Language Evolution and Computation Bibliography

Recently in the Vocalize-to-Localize framework (a functional stance just started in the Interaction Studies 2004-2005 issues we edited, Abry et al., 2004), we addressed the unification of two grounding attempts concerning the syllable and the foot in language ontogeny. Can the movement time of the pointing strokes of a child be predicted from her babbling rhythm? The answer for 6 babies (6-18 months) was a 2:1 pointing-to-syllable ratio. Implications for the grounding of the first words within this Pointing Frame will be examined. More tentatively we will suggest that babbling for protophonology together with pointing for protosyntax pave the way to language.

The present article raises the so far unaddressed question of the neural foundations of the linguistic processes underlying grammaticalization operations. Two fundamental adaptive neurolinguistic mechanisms are presented: The cerebellar-induced Kalman gain reduction in linguistic processing, and the basal ganglionic re-regulation of cortical unification operations.

Abstract: According to Pragmatics, the speaker's intention in declarative speech is to complete or correct the hearer's belief. But the age at which children begin to produce this type of communication is prior to their success in the 'false belief'test. Here, after proposing ...

Recursion is considered to be one of the hallmarks of human communication. Many theories have been proposed on how this important feature might once have originated. This paper critically examines previously proposed models and posits a new and clearly defined hypothesis: recursion might originate from language users who try to reuse as much of their previously gained linguistic knowledge as possible. We support this claim by providing results of a multi-agent computer simulation in which the agents invent their own communication system encompassing a recursive syntactic category system.

We present evidence that, for English, ambiguity is an active factor in the choice of relativization strategy and that, in speech, prosody plays a role in resolution of ambiguity over the internal role of the relativized constituent. The evidence is based on (semi-)automatic analysis and comparison of automatically-parsed written and spoken portions of the British National Corpus (BNC, Leech, 1992) and of the prosodically-transcribed Spoken English Corpus (SEC, Taylor and Knowles, 1988). The results are evaluated with respect to a model of parsing complexity and syntactic disambiguation (Briscoe 1987, 2000) building on Combinatory Categorial Grammar (Steedman, 2000) and this model is in turn motivated by an evolutionary account of linguistic coevolutionary adaptation of the syntactic and phonological prosodic systems to a solution which minimizes processing cost. To our knowledge this is the first work which investigates linguistic adaptations aimed at reducing ambiguity while making testable predictions about linguistic organization.

It is widely assumed that long-distance dependencies between elements are a unique feature of human language. Here we review recent evidence of long-distance correlations in sequences produced by non-human species and discuss two evolutionary scenarios for the evolution of human language in the light of these findings. Though applying their methodological framework, we conclude that some of Hauser, Chomsky and Fitch s central claims on language evolution are put into question to a different degree within each of those scenarios.

In the general context of dynamics of social consensus, we study an agent based model for the competition between two socially equivalent languages, addressing the role of bilingualism and social structure. In a regular network, we study the formation of linguistic domains and their interaction across the boundaries. We also analyse the dynamics on a small world network and on a network with community structure. In all cases, a final scenario of dominance of one language and extinction of the other is obtained (dominance-extinction state). In comparison with the regular network, smaller times for extinction are found in the small world network. In the network with communities instead, the average time for extinction does not give a characteristic time for the dynamics, and metastable states are observed at all time scales.

5 to 6 million years ago, 3.5 megabase of DNA duplicated from the long arm of the X to create a Hominid-specific stratum on the Y short arm. Such events constitute saltations and this particular change is a candidate for the speciation event for Australopithecus. Within the transposed block a gene pair - Protocadherin X. and Protocadherin Y - has been subject to accelerated evolution, with 16 amino acid changes in the Y protein and five in the X. The latter are particularly significant in that they include the introduction of two sulphur containing cysteines, that are likely to have changed the function of the molecule, and are expressed in both males and females. The sequence changes are seen as secondary to chromosomal rearrangements on the Y. (four deletions and a paracentric inversion), the latter representing the initiating events in successive speciations, and the former representing the sexually selected phase of accommodation that establishes a new mate recognition system. The paracentric inversion, which has not been dated, is a candidate for the sapiens speciation event. Cerebral asymmetry (the torque), may have been introduced at a late stage, perhaps as recently as 160KYA.

It is argued that compositionality, hierarchy and recursion, generally acknowledged to be universal features of human languages, can be explained as being emergent properties of the complex dynamics governing the establishment and evolution of a language in a population of language users, mainly on an intra-generational time scale, rather than being the result of a genetic selection process leading to a specialized language faculty that imposes those features upon language or than being mainly a cross-generational cultural phenomenon. This claim is supported with results from a computational language game experiment in which a number of autonomous software agents bootstrap a common compositional and recursive language.

The causal correlations between human genetic variants and linguistic (typological) features could represent the mechanism required for gradual, accretionary models of language evolution. The causal link is mediated by the process of cultural transmission of language across generations in a population of genetically biased individuals. The particular case of Tone, ASPM and Microcephalin is discussed as an illustration. It is proposed that this type of genetically-influenced linguistic bias, coupled with a fundamental role for genetic and linguistic diversities, provides a better explanation for the evolution of language and linguistic universals.

The fact that human beings universally put much energy and conviction in reporting events in daily conversations demands an explanation. After having observed that the selection of reportable events is based on unexpectedness and emotion, we make a few suggestions to show how the existence of narrative behaviour can be consistent with the socio-political theory of the origin of language.

This paper presents a simulation study exploring the role of cultural transmission in intention sharing (the ability to establish shared intentions in communications). This ability has been argued to be human-unique, and a low level of it has deprived animals of the possibility of developing human language. Our simulation results show that the adequate level of this ability to trigger a communal language is not very high, and that cultural transmission can indirectly optimize the average level of this ability in the population. This work extends the current discussion on the human-uniqueness of some language-related abilities, and provides better understanding on the role of cultural transmission in language evolution.

This paper presents a simulation study to explore the role of naming game in social structure, which is nearly neglected by contemporary studies from statistical physics that mainly discuss the dynamics of naming game in predefined mean-field or complex networks. Our foci include the dynamics of naming game under some simpler, distance restrictions, and the origin and evolution of primitive social clusters as well as their languages under these restrictions. This study extends the current work on the role of social structure in language games, and provides better understanding on the self-organizing process of lexical conventionalization during cultural transmission.

We argue that studying grammaticalisation is useful to evolutionary linguists, if we abstract away from linguistic description to the underlying cognitive mechanisms. We set out a unified approach to grammaticalisation that allows us to identify these mechanisms, and argue that they could indeed be sufficient for the initial emergence of linguistic signal-meaning associations.

In holistic theories of protolanguage, a vital step is the fractionation process where holistic utter- ances are broken down into segments, and segments associated with semantic components. One problem for this process may be the occurrence of counterexamples to any segment-meaning connection. The actual abundance of such counterexamples is a contentious issue (Smith, 2006; Tallerman, 2007). Here I present calculations of the prevalence of counterexamples in model languages. It is found that counterexamples are indeed abundant, much more numerous than positive examples for any plausible holistic language.

One question above all divides evolutionary psychologists. Do humans have digital minds in an analog world Or should this idea be completely reversed? Is it that we have analog minds, although unlike our primate relatives we inhabit a digital world This paper is a contribution intended in the philosophical spirit of Botha s (2003) Unravelling the Evolution of Language.

There are two possible sources of structure in language: biological evolution of the language faculty, or cultural evolution of language itself. Two recent models (Griffiths & Kalish, 2005; Kirby, Dowman, & Griffiths, 2007) make alternative claims about the relationship between innate bias and linguistic structure: either linguistic structure is largely determined by cultural factors (Kirby et al., 2007), with strength of innate bias being relatively unimportant, or the nature and strength of innate machinery is key (Griffiths & Kalish, 2005). These two competing possibilities rest on different assumptions about the learning process. We extend these models here to include a treatment of biological evolution, and show that natural selection for communication favours those conditions where the structure of language is primarily determined by cultural transmission.

It is widely assumed that, over their evolutionary history, languages increased in complexity from simple signals to protolanguages to complex syntactic structures. This papers investigates processes for increasing linguistic complexity while maintaining communicability across a pop- ulation. We assume that linguistic communicability is important for reliably exchanging infor- mation critical for coordination-based tasks. Interaction, needed for learning others languages and converging to communicability, bears a cost. There is a threshold of interaction (learning) effort beyond which convergence either doesn t pay or is practically impossible. Our central findings, established mainly through simulation, are: 1) There is an effort-dependent frontier of tractability for agreement on a language that balances linguistic complexity against linguis- tic diversity in a population. For a given maximum convergence effort either a) languages must be simpler, or b) their initial average communicability must be greater. Thus, if either conver- gence cost or high average communicability over time are important, then even agents who have the capability for using complex languages must not invent them from the start; they must start simple and grow. 2) A staged approach to increasing complexity, in which agents initially con- verge on simple languages and then use these to scaffold greater complexity, can outperform initially-complex languages in terms of overall effort to convergence. This performance gain improves with more complex final languages.

Over the last decade, computational models and simulations have been used to explore whether words could have initially become grounded and established in the earliest stages of language evolution through a process of self-organisation in a population. In this paper, a new model of this family is produced, with two major differences from previous models: the agents world consists of an infinite number of objects, and the agents categories have a flexible prototype structure. These changes result in a more realistic model, but also one which is more likely to fail. Simulation results revealed that coherent lexicons still emerged, but they were sensitive to certain model conditions, including the structure of the world.

Due to the nature of the subject, the field of language evolution has to rely largely on theoretical considerations. A coherent fundamental framework for approaching language evolution has to relate principles of evolution of complex traits with those governing the organization of cognitive processes, communication and natural language architecture. We suggest that by treating the language faculty as a complex trait with predefined functional interfaces, it is possible to delineate the evolutionary forces that have led to the emergence of natural language. We analyze embedding and recursion in communication, and propose a conceptual prerequisite for natural language and fully symbolic reference: a hierarchical way of conceptualization termed 'conceptual embedding' (the ability to nest concepts within concepts). We argue that parallel (multidimensional) interpretation and evolving abstract categories are the effects of conceptual embedding. We review and analyze relevant experiments in ethological literature and conclude that their results do not imply conceptual embedding in non-humans, which leads us to hypothesize that conceptual embedding may be a uniquely human trait. We go on to hypothesize that, initially, the selective force driving the development of the language faculty was towards enhanced conceptualization of reality that is functionally relevant in the absence of linguistic communication. According to this scenario, the invention of linguistic communication was a secondary event, dependent on conceptual embedding which supports the sophisticated conceptual underpinnings of linguistic meaning.

This paper presents a simulation model of self-organizing lexical networks. Its starting point is the notion of an association game in which the impact of varying community models is studied on the emergence of lexical networks. The paper reports on experiments whose results are in accordance with findings in the framework of the naming game. This is done by means of a multilevel network model in which the correlation of social and of linguistic networks is studied.

We demonstrate that polysemous links have a profound impact on the organization of the semantic graph, conforming it as a small-world network, based on the data from WordNet and A Thesaurus of Old English. We then show that the words with higher frequency and therefore with higher number of meanings construct the higher level of the hypernymy tree and this architecture is robust through the times. We suggest that our quantitative analysis of structural connection patterns in semantic networks provides insights into the functioning of neural architectures in the human brain. Furthermore, we discuss that vast quantities of variable and random utterances are converted into a small-world of fixed forms with fixed meanings by the cooperation between humans in the emergence of lexicon.

We present a series of studies investigating the formation, generative power, and evolution of toponyms (i.e. topographic names). The domain chosen for this project is the spatial concepts related to movement through the environment, one of the key sets of concepts to be grounded in autonomous agents. Concepts for spatial locations cannot be directly perceived and require representations built from interactions and inferred from ambiguous sensory data. A generative toponymic language game has been developed to allow the agents to interact, forming concepts for locations and spatial relations. The studies have shown that a grounded generative toponymic language may form and evolve in a population of agents interacting through language games. Initially, terms are grounded in simple spatial concepts directly experienced by the robots. The generative process then enables the robots to learn about and refer to locations beyond their direct experience, enabling concepts and toponyms to co-evolve.

General physical laws are evident as universal syntactic principles governing a computational system of the human language. The optimal space filling condition has to be satisfied in every system of efficient growth. This principle can be attested in syntax, exemplified as the Fibonacci (Fib)-patterns where each new term is the sum of the two that precede it. It will be shown that this rule accounts for the essential features of syntactic trees: limitations imposed on the number of arguments, and phase formation in derivations. The article provides a functional explanation of binary branching, labeling, and the properties of External and Internal Merge.

The defining problem of animal signalling theory is how reliable communication systems remain stable. The problem comes into sharp focus when signals take an arbitrary form, as human words do. Many researchers, particularly those in evolutionary linguistics, assume that the handicap principle is the only recognised solution to this paradox, and hence conclude that the process that underpins reliability in humans must be exceptional. However, there are other examples of cheap yet reliable signals in nature, and corresponding processes that may explain such examples. This paper reviews these alternatives and concludes that by far the most likely explanation of the stability of human communication is our ability to assess individual reputation: we hold the threat of social exclusion against those who signal unreliably and hence keep their utterances reliable.

We show that artificial language evolution involves the interplay of two opposing forces: pres- sure toward simple representations imposed by the dynamics of collective learning, and pres- sure towards complex representations imposed by requirements of agents tasks. The push-pull of these two forces results in the emergence of a language that is balanced: simple but not too simple. We introduce the classification game to study the emergence of these balanced languages and their properties. Our agents use artificial neural networks to learn how to solve tasks, and a simple counting algorithm to simultaneously learn a language as a form-meaning mapping. We show that task-language coupling drives the simplicity-complexity balance, and that both compositional and holistic languages can emerge.

Language is typically argued (or assumed) to be an adaptive trait in the Homo lineage, and various specific selection pressures are offered to explain why language would have increased fitness in a population. However, it is incoherent to discuss language as a monolithic entity: the set of properties that comprise the full, complex language faculty almost certainly evolved independently, and any pressure that buys one of these properties does not necessarily entail the others. Some recent work on kin selection starts by discussing the evolution of speech, but then moves on to the selective value of the exchange of information without indicating how our ancestors got from vocalization to propositions. This is too large a leap, and more specific mechanisms must be proposed if the hypotheses are to be seriously considered.

We combine information theory and cross-situational learning to develop a novel metric for quantifying the degree of regularity in the mappings between signals and meanings that can be inferred from exposure to language in context. We illustrate this metric using the results of two artificial language learning experiments, which show that learners are sensitive, with a high level of individual variation, to systematic regularities in the input. Analysing language using this measure of regularity allows us to explore in detail how language learning and language use can both generate linguistic variation, leading to language change, and potentially complexify language structure, leading to qualitative language evolution.

The ability to interpret, conceive and learn composite meaning is a pre- requisite for language use. Any computational model of the emergence and evolution of grammar needs to consider the emergence and evolution of such meaning. It needs to explain how interpretation deals with semantic ambi- guity, how rich meaning is conceptualised, and how it is acquired and con- ventionalised. Various computational models have been proposed that deal with one or a few of these aspects. It is however hard to integrate them given the diverse and often hard to align underlying conceptual and computational metaphors and paradigms. To remedy this we propose a constraint-based model of compositional semantics which affords a uniform and grounded treatment of its interpretation, its conceptualisation and its acquisition.

This paper shows how experiments on artificial language evolution can provide highly relevant results for important debates in linguistic theories. It reports on a series of experiments that in- vestigate how semantic roles can emerge in a population of artificial embodied agents and how these agents can build a network of constructions. The experiment also includes a fully oper- ational implementation of how event-specific participant-roles can be fused with the semantic roles of argument-structure constructions and thus contributes to the linguistic debate on how the syntax-semantics interface is organized.

In current literature, a number of standard lines of evidence reemerge in support of the hypothesis that the initial, bootstrapping stage of the evolution of language was gestural. However, one specific feature of gestural communication consistent with this hypothesis has been given surprisingly little attention. The visual modality makes gestural signals more secret than vocal signals (lack of broadcast transmission). The high relevance of secrecy is derived from the fundamental constraint on language evolution: the transfer of honest messages itself is a form of cooperation, and therefore not a naturally evolutionarily stable strategy. Consequently, greater secrecy of gestural communication constitutes a potentially important factor that should not fail to be represented in more comprehensive models of the emergence of protolanguage.

We examine the social prerequisites for symbolic communication by studying a language game embedded within a signaling game, in which cooperation is possible but unenforced. Despite incentives to cheat, and even with persistent cheating, the lateral inhibition dynamics commonly used in language game models remain resilient, as long as sufficient mechanisms are in place to detect deceit. However, unfairly antagonistic strategies can undermine lexical convergence. Symbolic communication, and hence human language, requires a delicate balance between restrained deception and revocable trust, but unconditional cooperation is unnecessary.

Scaling up the complexity of a language game brings about a towering scale-up in the uncer- tainty the agents are faced with when acquiring (lexical) form-meaning associations. The two most prominent assumptions influencing the uncertainty in models on word meaning concern (1) meaning transfer and (2) whether a form can be associated with only one part of meaning or any subset of parts of meaning. If meaning has internal structure (e.g. sets of attributes) this second assumption amounts to whether a form can be associated with only one attribute, giving rise to linear uncertainty, or any subset of attributes, resulting in exponential uncertainty. We first present a short overview of different models that each tried to tackle at least one of these assumptions. We propose a new model borrowing ideas from many of these models that can handle the exponential increase in uncertainty when removing both assumptions and allows scaling towards very large meaning spaces (i.e. worlds).

The iterated learning model (ILM), in which a language comes about via communication pressures exerted over successive generations of agents, has attracted much attention in recent years. Its importance lies in the focus on cultural emergence as opposed to biological evolution. The ILM simplifies a compositional language as the compression of an object space, motivated by a poverty of stimulus ''as not all objects in the space will be encountered by an individual in its lifetime. However, in the original ILM, every agent magically has a complete understanding of the surrounding object space, which weakens the relevance to natural language evolution. In this paper, we define each agent s meaning space as an internal self-organising map, allowing it to remain personal and potentially unique. This strengthens the parallels to real language as the agent s omniscience and mind-reading abilities that feature in the original ILM are removed. Additionally, this improvement motivates the compression of the language through a poverty of memory as well as a poverty of stimulus. Analysis of our new implementation shows maintenance of a compositional (structured) language. The effect of a (previously-implicit) generalisation parameter is also analysed; when each agent is able to generalise over a larger number of objects, a more stable compositional language emerges.

The language game approach is widely adopted to study conventionalization of linguistic knowledge. Most of contemporary models concentrate on the dynamics of language games in random or predefined social structures, but neglect the role of communicative constraints. This paper adopts one form of language games, the category game, to discuss whether some simple distance-related communicative constraint may affect the conventionalization of linguistic categories. By comparing the simulation results with those based on another form of language games, the naming game, we point out some essential differences between these two games which cause their distinct performances under the same communicative constraint. This study fills the gap between the dynamics of language games in random structures and that in complex networks, and suggests that internal properties of language games may reversely influence communicative constraints and social structures.

We study the self-organization of the consonant inventories through a complex network approach. We observe that the distribution of occurrence as well as cooccurrence of the consonants across languages follow a power-law behavior. The co-occurrence network of consonants exhibits a high clustering coefficient. We propose four novel synthesis models for these networks (each of which is a refinement of the earlier) so as to successively match with higher accuracy (a) the above mentioned topological properties as well as (b) the linguistic property of feature economy exhibited by the consonant inventories. We conclude by arguing that a possible interpretation of this mechanism of network growth is the process of child language acquisition. Such models essentially increase our understanding of the structure of languages that is influenced by their evolutionary dynamics and this, in turn, can be extremely useful for building future NLP applications.

Linguists speculate that human languages often evolve in rapid or punctuational bursts, sometimes associated with their emergence from other languages, but this phenomenon has never been demonstrated. We used vocabulary data from three of the world's major language groups -- Bantu, Indo-European, and Austronesian -- to show that 10 to 33\% of the overall vocabulary differences among these languages arose from rapid bursts of change associated with language-splitting events. Our findings identify a general tendency for increased rates of linguistic evolution in fledgling languages, perhaps arising from a linguistic founder effect or a desire to establish a distinct social identity.

When we transform thoughts into speech, we do something that no other animal ever achieves. Children acquire this ability effortlessly and without being taught, as though discovering how to walk. Damage to specific areas of the brain that are critical to language shows the profound selectivity of cerebral organization, underlining the exquisite biological structure of language and its computational features. Recent advances bring new insights into the neurogenetic basis of language, its development, and evolution, but also reveal deep holes in our understanding.

While Noah Webster may have produced the earliest compendium on American English, the divergence from British English dates from much earlier. Long before the publication of Webster's Dictionary in 1806, pronunciation in America and in Britain had begun to differ (1, 2). The Dictionary thus does not mark a fixed point when all Americans shifted abruptly from British to American English. The speciation, rather, was gradual, because individual speakers change gradually, by increments, in their lifetimes; individual changes also spread gradually from speaker to speaker.

We introduce an experimental paradigm for studying the cumulative cultural evolution of language. In doing so we provide the first experimental validation for the idea that cultural transmission can lead to the appearance of design without a designer. Our experiments involve the iterated learning of artificial languages by human participants. We show that languages transmitted culturally evolve in such a way as to maximize their own transmissibility: over time, the languages in our experiments become easier to learn and increasingly structured. Furthermore, this structure emerges purely as a consequence of the transmission of language over generations, without any intentional design on the part of individual language learners. Previous computational and mathematical models suggest that iterated learning provides an explanation for the structure of human language and link particular aspects of linguistic structure with particular constraints acting on language during its transmission. The experimental work presented here shows that the predictions of these models, and models of cultural evolution more generally, can be tested in the laboratory.

When people speak, they often insinuate their intent indirectly rather than stating it as a bald proposition. Examples include sexual come-ons, veiled threats, polite requests, and concealed bribes. We propose a three-part theory of indirect speech, based on the idea that human communication involves a mixture of cooperation and conflict. First, indirect requests allow for plausible deniability, in which a cooperative listener can accept the request, but an uncooperative one cannot react adversarially to it. This intuition is supported by a game-theoretic model that predicts the costs and benefits to a speaker of direct and indirect requests. Second, language has two functions: to convey information and to negotiate the type of relationship holding between speaker and hearer (in particular, dominance, communality, or reciprocity). The emotional costs of a mismatch in the assumed relationship type can create a need for plausible deniability and, thereby, select for indirectness even when there are no tangible costs. Third, people perceive language as a digital medium, which allows a sentence to generate common knowledge, to propagate a message with high fidelity, and to serve as a reference point in coordination games. This feature makes an indirect request qualitatively different from a direct one even when the speaker and listener can infer each other's intentions with high confidence.

Categories provide a coarse-grained description of the world. A fundamental question is whether categories simply mirror an underlying structure of nature or instead come from the complex interactions of human beings among themselves and with the environment. Here, we address this question by modeling a population of individuals who co-evolve their own system of symbols and meanings by playing elementary language games. The central result is the emergence of a hierarchical category structure made of two distinct levels: a basic layer, responsible for fine discrimination of the environment, and a shared linguistic layer that groups together perceptions to guarantee communicative success. Remarkably, the number of linguistic categories turns out to be finite and small, as observed in natural languages.

It has been claimed that a meaningful theory of cultural evolution is not possible because human beliefs and behaviors do not follow predictable patterns. However, theoretical models of cultural transmission and observations of the development of societies suggest that patterns in cultural evolution do occur. Here, we analyze whether two sets of related cultural traits, one tested against the environment and the other not, evolve at different rates in the same populations. Using functional and symbolic design features for Polynesian canoes, we show that natural selection apparently slows the evolution of functional structures, whereas symbolic designs differentiate more rapidly. This finding indicates that cultural change, like genetic evolution, can follow theoretically derived patterns.

Over the last 30 years, the idea that the processes producing cultural stability and change are analogous in important respects to those of biological evolution has become increasingly popular. Biological evolution is characterized by changing frequencies of genes in populations through time as a result of such processes as natural selection; likewise, cultural evolution refers to the changing distributions of cultural attributes in populations, which are affected by processes such as natural selection but also by others that have no analogue in genetic evolution. The fundamental, mathematically based theory that justified and spelled out the necessary modifications to standard population genetics theory to make it relevant to culture was laid out by Cavalli-Sforza and Feldman (1) and Boyd and Richerson (2) in the 1980s, on the basis of earlier papers, and Richard Dawkins (3) had already introduced the idea to the popular imagination with his concept of the meme as analogous to the gene. In the intervening period, the development of what has come to be called dual inheritance theory or gene ``culture coevolution theory has continued, and it has been accompanied by a slowly growing number of empirical case studies that apply these ideas to understanding patterned variation in cultural data. The article by Rogers and Ehrlich (4) in this issue of PNAS makes a significant contribution to this growing field by showing how different cultural evolutionary processes can be identified and distinguished from one another and how they differentially affect different kinds of cultural traits; it will certainly become a widely cited classic case study, and the dataset of descriptive traits of canoes from different Polynesian groups is likely to become a test bed for future cultural evolutionary studies.

It has recently been proposed [Dediu, D., Ladd, D.R., 2007. Linguistic tone is related to the population frequency of the adaptive haplogroups of two brain size genes, ASPM and Microcephalin. Proc. Natl Acad. Sci. USA 104(26), 10944-10949] that genetically coded linguistic biases can influence the trajectory of language change. However, the nature of such biases and the conditions under which they can become manifest have remained vague. The present paper explores computationally two plausible types of linguistic acquisition biases in a population of agents implementing realistic genetic, linguistic and demographic processes. One type of bias represents an innate asymmetric initial state (initial expectation bias) while the other an innate asymmetric facility of acquisition (rate of learning bias). It was found that only the second type of bias produces detectable effects on language through cultural transmission across generations and that such effects are produced even by weak biases present at low frequencies in the population. This suggests that learning preference asymmetries, very small at the individual level and not very frequent at the population level, can bias the trajectory of language change through the process of cultural transmission.

In signaling games the replicator dynamics does not almost always converge to states of perfect communication. A significant portion of the state space converges to components of Nash equilibria that characterize states of partial communication. Since these components consist of non-hyperbolic rest points, the significance of this result will depend on the dynamic behavior of specific perturbations of the replicator equations. In this paper we study selection-mutation dynamics of signaling games, which may be considered as one plausible perturbation of the replicator dynamics. We find that the long term behavior of the dynamics depends on the mutation rates of senders and receivers and on the relevance of communication.

The paper investigates the class of signaling games with the following properties: (a) the interests of sender and receiver coincide, (b) different signals incur differential costs, and (c) different events (meanings/types) have different probabilities. Necessary and sufficient conditions are presented for a profile to be evolutionarily stable and neutrally stable, and for a set of profiles to be an evolutionarily stable set.

The main finding is that a profile belongs to some evolutionarily stable set if and only if a maximal number of events can be reliably communicated. Furthermore, it is shown that under the replicator dynamics, a set of states with a positive measure is attracted to ``sub-optimal'' equilibria that do not belong to any asymptotically stable set.

The handicap principle has been applied to a number of different traits in the last three decades, but it is difficult to characterize its record, or even its perceived relevance, when it comes to an important human attribute ''spoken language. In some cases, assumptions regarding the energetic cost of speech, and the veracity of linguistically encoded messages, have failed to recognize critical aspects of human development, cognition, and social ecology. In other cases, the fact that speech contains honest (physiological) information, and tends to be used honestly with family and friends, has been overlooked. Speech and language are functionally related but they involve different resources. Individuals can increase the attractiveness of their speech, and of more stylized vocal and verbal performances, without enhancing linguistic structure or content; and they can modify their use of language without significant changes in the physical form of speech. That its production costs are normally low enables speech to be produced extravagantly in bids for status and mating relationships, and in evolution, may have allowed its content ''linguistic knowledge and structure ''to become complex.

The 'developmental stress hypothesis' attempts to provide a functional explanation of the evolutionary maintenance of song learning in songbirds. It argues that song learning can be viewed as an indicator mechanism that allows females to use learned features of song as a window on a male's early development, a potentially stressful period that may have long-term phenotypic effects. In this paper we formally model this hypothesis for the first time, presenting a population genetic model that takes into account both the evolution of genetic learning preferences and cultural transmission of song. The models demonstrate that a preference for song types that reveal developmental stress can evolve in a population, and that cultural transmission of these song types can be stable, lending more support to the hypothesis.

Cumulative cultural evolution is the term given to a particular kind of social learning, which allows for the accumulation of modifications over time, involving a ratchet-like effect where successful modifications are maintained until they can be improved upon. There has been great interest in the topic of cumulative cultural evolution from researchers from a wide variety of disciplines, but until recently there were no experimental studies of this phenomenon. Here, we describe our motivations for developing experimental methods for studying cumulative cultural evolution and review the results we have obtained using these techniques. The results that we describe have provided insights into understanding the outcomes of cultural processes at the population level. Our experiments show that cumulative cultural evolution can result in adaptive complexity in behaviour and can also produce convergence in behaviour. These findings lend support to ideas that some behaviours commonly attributed to natural selection and innate tendencies could in fact be shaped by cultural processes.

This paper assesses whether human communication systems undergo the same progressive adaptation seen in animal communication systems and concrete artefacts. Four experiments compared the fitness of ad hoc sign systems created under different conditions when participants play a graphical communication task. Experiment 1 demonstrated that when participants are organized into interacting communities, a series of signs evolve that enhance individual learning and promote efficient decoding. No such benefits are found for signs that result from the local interactions of isolated pairs of interlocutors. Experiments 2 and 3 showed that the decoding benefits associated with community evolved signs cannot be attributed to superior sign encoding or detection. Experiment 4 revealed that naive overseers were better able to identify the meaning of community evolved signs when compared with isolated pair developed signs. Hence, the decoding benefits for community evolved signs arise from their greater residual iconicity. We argue that community evolved sign systems undergo a process of communicative selection and adaptation that promotes optimized sign systems. This results from the interplay between sign diversity and a global alignment constraint; pairwise interaction introduces a range of competing signs and the need to globally align on a single sign-meaning mapping for each referent applies selection pressure.

The primary goal of this study was to investigate cultural transmission in young children, with specific reference to the phenomenon of overimitation. Diffusion chains were used to compare the imitation of 2- and 3-year-olds on a task in which the initial child in each chain performed a series of relevant and irrelevant actions on a puzzle box in order to retrieve a reward. Children in the chains witnessed the actions performed on one of two boxes, one which was transparent and so the lack of causality of the irrelevant actions was obvious, while the other was opaque and so the lack of causal relevance was not obvious. Unlike previous dyadic research in which children overimitate a model, the irrelevant actions were parsed out early in the diffusion chains. Even though children parsed out irrelevant actions, they showed fidelity to the method used to perform a relevant action both within dyads and across groups. This was true of 3-year-olds, and also 2-year-olds, therefore extending findings from previous research.

The question of how much the outcomes of cultural evolution are shaped by the cognitive capacities of human learners has been explored in several disciplines, including psychology, anthropology and linguistics. We address this question through a detailed investigation of transmission chains, in which each person passes information to another along a chain. We review mathematical and empirical evidence that shows that under general conditions, and across experimental paradigms, the information passed along transmission chains will be affected by the inductive biases of the people involved-the constraints on learning and memory, which influence conclusions from limited data. The mathematical analysis considers the case where each person is a rational Bayesian agent. The empirical work consists of behavioural experiments in which human participants are shown to operate in the manner predicted by the Bayesian framework. Specifically, in situations in which each person's response is used to determine the data seen by the next person, people converge on concepts consistent with their inductive biases irrespective of the information seen by the first member of the chain. We then relate the Bayesian analysis of transmission chains to models of biological evolution, clarifying how chains of individuals correspond to population-level models and how selective forces can be incorporated into our models. Taken together, these results indicate how laboratory studies of transmission chains can provide information about the dynamics of cultural evolution and illustrate that inductive biases can have a significant impact on these dynamics.

Genes and culture represent two streams of inheritance that for millions of years have flowed down the generations and interacted. Genetic propensities, expressed throughout development, influence what cultural organisms learn. Culturally transmitted information, expressed in behaviour and artefacts, spreads through populations, modifying selection acting back on populations. Drawing on three case studies, I will illustrate how this gene-culture coevolution has played a critical role in human evolution. These studies explore (i) the evolution of handedness, (ii) sexual selection with a culturally transmitted mating preference, and (iii) cultural niche construction and human evolution. These analyses shed light on how genes and culture shape each other, and on the significance of feedback mechanisms between biological and cultural processes.

The existence of social learning has been confirmed in diverse taxa, from apes to guppies. In order to advance our understanding of the consequences of social transmission and evolution of behaviour, however, we require statistical tools that can distinguish among diverse social learning strategies. In this paper, we advance two main ideas. First, social learning is diverse, in the sense that individuals can take advantage of different kinds of information and combine them in different ways. Examining learning strategies for different information conditions illuminates the more detailed design of social learning. We construct and analyse an evolutionary model of diverse social learning heuristics, in order to generate predictions and illustrate the impact of design differences on an organism's fitness. Second, in order to eventually escape the laboratory and apply social learning models to natural behaviour, we require statistical methods that do not depend upon tight experimental control. Therefore, we examine strategic social learning in an experimental setting in which the social information itself is endogenous to the experimental group, as it is in natural settings. We develop statistical models for distinguishing among different strategic uses of social information. The experimental data strongly suggest that most participants employ a hierarchical strategy that uses both average observed pay-offs of options as well as frequency information, the same model predicted by our evolutionary analysis to dominate a wide range of conditions.

In this paper, we explore how experimental studies of cultural transmission in adult humans can address general questions regarding the `who, what, when and how' of human cultural transmission, and consequently inform a theory of human cultural evolution. Three methods are discussed. The transmission chain method, in which information is passed along linear chains of participants, has been used to identify content biases in cultural transmission. These concern the kind of information that is transmitted. Several such candidate content biases have now emerged from the experimental literature. The replacement method, in which participants in groups are gradually replaced or moved across groups, has been used to study phenomena such as cumulative cultural evolution, cultural group selection and cultural innovation. The closed-group method, in which participants learn in groups with no replacement, has been used to explore issues such as who people choose to learn from and when they learn culturally as opposed to individually. A number of the studies reviewed here have received relatively little attention within their own disciplines, but we suggest that these, and future experimental studies of cultural transmission that build on them, can play an important role in a broader science of cultural evolution.

The articles in this theme issue seek to understand the evolutionary bases of social learning and the consequences of cultural transmission for the evolution of human behaviour. In this introductory article, we provide a summary of these articles (seven articles on the experimental exploration of cultural transmission and three articles on the role of gene-culture coevolution in shaping human behaviour) and a personal view of some promising lines of development suggested by the work summarized here.

Human language is unique among the communication systems of the natural world: it is socially learned and, as a consequence of its recursively compositional structure, offers open-ended communicative potential. The structure of this communication system can be explained as a consequence of the evolution of the human biological capacity for language or the cultural evolution of language itself. We argue, supported by a formal model, that an explanatory account that involves some role for cultural evolution has profound implications for our understanding of the biological evolution of the language faculty: under a number of reasonable scenarios, cultural evolution can shield the language faculty from selection, such that strongly constraining language-specific learning biases are unlikely to evolve. We therefore argue that language is best seen as a consequence of cultural evolution in populations with a weak and/or domain-general language faculty.

Much recent work stresses the role of embodiment and action in thought and reason, and celebrates the power of transmitted cultural and environmental structures to transform the problem-solving activity required of individual brains. By apparent contrast, much work in evolutionary psychology has stressed the selective fit of the biological brain to an ancestral environment of evolutionary adaptedness, with an attendant stress upon the limitations and cognitive biases that result. On the face of it, this suggests either a tension or, at least, a mismatch, with the symbiotic dyad of cultural evolution and embodied cognition. In what follows, we explore this mismatch by focusing on three key ideas: cognitive niche construction; cognitive modularity; and the existence (or otherwise) of an evolved universal human nature. An appreciation of the power and scope of the first, combined with consequently more nuanced visions of the latter two, allow us to begin to glimpse a much richer vision of the combined interactive potency of biological and cultural evolution for active, embodied agents.

A growing set of observational studies documenting putative cultural variations in wild animal populations has been complemented by experimental studies that can more rigorously distinguish between social and individual learning. However, these experiments typically examine only what one animal learns from another. Since the spread of culture is inherently a group-level phenomenon, greater validity can be achieved through `diffusion experiments', in which founder behaviours are experimentally manipulated and their spread across multiple individuals tested. Here we review the existing corpus of 33 such studies in fishes, birds, rodents and primates and offer the first systematic analysis of the diversity of experimental designs that have arisen. We distinguish three main transmission designs and seven different experimental/control approaches, generating an array with 21 possible cells, 15 of which are currently represented by published studies. Most but not all of the adequately controlled diffusion experiments have provided robust evidence for cultural transmission in at least some taxa, with transmission spreading across populations of up to 24 individuals and along chains of up to 14 transmission events. We survey the achievements of this work, its prospects for the future and its relationship to diffusion studies with humans discussed in this theme issue and elsewhere.

With the launch of this journal, the term 'biolinguistics' gains new visibility and credibility, but a clear definition has yet to emerge. In their Editorial in the journal's inaugural issue, Boeckx & Grohmann (2007: 2) draw a distinction between “weak” and “strong” senses of the term. ...

The mathematical model for language competition developed by Abrams and Strogatz allows the evolution of the numbers of monolingual speakers of two competing languages to be estimated. In this paper, we extend the model to examine the role of bilingualism and social structure, neither of which are addressed in the previous model. We consider the impact of two strategies for language maintenance: (1) adjusting the status of the endangered language; and (2) adjusting the availability of monolingual and bilingual educational resources. The model allows us to predict for which scenarios of intervention language maintenance is more likely to be achieved. Qualitative analysis of the model indicates a set of intervention strategies by which the likelihood of successful maintenance is expected to increase.

Vocal learning is a critical behavioral substrate for spoken human language. It is a rare trait found in three distantly related groups of birds-songbirds, hummingbirds, and parrots. These avian groups have remarkably similar systems of cerebral vocal nuclei for the control of ...

A compositionality-regularity coevolution model is adopted to explore the effect of social structure on language emergence and maintenance. Based on this model, we explore language evolution in three experiments, and discuss the role of a popular agent in language evolution, the relationship between mutual understanding and social hierarchy, and the effect of inter-community communications and that of simple linguistic features on convergence of communal languages in two communities. This work embodies several important interactions during social learning, and introduces a new approach that manipulates individuals' probabilities to participate in social interactions to study the effect of social structure. We hope it will stimulate further theoretical and empirical explorations on language evolution in a social environment.

This study investigates how more advanced joint attentional mechanisms, rather than only shared attention between two agents and an object, can be implemented and how they influence the results of language games played by these agents. We present computer simulations with language games showing that adding constructs that mimic the three stages of joint attention identified in children's early development (checking attention, following attention, and directing attention) substantially increase the performance of agents in these language games. In particular, the rates of improved performance for the individual attentional mechanisms have the same ordering as that of the emergence of these mechanisms in infants' development. These results suggest that language evolution and joint attentional mechanisms have developed in a co-evolutionary way, and that the evolutionary emergence of the individual attentional mechanisms is ordered just like their developmental emergence.

Learning the meanings of words requires coping with referential uncertainty - a learner hearing a novel word cannot be sure which aspects or properties of the referred object or event comprise the meaning of the word. Data from developmental psychology suggest that human learners grasp the important aspects of many novel words after just a few exposures, a phenomenon known as fast mapping. Traditionally, word learning is viewed as a mapping task, in which the learner has to map a set of forms onto a set of pre-existing concepts. We criticise this approach and argue instead for a flexible nature of the coupling between form and meanings as a solution to the problem of referential uncertainty. We implemented and tested the model in populations of humanoid robots that play situated language games about objects in their shared environment. Results show that the model can handle an exponential increase in uncertainty and allows scaling towards very large meaning spaces, while retaining the ability to grasp an operational meaning almost instantly for a great number of words. In addition, the model captures some aspects of the flexibility of form-meaning associations found in human languages. Meanings of words can shift between being very specific (names) and general (e.g. 'small'). We show that this specificity is biased not by the model itself but by the distribution of object properties in the world.

Like any other biological trait, communication can be studied from at least four perspectives: mechanistic, ontogenetic, functional, and phylogenetic. In this article, we focus on the following phylogenetic question: how can communication emerge, given that both signal-producing and signal-responding abilities seem to be adaptively neutral until the complementary ability is present in the population? We explore the problem of co-evolution of speakers and hearers with artificial life simulations: a population of artificial neural networks evolving a food call system. The core of the article is devoted to a careful analysis of the complex evolutionary dynamics demonstrated by our simple simulation. Our analyses reveal an important factor, which might solve the phylogenetic problem: the spontaneous production of good (meaningful) signals by speakers because of the need for organisms to categorize their experience in adaptively relevant ways. We discuss our results with respect both to previous simulative work and to the biological literature on the evolution of communication.

One important difference between existing accounts of protolanguage lies in their assumptions on the semantic complexity of protolinguistic utterances. I bring evidence about the nature of linguistic communication to bear on the plausibility of these assumptions, and show that communication is fundamentally inferential and characterised by semantic uncertainty. This not only allows individuals to maintain variation in linguistic representation, but also imposes a selection pressure that meanings be reconstructible from context. I argue that protolanguage utterances had varying degrees of semantic complexity, and developed into complex language gradually, through the same processes of re-analysis and analogy which still underpin continual change in modern languages.

Much of the debate concerning the question ''Was Protolanguage Holophrastic?'' assumes that protolanguage existed as a single, stable transitional form between communication systems akin to those of modern primates and human languages as we know them today. The present paper argues for a spectrum of protolanguages preceding modern languages emphasizing that (i) protospeech was intertwined with protosign and gesture; (ii) grammar emerged from a growing population of constructions; and (iii) an increasing protolexicon drove the emergence of phonological structure. This framework weakens arguments for the view that the earliest protolanguages were not holophrastic while advancing the claim that protolanguages became increasingly compositional over time en route to the emergence of true languages.

It is possible for a language to emerge with no direct linguistic history or outside linguistic influence. Al-Sayyid Bedouin Sign Language (ABSL) arose about 70 years ago in a small, insular community with a high incidence of profound prelingual neurosensory deafness. In ABSL, we have been able to identify the beginnings of phonology, morphology, syntax, and prosody. The linguistic elements we find in ABSL are not exclusively holistic, nor are they all compositional, but a combination of both. We do not, however, find in ABSL certain features that have been posited as essential even for a proto-language. ABSL has a highly regular syntax as well as word-internal compounding, also highly regular but quite distinct from syntax in its patterns. ABSL, however, has no discernable word-internal structure of the kind observed in more mature sign languages: no spatially organized morphology and no evident duality of phonological patterning.

In dealing with the nature of protolanguage, an important formative factor in its development, and one that would surely have influenced that nature, has too often been neglected: the precise circumstances under which protolanguage arose. Three factors are involved in this neglect: a failure to appreciate radical differences between the functions of language and animal communication, a failure to relate developments to the overall course of human evolution, and the supposition that protolanguage represents a package, rather than a series of separate developments that sequentially impacted the communication of pre-humans. An approach that takes these factors into account is very briefly suggested.

Two opposing accounts of early language evolution, the compositional and the holistic, have become the subject of lively debate. It has been argued that an evolving compositional protolanguage would not be useful for communication until it reached a certain level of grammatical complexity. This paper offers a new, discourse-oriented perspective on the debate. It argues that discourse should be viewed, not as a level of language structure beyond the sentence , but as sequenced communicative behaviour, typically but not uniquely involving language. This provides for continuity from exchanges making use of simple communicative resources such as single words and gestures to those making use of complex grammatical conventions. Supporting evidence comes from child language and from original experiments with adults using constrained language systems. The paper shows that the utility of emerging compositional language is not dependent on some critical level of complexity, and so defends the compositional account.

A modular analysis of spontaneous language use provides support for the existence of an identifiable step in language evolution, protolanguage. Our suggestion is that a grammarless form of expression would have evolved to signal unexpected events, a behavior still prevalent in our species. Words could not be so specific as to refer to whole, non-recurring, situations. They referred to elements such as objects or locations, and the communicated event was inferred metonymically. Compositionality was achieved, without syntax, through multi-metonymy, as words referring to elements of the same situation were concatenated into proto-utterances.

We approach the issue of holophrasis versus compositionality in the emergence of protolanguage by analyzing the earliest combinatorial constructions in child, bonobo, and chimpanzee: messages consisting of one symbol combined with one gesture. Based on evidence from apes learning an interspecies visual communication system and children acquiring a first language, we conclude that the potential to combine two different kinds of semiotic element '' deictic and representational '' was fundamental to the protolanguage forming the foundation for the earliest human language. This is a form of compositionality, in that each communicative element stands for a single semantic element. The conclusion that human protolanguage was exclusively holophrastic '' containing a proposition in a single word '' emerges only if one considers the symbol alone, without taking into account the gesture as a second element comprising the total message.

If protolanguage was a holistic system where complex meanings were conveyed using unanalysed forms, there must be some process (analysis) which delivered up the elements of modern language from this system. This paper draws on evidence from computational modelling, developmental and historical linguistics and comparative psychology to evaluate the plausibility of the analysis process. While some of the criticisms levelled at analysis can be refuted using such evidence, several areas are highlighted where further evidence is required to decide key issues. More generally, the debate over the nature of protolanguage offers a framework for developing and showcasing a modern, evidence-based evolutionary linguistics.

Early humans formed language units consisting of global and discrete dimensions of semiosis in dynamic opposition, or growth points. At some point, gestures gained the power to orchestrate actions, manual and vocal, with significances other than those of the actions themselves, giving rise to cognition framed in dual terms. However, our proposal emphasizes natural selection of joint gesture-speech, not gesture-first in language origin.

This paper challenges recent assumptions that holophrastic utterances could be planned, processed, stored and retrieved from storage, focussing on three specific issues: (i) Problems in conceptual planning of multi-proposition utterances of the type proposed by Arbib (2005), Mithen (2005); (ii) The question of whether holophrastic protolanguage could have been processed by a special holistic mode, the precursor to a projected idiom mode in modern language; (iii) The implications for learning a holophrastic proto-lexicon in light of lexical constraints on word learning. Modern speakers only plan utterances in clause-sized units, and it is improbable that protolanguage speakers had more complex abilities. Moreover, the production and comprehension of idioms sheds no light on a putative holistic mode of language processing, since idioms are not processed in this way. Finally, innate constraints on learning lexical items preclude the types of word meanings proposed by proponents of holophrastic protolanguage.

The Complex-First Paradox regards the semantics of nouns and consists of a set of together incompatible, but individually well confirmed propositions about the evolution and development of language, the semantics of word classes and the cortical realization of word meaning. Theoretical and empirical considerations support the view that the concepts expressed by concrete nouns are more complex and their neural realizations more widely distributed in cortex than those expressed by other word classes. For a cortically implemented syntax ``semantics interface, the more widely distributed a concept s neural realization is, the more effort it takes to establish a link between the concept and its expression. If one assumes the principle that in ontogeny and phylogeny capabilities demanding more effort develop, respectively, evolve later than those demanding less effort, the empirical observation seems paradoxical that the meanings of concrete nouns, in ontogeny and phylogeny, are acquired earlier than those of other word classes.

Abstract The object of inquiry in linguistics is the human ability to acquire and use a natural language, and the goal of linguistic theory is an explicit characterization of that ability. Looking at the communicative abilities of other species, it becomes clear that our linguistic ...

We focus on the evolution of action capabilities which set the stage for language, rather than analyzing how further brain evolution built on these capabilities to yield a language-ready brain. Our framework is given by the Mirror System Hypothesis, which charts a progression from a monkey-like mirror neuron system (MNS) to a chimpanzee-like mirror system that supports simple imitation and thence to a human-like mirror system that supports complex imitation and language. We present the MNS2 model, a new model of action recognition learning by mirror neurons of the macaque brain and augmented competitive queuing, a model of opportunistic scheduling of action sequences as background for analysis of modeling strategies for ``simple imitation'' as seen in the great apes and ``complex/goal-directed imitation'' as seen in humans. Implications for the study of language are briefly noted.

I discuss the ubiquity of power law distributions in language organisation (and elsewhere), and argue against Miller's (The mating mind: How sexual choice shaped the evolution of human nature, William Heinemann, London, 2000) argument that large vocabulary size is a consequence of sexual selection. Instead I argue that power law distributions are evidence that languages are best modelled as dynamical systems but raise some issues for models of iterated language learning.

The term embodiment identifies a theory that meaning and semantics cannot be captured by abstract, logical systems, but are dependent on an agent's experience derived from being situated in an environment. This theory has recently received a great deal of support in the cognitive science literature and is having significant impact in artificial intelligence. Memetics refers to the theory that knowledge and ideas can evolve more or less independently of their human-agent substrates. While humans provide the medium for this evolution, memetics holds that ideas can be developed without human comprehension or deliberate interference. Both theories have profound implications for the study of language -- its potential use by machines, its acquisition by children and of particular relevance to this special issue, its evolution. This article links the theory of memetics to the established literature on semantic space, then examines the extent to which these memetic mechanisms might account for language independently of embodiment. It then seeks to explain the evolution of language through uniquely human cognitive capacities which facilitate memetic evolution.

This symposium includes a selection of articles on the origins and evolution of language. These are extended version of selected papers presented at ``EVOLANG6: The Sixth International Conference on the Evolution of Language'' that was held in Rome in April 2006. This selection of papers provides a multi-methodological view of different approaches to, and theoretical explanations of, the evolution of language.

Chicken-Hawk is a social-dilemma game that distinguishes uncoordinated from coordinated cooperation. In tests with players belonging to a culturally homogeneous population, natural-language ``cheap talk'' led to efficient coordination, while nonlinguistic signaling yielded uncoordinated altruism. In a subsequent test with players from a moderately more heterogeneous population nearby, the ``cheap talk'' condition still produced better coordination than other signaling conditions, but at a lower level and with fewer acts of altruism overall. Implications are: (1) without language, even willing cooperators coordinate poorly; (2) given a sufficiently homogeneous social group, language can coordinate cooperation in the face of opportunities for anonymous defection; (3) coordination therefore depends not on merely a general propensity to cooperate but on the overlap of social identities, which are always costly to acquire and maintain. So far as linguistic variation establishes how much social identities overlap, natural-language ``cheap talk'' is self-insuring, suggesting that linguistic variation is itself adaptive.

Many scholars assume a connection between the evolution of language and that of distinctively human group-level morality. Unfortunately, such thinkers frequently downplay a central implication of modern Darwinian theory, which precludes the possibility of innate psychological mechanisms evolving to benefit the group at the expense of the individual. Group level moral regulation is indeed central to public life in all known human communities. The production of speech acts would be impossible without this. The challenge, therefore, is to explain on a Darwinian basis how life could have become subject to the rule of law. Only then will we have an appropriate social framework in which to contextualize our models of how language may have evolved.

In this paper, we claim that the problem of conditionals should be dealt with by carefully distinguishing between thinking conditional propositions and conditional thinking, i.e. thinking on the basis of some supposition. This distinction deserves further investigation, if we are to make sense of some old and new experimental data concerning the understanding and the assertion of conditional sentences. Here we will argue that some of these data seem to refute the mental models theory of conditional reasoning, setting the ground for a different approach to the cognitive study of conditionals.

The mental model theory claims that the ability to falsify is at the core of human rationality. We assume that cognitive conflicts (CCs) and socio-cognitive conflicts (SCCs) induce falsification, and thus improve syllogistic reasoning performance. Our first study assesses adults' ability to reason in two different conditions in a single experimental session. In both conditions the participants are presented with conclusions alternative to their own. In the CC condition they are told that these conclusions are casual, in the SCC condition they are told they have been produced by another individual. The second study is analogous to the first, with the exception that the participants deal with the two conditions in two different experimental sessions. The overall results reveal that falsification is enhanced by conflicts experienced at the cognitive level. The results also reveal that learning to reason occurs in adults, when tested in two distinct experimental periods.

Primate vocal communication is very different from human language. Differences are most pronounced in call production. Differences in production have been overemphasized, however, and distracted attention from the information that primates acquire when they hear vocalizations. In perception and cognition, continuities with language are more apparent. We suggest that natural selection has favored nonhuman primates who, upon hearing vocalizations, form mental representations of other individuals, their relationships, and their motives. This social knowledge constitutes a discrete, combinatorial system that shares several features with language. It is probably a general primate characteristic whose appearance pre-dates the evolution of spoken language in our hominid ancestors. The prior evolution of social cognition created individuals who were preadapted to develop language. Several features thought to be unique to language --like discrete combinatorics and the encoding of propositional information-- were not introduced by language. They arose, instead, because understanding social life and predicting others' behavior requires a particular style of thinking.

Sign languages provide direct evidence for the relation between human languages and the body that engenders them. We discuss the use of the hands to create symbols and the role of the body in sign language verb systems, especially in two quite recently developed sign languages, Israeli Sign Language and Al-Sayyid Bedouin Sign Language.

We agree that much of language evolution is likely to be adaptation of languages to properties of the brain. However, the attempt to rule out the existence of language-specific adaptations a priori is misguided. In particular, the claim that adaptation to cannot occur is false. Instead, the details of gene-culture coevolution in language are an empirical matter.

Christiansen & Chater (C&C) argue persuasively that Universal Grammar (UG) could not have arisen through evolutionary processes. I provide additional suggestions to strengthen the argument against UG evolution. Further, I suggest that C&C's solution to the logical problem of language evolution faces several problems. Widening the focus to mechanisms of general cognition and inclusion of animal communication research might overcome these problems.

Christiansen & Chater's (C&C's) arguments share with memetics the ideas that language is an evolving organism and that brain capacities shape language by influencing the fitness of memes, although memetics also claims that memes in turn shape brains. Their rejection of meme theory is based on falsely claiming that memes must be consciously selected by sighted watchmakers.

Languages emerge in response to the negotiation of shared meaning in social groups, where transparency of grammar is necessitated by demands of communication with relative strangers needing to consult on a wide range of topics (Ragir 2002). This communal exchange is automated and stabilized through activity-dependent fine-tuning of information-specific neural connections during postnatal growth and social development.

Christiansen & Chater's (C&C's) argument rests on an assumption that convergent cultural evolution can produce similar (complex) behaviours in isolated populations. In this commentary, I describe how experiments recently carried out by Caldwell and colleagues can contribute to the understanding of such phenomena.

Evolutionary contingencies select organisms based on what they can do; brains and other evolved structures serve their behavior. Arguments that brains drive language structure get the direction wrong; with functional issues unacknowledged, interactions between central structures and periphery are overlooked. Evidence supports a peripherally driven central organization. If language modules develop like other brain compartments, then environmental consistencies can engender both structural and functional language units (e.g., the different phonemic, semantic, and grammatical structures of different languages).

It is widely assumed that human learning and the structure of human languages are intimately related. This relationship is frequently suggested to derive from a language-specific biological endowment, which encodes universal, but communicatively arbitrary, principles of language structure (a Universal Grammar or UG). How might such a UG have evolved? We argue that UG could not have arisen either by biological adaptation or non-adaptationist genetic processes, resulting in a logical problem of language evolution. Specifically, as the processes of language change are much more rapid than processes of genetic change, language constitutes a both over time and across different human populations, and, hence, cannot provide a stable environment to which language genes could have adapted. We conclude that a biologically determined UG is not evolutionarily viable. Instead, the original motivation for UG arises because language has been shaped to fit the human brain, rather than vice versa. Following Darwin, we view language itself as a complex and interdependent which evolves under selectional pressures from human learning and processing mechanisms. That is, languages themselves are shaped by severe selectional pressure from each generation of language users and learners. This suggests that apparently arbitrary aspects of linguistic structure may result from general learning and processing biases deriving from the structure of thought processes, perceptuo-motor factors, cognitive limitations, and pragmatics.

Our target article argued that a genetically specified Universal Grammar (UG), capturing arbitrary properties of languages, is not tenable on evolutionary grounds, and that the close fit between language and language learners arises because language is shaped by the brain, rather than the reverse. Few commentaries defend a genetically specified UG. Some commentators argue that we underestimate the importance of processes of cultural transmission; some propose additional cognitive and brain mechanisms that may constrain language and perhaps differentiate humans from nonhuman primates; and others argue that we overstate or understate the case against co-evolution of language genes. In engaging with these issues, we suggest that a new synthesis concerning the relationship between brains, genes, and language may be emerging.

Recognising that signed languages are true languages adds to the variety of forms that languages can take. Such recognition also allows one to differentiate those aspects of language that depend on the medium (voiced or signed) from those that depend on more cognitive aspects. At least some aspects of language, such as symbolic representation, time markers, and generativity, may derive from the communication of the products of mental time travel, and from the sharing of remembered past and planned future episodes.

Universal Grammar (UG) is indeed evolutionarily implausible. But if languages are just to a large primate brain, it is hard to see why other primates do not have complex languages. The answer is that humans have evolved a specialized and uniquely human cognitive architecture, whose main function is to compute mappings between arbitrary signals and communicative intentions. This underlies the development of language in the human species.

Selection through iterated learning explains no more than other non-functional accounts, such as Universal Grammar (UG), why language is so well designed for communicative efficiency. It does not predict several distinctive features of language, such as central embedding, large lexicons, or the lack of iconicity, which seem to serve communication purposes at the expense of learnability.

Language is shaped by its environment, which includes not only the brain, but also the public context in which speech acts are effected. To fully account for why language has the shape it has, we need to examine the constraints imposed by language use as a sequentially organized joint activity, and as the very conduit for linguistic diffusion and change.

Meaning construction through language requires advanced mental operations also necessary for other higher-order, specifically human behaviors. Biological evolution slowly improved conceptual mapping capacities until human beings reached the level of double-scope blending, perhaps 50 to 80 thousand years ago, at which point language, along with other higher-order human behaviors, became possible. Languages are optimized to be driven by the principles and powers of double-scope blending.

Historical language change (), like evolution itself, is a fact; and its implications for the biological evolution of the human capacity for language acquisition () have been ably explored by many contemporary theorists. However, Christiansen & Chater's (C&C's) revolutionary call for a replacement of phylogenetic models with glossogenetic cultural models is based on an inadequate understanding of either. The solution to their lies before their eyes, but they mistakenly reject it due to a supposed Gene/;culture co-evolution poses a series of difficult theoretical and empirical problems that will be resolved by subtle thinking, adequate models, and careful cross-disciplinary research, not by oversimplified manifestos.

This commentary aims to highlight what exactly is controversial about the traditional Universal Grammar (UG) hypothesis and what is not. There is widespread agreement that we are not born that language universals exist, that grammar exists, and that adults have domain-specific representations of language. The point of contention is whether we should assume that there exist unlearned syntactic universals that are arbitrary and specific to Language.

We propose that some aspects of language evolved to fit the brain, whereas other aspects co-evolved with the brain. Cladistic analysis indicates that common basic structures of both action and grammar arose in phylogeny six million years ago and in ontogeny before age two, with a shared prefrontal neural substrate. In contrast, mirror neurons, found in both humans and monkeys, suggest that the neural basis for intersubjectivity evolved before language. Natural selection acts upon genes controlling the neural substrates of these phenotypic language functions.

Christiansen & Chater (C&C) suggest that language is an organism, like us, and that our brains were not selected for Universal Grammar (UG) capacity; rather, languages were selected for learnability with minimal trial-and-error experience by our brains. This explanation is circular: Where did our brain's selective capacity to learn all and only UG-compliant languages come from?

That language is shaped to fit the human brain is close to the Chomskyan position. The target article by Christiansen & Chater (C&C) assumes an entity, outside individual heads. What is the nature of this entity? Linguistic niche-construction and co-evolution of language and genes are possible, with some of what evolved being language-specific. Recent generative theory postulates much less than the old Universal Grammar (UG).

The claim that language is shaped by the brain is weakened by lack of clear specification of what necessary and sufficient properties the brain actually imposes. To account for human intellectual superiority, it is proposed that language did require special brain evolution (Deacon 1997), but that what evolved was a merely quantitative change rather than a radically new invention.

Neural circuits linking local operations in the cortex and the basal ganglia confer reiterative capacities, expressed in seemingly unrelated human traits such as speech, syntax, adaptive actions to changing circumstances, dancing, and music. Reiteration allows the formation of a potentially unbounded number of sentences from a finite set of syntactic processes, obviating the need for the hypothetical

The proposal that language has evolved to conform to general cognitive and learning constraints inherent in the human brain calls for specification of these mechanisms. We propose that just as cognition appears to be grounded in cross-modal perceptual-motor capabilities, so too must language. Evidence for perceptual-motor grounding comes from non-arbitrary sound-to-meaning correspondences and their role in word learning.

We suggest there is somewhat more potential than Christiansen & Chater (C&C) allow for genetic adaptations specific to language. Our uniquely cooperative social system requires sophisticated language skills. Learning and performance of some culturally transmitted elements in animals is genetically based, and we give examples of features of human language that evolve slowly enough that genetic adaptations to them may arise.

Christiansen & Chater (C&C) have taken the interactionist approach to linguistic universals to an extreme, adopting the metaphor of language as an organism. This metaphor adds no insights to five decades of analyzing language universals as the result of interaction of linguistically unique and general cognitive systems. This metaphor is also based on an outmoded view of classical Darwinian evolution and has no clear basis in biology or cognition.

I identify a number of problematic aspects of Christiansen & Chater's (C&C's) contribution. These include their suggestion that subjacency and binding reflect non-domain-specific mechanisms; that proto-language is a ; and that non-adaptationism requires overly rich innate structures, and is incompatible with acceptable evolutionary processes. It shows that a fully UG (Universal Grammar)-free version of the authors' neo-adaptationism would be incoherent.

Christiansen & Chater (C&C) focus solely on general-purpose cognitive processes in their elegant conceptualization of language evolution. However, numerous developmental facts attested in L1 acquisition confound C&C's subsequent claim that the logical problem of language acquisition now plausibly recapitulates that of language evolution. I argue that language acquisition should be viewed instead as a multi-layered construction involving the interplay of general and domain-specific learning mechanisms.

We agree that language adapts to the brain, but we note that language also has to adapt to brain-external constraints, such as those arising from properties of the cultural transmission medium. The hypothesis that Christiansen & Chater (C&C) raise in the target article not only has profound consequences for our understanding of language, but also for our understanding of the biological evolution of the language faculty.

Christiansen & Chater (C&C) suggest that language is itself an evolutionary system, and that natural languages to be easy to learn and process. The tight economy of the world's case-marking systems lends support to this hypothesis. Only two major case systems occur, cross-linguistically, and noun phrases are seldom overtly case-marked wherever zero-marking would be functionally practical.

Though we agree with their argument that language is shaped by domain-general learning processes, Christiansen & Chater (C&C) neglect to detail how the development of these processes shapes language change. We discuss a number of examples that show how developmental processes at multiple levels and timescales are critical to understanding the origin of domain-general mechanisms that shape language evolution.

Language learning is not primarily driven by a motivation to describe invariant features of the world, but rather by a strong force to be a part of the social group, which by definition is not invariant. It is not sufficient for language to be fit for the speaker's perceptual motor system. It must also be fit for social interactions.

The article reviews computational studies of language change. Computer models of change are helpful because of the complexity of the behavior involved: an entire population of complex, interacting agents must be accounted for. Computational studies frequently bring to light hidden implications of theories, which make them relevant to the theoretical development of both acquisition and change. Studies of language change have focused on discovering mathematical properties of dynamical systems, or on simulating populations of speakers that interact with one another and change their internal states as a result. Models of lexical (including phonological) and syntactic change are considered. Computational models of change have proved useful tools for testing theories of language change, and will prove more useful as the field matures to include more systematic studies of the effects of varying model parameters in complex simulations.

Large linguistic databases, especially databases having a global coverage, such as the World Atlas of Language Structures, the Automated Similarity Judgment Program, and Ethnologue, are making it possible to systematically investigate many aspects of how languages change and compete for viability. Agent-based computer simulations supplement such empirical data by analyzing the necessary and sufficient parameters for the current global distributions of languages or linguistic features. By combining empirical datasets with simulations and applying quantitative methods, it is now possible to address fundamental questions, such as 'what are the relative rates of change in different parts of languages?', 'why are there a few large language families, many intermediate ones, and even more small ones?', 'do small languages change faster or slower than large ones?', or 'how does the borrowing of words relate to the borrowing of structural features?'

The field of language dynamics encompasses the study and modeling of how languages develop (language evolution), change, and interact (language competition). It contrasts with traditional historical linguistics in several ways: the focus is on the world's linguistic diversity rather than just on specific languages or language families; methods are quantitative rather than qualitative; computer simulations are employed for elucidating situations that are not immediately observable, being too complex or pertaining to prehistory; the data used are systematic ones gathered in large databases rather than data that happen to be available for select languages. A crucial feature of the methodology is the fine-tuning of simulation models through empirical observations of quantitative distributions such as those of speaker populations or of grammatical features shared among languages.

Set representations are explicitly expressed in natural language. For example, many languages distinguish between sets and subsets (all vs. some), as well as between singular and plural sets (a cat vs. some cats). Three experiments explored the hypothesis that these representations are language specific, and thus absent from the conceptual resources of non-linguistic animals. We found that rhesus monkeys spontaneously discriminate sets based on a conceptual singular-plural distinction. Under conditions that do not elicit comparisons based on approximate magnitudes or one-to-one correspondance, rhesus monkeys distinguished between singular and plural sets (1 vs. 2 and 1 vs. 5), but not between two plural sets (2 vs. 3, 2 vs. 4, and 2 vs. 5). These results suggest that set-relational distinctions are not a privileged part of natural language, and may have evolved in a non-linguistic species to support domain general quantitative computations.

There is a surprising degree of overlapping structure evident across the languages of the world. One factor leading to cross-linguistic similarities may be constraints on human learning abilities. Linguistic structures that are easier for infants to learn should predominate in human languages. If correct, then (a) human infants should more readily acquire structures that are consistent with the form of natural language, whereas (b) non-human primates patterns of learning should be less tightly linked to the structure of human languages. Prior experiments have not directly compared laboratory-based learning of grammatical strucutures by human infants and non-human primates, especially under comparable testing conditions and with similar materials. Five experiments with 12-month-old human infants and adult cotton-top tamarin monkeys addressed these predictions, employing comparable methods (familiarization-discrimination) and materials. Infants rapidly acquired complex grammatical structures by using statistically predictive patterns, failing to learn structures that lacked such patterns. In contrast, the tamarins only exploited predictive patterns when learning relatively simple grammatical structures. Infant learning abilities may serve both to facilitate natural language acquisition and to impose constraints on the structure of human languages.

Language emergence and evolution have recently gained growing attention through multi-agent models and mathematical frameworks to study their behavior. Here we investigate further the Naming Game, a model able to account for the emergence of a shared vocabulary of form-meaning associations through social/cultural learning. Due to the simplicity of both the structure of the agents and their interaction rules, the dynamics of this model can be analyzed in great detail using numerical simulations and analytical arguments. This paper first reviews some existing results and then presents a new overall understanding.

We examine an evolutionary naming-game model where communicating agents are equipped with an evolutionary selected learning ability. Such a coupling of biological and linguistic ingredients results in an abrupt transition: upon a small change of a model control parameter a poorly communicating group of linguistically unskilled agents transforms into almost perfectly communicating group with large learning abilities. When learning ability is kept fixed, the transition appears to be continuous. Genetic imprinting of the learning abilities proceeds via Baldwin effect: initially unskilled communicating agents learn a language and that creates a niche in which there is an evolutionary pressure for the increase of learning ability. Our model suggests that when linguistic (or cultural) processes became intensitive enough, a transition took place where both linguistic performance and biological endowment of our species experienced an abrupt change that perhaps triggered the rapid expansion of human civilization.

The time evolution of Earth with her cities, languages and countries is considered in terms of the multiplicative noise1 and the fragmentation processes, where the related families, size distributions, lifetimes, bilinguals, etc. are studied. Earlier we treated the cities and the languages differently (and as connected; languages split since cities split, etc.). Hence, two distributions are obtained in the same computation at the same time. The same approach is followed here and Pareto-Zipf law for the distribution of the cities, log-normal for the languages, decreasing exponential for the city families (countries) in the rank order over population, and power law -2 for the language families over the number of languages in rank order are obtained theoretically in this combination for the first time (up to our knowledge) in the literature; all of which are in good agreement with the present empirical data.

It is argued that the present log-normal distribution of language sizes is, to a large extent, a consequence of demographic dynamics within the population of speakers of each language. A two-parameter stochastic multiplicative process is proposed as a model for the population dynamics of individual languages, and applied over a period spanning the last ten centuries. The model disregards language birth and death. A straightforward fitting of the two parameters, which statistically characterize the population growth rate, predicts a distribution of language sizes in excellent agreement with empirical data. Numerical simulations, and the study of the size distribution within language families, validate the assumptions at the basis of the model.

A formulation of bit-string models of language evolution, based on differential equations for the population speaking each language, is introduced and preliminarily studied. Connections with replicator dynamics and diffusion processes are pointed out. The stability of the dominance state, where most of the population speaks a single language, is analyzed within a mean-field-like approximation, while the homogeneous state, where the population is evenly distributed among languages, can be studied. This analysis discloses the existence of a bistability region, where dominance coexists with homogeneity as possible asymptotic states. Numerical resolution of the differential system validates these findings.

We investigate a set of stochastic models of biodiversity, population genetics, language evolution and opinion dynamics on a network within a common framework. Each node has a state, 0 < xi < 1, with interactions specified by strengths mij. For any set of mij we derive an approximate expression for the mean time to reach fixation or consensus (all xi=0 or 1). Remarkably in a case relevant to language change this time is independent of the network structure.

We investigate different opinion formation models on adaptive network topologies. Depending on the dynamical process, rewiring can either (i) lead to the elimination of interactions between agents in different states, and accelerate the convergence to a consensus state or break the network in noninteracting groups or (ii), counterintuitively, favor the existence of diverse interacting groups for exponentially long times. The mean-field analysis allows us to elucidate the mechanisms at play. Strikingly, allowing the interacting agents to bear more than one opinion at the same time drastically changes the model's behavior and leads to fast consensus.

We study a modified version of the naming game, a recently introduced model which describes how shared vocabulary can emerge spontaneously in a population without any central control. In particular, we introduce a mechanism that allows a continuous interchange with the external inventory of words. A playing strategy, influenced by the hierarchical structure that individuals' reputation defines in the community, is implemented. We analyze how these features influence the convergence times, the cognitive efforts of the agents, and the scaling behavior in memory and time.

We investigate the coevolutionary dynamics of opinions and networks based upon majority-preference (MP) and minority-avoidance (MA) rules. Under MP, individuals adopt the majority opinion among their neighbors; while in MA individuals can break the link to one holding a minority and different opinion, and rewire either to neighbors of their neighbors with the same opinion or to a random one from the whole population except their nearest neighbors. We study opinion formation as a result of combination of these two competing rules, with a parameter tuning the balance between them. We find that the underlying network can be self-organized into connected communities with like-minded individuals belonging to the same group; thus a broad variety of opinions coexist. Diverse opinions disappear in a population in which all individuals share a uniform opinion, when the model parameter exceeds a critical value. Furthermore, we show that an increasing tendency to redirect to neighbors of neighbors is more likely to result in a consensus of opinion.

We investigate a prototypical agent-based model, the naming game, on two-dimensional random geometric networks. The naming game {[}Baronchelli , J. Stat. Mech.: Theory Exp. (2006) P06014] is a minimal model, employing local communications that captures the emergence of shared communication schemes (languages) in a population of autonomous semiotic agents. Implementing the naming games with local broadcasts on random geometric graphs, serves as a model for agreement dynamics in large-scale, autonomously operating wireless sensor networks. Further, it captures essential features of the scaling properties of the agreement process for spatially embedded autonomous agents. Among the relevant observables capturing the temporal properties of the agreement process, we investigate the cluster-size distribution and the distribution of the agreement times, both exhibiting dynamic scaling. We also present results for the case when a small density of long-range communication links are added on top of the random geometric graph, resulting in a ``small-world''-like network and yielding a significantly reduced time to reach global agreement. We construct a finite-size scaling analysis for the agreement times in this case.

A simple model of language evolution proposed by Komarova, Niyogi, and Nowak is characterized by a payoff in communicative function and by an error in learning that measure the accuracy in language acquisition. The time scale for language change is generational, and the model's equations in the mean-field approximation are a particular case of the replicator-mutator equations of evolutionary dynamics. In well-mixed populations, this model exhibits a critical coherence threshold; i.e., a minimal accuracy in the learning process is required to maintain linguistic coherence. In this work, we analyze in detail the effects of different fitness-based dynamics driving linguistic coherence and of the network of interactions on the nature of the coherence threshold by performing numerical simulations and theoretical analyses of three different models of language change in finite populations with two types of structure: fully connected networks and regular random graphs. We find that although the threshold of the original replicator-mutator evolutionary model is robust with respect to the structure of the network of contacts, the coherence threshold of related fitness-driven models may be strongly affected by this feature.

We propose an asymmetric negotiation strategy to investigate the influence of high-degree agents on the agreement dynamics in a structured language game, the naming game. We introduce a model parameter, which governs the frequency of high-degree agents acting as speakers in communication. It is found that there exists an optimal value of the parameter that induces the fastest convergence to a global consensus on naming an object for both scale-free and small-world naming games. This phenomenon indicates that, although a strong influence of high-degree agents favors consensus achievement, very strong influences inhibit the convergence process, making it even slower than in the absence of influence of high-degree agents. Investigation of the total memory used by agents implies that there is some trade-off between the convergence speed and the required total memory. Other quantities, including the evolution of the number of different names and the relationship between agents' memories and their degrees, are also studied. The results are helpful for better understanding of the dynamics of the naming game with asymmetric negotiation strategy.

This paper presents computer simulations of language populations and the development of language families, showing how a simple model can lead to distributions similar to those observed empirically by Wichmann (2005) and others. The model combines features of two models used in earlier work for the simulation of competition among languages: the `Viviane' model for the migration of peoples and the propagation of languages, and the `Schulze' model, which uses bit-strings as a way of characterising structural features of languages.

A hard look in the mirror, as they say, is good for fitness and vitality. The time seems ripe, then, fifty years after the birth of modern linguistics, to reexamine its foundations. Or rather, the rubble, as the editors of Probabilistic linguistics suggest: corpus statistics, Markov ...

This is a reply to Ramon Ferrer-I-Cancho's paper in this issue ``Some Word Order Biases from Limited Brain Resources: A Mathematical Approach.'' In this reply, I challenge the Euclidean distance model proposed in that paper by proposing a simple alternative model based on linear ordering.

In this paper, we propose a mathematical framework for studying word order optimization. The framework relies on the well-known positive correlation between cognitive cost and the Euclidean distance between the elements (e.g. words) involved in a syntactic link. We study the conditions under which a certain word order is more economical than an alternative word order by proposing a mathematical approach. We apply our methodology to two different cases: (a) the ordering of subject (S), verb (V) and object (O), and (b) the covering of a root word by a syntactic link. For the former, we find that SVO and its symmetric, OVS, are more economical than OVS, SOV, VOS and VSO at least 2/3 of the time. For the latter, we find that uncovering the root word is more economical than covering it at least 1/2 of the time. With the help of our framework, one can explain some Greenbergian universals. Our findings provide further theoretical support for the hypothesis that the limited resources of the brain introduce biases toward certain word orders. Our theoretical findings could inspire or illuminate future psycholinguistics or corpus linguistics studies.

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 vowels) 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 and show 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. We validate the above observations by proposing a quantitative measure of perceptual contrast as well as feature economy and subsequently comparing the results obtained due to these quantifications with those where we assume that the vowel inventories had evolved just by chance.

An earlier study [24] concluded, based on computer simulations and some inferences from empirical data, that languages will change the more slowly the larger the population gets. We replicate this study using a more complete language model for simulations (the Schulze model combined with a Barabasi-Albert network) and a richer empirical dataset [12]. Our simulations show either a negligible or a strong dependence of language change on population sizes, depending on the parameter settings; while empirical data, like some of the simulations, show a negligible dependence.

Evolutionary theorists since Darwin have been interested in the parallels and interactions between biological and cultural evolution. Recent applications of empirical techniques originally developed to analyze molecular genetic data to linguistic data offer new insights into the historical evolution of language, revealing fascinating parallels between language change and biological evolution. This work offers considerable potential toward unified theories of genetic and cultural change.

Structured meaning-signal mappings, i.e., mappings that preserve neighborhood relationships by associating similar signals with similar meanings, are advantageous in an environment where signals are corrupted by noise and sub-optimal meaning inferences are rewarded as well. The evolution of these mappings, however, cannot be explained within a traditional language evolutionary game scenario in which individuals meet randomly because the evolutionary dynamics is trapped in local maxima that do not reflect the structure of the meaning and signal spaces. Here we use a simple game theoretical model to show analytically that when individuals adopting the same communication code meet more frequently than individuals using different codes-a result of the spatial organization of the population-then advantageous linguistic innovations can spread and take over the population. In addition, we report results of simulations in which an individual can communicate only with its K nearest neighbors and show that the probability that the lineage of a mutant that uses a more efficient communication code becomes fixed decreases exponentially with increasing K. These findings support the mother tongue hypothesis that human language evolved as a communication system used among kin, especially between mothers and offspring.

The language game approach has recently been adopted to explore the conventionalization of linguistic knowledge in a social environment. Most contemporary studies focus on the dynamics of language games in random or predefined social networks, but neglect the reverse roles of communicative constraints in language evolution and social structures. This article, based on two forms of language games (the naming game and the category game), examines whether a simple, distance-based communicative constraint can affect the conventionalization of linguistic knowledge. The study bridges the gap between random networks and complex social structures, and illustrates that the internal properties of language games can influence the effects of communicative constraints and social structures.

This paper suggests a model of the process through which a set of symbols, initially without any intrinsic meaning, acquires endogenously a conventional and socially shared meaning. This model has two related aspects. The first is the cognitive aspect, represented by the process through which each agent processes the information gathered during the interactions with other agents. In this paper, the agents are endowed with the cognitive skills necessary to categorize the input in a lexicographic way, a categorization process that is implemented by the means of data mining techniques. The second aspect is the social one, represented by the process of reiterate interactions among the agents who compose a population. The framework of this social process is that of evolutionary game theory, with a population of agents who are randomly matched in each period in order to play a game that, in this paper, is a kind of signaling game. The simulations show that the emergence of a socially shared meaning associated to a combination of symbols is, under the assumptions of this model, a statistically inevitable occurrence.

Phylogenetic methods have revolutionised evolutionary biology and have recently been applied to studies of linguistic and cultural evolution. However, the basic comparative data on the languages of the world required for these analyses is often widely dispersed in hard to obtain sources. Here we outline how our Austronesian Basic Vocabulary Database (ABVD) helps remedy this situation by collating wordlists from over 500 languages into one web-accessible database. We describe the technology underlying the ABVD and discuss the benefits that an evolutionary bioinformatic approach can provide. These include facilitating computational comparative linguistic research, answering questions about human prehistory, enabling syntheses with genetic data, and safe-guarding fragile linguistic information.

The standard three-state voter model is enlarged by including the outside pressure favouring one of the three language choices and by adding some biased internal random noise. The Monte Carlo simulations are motivated by states with the population divided into three groups of various affinities to each other. We show the crucial influence of the boundaries for moderate lattice sizes like 500 x 500. By removing the fixed boundary at one side, we demonstrate that this can lead to the victory of one single choice. Noise in contrast stabilizes the choices of all three populations. In addition, we compute the persistence probability, i.e., the number of sites who have never changed their opinion during the simulation, and we consider the case of ``rigid-minded'' decision makers.

This paper proposes how to build a syntactic network based on syntactic theory and presents some statistical properties of Chinese syntactic dependency networks based on two Chinese treebanks with different genres. The results show that the two syntactic networks are small-world networks, and their degree distributions obey a power law. The finding, that the two syntactic networks have the same diameter and different average degrees, path lengths, clustering coefficients and power exponents, can be seen as an indicator that complexity theory can work as a means of stylistic study. The paper links the degree of a vertex with a valency of a word, the small world with the minimized average distance of a language, that reinforces the explanations of the findings from linguistics.

The phenomenon of human language is widely studied from various points of view. It is interesting not only for social scientists, antropologists or philosophers, but also for those, interested in the network dynamics. In several recent papers word web, or language as a graph has been investigated [R.F. Cancho, R. Sole, The small world of human language, Proc. R. Soc. London B 268 (2001) 2261-2265; A.E. Motter, P.S. de Moura, Lai Ying-Cheng, P. Dasgupta, Topology of the conceptual network of language, Phys. Rev. E 65 (2002) R 065102; M. Steyvers, J.B. Tenenbaum, The large-scale structure of semantic networks: Statistical analysis and a model of semantic growth, Cogn. Sci. 29 (2005) 41-78]. In this paper I revise recent studies of syntactical word web [R.F. Cancho, R. Sole, The small world of human language, Proc. R. Soc. London B 268 (2001) 2261-2265; S.N. Dorogovtsev, J.F.F. Mendes, Language as an evolving word web, Proc. R. Soc. London B 268 (2001) 2603-2606]. I present a model of growing network in which such processes as node addition, edge rewiring and new link creation are taken into account. I argue, that this model is a satisfactory minimal model explaining measured data [R.F. Cancho, R. Sole, The small world of human language, Proc. R. Soc. London B 268 (2001) 2261-2265; M. Markosova, P. Nather, Language as a graph, in: V. Kvasnicka, P. Trebaticky, J. Pospichal (Eds.), Mind, Intelligence and Life, Kelemen, STU Bratislava, 2007, pp. 298-307 (in Slovak)].

We have recently investigated the evolution of linguistic diversity by means of a simple spatial model that considers selective geographic colonization, linguistic anomalous diffusion and mutation. In the model, regions of the lattice are characterized by the amount of resources available to populations which are going to colonize the region. In that approach, the resources were ascribed in a randomly and uncorrelated way. Here, we extend the previous model and introduce a degree of correlation for the resource landscape. A change of the qualitative scenario is observed for high correlation, where the increase of the linguistic diversity on area is faster than for low correlated landscapes. For low correlated landscapes, the dependence of diversity on area shows two scaling regimes, while we observe the rising of another scaling region for high correlated landscapes.

Chinese is spoken by the largest number of people in the world, and it is regarded as one of the most important languages. In this paper, we explore the statistical properties of Chinese language networks (CLNs) within the framework of complex network theory. Based on one of the largest Chinese corpora, i.e. People's Daily Corpus, we construct two networks (CLN1 and CLN2) from two different respects, with Chinese words as nodes. In CLN1, a link between two nodes exists if they appear next to each other in at least one sentence; in CLN2, a link represents that two nodes appear simultaneously in a sentence. We show that both networks exhibit small-world effect, scale-free structure, hierarchical organization and disassortative mixing. These results indicate that in many topological aspects Chinese language shapes complex networks with organizing principles similar to other previously studied language systems, which shows that different languages may have some common characteristics in their evolution processes. We believe that our research may shed some new light into the Chinese language and find some potentially significant implications.

We propose a paleoeconomic coevolutionary explanation for the origin of speech in modern humans. The coevolutionary process, in which trade facilitates speech and speech facilitates trade, gives rise to multiple stable trajectories. While a `trade-speech' equilibrium is not an inevitable outcome for modern humans, we find it is a relatively likely scenario given our species evolved in Africa under climatic conditions supporting relatively high population densities. The origin of human speech is not independent of economic institutions-the economics of early human trade can provide additional insight to help explain the physiological emergence of human speech.

Change and variation, while inherent to language, might be seen as running counter to human communicative needs. However, variation also gives language the power to convey reliable indexical information about the speaker. This has been argued to play a significant role in allowing the establishment of large communities based on cooperative exchange (M. Enquist and O. Leimar 1993, The evolution of cooperation in mobile organisms. Animal Behaviour 45: 747a757; R. I. M. Dunbar 1996, Grooming, Gossip and the Evolution of Language. London: Faber and Faber), although there has been little experimental investigation of the hypothesis. Here I present a preliminary study intended to help fill this gap. Participants played an online team game in which they negotiated anonymously for resources using an artificial language. Players succeeded in using linguistic cues to distinguish between their teammates and their opponents, and displayed between-team variation in the use of the language.

Social networks play an important role in determining the dynamics and outcome of language change. Early empirical studies only examine small-scale local social networks, and focus on the relationship between the individual speakers' linguistic behaviors and their characteristics in the network. In contrast, computer models can provide an efficient tool to consider large-scale networks with different structures and discuss the long-term effect of individuals' learning and interaction on language change. This paper presents an agent-based computer model which simulates language change as a process of innovation diffusion, to address the threshold problem of language change. In the model, the population is implemented as a network of agents with age differences and different learning abilities, and the population is changing, with new agents born periodically to replace old ones. Four typical types of networks and their effect on the diffusion dynamics are examined. When the functional bias is sufficiently high, innovations always diffuse to the whole population in a linear manner in regular and small-world networks, but diffuse quickly in a sharp S-curve in random and scale-free networks. The success rate of diffusion is higher in regular and small-world networks than in random and scale-free networks. In addition, the model shows that as long as the population contains a small number of statistical learners who can learn and use both linguistic variants statistically according to the impact of these variants in the input, there is a very high probability for linguistic innovations with only small functional advantage to overcome the threshold of diffusion.

Simulations mostly by physicists of the competition between adult languages since 2003 are reviewed. The Viviane and Schulze models give good and reasonable agreement, respectively, with the empirical histogram of language sizes. Also the numbers of different languages within one language family is modeled reasonably in an intermediate range. Bilingualism is now incorporated into the Schulze model. Also the rate at which the majority shifts from one language to another is found to be nearly independent of the population size, or to depend strongly on it, according to details of the Schulze model. Other simulations, like Nettle-Culicover-Nowak, are reviewed more briefly.

Linguistic complexity is a measure of the cognitive difficulty of human language processing. The present paper proposes dependency distance, in the framework of dependency grammar, as an insightful metric of complexity. Three hypotheses are formulated: (1) The human language parser prefers linear orders that minimize the average dependency distance of the recognized sentence (2) There is a threshold that the average dependency distance of most sentences or texts of human languages does not exceed (3) Grammar and cognition combine to keep dependency distance within the threshold. Twenty corpora from different languages with dependency syntactic annotation are used to test these hypotheses. The paper reports the average dependency distance in these corpora and analyzes the factors which influence dependency distance. The findings - that average dependency distance has a tendency to be minimized in human language and that there is a threshold of less than 3 words in average dependency distance and grammar plays an important role in constraining distance -support all three hypotheses, although some questions are still open for further research.

That almost all language networks are small-world and scale-free raises the question of whether syntax plays a role to measure the complexity of a language network. To answer this question, we built up two random language (dependency) networks based on a dependency syntactic network and investigated the complexity of these three language networks to see if the non-syntactic ones have network indicators similar to the syntactic one. The results show that all the three networks are small-world and scale-free. While syntax influences the indicators of a complex network, scale-free is only a necessary but not sufficient condition to judge whether a network is syntactic or non-syntactic. The network analysis focuses on the global organization of a language, it may not reflect the subtle syntactic differences of the sentence structure.

Binary trait data record the presence or absence of distinguishing traits in individuals. We treat the problem of estimating ancestral trees with time depth from binary trait data. Simple analysis of such data is problematic. Each homology class of traits has a unique birth event on the tree, and the birth event of a trait that is visible at the leaves is biased towards the leaves. We propose a model-based analysis of such data and present a Markov chain Monte Carlo algorithm that can sample from the resulting posterior distribution. Our model is based on using a birth-death process for the evolution of the elements of sets of traits. Our analysis correctly accounts for the removal of singleton traits, which are commonly discarded in real data sets. We illustrate Bayesian inference for two binary trait data sets which arise in historical linguistics. The Bayesian approach allows for the incorporation of information from ancestral languages. The marginal prior distribution of the root time is uniform. We present a thorough analysis of the robustness of our results to model misspecification, through analysis of predictive distributions for external data, and fitting data that are simulated under alternative observation models. The reconstructed ages of tree nodes are relatively robust, whereas posterior probabilities for topology are not reliable.

Communication is ubiquitous in biology, and agreement on terms essential for scientific progress. Yet there is no agreed definition of biological communication. Definitions couched in terms of adaptation are often used, but there is significant variability in exactly which criteria are invoked. An alternative is to define communication in terms of information transfer. This article reviews the merits of these approaches, and argues that the former is to be preferred, so long as we demand that both the signal and the response be adaptive, rather than just one or the other, as is common. Specific concerns with the definition are addressed, and it is then explained why an account of communication predicated on information transfer is necessarily derivative upon such an approach. Other alternatives and some variants of the adaptationist definition are also briefly discussed.

In February 1837 -- even before he sailed on the Beagle -- Charles Darwin wrote to his sister Caroline, discussing the linguist Sir John Herschel's idea that modern languages were descended from a common ancestor. If this were really the case, it cast doubt on the Biblical chronology of the world: ``[E]veryone has yet thought that the six thousand odd years has been the right period but Sir J. thinks that a far greater number must have passed since the Chinese [and] the Caucasian languages separated from one stock''.

The example of language change was a lifelong influence on Darwin's thought (see Figure 1). In The Origin of Species, he argued that our ability to order languages genealogically, despite their having changed and divided at different rates, shows that the same can be done for species [2]. And in The Descent of Man, he noted that: ``The formation of different languages and of distinct species, and the proofs that both have been developed through a gradual process, are curiously parallel''.
...

Abstract Keywords in scientific articles have found their significance in information filtering and classification. In this article, we empirically investigated statistical characteristics and evolutionary properties of keywords in a very famous journal, namely Proceedings of the ...

This paper is part of an ongoing research program to understand the cognitive and functional bases for the origins and evolution of spatial language. Following a cognitive-functional approach, we first investigate the cross-linguistic variety in spatial language, with special attention for spatial perspective. Based on this language-typological data, we hypothesize which cognitive mechanisms are needed to explain this variety and argue for an interdisciplinary approach to test these hypotheses. We then explain how experiments in artificial language evolution can contribute to that and give a concrete example.

In the 1980s, a lot of ink was spent on the question of symbol grounding, largely triggered by Searle's Chinese room theory (Searle 1980).Searle's article had the advantage ofstirring up discussion about when and how symbols could be about things in the world,whether intelligence involves representations or not,what embodiment means, and under what conditions cognition is embodied.But almost 25 years ofphilosophical discussion have shed little light on the issue, partly because the discussion has been mixed up with emotional arguments whether artificial intelligence (AI) is possible or not.However,today I believe that sufficient progress has been made in cognitive science and AI so that we can say that the symbol grounding problem has been solved. This chapter briefly discusses the issues of symbols,meanings,and embodiment (the main themes ofthe workshop),why I claim the symbol grounding problem has been solved, and what we should do next.

Construction grammar theories argue that the selection and realization of a verb's arguments is largely taken care of by constructions, as opposed to lexicalist theories which constrain the argument structure of a verb in the lexicon. So far, however, no operational definition has been given which shows how this 'fusion' of a verb's arguments with the roles of a construction can be achieved. This paper reports a fully implemented solution in Fluid Construction Grammar in which the lexical entry of the verb lists its 'potential valences' from which constructions can select.

Origin and Evolution of Languages has a strong interdisciplinary flavour designed to highlight the true complexity of the debates in the field. Many of the models and theories conjectured can only receive their validation from a convergence of arguments developed across disciplines. The book underscores this dimension by including contribution from disciplines that have been wary, traditionally, of extending beyond their borders: linguistics (different branches thereof), philosophy, history and prehistory, archaeology, anthropology, genetics, computer-modelling. The presentation is intended to encompass both the agreements and disjunctures characteristic of the field and insisted on laying open propositions that clearly differ from, possibly even enter into contradiction with one another. While several teams of researchers active in the fields of genetics, linguistics, anthropology and archaeology have come up with new proposals in favor of the `New Synthesis,' many competing hypotheses and models continue to be explored in areal linguistics, language contact, wave-like diffusion. On the anthropological scene, criticisms of the monogenetic model have set up new debates and counter-arguments. Approaching the issue of the origin and evolution of human languages within a Darwinian paradigm remains problematic. On the archaeological scene, not all reconstructions are proving compatible with current models for the circulation of techniques, myths and cultures. On the linguistic scene, raising again the issue of the origin / evolution of humankind and of languages in an evolutionary, cognitive, social and cultural perspective or in terms of generational transmission and acquisition, may induce a reconsideration of linguistic theories in search of universals as well as most theories of change and variation. All contributors are world-renowned experts in their domain.

Table of Contents

1. Introduction. -- Serge Cleuziou, Jean Paul Demoule, Pierre Encreve, Bernard Laks
Part One : ab originem
2. Genetic evolution and the evolution of languages. -- L.L.Cavalli-Sforza
3. Languages, genes, and prehistory, with special reference to europe. -- Bernard Comrie
4. Poor design features in language as clues to its prehistory. -- Andrew Carstairs-McCarthy
5. What can we learn about the earliest human language by comparing languages known today? -- Lyle Campbell
6. Conceptualization, communication, and the origins of grammar. -- Frederick J. Newmeyer
7. The origin of language as a product of the evolution of modern cognition. -- Gilles Fauconnier and Mark Turner
Part Two : post originem
8. Genetics and language: comparatism and genealogy in perspective. -- Bernard Laks
9. Simulating the expansion of farming and the differentiation of european languages. -- Domenico Parisi, Francesco Antinucci, Francesco Natale, Federico Cecconi
10. On Renfrew's hypothesis of the near-eastern origins of the indo-european urheimat. -- Jean-Paul Demoule
11. New perspectives on the origin of languages. -- Merrit Ruhlen
12. Linguistic history and computational cladistics. -- Don Ringe and Tandy Warnow
13. What do creoles and pidgins tell us about the evolution of languages? -- Salikoko S. Mufwene
14. Linguistics and archeology. -- Serge Cleuziou

This book explores the origin and evolution of speech. The human speech system is in a league of its own in the animal kingdom and its possession dwarfs most other evolutionary achievements. During every second of speech we unconsciously use about 225 distinct muscle actions. To investigate the evolutionary origins of this prodigious ability, Peter MacNeilage draws on work in linguistics, cognitive science, evolutionary biology, and animal behaviour. He puts forward a neo-Darwinian account of speech as a process of descent in which ancestral vocal capabilities became modified in response to natural selection pressures for more efficient communication. His proposals include the crucial observation that present-day infants learning to produce speech reveal constraints that were acting on our ancestors as they invented new words long ago. This important and original investigation integrates the latest research on modern speech capabilities, their acquisition, and their neurobiology, including the issues surrounding the cerebral hemispheric specialization for speech. It will interest a wide range of readers in cognitive, neuro-, and evolutionary science, as well as all those seeking to understand the nature and evolution of speech and human communication.

Table of Contents

Part 1 Introduction
1. Background: The Intellectual Context
2. Getting to the Explanation of Speech
Part 2 Speech and its origin: The Frame/Content Theory
3. The Nature of Modern Hominid Speech
4. Speech in Deep TIme: How Speech Got Started
Part 3 The Relation Between Ontogeny and Phylogeny
5. Ontogeny and Phylogeny 1: The Frame Stage
6. Ontogeny and Phylogeny 2: The Frame/Content Stage
7. The Origin of Words: How Frame-Stage Patterns Acquired Meanings
Part 4 Brain Organization and the Evolution of Speech
8. Evolution of brain Organization for Speech: Background
9. A Dual Brain System for the Frame/Content Mode
10. Evolution of Cerebral Hemispheric Specialization for Speech
Part 5 The Frame/Content Theory and Generative Linguistics
11. Generative Phonology and the Origin of Speech
12. The Generative Approach to Speech Acquisition
Part 6 A Perspective on Speech From Manual Evolution
13. An Amodal Phonology? Implications of the Existence of Sign Language
Part 7 Last Things
14. Ultimate Causes: Genes and Memes
15. Conclusions

Examines the processes by which languages change, from the macroecological perspective of competition and natural selection. This work looks at such themes as: natural selection in language; the actuation question and the invisible hand that drives evolution; multilingualism and language contact; and, language birth and language death.

Human communication is grounded in fundamentally cooperative, even shared, intentions. In this original and provocative account of the evolutionary origins of human communication, Michael Tomasello connects the fundamentally cooperative structure of human communication (initially discovered by Paul Grice) to the especially cooperative structure of human (as opposed to other primate) social interaction.

Tomasello argues that human cooperative communication rests on a psychological infrastructure of shared intentionality (joint attention, common ground), evolved originally for collaboration and culture more generally. The basic motives of the infrastructure are helping and sharing: humans communicate to request help, inform others of things helpfully, and share attitudes as a way of bonding within the cultural group. These cooperative motives each created different functional pressures for conventionalizing grammatical constructions. Requesting help in the immediate you-and-me and here-and-now, for example, required very little grammar, but informing and sharing required increasingly complex grammatical devices.

Drawing on empirical research into gestural and vocal communication by great apes and human infants (much of it conducted by his own research team), Tomasello argues further that humans' cooperative communication emerged first in the natural gestures of pointing and pantomiming. Conventional communication, first gestural and then vocal, evolved only after humans already possessed these natural gestures and their shared intentionality infrastructure along with skills of cultural learning for creating and passing along jointly understood communicative conventions. Challenging the Chomskian view that linguistic knowledge is innate, Tomasello proposes instead that the most fundamental aspects of uniquely human communication are biological adaptations for cooperative social interaction in general and that the purely linguistic dimensions of human communication are cultural conventions and constructions created by and passed along within particular cultural groups.

Table of Contents
1 A Focus on Infrastructure 1
2 Primate Intentional Communication 13
3 Human Cooperative Communication 57
4 Ontogenetic Origins 109
5 Phylogenetic Origins 169
6 The Grammatical Dimension 243
7 From Ape Gestures to Human Language 319

Case languages use an inflectional category system for marking event structure. The research in this thesis investigates how such a grammatical system can be developed as the consequence of distributed processes whereby language users continuously shape and reshape their language in locally situated communicative interactions. Since these processes are notoriously difficult to grasp in natural languages, this thesis offers additional evidence from computational simulations in which autonomous artificial agents self-organise a case-like grammar with similar properties as found in case languages such as German, Latin and Turkish.

This thesis hypothesises that language users gradually build their grammar in order to optimise their communicative success and expressiveness while at the same time reducing the cognitive effort needed for semantic interpretation. In the experiments, artificial agents engage in a series of `language games' in which the speaker has to describe a dynamic event to the hearer. The agents are equipped with diagnostics for autonomously detecting communicative problems, repair strategies for solving these problems, and alignment strategies for coordinating their linguistic inventories with each other. Through comparative simulations, this thesis aims at demonstrating which communicative and external pressures and which cognitive mechanisms are minimally required for the formation of a case grammar.

Two innovating experiments are reported. The first experiment offers the first multi-agent simulations ever that involve polysemous categories. The agents are capable of inventing grammatical markers for indicating event structure and of generalising these markers to semantic roles by performing analogical reasoning over events. Extension by analogy occurs as a side-effect of the need to optimise communicative success and is accompanied by careful abstraction, which yields an increased productivity of the categories. In the second experiment, the agents are capable of combining markers into larger argument structure constructions through pattern formation. The results show that languages become unsystematic if the linguistic inventory is unstructured and contains multiple levels of organisation. This thesis demonstrates that this problem of systematicity can be solved using multi-level selection.

All the experiments are implemented in Fluid Construction Grammar. This thesis presents the first computational formalisation of argument structure in a construction-based approach that works for both production and parsing. It implements the `fusion' of the participant roles of events with the semantic roles of argument structure constructions. This representation aims at maximal fluidity and introduces some novel concepts in linguistics. Instead of containing a fixed predicate frame, verbs list their `potential valents' from which the `actual valency' is selected by argument structure constructions.

Even though the experiments involve the formation of artificial languages, the results are highly relevant for natural language research as well. This thesis therefore engages in an interdisciplinary dialogue with linguistics and contributes to some currently ongoing debates such as the formalisation of argument structure in construction grammar, the organisation of the linguistic inventory, the status of semantic maps and thematic hierarchies and the mechanisms for explaining grammaticalization.