Language Evolution and Computation Bibliography

The verb inflections of Bengali underwent a series of phonological change between 10th and 18th centuries, which gave rise to several modern dialects of the language. In this paper, we offer a functional explanation for this change by quantifying the functional pressures of ease of articulation, perceptual contrast and learnability through objective functions or constraints, or both. The multi-objective and multi-constraint optimization problem has been solved through genetic algorithm, whereby we have observed the emergence of Pareto-optimal dialects in the system that closely resemble some of the real ones.

In this work, we attempt to capture patterns of co-occurrence across vowel systems and at the same time figure out the nature of the force leading to the emergence of such patterns. For this purpose we define a weighted network where the vowels are the nodes and an edge between two nodes (read vowls) signify their co-occurrence likelihood over the vowel inventories. Through this network we identify communities of vowels, which essentially reflect their patterns of co-occurrence across languages. We observe that in the assortative vowel communities the constituent nodes (read vowels) are largely uncorrelated in terms of their features indicating that they are formed based on the principle of maximal perceptual contrast. However, in the rest of the communities, strong correlations are reflected among the constituent vowels with respect to their features indicating that it is the principle of feature economy that binds them together.

This paper discusses the reconstruction of the Elamite language s phonology from its orthography using the Gradual Learning Algorithm, which was re-purposed to learn underlying phonological forms from surface orthography. Practical issues are raised regarding the difficulty of mapping between orthography and phonology, and Optimality Theory s neglected Lexicon Optimization module is highlighted.

A number of Russian verbs lack 1sg non-past forms. These paradigmatic gaps are puzzling because they seemingly contradict the highly productive nature of inflectional systems. We model the persistence and spread of Russian gaps via a multi-agent model with Bayesian learning. We ran three simulations: no grammar learning, learning with arbitrary analogical pressure, and morphophonologically conditioned learning. We compare the results to the attested historical development of the gaps. Contradicting previous accounts, we propose that the persistence of gaps can be explained in the absence of synchronic competition between forms.

In this paper, we put forward an information theoretic definition of the redundancy that is observed across the sound inventories of the world's languages. Through rigorous statistical analysis, we find that this redundancy is an invariant property of the consonant inventories. The statistical analysis further unfolds that the vowel inventories do not exhibit any such property, which in turn points to the fact that the organizing principles of the vowel and the consonant inventories are quite different in nature.

There is an ongoing debate about whether the words in the first languages spoken by humans expressed single concepts or complex holophrases. A computer model was used to investigate the nature of the protolanguages that would arise if speakers could associate words and meanings, but lacked any productive ability beyond saying the word whose past uses most closely matched the meaning that they wished to express. It was found that both words expressing single concepts, and holophrastic words could arise, depending on the conceptual and articulatory abilities of the agents. However, most words were of an intermediate type, as they expressed more than a single concept but less than a holophrase. The model therefore demonstrates that protolanguages may have been of types that are not usually considered in the debate over the nature of the first human languages.

This paper describes a study in the use of digital evolution to produce cooperative communication behavior in a population of digital organisms. The results demonstrate that digital evolution can produce organisms capable of distributed problem solving through interactions between members of the population and their environment. Specifically, the organisms cooperate to distribute among the population the largest value sensed from the environment. These digital organisms have no 'built-in' ability to perform this task; each population begins with a single organism that has only the ability to self-replicate. Over thousands of generations, random mutations and natural selection produce an instruction sequence that realizes this behavior, despite continuous turnover in the population.

Language can be viewed as a complex adaptive system which is continuously shaped and reshaped by the actions of its users as they try to solve communicative problems. To maintain coherence in the overall system, different language elements (sounds, words, grammatical constructions) compete with each other for global acceptance. This paper examines what happens when a language system uses systematic structure, in the sense that certain meaning-form conventions are themselves parts of larger units. We argue that in this case multi-level selection occurs: at the level of elements (e.g. tense affixes) and at the level of larger units in which these elements are used (e.g. phrases). Achieving and maintaining linguistic coherence in the population under these conditions is non-trivial. This paper shows that it is nevertheless possible when agents take multiple levels into account both for processing meaning-form associations and for consolidating the language inventory after each interaction.

Language learning dynamics is modelled by an ensemble of individuals consisting of the grammar carriers and the learners. Increasing the system population size results into the transition from the individual to the collective mode of learning. At low communication level, different grammars coexist in their own survival niches. Enhancement of the communication level in purely collective mode, when all individuals are the part of general communication network, leads to the selection of the fittest grammar. Adding the individual mode of learning results into the formation of the quasigrammar, with the dominant grammar prevailing over the set of coexisting grammars.

In this paper we describe a model in which artificial evolution is employed to design neural mechanisms that control the motion of two autonomous robots required to communicate through sound to perform a common task. The results of this work are a proof-of-concept : they demonstrate that evolution can exploit a very simple sound communication system, to design the mechanisms that allow the robots cooperate by employing acoustic interactions. The analysis of the evolved strategies uncover the basic properties of the communication protocol.

This paper presents computer simulations which investigate the effect that different group sizes have on the emergence of compositional structures in languages. The simulations are based on a model that integrates the language game model with the iterated learning model. The simulations show that compositional structures tend to emerge more extensively for larger groups, which has a positive effect on the time in which the languages develop and on communicative success, which may even have an optimal group size. A mathematical analysis of the time of convergence is presented that provides an approximate explanation of the results. The paper concludes that increasing group sizes among humans could not only have triggered the origins of language, but also facilitated the evolution of more complex languages.

A multi-agent computational model is proposed to simulate language evolution in an acquisition framework. This framework involves many major forms of cultural transmission, and the simulation results of the model systematically examine the role of cultural transmission in language emergence and maintenance. In addition, this study discusses the effects of conventionalization during horizontal transmission on diffusing linguistic innovations, maintaining high levels of linguistic understandability, and triggering inevitable changes in the communal languages across generations. All these reflect that conventionalization could be a self-organizing property of the human communication system that drives language evolution.

We review some of the main theories about how language emerged. We suggest that including the study of the emergence of artificial languages, in simulation settings, allows us to ask a more general question, namely, what are the minimal initial conditions for the emergence of language? This is a very important question from a technological viewpoint, because it is very closely tied to questions of intelligence and autonomy. We identify anticipation as being a key underlying computational principle in the emergence of language. We suggest that this is in fact present implicitly in many of the theories in contention today. Focused simulations that address precise questions are necessary to isolate the roles of the minimal initial conditions for the emergence of language.

Human language is based on grammatical rules. Cultural evolution allows these rules to change over time. Rules compete with each other: as new rules rise to prominence, old ones die away. To quantify the dynamics of language evolution, we studied the regularization of English verbs over the past 1,200 years. Although an elaborate system of productive conjugations existed in English's proto-Germanic ancestor, Modern English uses the dental suffix, '-ed', to signify past tense. Here we describe the emergence of this linguistic rule amidst the evolutionary decay of its exceptions, known to us as irregular verbs. We have generated a data set of verbs whose conjugations have been evolving for more than a millennium, tracking inflectional changes to 177 Old-English irregular verbs. Of these irregular verbs, 145 remained irregular in Middle English and 98 are still irregular today. We study how the rate of regularization depends on the frequency of word usage. The half-life of an irregular verb scales as the square root of its usage frequency: a verb that is 100 times less frequent regularizes 10 times as fast. Our study provides a quantitative analysis of the regularization process by which ancestral forms gradually yield to an emerging linguistic rule.

Greek speakers say 'omicronupsilonrho', Germans 'schwanz' and the French 'queue' to describe what English speakers call a 'tail', but all of these languages use a related form of 'two' to describe the number after one. Among more than 100 Indo-European languages and dialects, the words for some meanings (such as 'tail') evolve rapidly, being expressed across languages by dozens of unrelated words, while others evolve much more slowly--such as the number 'two', for which all Indo-European language speakers use the same related word-form. No general linguistic mechanism has been advanced to explain this striking variation in rates of lexical replacement among meanings. Here we use four large and divergent language corpora (English, Spanish, Russian and Greek) and a comparative database of 200 fundamental vocabulary meanings in 87 Indo-European languages to show that the frequency with which these words are used in modern language predicts their rate of replacement over thousands of years of Indo-European language evolution. Across all 200 meanings, frequently used words evolve at slower rates and infrequently used words evolve more rapidly. This relationship holds separately and identically across parts of speech for each of the four language corpora, and accounts for approximately 50\% of the variation in historical rates of lexical replacement. We propose that the frequency with which specific words are used in everyday language exerts a general and law-like influence on their rates of evolution. Our findings are consistent with social models of word change that emphasize the role of selection, and suggest that owing to the ways that humans use language, some words will evolve slowly and others rapidly across all languages.

Collaborative tagging has been quickly gaining ground because of its ability to recruit the activity of web users into effectively organizing and sharing vast amounts of information. Here we collect data from a popular system and investigate the statistical properties of tag cooccurrence. We introduce a stochastic model of user behavior embodying two main aspects of collaborative tagging: (i) a frequency-bias mechanism related to the idea that users are exposed to each other's tagging activity; (ii) a notion of memory, or aging of resources, in the form of a heavy-tailed access to the past state of the system. Remarkably, our simple modeling is able to account quantitatively for the observed experimental features with a surprisingly high accuracy. This points in the direction of a universal behavior of users who, despite the complexity of their own cognitive processes and the uncoordinated and selfish nature of their tagging activity, appear to follow simple activity patterns.

The correlations between interpopulation genetic and linguistic diversities are mostly noncausal (spurious), being due to historical processes and geographical factors that shape them in similar ways. Studies of such correlations usually consider allele frequencies and linguistic groupings (dialects, languages, linguistic families or phyla), sometimes controlling for geographic, topographic, or ecological factors. Here, we consider the relation between allele frequencies and linguistic typological features. Specifically, we focus on the derived haplogroups of the brain growth and development-related genes ASPM and Microcephalin, which show signs of natural selection and a marked geographic structure, and on linguistic tone, the use of voice pitch to convey lexical or grammatical distinctions. We hypothesize that there is a relationship between the population frequency of these two alleles and the presence of linguistic tone and test this hypothesis relative to a large database (983 alleles and 26 linguistic features in 49 populations), showing that it is not due to the usual explanatory factors represented by geography and history. The relationship between genetic and linguistic diversity in this case may be causal: certain alleles can bias language acquisition or processing and thereby influence the trajectory of language change through iterated cultural transmission.

Human language arises from biological evolution, individual learning, and cultural transmission, but the interaction of these three processes has not been widely studied. We set out a formal framework for analyzing cultural transmission, which allows us to investigate how innate learning biases are related to universal properties of language. We show that cultural transmission can magnify weak biases into strong linguistic universals, undermining one of the arguments for strong innate constraints on language learning. As a consequence, the strength of innate biases can be shielded from natural selection, allowing these genes to drift. Furthermore, even when there is no natural selection, cultural transmission can produce apparent adaptations. Cultural transmission thus provides an alternative to traditional nativist and adaptationist explanations for the properties of human languages.

Numerous studies indicate strong associations between languages and genes among human populations at the global scale, but all broader scale genetic and linguistic patterns must arise from processes originating at the community level. We examine linguistic and genetic variation in a contact zone on the eastern Indonesian island of Sumba, where Neolithic Austronesian farming communities settled and began interacting with aboriginal foraging societies approximately 3,500 years ago. Phylogenetic reconstruction based on a 200-word Swadesh list sampled from 29 localities supports the hypothesis that Sumbanese languages derive from a single ancestral Austronesian language. However, the proportion of cognates (words with a common origin) traceable to Proto-Austronesian (PAn) varies among language subgroups distributed across the island. Interestingly, a positive correlation was found between the percentage of Y chromosome lineages that derive from Austronesian (as opposed to aboriginal) ancestors and the retention of PAn cognates. We also find a striking correlation between the percentage of PAn cognates and geographic distance from the site where many Sumbanese believe their ancestors arrived on the island. These language-gene-geography correlations, unprecedented at such a fine scale, imply that historical patterns of social interaction between expanding farmers and resident hunter-gatherers largely explain community-level language evolution on Sumba. We propose a model to explain linguistic and demographic coevolution at fine spatial and temporal scales.

All human languages perform the same function, and the set of distinctions that they use to do so is probably highly constrained. The constraints come from the universal architecture of the human mind, which influences language form through the way it hears, articulates, remembers, and learns. However, within these constraints, there is latitude for variation from language to language. For example, the major categories of subject, verb, and object vary in their typical order, and some languages signal grammatical distinctions primary by syntax, or the combinatorics of words, whereas others achieve this mainly through morphology, or the internal mutation of words. What determines the historical evolution of any particular language across the possibility space formed by these different options? In this issue of PNAS, Dediu and Ladd (1) present evidence suggestive of an answer that has seldom been considered before, which is that interpopulation genetic differences may play a role.

The natural communication of apes may hold clues about language origins, especially because apes frequently gesture with limbs and hands, a mode of communication thought to have been the starting point of human language evolution. The present study aimed to contrast brachiomanual gestures with orofacial movements and vocalizations in the natural communication of our closest primate relatives, bonobos (Pan paniscus) and chimpanzees (Pan troglodytes). We tested whether gesture is the more flexible form of communication by measuring the strength of association between signals and specific behavioral contexts, comparing groups of both the same and different ape species. Subjects were two captive bonobo groups, a total of 13 individuals, and two captive chimpanzee groups, a total of 34 individuals. The study distinguished 31 manual gestures and 18 facial/vocal signals. It was found that homologous facial/vocal displays were used very similarly by both ape species, yet the same did not apply to gestures. Both within and between species gesture usage varied enormously. Moreover, bonobos showed greater flexibility in this regard than chimpanzees and were also the only species in which multimodal communication (i.e., combinations of gestures and facial/vocal signals) added to behavioral impact on the recipient.

We investigate in a series of laboratory experiments how costs and benefits of linguistic communication affect the emergence of simple languages in a coordination task when no common language is available in the beginning. The experiment involved pairwise computerized communication between 152 subjects involved in at least 60 rounds. The subjects had to develop a common code referring to items in varying lists of geometrical figures distinguished by up to three features. A code had to be made of a limited repertoire of letters. Using letters had a cost. We are interested in the question of whether a common code is developed, and what enhances its emergence. Furthermore, we explore the emergence of compositional, protogrammatical structure in such codes. We compare environments that differ in terms of available linguistic resources (number of letters available) and in terms of stability of the task environment (variability in the set of figures). Our experiments show that a too small repertoire of letters causes coordination failures. Cost efficiency and role asymmetry are important factors enhancing communicative success. In stable environments, grammars do not seem to matter much, and instead efficient arbitrary codes often do better. However, in an environment with novelty, compositional grammars offer considerable coordination advantages and therefore are more likely to arise.

This paper studies the evolution of a proto-language in a finite population under the frequency-dependent Moran process. A proto-language can be seen as a collection of concept-to-sign mappings. An efficient proto-language is a bijective mapping from objects of communication to used signs and vice versa. Based on the comparison of fixation probabilities, a method for deriving conditions of evolutionary stability in a finite population [Nowak et al., 2004. Emergence of cooperation and evolutionary stability in finite populations. Nature 428, 246-650], it is shown that efficient proto-languages are the only strategies that are protected by selection, which means that no mutant strategy can have a fixation probability that is greater than the inverse population size. In passing, the paper provides interesting results about the comparison of fixation probabilities as well as Maynard Smith's notion of evolutionary stability for finite populations [Maynard Smith, 1988. Can a mixed strategy be stable in a finite population? J. Theor. Biol. 130, 247-251] that are generally true for games with a symmetric payoff function.

For the benefit of linguists new to the field of language evolution, the author sets out the issues that need to be distinguished in any research on it. He offers a guided tour of contemporary approaches, including the work of linguists (Bickerton, Carstairs-McCarthy, Chomsky, Hurford, Jackendoff, Pinker, Wray), animal behaviour experts (Dunbar, Hauser, Premack, Savage-Rumbaugh), neurophysiologists (Arbib, Calvin), psychologists (Corballis, Donald), archaeologists (Davidson), and computer modellers (Batali, Kirby, Steels). He criticises the expectation that recent discoveries such as `mirror neurons' and the FOXP2 gene will provide easy answers. He emphasises the extremely interdisciplinary nature of this field, and also the importance of involvement in it by linguists, after more than a century of neglect.

Three factors contribute to the evolutionary development of an organism: (i) historical accident (the genetic raw material available for natural selection to work on); (ii) adaptation through natural selection; (iii) nonbiological (especially physical) constraints. The same factors apply in principle to characteristics of an organism, such as the biological basis of the capacity for language in humans. From the point of view of these three factors, the author discusses recent contributions from linguists to language evolution research, including the contributions in this volume. He emphasises the importance of language evolution research for the development of linguistic theory, and the consequent need for more linguists to get involved in language evolution research.

This paper argues for an alternative answer to Carstairs-McCarthy's (1999) question ``Why do all languages distinguish between NPs and sentences?'' While agreeing on basic philosophical points with Carstairs-McCarthy, such as the lack of a distinction between truth and reference independent of grammar, I argue that the S/NP distinction has its roots in the basic communicative distinction between Topic and Comment. In the very earliest mental processes, long antedating language, binary structure can be found, with components that one can associate with the functions of identifying or locating an object and representing some information about it. When private thought went public, the earliest messages in any code with rudimentary syntax were of similar bipartite structure, with one part conveying information presumed to be already known to the hearer, and identifying the object that the message is about. The other part of the bipartite message conveyed information presumed to be new to the hearer. This bipartite structure, with its concomitant distinction between types of expression that could fulfil the respective roles, was central enough to the main function of public language, namely communication, that it was never eroded away, and is the basis of the bipartite structure found universally in languages today.

A strong constraint on the arithmetical combinations allowed in compound numerals, called the Packing Strategy, applies very widely to numeral systems across the world. A previous attempt to explain the existence of the strong universal constraint, in terms of a gradual socio-historical process of standardization, will not scale up to higher-valued numerals. It is proposed that the real explanation for the Packing Strategy is that it reflects two natural principles applied in the practical task of counting objects. These two principles, `Go as far as you can with the resources you have', and `Minimize the number of entities you are dealing with', are not specific to the counting task, but are of more general application to practical tasks.

The dominant theory of the evolution of complex language from protolanguage can be termed the synthetic approach. Under this view, single words arose first in evolution, and were combined as syntax evolved. More recently, an alternative scenario for protolanguage has been proposed, which we can term the holistic approach. Scholars subscribing to this view propose that words emerge from longer, entirely arbitrary strings of sounds - non-compositional utterances - via a process of fractionation. Such holistic utterances initially have no internal structure, but represent whole messages. The idea is that over time, chance phonetic similarities are observed between sections of utterances, and that if similar meanings can be ascribed to these strings, then 'words' will emerge. This paper dissects the main ideas found in the holistic approach, and argues on a number of grounds that it is conceptually and empirically flawed. A proposal that protolanguage developed out of an earlier holistic primate communication system is hard to sustain, in view of differences between primate vocalization and language. Evidence against the holistic approach is offered on the basis of known facts about the historical development of natural languages, and a conclusion is drawn in favour of synthetic models of protolanguage.

Humans possess a number concept that differs from its predecessors in animal cognition in two crucial respects: (1) it is based on a numerical sequence whose elements are not confined to quantitative contexts, but can indicate cardinal/quantitative as well as ordinal and even nominal properties of empirical objects (e.g. `five buses': cardinal; `the fifth bus': ordinal; `the #5 bus': nominal), and (2) it can involve recursion and, via recursion, discrete infinity. In contrast to that, the predecessors of numerical cognition that we find in animals and human infants rely on finite and iconic representations that are limited to cardinality and do not support a unified concept of number.

In this paper, I argue that the way such a unified number concept could evolve in humans is via verbal sequences that are employed as numerical tools, that is, sequences of words whose elements are associated with empirical objects in number assignments. In particular, I show that a certain kind of number words, namely the counting sequences of natural languages, can be characterised as a central instance of verbal numerical tools.

I describe a possible scenario for the emergence of such verbal numerical tools in human history that starts from iconic roots and that suggests that in a process of co-evolution, the gradual emergence of counting sequences and the development of an increasingly comprehensive number concept supported each other.

On this account, it is language that opened the way for numerical cognition, suggesting that it is no accident that the same species that possesses the language faculty as a unique trait, should also be the one that developed a systematic concept of number.

We explore the proposal that the linguistic forms and structures employed by our earliest language-using ancestors might have been significantly different from those observed in the languages we are most familiar with today, not because of a biological difference between them and us, but because the communicative context in which they operated was fundamentally different from that of most modern humans. Languages that are used predominantly for esoteric (intra-group) communication tend to have features that are semantically and grammatically 'complex', while those used also (or even exclusively) for exoteric (inter-group) communication become 'simplified' towards rule-based regularity and semantic transparency. Drawing on a range of contemporary data, we propose a psycholinguistic explanation for why esotericity would promote such complexity, and argue that this is the natural default setting for human language. This being so, it should be taken into account when modelling the evolution of language, for some of the features that are normally viewed as fundamental - including the notion of fully developed underlying rule-based systematicity - may, in fact, be cultural add-ons.

Abstract: This paper compares the nascent theory of the'semiotic symbol'in cognitive science with its computational relative. It finds that the semiotic symbol as it is understood in recent practical and theoretical work does not have the resources to explain the role of symbols ...

It has long been asserted that the evolutionary path to spoken language was paved by manual-gestural behaviors, a claim that has been revitalized in response to recent research on mirror neurons. Renewed interest in the relationship between manual and vocal behavior draws attention to its development. Here, the pointing and vocalization of 16.5-month-old infants are reported as a function of the context in which they occurred. When infants operated in a referential mode, the frequency of simultaneous vocalization and pointing exceeded the frequency of vocalization-only and pointing-only responses by a wide margin. In a non-communicative context, combinatorial effects persisted, but in weaker form. Manual-vocal signals thus appear to express the operation of an integrated system, arguably adaptive in the young from evolutionary times to the present. It was speculated, based on reported evidence, that manual behavior increases the frequency and complexity of vocal behaviors in modern infants. There may be merit in the claim that manual behavior facilitated the evolution of language because it helped make available, early in development, behaviors that under selection pressures in later ontogenetic stages elaborated into speech.

This paper illustrates how external (or {\em social}) symbol grounding can be studied in simulations with large populations. We discuss how we can simulate language evolution in a relatively complex environment which has been developed in the context of the New Ties project. This project has the objective of evolving a cultural society and, in doing so, the agents have to evolve a communication system that is grounded in their interactions with their virtual environment and with other individuals. A preliminary experiment is presented in which we investigate the effect of a number of learning mechanisms. The results show that the social symbol grounding problem is a particularly hard one; however, we provide an ideal platform to study this problem.

We examine the evolution of the vocabulary of a group of individuals (linguistic agents) on a scale-free network, using Monte Carlo simulations and assumptions from evolutionary game theory. It is known that when the agents are arranged in a two-dimensional lattice structure and interact by diffusion and encounter, then their final vocabulary size is the maximum possible. Knowing all available words is essential in order to increase the probability to 'survive' by effective reproduction. On scale-free networks we find a different result. It is not necessary to learn the entire vocabulary available. Survival chances are increased by using the vocabulary of the 'hubs' (nodes with high degree). The existence of the 'hubs' in a scale-free network is the source of an additional important fitness generating mechanism. (C) 2007 Elsevier B.V. All rights reserved.

The differential equation of Abrams and Strogatz for the competition between two languages is compared with agent-based Monte Carlo simulations for fully connected networks as well as for lattices in one, two and three dimensions, with up to 10(9) agents. In the case of socially equivalent languages, agent-based models and a mean-field approximation give grossly different results.

We review the behavior of a recently introduced model of agreement dynamics, called the ``Naming Game.'' This model describes the self-organized emergence of linguistic conventions and the establishment of simple communication systems in a population of agents with pairwise local interactions. The mechanisms of convergence towards agreement strongly depend on the network of possible interactions between the agents. In particular, the mean-field case in which all agents communicate with all the others is not efficient, since a large temporary memory is requested for the agents. On the other hand, regular lattice topologies lead to a fast local convergence but to a slow global dynamics similar to coarsening phenomena. The embedding of the agents in a small-world network represents an interesting tradeoff: a local consensus is easily reached, while the long-range links allow to bypass coarsening-like convergence. We also consider alternative adaptive strategies which can lead to faster global convergence.(c) 2007 American Institute of Physics.

In this paper we present the `grounded adaptive agent' computational framework for studying the emergence of communication and language. This modeling framework is based on simulations of population of cognitive agents that evolve linguistic capabilities by interacting with their social and physical environment (internal and external symbol grounding). These models provide an integrative vision of language where the linguistic abilities of cognitive agents strictly depend on other social, sensorimotor, neural and cognitive capabilities. Here language is not seen as an isolated and dedicated symbol processing system, but rather as a heterogeneous set of artifacts implicated in cultural and cognitive activities. The proposed modeling approach is also closely related to embodied cognition theories of the grounding of language in the organism's perceptual and motor systems.

Recurrent neural networks are often employed in the cognitive science community to process symbol sequences that represent various natural language structures. The aim is to study possible neural mechanisms of language processing and aid in development of artificial language processing systems. We used data sets containing recursive linguistic structures and trained the Elman simple recurrent network (SRN) for the next-symbol prediction task. Concentrating on neuron activation clusters in the recurrent layer of SRN we investigate the network state space organization before and after training. Given a SRN and a training stream, we construct predictive models, called neural prediction machines, that directly employ the state space dynamics of the network. We demonstrate two important properties of representations of recursive symbol series in the SRN. First, the clusters of recurrent activations emerging before training are meaningful and correspond to Markov prediction contexts. We show that prediction states that naturally arise in the SRN initialized with small random weights approximately correspond to states of Variable Memory Length Markov Models (VLMM) based on individual symbols (i.e. words). Second, we demonstrate that during training, the SRN reorganizes its state space according to word categories and their grammatical subcategories, and the next-symbol prediction is again based on the VLMM strategy. However, after training, the prediction is based on word categories and their grammatical subcategories rather than individual words. Our conclusion holds for small depths of recursions that are comparable to human performances. The methods of SRN training and analysis of its state space introduced in this paper are of a general nature and can be used for investigation of processing of any other symbol time series by means of SRN.

Gold's [1967. Language identification in the limit. Information and Control, 16, 447–474] celebrated work on learning in the limit has been taken, by many cognitive scientists, to have powerful negative implications for the learnability of language from positive data (ie, from ...

Specifying the factors that contribute to the universality of color categorization across individuals and cultures is a longstanding and still controversial issue in psychology, linguistics, and anthropology. The present article approaches this issue through the simulated evolution of color lexicons. It is shown that the combination of a minimal perceptual psychology of discrimination, simple pragmatic constraints involving communication, and simple learning rules are enough to evolve color naming systems. Implications of this result for psychological theories of color categorization and the evolution of color naming systems in human societies are discussed.

Phonologies are characterised by regularity, from the stereotyped phonetic characteristics of allophones to the contextually conditioned alternations between them. Most models of grammar account for regularity by hypothesising that there is only a limited set of symbols for expressing underlying forms, and that an independent grammar algorithm transforms symbol sequences into an output representation. However, this explanation for regularity is called into question by research which suggests that the mental lexicon records rich phonetic detail that directly informs production. Given evidence for biases favouring previously experienced forms at many levels of production and perception, I argue that positive feedback within a richly detailed lexicon can produce regularity over many cycles of production and perception. Using simulation as a tool, I show that under the influence of positive feedback, gradient biases in usage can convert an initially gradient and variable distribution of lexical behaviours into a more categorical and simpler pattern.

It has been suggested that iconic graphical signs evolve into symbolic graphical signs through repeated usage. This article reports a series of interactive graphical communication experiments using a ``pictionary'' task to establish the conditions under which the evolution might occur. Experiment 1 rules out a simple repetition based account in favor of an account that requires feedback and interaction between communicators. Experiment 2 shows how the degree of interaction affects the evolution of signs according to a process of grounding. Experiment 3 confirms the prediction that those not involved directly in the interaction have trouble interpreting the graphical signs produced in Experiment 1. On the basis of these results, this article argues that icons evolve into symbols as a consequence of the systematic shift in the locus of information from the sign to the users' memory of the sign's usage supported by an interactive grounding process.

Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute a posterior distribution over languages by combining a prior (representing their inductive biases) with the evidence provided by linguistic data. We show that when learners sample languages from this posterior distribution, iterated learning converges to a distribution over languages that is determined entirely by the prior. Under these conditions, iterated learning is a form of Gibbs sampling, a widely-used Markov chain Monte Carlo algorithm. The consequences of iterated learning are more complicated when learners choose the language with maximum posterior probability, being affected by both the prior of the learners and the amount of information transmitted between generations. We show that in this case, iterated learning corresponds to another statistical inference algorithm, a variant of the expectation-maximization (EM) algorithm. These results clarify the role of iterated learning in explanations of linguistic universals and provide a formal connection between constraints on language acquisition and the languages that come to be spoken, suggesting that information transmitted via iterated learning will ultimately come to mirror the minds of the learners.

The emergence of shared symbol systems is considered to be a pivotal moment in human evolution and human development. These changes are normally explained by reference to changes in people's internal cognitive processes. We present 2 experiments which provide evidence that changes in the external, collaborative processes that people use to communicate can also affect the structure and organization of symbol systems independently of cognitive change. We propose that mutual-modifiability--opportunities for people to edit or manipulate each other's contributions--is a key constraint on the emergence of complex symbol systems. We discuss the implications for models of language development and the origins of compositionality.

Until recently, models of communication have explicitly or implicitly assumed that the goal of a communication system is just maximizing the information transfer between signals and 'meanings'. Recently, it has been argued that a natural communication system not only has to maximize this quantity but also has to minimize the entropy of signals, which is a measure of the cognitive cost of using a word. The interplay between these two factors, i.e. maximization of the information transfer and minimization of the entropy, has been addressed previously using a Monte Carlo minimization procedure at zero temperature. Here we derive analytically the globally optimal communication systems that result from the interaction between these factors. We discuss the implications of our results for previous studies within this framework. In particular we prove that the emergence of Zipf's law using a Monte Carlo technique at zero temperature in previous studies indicates that the system had not reached the global optimum.

We analyze here a particular kind of linguistic network where vertices represent words and edges stand for syntactic relationships between words. The statistical properties of these networks have been recently studied and various features such as the small-world phenomenon and a scale-free distribution of degrees have been found. Our work focuses on four classes of words: verbs, nouns, adverbs and adjectives. Here, we use spectral methods sorting vertices. We show that the ordering clusters words of the same class. For nouns and verbs, the cluster size distribution clearly follows a power-law distribution that cannot be explained by a null hypothesis. Long-range correlations are found between vertices in the ordering provided by the spectral method. The findings support the use of spectral methods for detecting community structure.

Information transfer plays a central role in the biology of most organisms, particularly social species [1, 2]. Although the neurophysiological processes by which signals are produced, conducted, perceived, and interpreted are well understood, the conditions conducive to the evolution of communication and the paths by which reliable systems of communication become established remain largely unknown. This is a particularly challenging problem because efficient communication requires tight coevolution between the signal emitted and the response elicited [3]. We conducted repeated trials of experimental evolution with robots that could produce visual signals to provide information on food location. We found that communication readily evolves when colonies consist of genetically similar individuals and when selection acts at the colony level. We identified several distinct communication systems that differed in their efficiency. Once a given system of communication was well established, it constrained the evolution of more efficient communication systems. Under individual selection, the ability to produce visual signals resulted in the evolution of deceptive communication strategies in colonies of unrelated robots and a concomitant decrease in colony performance. This study generates predictions about the evolutionary conditions conducive to the emergence of communication and provides guidelines for designing artificial evolutionary systems displaying spontaneous communication.

The emergence of communication is considered one of the major transitions in evolution. Recent work using robot-based simulation shows that communication arises spontaneously. While deceptive communication arises in a purely competitive setting, cooperative communication arises only subject to group or kin selection.

Evolutionary language games have proved a useful tool to study the evolution of communication codes in communities of agents that interact among themselves by transmitting and interpreting a fixed repertoire of signals. Most studies have focused on the emergence of Saussurean codes (i.e., codes characterized by an arbitrary one-to-one correspondence between meanings and signals). In this contribution, we argue that the standard evolutionary language game framework cannot explain the emergence of compositional codes-communication codes that preserve neighborhood relationships by mapping similar signals into similar meanings-even though use of those codes would result in a much higher payoff in the case that signals are noisy. We introduce an alternative evolutionary setting in which the meanings are assimilated sequentially and show that the gradual building of the meaning-signal mapping leads to the emergence of mappings with the desired compositional property.

Abstract 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. Multi-agent planning generally assumes that agents share a common means of communication; however, in building robust distributed systems it is important to address potential miscoordination 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. We establish a formal framework for the problem, and identify a collection of necessary and sufficient properties for decision problems that allow agents to employ probabilistic updating schemes in order to learn how to interpret what others are communicating. Solving the problem optimally is often intractable, but our approach enables agents using different languages to converge upon coordination over time. Our experimental work establishes how these methods perform when applied to problems of varying complexity.

Abstract Current debates about “Darwinizing culture” have typically focused on the validity of memetics. In this article we argue that meme-like inheritance is not a necessary requirement for descent with modification. We suggest that an alternative and more productive way of ...

1 Introduction Morality is one of the few topics in academe endowed with its own protective spell. A biologist is not blinded by her biological nature to the workings of biology. An economist is not confused by his own economic activity when he tries to understand the workings of ...

A formal language is called factorizable if any substring of a word in it also belongs to the language. Symbolic sequences from symbolic dynamics make factorizable languages by definition. In studying avoided and under-represented strings in bacterial genomes we have defined a factorizable language for each complete genome. Recently, in studying the problem of uniqueness of reconstruction of a protein sequence from its constituent K-peptides we encounter again factorizable language which helps to build a finite state automaton to recognize the uniqueness of reconstruction. We present a brief review of these applications of factorizable languages from dynamics to biology.

This article explores the evolution of language, focusing on insights derived from observations and experiments in animals, guided by current theoretical problems that were inspired by the generative theory of grammar, and carried forward in substantial ways to the present by psycholinguists working on child language acquisition. We suggest that over the past few years, there has been a shift with respect to empirical studies of animals targeting questions of language evolution. In particular, rather than focus exclusively on the ways in which animals communicate, either naturally or by means of artificially acquired symbol systems, more recent work has focused on the underlying computational mechanisms subserving the language faculty and the ability of nonhuman animals to acquire these in some form. This shift in emphasis has brought biologists studying animals in closer contact with linguists studying the formal aspects of language, and has opened the door to a new line of empirical inquiry that we label evolingo. Here we review some of the exciting new findings in the evolingo area, focusing in particular on aspects of semantics and syntax.With respect to semantics, we suggest that some of the apparently distinctive and uniquely linguistic conceptual distinctions may have their origins in nonlinguistic conceptual representations; as one example, we present data on nonhuman primates and their capacity to represent a singular-plural distinction in the absence of language. With respect to syntax, we focus on both statistical and rule-based problems, especially the most recent attempts to explore different layers within the Chomsky hierarchy; here, we discuss work on tamarins and starlings, highlighting differences in the patterns of results as well as differences in methodology that speak to potential issues of learnability. We conclude by highlighting some of the exciting questions that lie ahead, as well as some of the methodological challenges that face both comparative and developmental studies of language evolution.

Modern linguistic typology is increasingly less concerned with what is possible in human languages (universals) and increasingly more with the question ``what's where why?'' (Bickel 2007). Moreover, as several recent papers in this journal show, typologists increasingly turn to quantitative approaches as a means to understanding typological distributions. In order to provide the quantitative study of typological distributions with a firm methodological foundation it is preferable to gain a grasp of simple facts before starting to ask the more complicated questions. In this article the only assumptions we make about languages are that (i) they may be partly described by a set of typological characteristics, each of which may either be found or not found in any given language; that (ii) languages may be genealogically related or not; and that (iii) languages are spoken in certain places. Given these minimal assumptions we can begin to ask how to express the differences and similarities among languages as functions of the geographical distances among them, whether different functions apply to genealogically related and unrelated languages, and whether it is possible to distinguish in some quantitative way between languages that are related and languages that are not, even when the languages in question are spoken at great distances from one another. Moreover, we may investigate the effects that factors such as ecology, migration, and rates of linguistic change or diffusion have on the degree of similarities among languages in cases where they are either related or unrelated. We will approach these questions from two perspectives. The first perspective is an empirical one, where observations primarily derive from analyses of the data of Haspelmath et al. (eds.) (2005). The second perspective is a computational one, where simulations are drawn upon to test the effects of different parameters on the development of structural linguistic diversity.

This paper investigates a mechanism of linguistic and genetic coevolution in Native Central and South America. This mechanism proposes that a process of population fissions, expansions into new territories, and isolation of ancestral and descendant groups will produce congruent language and gene trees. To evaluate this population fissions mechanism, we collected published mtDNA sequences for 1,381 individuals from 17 Native Central and South American populations. We then tested the hypothesis that three well-known language classifications also represented the genetic structure of these populations. We rejected the hypothesis for each language classification. Our tests revealed linguistic and genetic correspondence in several shallow branches common to each classification, but no linguistic and genetic correspondence in the deeper branches contained in two of the language classifications. We discuss the possible causes for the lack of congruence between linguistic and genetic structure in the region, and describe alternative mechanisms of linguistic and genetic correspondence and their predictions.

This article deals with the typology of the case marking of semantic core roles. The competing economy considerations of hearer (disambiguation) and speaker (minimal effort) are formalized in terms of EVOLUTIONARY GAME THEORY. It is shown that the case-marking patterns that are attested in the languages of the world are those that are evolutionarily stable for different relative weightings of speaker economy and hearer economy, given the statistical patterns of language use that were extracted from corpora of naturally occurring conversations.

In this article we discuss the notion of a linguistic universal, and possible sources of such invariant properties of natural languages. In the first part, we explore the conceptual issues that arise. In the second part of the paper, we focus on the explanatory potential of horizontal evolution. We particularly focus on two case studies, concerning Zipf's Law and universal properties of color terms, respectively. We show how computer simulations can be employed to study the large scale, emergent, consequences of psychologically and psychologically motivated assumptions about the working of horizontal language transmission.

Cultural transmission of information plays a central role in shaping human knowledge. Some of the most complex knowledge that people acquire, such as languages or cultural norms, can only be learned from other people, who themselves learned from previous generations. The prevalence of this process of iterated learning as a mode of cultural transmission raises the question of how it affects the information being transmitted. Analyses of iterated learning under the assumption that the learners are Bayesian agents predict that this process should converge to an equilibrium that reflects the inductive biases of the learners. An experiment in iterated function learning with human participants confirms this prediction, providing insight into the consequences of intergenerational knowledge transmission and a method for discovering the inductive biases that guide human inferences.

Human speech involves species-specific anatomy deriving from the descent of the tongue into the pharynx. The human tongue's shape and position yields the 1:1 oral-to-pharyngeal proportions of the supralaryngeal vocal tract. Speech also requires a brain that can ``reiterate''--freely reorder a finite set of motor gestures to form a potentially infinite number of words and sentences. The end points of the evolutionary process are clear. The chimpanzee lacks a supralaryngeal vocal tract capable of producing the ``quantal'' sounds which facilitate both speech production and perception and a brain that can reiterate the phonetic contrasts apparent in its fixed vocalizations. The traditional Broca-Wernicke brain-language theory is incorrect; neural circuits linking regions of the cortex with the basal ganglia and other subcortical structures regulate motor control, including speech production, as well as cognitive processes including syntax. The dating of the FOXP2 gene, which governs the embryonic development of these subcortical structures, provides an insight on the evolution of speech and language. The starting points for human speech and language were perhaps walking and running. However, fully human speech anatomy first appears in the fossil record in the Upper Paleolithic (about 50,000 years ago) and is absent in both Neanderthals and earlier humans.

This paper investigates probability distributions of dependency distances in six texts extracted from a Chinese dependency treebank. The fitting results reveal that the investigated distribution can be well captured by the right truncated Zeta distribution. In order to restrict the model only to natural language, two samples with randomly generated governors are investigated. One of them can be described e.g. by the Hyperpoisson distribution, the other satisfies the Zeta distribution. The paper also presents a study on sequential plot and mean dependency distance of six texts with three analyses (syntactic, and two random). Of these three analyses, syntactic analysis has a minimum (mean) dependency distance.

We consider communication when there is no agreement about symbols and meanings. We treat it within the framework of reinforcement learning. This framework enables us to talk about emotional coupling and to consider the emergence of communication. We apply different reinforcement learning models in our studies and simplify the problem as much as possible. We show that the modelling of the other agent is insufficient in the simplest possible case, unless the intentions can also be modelled. The model of the agent and its intentions enable quick agreements about symbol-meaning association. We show that when both agents assume an intention model about the other agent then the symbol-meaning association process can be spoiled and symbol meaning association may become hard.

We investigate a model of language evolution, based on population game dynamics with learning. Specifically, we examine the case of two genetic variants of universal grammar (UG), the heart of the human language faculty, assuming each admits two possible grammars. The dynamics are driven by a communication game. We prove using dynamical systems techniques that if the payoff matrix obeys certain constraints, then the two UGs are stable against invasion by each other, that is, they are evolutionarily stable. These constraints are independent of the learning process. Intuitively, if a mutation in UG results in grammars that are incompatible with the established languages, then it will die out because individuals with the mutation will be unable to communicate and therefore unable to realize any potential benefit of the mutation. An example for which the proofs do not apply shows that compatible mutations may or may not be able to invade, depending on the population's history and the learning process. These results suggest that the genetic history of language is constrained by the need for compatibility and that mutations in the language faculty may have died out or taken over depending more on historical accident than on any simple notion of relative fitness.

Speech sounds of the languages all over the world show remarkable patterns of cooccurrence. In this work, we attempt to automatically capture the patterns of cooccurrence of the consonants across languages and at the same time figure out the nature of the force leading to the emergence of such patterns. For this purpose we define a weighted network where the consonants are the nodes and an edge between two nodes (read consonants) signify their co-occurrence likelihood over the consonant inventories. Through this network we identify communities of consonants that essentially reflect their patterns of co-occurrence across languages. We test the goodness of the communities and observe that the constituent consonants frequently occur in such groups in real languages also. Interestingly, the consonants forming these communities reflect strong correlations in terms of their features, which indicate that the principle of feature economy acts as a driving force towards community formation. In order to measure the strength of this force we propose an information theoretic definition of feature economy and show that indeed the feature economy exhibited by the consonant communities are substantially better than those if the consonant inventories had evolved just by chance.

A quantitative method is suggested, where meanings of words, and grammatic rules about these, of a vocabulary are represented by real numbers. People meet randomly, and average their vocabularies if they are equal; otherwise they either copy from higher hierarchy or stay idle. The presence of teachers broadcasting the same (but arbitrarily chosen) vocabulary leads the language formations to converge more quickly.

In open systems of artificial agents, the meaning of communication in part emerges from ongoing interaction processes. In this paper, we present the empirical semantics approach to inductive derivation of communication semantics that can be used to derive this emergent semantics of communication from observations. The approach comes in two complementary variants: One uses social systems theory, focusing on system expectation structures and global utility maximisation, and the other is based on symbolic interactionism, focusing on the viewpoint and utility maximisation of the individual agent. Both these frameworks make use of the insight that the most general meaning of agent utterances lies in their expectable consequences in terms of observable events, and thus they strongly demarcate themselves from traditional approaches to the semantics and pragmatics of agent communication languages.

Much debate has been stimulated by the recent hypothesis that human language consists of a faculty that is shared with non-human animals (faculty of language in a broad sense; FLB) and a faculty that is specific to human language (faculty of language in a narrow sense; FLN). This hypothesis has encouraged a tendency to emphasize one component of FLN: the cognitive operation of recursion. In consequence, non-syntactical, yet unique, aspects of human language have been neglected. One of these properties consists of vocal learning that enables an abundance of learned syllables. I suggest that FLN is not an independent faculty, but an 'emergent' property, arising from interactions between several other non-syntactical subfaculties of FLB, including vocal learning ability.

The language competition model of Viviane de Oliveira et al. is modified by associating with each language a string of 32 bits. Whenever a language changes in this Viviane model, also one randomly selected bit is flipped. If then only languages with different bit-strings are counted as different, the resulting size distribution of languages agrees with the empirically observed slightly asymmetric log-normal distribution. Several other modifications were also tried but either had more free parameters or agreed less well with reality.

This paper presents computational experiments that illustrate how one can precisely conceptualize language evolution as a Darwinian process. We show that there is potentially a wide diversity of replicating units and replication mechanisms involved in language evolution. Computational experiments allow us to study systemic properties coming out of populations of linguistic replicators: linguistic replicators can adapt to specific external environments; they evolve under the pressure of the cognitive constraints of their hosts, as well as under the functional pressure of communication for which they are used; one can observe neutral drift; coalitions of replicators may appear, forming higher level groups which can themselves become subject to competition and selection.

This study examined two questions regarding the emergence of adjectives that describe the Big Five Personality dimensions and when they emerged into the modern English lexicon: (1) Did the terms that describe these qualities appear simultaneously or sequentially? (2) Can the emergence of these terms be linked to specific historical eras? Results showed that the adjective descriptors for Openness appeared in the modern lexicon significantly later than those for Agreeableness, Extraversion, and Conscientiousness. The historical context surrounding the emergence of Openness was presented and the implications of these findings for understanding personality were discussed.

Modern semiotics is a branch of logics that formally defines symbol-based communication. In recent years, the semiotic classification of signs has been invoked to support the notion that symbols are uniquely human. Here we show that alarm-calls such as those used by African vervet monkeys (Cercopithecus aethiops), logically satisfy the semiotic definition of symbol. We also show that the acquisition of vocal symbols in vervet monkeys can be successfully simulated by a computer program based on minimal semiotic and neurobiological constraints. The simulations indicate that learning depends on the tutor-predator ratio, and that apprenticegenerated auditory mistakes in vocal symbol interpretation have little effect on the learning rates of apprentices (up to 80\% of mistakes are tolerated). In contrast, just 10\% of apprentice-generated visual mistakes in predator identification will prevent any vocal symbol to be correctly associated with a predator call in a stable manner. Tutor unreliability was also deleterious to vocal symbol learning: a mere 5\% of ``lying'' tutors were able to completely disrupt symbol learning, invariably leading to the acquisition of incorrect associations by apprentices. Our investigation corroborates the existence of vocal symbols in a non-human species, and indicates that symbolic competence emerges spontaneously from classical associative learning mechanisms when the conditioned stimuli are self-generated, arbitrary and socially efficacious. We propose that more exclusive properties of human language, such as syntax, may derive from the evolution of higher-order domains for neural association, more removed from both the sensory input and the motor output, able to support the gradual complexification of grammatical categories into syntax.

We present a modified naming game by introducing weights of words in the evolution process. We assign the weight of a word spoken by an agent according to its connectivity, which is a natural reflection of the agent's influence in population. A tunable parameter is introduced, governing the word weight based on the connectivity of agents. We consider the scale-free topology and concentrate on the efficiency of reaching the final consensus, which is of high importance in the self-organized system. Interestingly, it is found that there exists an optimal parameter value, leading to the fastest convergence. This indicates appropriate hub's effects favor the achievement of consensus. The evolution of distinct words helps to give a qualitative explanation of this phenomena. Similar nontrivial phenomena are observed in the total memory of agents with a peak in the middle range of parameter values. Other relevant characters are provided as well, including the time evolution of total memory and success rate for different parameter values as well as the average degree of the network, which are helpful for understanding the dynamics of the modified naming game in detail.

We propose a Finite-Memory Naming Game (FMNG) model with respect to the bounded rationality of agents or finite resources for information storage in communication systems. We study its dynamics on several kinds of complex networks, including random networks, small-world networks and scale-free networks. We focus on the dynamics of the FMNG affected by the memory restriction as well as the topological properties of the networks. Interestingly, we found that the most important quantity, the convergence time of reaching the consensus, shows some non-monotonic behaviors by varying the average degrees of the networks with the existence of the fastest convergence at some specific average degrees. We also investigate other main quantities, such as the success rate in negotiation, the total number of words in the system and the correlations between agents of full memory and the total number of words, which clearly explain the nontrivial behaviors of the convergence. We provide some analytical results which help better understand the dynamics of the FMNG. We finally report a robust scaling property of the convergence time, which is regardless of the network structure and the memory restriction.

This paper presents the results of the application of a bit-string model of languages (Schulze and Stauffer 2005) to problems of taxonomic patterns. The questions addressed include the following: (1) Which parameters are minimally ne eded for the development of a taxonomic dynamics leading to the type of distribution of language family sizes currently attested (as measured in the i number of languages per family), which appears to be a power-law? (2) How may such a model be coupled with one of the dynamics of speaker populations leading to the type of language size seen today, which appears to follow a log-normal distribution?

In order to persist, language must be transmitted from generation to generation through a repeated cycle of use and learning. This process of iterated learning has been explored extensively in recent years using computational and mathematical models. These models have shown how compositional syntax provides language with a stability advantage and that iterated learning can induce linguistic adaptation. This paper presents an extension to previous idealised models to allow linguistic agents flexibility and choice in how they construct the semantics of linguistic expressions. This extension allows us to examine the complete dynamics of mixed compositional and holistic languages, look at how semantics can evolve culturally, and how communicative contexts impact on the evolution of meaning structure.

It is well known that perspective alignment plays a major role in the planning and interpretation of spatial language. In order to understand the role of perspective alignment and the cognitive processes involved, we have made precise complete cognitive models of situated embodied agents that self-organise a communication system for dialoging about the position and movement of real world objects in their immediate surroundings. We show in a series of robotic experiments which cognitive mechanisms are necessary and sufficient to achieve successful spatial language and why and how perspective alignment can take place, either implicitly or based on explicit marking.

This chapter discusses how Darwin's evolution theory can be applied to explain language evolution at a cultural level. So, rather than viewing language evolution as a process in which the users adapt biologically to learn language, languages themselves adapt to the learning abilities of individuals. Within this framework, languages evolve through variation, competition and selection. Invention and learning are identified as variation mechanisms; learnability, transmission bottlenecks and stability are pressures for competition; and optimising for success is a good selection mechanism. Rather than studying the language development in individual users, this chapter illustrates how artificial multi-agent systems equipped with these principles can self-organise a compositional language from scratch. It is argued that this model offers a good alternative to many standard approaches in linguistics.

Jean-Louis Dessalles explores the co-evolutionary paths of biology, culture, and the great human edifice of language, linking the evolution of the language to the general evolutionary history of humankind. He provides searchingly original answers to such fundamental paradoxes as to whether we acquired our greatest gift in order to talk or so as to be able to think, and as to why human beings should, as experience constantly confirms, contribute information for the well-being of others at their own expense and for no apparent gain: which if this is one of language's main functions appears to make its possession, in Darwinian terms, a disadvantage. Dr Dessalles looks for solutions in the early history of human species and considers the degree to which language evolved as a means of choosing profitable coalition partners and maximizing individual success within a competitive social environment.

The author opens with a discussion of the differences between animal and human communication and the biological foundations of language. He looks at the physiological preconditions for language evolution and the early evolution of meaning and communication. He then embarks on an important and original account of the natural history of conversation. Here he considers the roles of language in supporting social cohesion and information exchange.

This challenging and original account will appeal to all those interested in the origins of language and the evolution of human behaviour.

Table of Contents

Part I The Place of Language in Human Evolutionary History
1. Animal and Human Communication
2. Culture, Languages, and Language
3. The Biological Roots of Language
4. Misapprehensions about the Origins of Language
5. Language as an Evolutionary Curiosity
6. The Local Optimality of Language
Part II The Functional Anatomy of Speech
7. Putting Sounds Together
8. Protolanguage
9. The Mechanics of Syntax
10. Syntax and Meaning
11. The Structure of Meanings
12. The Emergence of Meaning
Part III The Ethology of Language
13. Conversation Behaviour
14. Language as Information
15. The Birth of Argumentation
16. Language as an Evolutionary Paradox
17. The Political Origins of Language
18. Epilogue

In this, the first of two ground-breaking volumes on the nature of language in the light of the way it evolved, James Hurford looks at how the world first came to have a meaning in the minds of animals and how in humans this meaning eventually came to be expressed as language. He reviews a mass of evidence to show how close some animals, especially primates and more especially apes, are to the brink of human language. Apes may not talk to us but they construct rich cognitive representations of the world around them, and here, he shows, are the evolutionary seeds of abstract thought - the means of referring to objects, the memory of events, even elements of the propositional thinking philosophers have hitherto reserved for humans. What then, he asks, is the evolutionary path between the non-speaking minds of apes and our own speaking minds? Why don't apes communicate the richness of their thoughts to each other? Why do humans alone have a unique disposition to reveal their thoughts in complex detail? Professor Hurford searches a wide range of evidence for the answers to these central questions, including degrees of trust, the role of hormones, the ability to read minds, and the willingness to cooperate.

Expressing himself congenially in consistently colloquial language the author builds up a vivid picture of how mind, language, and meaning evolved over millions of years. His book is a landmark contribution to the understanding of linguistic and thinking processes, and the fullest account yet published of the evolution of language and communication.

Table of Contents

Part I Meaning Beford Communication
1. Let's Agree on Terms
2. Animals Approach Human Cognition
3. A New Kind of Memory Evolves
4. Animals Form proto-propositions
5. Towards Human Semantics
Part II Communication: What and Why?
6. Communication by Dyadic Acts
7. Going Triadic: Precursors of Reference
8. Why Communicate? Squaring With Evolutionary Theory
9. Cooperation, Fair Play and Trust in Primates
10. Epilogue

A compelling look at the quest for the origins of human language from an accomplished linguist. Language is a distinctly human gift. However, because it leaves no permanent trace, its evolution has long been a mystery, and it is only in the last fifteen years that we have begun to understand how language came into being. ``The First Word'' is the compelling story of the quest for the origins of human language. The book follows two intertwined narratives. The first is an account of how language developed, how the random and layered processes of evolution wound together to produce a talking animal: us. The second addresses why scientists are at last able to explore the subject. For more than a hundred years, language evolution was considered a scientific taboo. Kenneally focuses on figures like Noam Chomsky and Steven Pinker, along with cognitive scientists, biologists, geneticists, and animal researchers, in order to answer the fundamental question: Is language a uniquely human phenomenon? ``The First Word'' is the first book of its kind written for a general audience. Sure to appeal to fans of Steven Pinker's ``The Language Instinct'' and Jared Diamond's ``Guns, Germs, and Steel,'' Kenneally's book is set to join them as a seminal account of human history.

This volume brings together studies from diverse disciplines, showing how they can inform and stimulate each other. It includes work in linguistics, psychology, neuroscience, anthropology and computer science. New empirical work is reported on both human and animal communication, using some novel techniques that have only recently become workable.

A principal theme is the importance of studies involving artificial agents, their contribution to the body of knowledge on the emergence of communication and language, and the role of simulations in exploring some of the most significant issues. A number of different synthetic systems are described, showing how communication can emerge in natural and artificial organisms. Theories on the origins of language are supported by computational and robotic experiments.

Worldwide contributors to this volume include some of the most influential figures in the field, delivering essential reading for researchers and graduates in the area, as well as providing fascinating insights for a wider readership.

Contents

Introduction

Current Work and Open Problems: A Roadmap for Research into the Emergence of Communication and Language by Chrystopher L. Nehaniv, Caroline Lyon, and Angelo Cangelosi

Section 1: Empirical Investigations on Human Language

• Evolving Meaning: The Roles of Kin Selection, Allomothering and Paternal Care in Language Evolution by W. Tecumseh Fitch
• `Needs only' Analysis in Linguistic Ontogeny and Phylogeny by Alison Wray
• Clues from Information Theory Indicating a Phased Emergence of Grammar by Caroline Lyon, Chrystopher L. Nehaniv and Bob Dickerson
• Emergence of a Communication System: International Sign by Rachel Rosenstock
• Distributed Language: Biomechanics, Functions, and the Origins of Talk by Stephen J. Cowley

Section 2: Synthesis of Communication and Language in Artificial Systems

• The Recruitment Theory of Language Origins by Luc Steels
• In silico Evolutionary Developmental Neurobiology and the Origin of Natural Language by Eors Szathmary, Zoltan Szatmary, Peter Ittzes, Gergo Orban, Istvan Zachar, Ferenc Huszar, Anna Fedor, Mate Varga, Szabolcs Szamado
• Communication in Natural and Artificial Organisms: Experiments in Evolutionary Robotics by Davide Marocco and Stefano Nolfi
• From Vocal Replication to Shared Combinatorial Speech Codes: A Small Step for Evolution, a Big Step for Language by Pierre-Yves Oudeyer
• Learning and Transition of Symbols: Towards a Dynamical Model of a Symbolic Individual by Takashi Hashimoto and Akira Masumi
• Language Change among `Memoryless Learners' Simulated in Language Dynamics Equations by Makoto Nakamura, Takashi Hashimoto and Satoshi Tojo
• The Evolution of Meaning-space Structure through Iterated Learning by Simon Kirby
• The Emergence of Language: How to Simulate It by Domenico Parisi and Marco Mirolli
• Lexical Acquisition with and without Metacommunication by Jonathan Ginzburg and Zoran Macura
• Agent Based Modelling of Communication Costs: Why Information Can be Free by Ivana Cace and Joanna Bryson
• Language Change and the Inference of Meaning by Andrew D. M. Smith
• Language, Perceptual Categories and their Interaction: Insights from Computational Modelling by Tony Belpaeme and Joris Bleys

Section 3: Insights from Animal Communication

• Emergence of Linguistic Communication: Studies on Grey Parrots by Irene M. Pepperberg
• A Possible Role for Selective Masking in the Evolution of Complex, Learned Communication Systems by Graham R. S. Ritchie and Simon Kirby
• The Natural History of Human Language: Bridging the Gaps without Magic by Bjorn Merker and Kazuo Okanoya
• Neural Substrates for String-Context Mutual Segmentation: A Path to Human Language by Kazuo Okanoya and Bjorn Merker

The original idea for this book came from the successful 2nd International Symposium on the Emergence and Evolution of Linguistic Communication (EELC '05) held in Hatfield, UK, in April 2005. Grants from the British Academy and the UK Engineering and Physical Sciences Research Council in support of this workshop are gratefully acknowledged.

A lot of conventions emerge in gradual stages without being centrally imposed. The most significant and complex example in our human society is undoubtedly human language which evolved according to our need for communication. Also in artificial multi-agent systems, e.g. mobile robots or software agents, it is often desirable that agents can reach a convention in a distributed way. To make this possible, it is important to have a sound grasp of the mechanism by which conventions arise.

In this thesis we define a theoretical framework that enables us to examine this process carefully. We make a strict distinction between the description of the convention problem on the one hand and the solution to this problem in terms of an agent design on the other. A convention problem specifies the preconditions any type of agent must comply with. This includes (i) the space of alternatives from which the convention is to be chosen, (ii) the interaction model between the agents, which determines which agents interact at what time and (iii) the amount, nature and direction of information transmitted between the agents during an interaction. A particular agent design solves a convention problem if a population of such agents will reach an agreement in a reasonable time, under the given restrictions.

We focus on the class of convention problems with a global interaction model: every agent is equally likely to interact with any other agent. We argue that for these convention problems the performance of an agent can be predicted by inspecting the properties of the agent's response function. This response function captures the average behavior of an agent when interacting with agents from a non-changing population.

We apply this analytical technique to different sorts of convention problems. For the more simple convention problems we define general, sufficient properties which guarantee that a convention will arise after a certain amount of time when an agent possesses these. For the more difficult convention problems we confine ourselves to the construction of agents who, we can show, will solve the problem. Finally, our framework is applied to the problem of language evolution in artificial agents. This is a complicated domain for which precise mathematical results are very difficult to obtain. We will focus on the naming game, a relatively simple instance in the paradigm of languages games. In certain instances our analysis will surface problems of convergence that have not been noticed before. This shows on the one hand that it is important to theoretically substantiate computer experiments in language evolution and on the other that the framework introduced in this thesis is very suitable to this extent.