception, in which combinatoriality is spread across words and constructions, has both empirical advantages and greater evolutionary plausibility.

We examine the question of which aspects of language are uniquely human and uniquely linguistic in light of recent suggestions by Hauser, Chomsky, and Fitch that the only such aspect is syntactic recursion, the rest of language being either specific to humans but not to language (e.g. words and concepts) or not specific to humans (e.g. speech perception). We find the hypothesis problematic. It ignores the many aspects of grammar that are not recursive, such as phonology, morphology, case, agreement, and many properties of words. It is inconsistent with the anatomy and neural control of the human vocal tract. And it is weakened by experiments suggesting that speech perception cannot be reduced to primate audition, that word learning cannot be reduced to fact learning, and that at least one gene involved in speech and language was evolutionarily selected in the human lineage but is not specific to recursion. The recursion-only claim, we suggest, is motivated by Chomsky's recent approach to syntax, the Minimalist Program, which de-emphasizes the same aspects of language. The approach, however, is sufficiently problematic that it cannot be used to support claims about evolution. We contest related arguments that language is not an adaptation, namely that it is 'perfect,' non-redundant, unusable in any partial form, and badly designed for communication. The hypothesis that language is a complex adaptation for communication which evolved piecemeal avoids all these problems.

The emergence of human communication systems is typically investigated via 2 approaches with complementary strengths and weaknesses: naturalistic studies and computer simulations. This study was conducted with a method that combines these approaches. Pairs of participants played video games requiring communication. Members of a pair were physically separated but exchanged graphic signals through a medium that prevented the use of standard symbols (e.g., letters). Communication systems emerged and developed rapidly during the games, integrating the use of explicit signs with information implicitly available to players and silent behavior-coordinating procedures. The systems that emerged suggest 3 conclusions: (a) signs originate from different mappings; (b) sign systems develop parsimoniously; (c) sign forms are perceptually distinct, easy to produce, and tolerant to variations.

Whether simple syntax (in the form of simple word order) can emerge during the emergence of lexicon is studied from a simulation perspective; a multiagent computational model is adopted to trace a lexicon-syntax coevolution through iterative communications. Several factors that may affect this self-organizing process are discussed. An indirect meaning transference is simulated to study the effect of nonlinguistic information in listener's comprehension. Besides the theoretical and empirical argumentations, this computational model, following the Emergentism, demonstrates an adaptation of syntax from some domain-general abilities, which provides an argumentation against the Innatism.

In this paper, after a brief review of current computational models on language emergence, a multi-agent model is introduced to simulate the emergence of a compositional language from a holistic signaling system, through iterative interactions among heterogeneous agents. A coevolution of lexicon and syntax (in the form of simple word order) is tracked during communications with indirect meaning transference, in which the listener's comprehension is based on interactions of linguistic and nonlinguistic information, and the feedback is not a direct meaning check. In this model, homonymous and synonymous rules emerge inevitably, and a sufficiently developed communication system is available only when a homonym-avoidance mechanism is adopted. In addition, certain degrees of heterogeneity regarding agent's natural characteristics and linguistic behaviors do not significantly affect language emergence. Finally, based on theories of complex networks, a preliminary study of social structure's influence on language emergence is given, and a coevolution of the emergence of language and that of simple social structure is implemented.

In this article we investigate what learners acquire when their input contains in- consistent grammatical morphemes such as those present in pidgins and incipi- ent creoles. In particular, we ask if learners acquire variability veridically or if they change it, making the language more regular as they learn it. In Experi- ment 1 we taught adult participants an artificial language containing unpredict- able variation in 1 grammatical feature. We manipulated the amount of inconsis- tency and the meaning of the inconsistent item. Postexposure testing showed that participants learned the language, including the variable item, despite the presence of inconsistency. However, their use of variable items reflected their input. Participants exposed to consistent patterns produced consistent patterns, and participants exposed to inconsistency reproduced that inconsistency; they did not make the language more consistent. The meaning of the inconsistent item had no effect. In Experiment 2 we taught adults and 5- to 7-year-old chil- dren a similar artificial language. As in Experiment 1, the adults did not regu- larize the language. However, many children did regularize the language, impos- ing patterns that were not the same as their input. These results suggest that children and adults do not learn from variable input in the same way. Moreover, they suggest that children may play a unique and important role in creole for- mation by regularizing grammatical patterns.

There has been a rapid increase in the use of phylogenetic methods to study the evolution of languages and culture. Languages fit a tree model of evolution well, at least in their basic vocabulary, challenging the view that blending, or admixture among neighbouring groups, was predominant in cultural history. Here, we argue that we can use language trees to test hypotheses about not only cultural history and diversification, but also bio-cultural adaptation. Phylogenetic comparative methods take account of the non-independence of cultures (Galton's problem), which can cause spurious statistical associations in comparative analyses. Advances in phylogenetic methods offer new possibilities for the analysis of cultural evolution, including estimating the rate of evolution and the direction of coevolutionary change of traits on the tree. They also enable phylogenetic uncertainty to be incorporated into the analyses, so that one does not have to treat phylogenetic trees as if they were known without error.

Research into the emergence and evolution of human language has received unprecedented attention during the past 15 years. Efforts to better understand the processes of language emergence and evolution have proceeded in two main directions: from the top-down (linguists) and from the bottom-up (cognitive scientists). Language can be viewed as an invading process that has had profound impact on the human phenotype at all levels, from the structure of the brain to modes of cultural interaction. In our view, the most effective way to form a connection between the two efforts (essential if theories for language evolution are to reflect the constraints imposed on language by the brain) lies in computational modelling, an approach that enables numerous hypotheses to be explored and tested against objective criteria and which suggest productive paths for empirical researchers to then follow. Here, with the aim of promoting the cross-fertilization of ideas across disciplines, we review some of the recent research that has made use of computational methods in three principal areas of research into language evolution: language emergence, language change, and language death.

We analyze the time evolution of a system of two coexisting languages (Castillian Spanish and Galician, both spoken in northwest Spain) in the framework of a model given by Abrams and Strogatz [Nature 424, 900 (2003)]. It is shown that, contrary to the model's initial prediction, a stable bilingual situation is possible if the languages in competition are similar enough. Similarity is described with a simple parameter, whose value can be estimated from fits of the data.

This paper describes a new model on the evolution and induction of compositional structures in the language of a population of (simulated) robotic agents. The model is based on recent work in language evolution modelling, including the iterated learning model, the language game model and the Talking Heads experiment. It further adopts techniques recently developed in the field of grammar induction. The paper reports on a number of different experiments done with this new model and shows certain conditions under which compositional structures can emerge. The paper confirms previous findings that a transmission bottleneck serves as a pressure mechanism for the emergence of compositionality, and that a communication strategy for guessing the references of utterances aids in the development of qualitatively `good' languages. In addition, the results show that the emerging languages reflect the structure of the world to a large extent and that the development of a semantics, together with a competitive selection mechanism, produces a faster emergence of compositionality than a predefined semantics without such a selection mechanism.

Similar to biological evolution and speciation we define a language through a string of 8 or 16 bits. The parent gives its language to its children, apart from a random mutation from zero to one or from one to zero; initially all bits are zero. The Verhulst deaths are taken as proportional to the total number of people, while in addition languages spoken by many people are preferred over small languages. For a fixed population size, a sharp phase transition is observed: For low mutation rates, one language contains nearly all people; for high mutation rates, no language dominates and the size distribution of languages is roughly log-normal as for present human languages. A simple scaling law is valid.

Recently, individual-based models originally used for biological purposes revealed interesting insights into processes of the competition of languages. Within this new field of population dynamics a model considering sexual populations with ageing is presented. The agents are situated on a lattice and each one speaks one of two languages or both. The stability and quantitative structure of an interface between two regions, initially speaking different languages, is studied. We find that individuals speaking both languages do not prefer any of these regions and have a different age structure than individuals speaking only one language.

The faculty of language is unique to the human species. This implies that there are human-specific biological changes that lie at the basis of human language. However, it is not clear what the nature of such changes are, and how they could be shaped by evolution. In this paper, emphasis is laid on describing language in a Chomskyan manner, as a mental object. This serves as a standpoint to speculate about the biological basis of the emergence and evolution of language.

When the sizes of language families of the world, measured by the number of languages contained in each family, are plotted in descending order on a diagram where the x-axis represents the place of each family in the rank-order (the largest family having rank 1, the next-largest, rank 2, and so on) and the y-axis represents the number of languages in the family determining the rank-ordering, it is seen that the distribution closely approximates a curve defined by the formula $y=ax^{[minus sign]b}$. Such `power-law' distributions are known to characterize a wide range of social, biological, and physical phenomena and are essentially of a stochastic nature. It is suggested that the apparent power-law distribution of language family sizes is of relevance when evaluating overall classifications of the world's languages, for the analysis of taxonomic structures, for developing hypotheses concerning the prehistory of the world's languages, and for modelling the future extinction of language families.

Sound is a medium used by humans to carry information. The existence of this kind of medium is a pre-requisite for language. It is organized into a code, called speech, which provides a repertoire of forms that is shared in each language community. This code is necessary to support the linguistic interactions that allow humans to communicate. How then may a speech code be formed prior to the existence of linguistic interactions? Moreover, the human speech code is characterized by several properties: speech is digital and compositional (vocalizations are made of units re-used systematically in other syllables); phoneme inventories have precise regularities as well as great diversity in human languages; all the speakers of a language community categorize sounds in the same manner, but each language has its own system of categorization, possibly very different from every other. How can a speech code with these properties form? These are the questions we will approach in the paper. We will study them using the method of the artificial. We will build a society of artificial agents, and study what mechanisms may provide answers. This will not prove directly what mechanisms were used for humans, but rather give ideas about what kind of mechanism may have been used. This allows us to shape the search space of possible answers, in particular by showing what is sufficient and what is not necessary. The mechanism we present is based on a low-level model of sensorymotor interactions. We show that the integration of certain very simple and non language-specific neural devices allows a population of agents to build a speech code that has the properties mentioned above. The originality is that it pre-supposes neither a functional pressure for communication, nor the ability to have coordinated social interactions (they do not play language or imitation games). It relies on the self-organizing properties of a generic coupling between perception and production both within agents, and on the interactions between agents.

Complex networks have received substantial attention from physics recently. Here we review from a physics perspective the different linguistic networks that have been studied. We focus on syntactic dependency networks and summarize some recent strong results that suggest new possible ways of understanding the universal properties of world languages.

On the one hand, numerous hypotheses have been put forward to account for the emergence of language during the last million years of human evolution. On the other hand, a large majority of linguists considers that nothing can be said about past languages before 8,000 or 10,000 years in the past, given our current knowledge on modern languages. A large gap obviously separates such approaches and conceptions, and has to be crossed to provide a better account of the development of our communicative system. To partially bridge the gap between the former domains, we aim at proposing a plausible scenario for the emergence of languages, with an emphasis on the development of linguistic diversity. The present study will address the question of the monogenesis or polygenesis of modern languages, which is often implicitly biased toward the first hypothesis. Probabilistic and computational models, as well as palaeo-demographic data and evolutionary considerations, will constitute the key points of our proposals.

This paper presents a learning-via-compression approach to unsupervised acquisition of word forms with no a priori knowledge. Following the basic ideas in Solomonoff's theory of inductive inference and Rissanen's MDL framework, the learning is formulated as a process of inferring regularities, in the form of string patterns (i.e., words), from a given set of data. A segmentation algorithm is designed to segment each input utterance into a sequence of word candidates giving an optimal sum of description length gain (DLG). The learning model has a lexical refinement module to exploit this algorithm to derive finer-grained word candidates recursively until no more compression effect is available. Experimental results on an infant-directed speech corpus show that this approach reaches a state-of-art performance in terms of precision and recall of both words and word boundaries

This chapter explores the psychological basis of lexical ambiguity. We compare three ways of meaning calculation, including meanings listed in dictionaries, meanings provided by human subjects, and meanings analyzed by a linguistic theory. Two experiments were conducted using both Chinese and English data. The results suggest that while the numbers of meanings obtained by different methods are significantly different from one another, they are also significantly correlated. Different ways of meaning calculation produce distinct numbers of meanings, though on a relative scale, words with more meanings tend to have greater numbers of meanings throughout. Dictionary meanings are to be distinguished from meanings obtained from subjects both in content and in numbers. These results are then discussed with regard to their methodological implications for further research on psycho- semantics and semantic change.

The evolutionary process works by modifying pre-existing mechanisms, which makes continuity likely. A review of the evidence available to date suggests that there are many aspects of language that show evolutionary continuity, though the direct evidence for syntax and grammar is less clear. However, the universal features of grammar in modern human languages appear to be essentially descriptions of aspects of our basic conceptual universe. It is argued that the most parsimonious model of language evolution involves an increase in conceptual/semantic complexity, which in turn drove the acquisition of syntax and grammar. In this model, universal features of grammar are actually simply reflections of our internal conceptual universe, which are manifested culturally in a variety of ways that are consistent with our pre-linguistic cognitive abilities. This explains both why grammatical rules vary so much across languages, as well as the fact that the commonalities appear to be inherently semantic in nature. An understanding of the way in which concepts are instantiated in the brain, combined with a comparative perspective on brain structure/function relationships, suggest a tight relationship between increasing brain size during hominid evolution and increasing conceptual complexity. A simulation using populations of interacting artificial neural-net agents illustrating this hypothesis is described. The association of brain size and conceptual complexity suggests that language has a deep ancestry.

Language is a symbiotic organism. Language is neither an organ, nor is it an instinct. In the past two and a half million years, we have acquired a genetic predisposition to serve as the host for this symbiont. Like any true symbiont, language enhances our reproductive fitness ...

Complex networks have received substantial attention from physics recently. Here we review from a physics perspective the different linguistic networks that have been studied. We focus on syntactic dependency networks and summarize some recent new results that suggest new possible ways of understanding the universal properties of world languages.

In this paper, I briefly review some comparative data that provide an empirical basis for research on the evolution of music making in humans. First, a brief comparison of music and language leads to discussion of design features of music, suggesting a deep connection between the biology of music and language. I then selectively review data on animal ``music.'' Examining sound production in animals, we find examples of repeated convergent evolution or analogy (the evolution of vocal learning of complex songs in birds, whales, and seals). A fascinating but overlooked potential homology to instrumental music is provided by manual percussion in African apes. Such comparative behavioral data, combined with neuroscientific and developmental data, provide an important starting point for any hypothesis about how or why human music evolved. Regarding these functional and phylogenetic questions, I discuss some previously proposed functions of music, including Pinker's ``cheesecake'' hypothesis; Darwin's and others' sexual selection model; Dunbar's group ``grooming'' hypothesis; and Trehub's caregiving model. I conclude that only the last hypothesis receives strong support from currently available data. I end with a brief synopsis of Darwin's model of a songlike musical ``protolanguage,'' concluding that Darwin's model is consistent with much of the available evidence concerning the evolution of both music and language. There is a rich future for empirical investigations of the evolution of music, both in investigations of individual differences among humans, and in interspecific investigations of musical abilities in other animals, especially those of our ape cousins, about which we know little.

Empirical data have recently begun to inform debates on the evolutionary origins of music. In this paper we discuss some of our recent findings and related theoretical issues. We claim that theories of the origins of music will be usefully constrained if we can determine which aspects of music perception are innate, and, of those, which are uniquely human and specific to music. Comparative research in nonhuman animals, particularly nonhuman primates, is thus critical to the debate. In this paper we focus on the preferences that characterize most humans' experience of music, testing whether similar preferences exist in nonhuman primates. Our research suggests that many rudimentary acoustic preferences, such as those for consonant over dissonant intervals, may be unique to humans. If these preferences prove to be innate in humans, they may be candidates for music-specific adaptations. To establish whether such preferences are innate in humans, one important avenue for future research will be the collection of data from different cultures. This may be facilitated by studies conducted over the internet.

Humans never run out of things to say. We explain, we cajole, we gossip, and we flirt--all with the help of language. But how in the space of several million years did we evolve from an ordinary primate that that could not talk to the strange human primate that can't shut up? In this fascinating, thought-provoking book, Robbins Burling presents the most convincing account of the origins of language ever published, shedding new light on how speech affects the way we think, behave, and relate to each other, and offering us a deeper understanding of the nature of language itself. Burling argues that comprehension, rather than production, was the driving force behind the evolution of language--we could understand words before we could produce them. As he develops this insight, he investigates the first links between signs, sounds, and meanings and explores the beginnings of vocabulary and grammar. He explains what the earliest forms of communication are likely to have been, how they worked, and why they were deployed, suggesting that when language began it was probably much more dependent on words like 'poke' or 'whoosh,' words whose sounds have a close association with what they refer to. Only gradually did language develop the immense vocabulary it has today. Burling also examines the qualities of mind and brain needed to support the operations of language and the selective advantages they offered those able to use them. Written in a crystal-clear style, constantly enlivened by flashes of wit and humor, here is the definitive account on the birth of language.

Table of Contents

1. In The Beginning
2. Smiles, Winks, and Words
3. Truths and Lies
4. The Mind and Language
5. Signs and Symbols
6. Icons Gained and Icons Lost
7. From A Few Sounds To Many Words
8. Syntax: Wired and Learned
9. Step By Step To Grammar
10. Power, Gossip, and Seduction
11. What Has Language Done To Us?

Sverker Johansson has written an unusual book on language origins, with its emphasis on empirical evidence rather than theory-building. This is a book for the student or researcher who prefers solid data and well-supported conclusions, over speculative scenarios. Much that has been written on the origins of language is characterized by hypothesizing largely unconstrained by evidence. But empirical data do exist, and the purpose of this book is to integrate and review the available evidence from all relevant disciplines, not only linguistics but also, e.g., neurology, primatology, paleoanthropology, and evolutionary biology. The evidence is then used to constrain the multitude of scenarios for language origins, demonstrating that many popular hypotheses are untenable. Among the issues covered: (1) Human evolutionary history, (2) Anatomical prerequisites for language, (3) Animal communication and ape 'language', (4) Mind and language, (5) The role of gesture, (6) Innateness, (7) Selective advantage of language, (8) Proto-language.

Table of Contents
1. Introduction 1
2. What is language? 5
3. The theory of evolution 13
4. Human origins and evolution 41
5. Anatomical and neurological prerequisites 77
6. Animal communication in the wild 119
7. Can nonhumans be taught language? 129
8. Language, mind, and self 143
9. Hypotheses of language origins 157
10. Why did language evolve? 193
11. Protolanguage 219
12. Conclusions 243

1. Introduction Part 1 -- Language Emergence
2. Speech and language - a human trait defined by molecular genetics -- King Chow
3. Conceptual complexity and the brain: understanding language origins -- P. Thomas Schoenemann
4. The emergence of grammar from perspective -- Brian MacWhinney
5. Polygenesis of linguistic strategies: a scenario for the emergence of languages -- Christophe Coupe Jand ean-Marie Hombert

Part 2 -- Language Acquisition
6. Multiple-cue integration in language acquisition: a connectionist model of speech segmentation and rule-like behavior -- Morten Christiansen, Christopher M. Conway and Suzanne Curtin
7. Unsupervised lexical learning as inductive inference via compression -- Chunyu Kit
8. The origin of linguistic irregularity -- Charles Yang

Part 3 -- Language Change
9. The language organism: the Leiden theory of language evolution -- George van Driem
10. Taxonomy, typology, and historical linguistics -- Merritt Ruhlen
11. Modeling language evolution -- Felipe Cucker, Steve Smale and Ding-Xuan Zhou

Part 4 -- Language and Complexity
12. Language and complexity -- Murray Gell-Mann
13. Language acquisition as a complex adaptive system -- John H. Holland
14. How many meanings does a word have? Meaning estimation in Chinese and English -- Charles Lin and Kathleen Ahrens
15. Typology and complexity -- Randy LaPolla
16. Creoles and complexity -- Bernard Comrie

The mental lexicon is a complex structure organised in terms of phonology, semantics and syntax, among other levels. In this thesis I propose that this structure can be explained in terms of the pressures acting on it: every aspect of the organisation of the lexicon is an adaptation ultimately related to the function of language as a tool for human communication, or to the fact that language has to be learned by subsequent generations of people. A collection of methods, most of which are applied to a Spanish speech corpus, reveal structure at different levels of the lexicon. ...

The origins of language have become a hotly debated topic in the last decade. Researchers from many fields have been trying to explain them using the tools that their specialisations provide. Also in Artificial Intelligence their has been interest in the topic, and computer science provides a very powerful tool for studying language as a complex dynamical system: multi-agent systems. In the thesis, we present four models based on the ``Language Game'' paradigm developed by Luc Steels for studying language. We support a gradualist view of the evolution of language, and we provide partial answers to three issues that need to be clarified in order to strengthen this position: (1) each stadium of the developing communication system must be viable and successful in its own right; (2) their must be a path from each intermediate communication system to the next; and (3) the users of the communication systems must be able to transition from one system to the next using only local data. Three models describe intermediate communication systems: a system in which the agents can exchange single words referencing one referent; a system in which the agents can exchange several words referencing one referent; and a system in which the agents can exchange complex, structured utterances referencing several referents. Experiments with these systems show that each system is a successful communication system in its own right. The fourth model attempts to show that the agents can make the transition from one communication system to the next successfully and on their own. Agents in this system have the cognitive capacities for two systems: (1) and (2), and have internal pressures that can push either of the communication systems as the one to use, based on different criteria. Two criteria push the agents from system (1) to system (2), yielding interesting results. The models and experiments in the thesis give positive results for each of the issues to be clarified, thereby supporting the gradual evolution hypothesis.

The origins of human language, with its extraordinarily complex structure and multitude of functions, remains among the most challenging problems for evolutionary biology and the cognitive sciences. Although many will agree progress on this issue would have important consequences for linguistic theory, many remain sceptical about whether the topic is amenable to rigorous, scientific research at all. Complementing recent developments toward better empirical validation, this thesis explores how formal models from both linguistics and evolutionary biology can help to constrain the many theories and scenarios in this field.

I first review a number of foundational mathematical models from three branches of evolutionary biology -- population genetics, evolutionary game theory and social evolution theory -- and discuss the relation between them. This discussion yields a list of ten requirements on evolutionary scenarios for language, and highlights the assumptions implicit in the various formalisms. I then look in more details at one specific step-by-step scenario, proposed by Ray Jackendoff, and consider the linguistic formalisms that could be used to characterise the evolutionary transitions from one stage to the next. I conclude from this review that the main challenges in evolutionary linguistics are to explain how three major linguistic innovations -- combinatorial phonology, compositional semantics and hierarchical phrase-structure -- could have spread through a population where they are initially rare.

In the second part of the thesis, I critically evaluate some existing formal models of each of these major transitions and present three novel alternatives. In an abstract model of the evolution of speech sounds (viewed as trajectories through an acoustic space), I show that combinatorial phonology is a solution for robustness against noise and the only evolutionary stable strategy (ESS). In a model of the evolution of simple lexicons in a noisy environment, I show that the optimal lexicon uses a structured mapping from meanings to sounds, providing a rudimentary compositional semantics. Lexicons with this property are also ESS's. Finally, in a model of the evolution and acquisition of context-free grammars, I evaluate the conditions under which hierarchical phrase-structure will be favoured by natural selection, or will be the outcome of a process of cultural evolution.

In the last chapter of the thesis, I discuss the implications of these models for the debates in linguistics on innateness and learnability, and on the nature of language universals. A mainly negative point to make is that formal learnability results cannot be used as evidence for an innate, language-specific specialisation for language. A positive point is that with the evolutionary models of language, we can begin to under- stand how universal properties and tendencies in natural languages can result from the intricate interaction between innate learning biases and a process of cultural evolution over many generations.