Language Evolution and Computation Bibliography

It has recently been suggested that grammaticalization can be fruitfully explained by the glossogenetic mechanisms for language evolution and historical change. Contrary to this position, it is here argued that the incorporation of grammaticalization processes in the glossogenetic ontology is far from unproblematic.

Languages are far more complex than they need to be for one-to-one communication. This paper attempts to answer the question as to why that should be. The answer, it is suggested, lies in the evolution of story-telling, legend and myth as culturally-important means of expression. Myth may not mark the dawn of proto- or rudimentary language, or even the beginnings of full language, but its existence accounts at least in part for the evolution of linguistic complexity. Language co-evolved with mythology in symbolic frameworks which extended, to the limits of cognition, the capacity for verbal expression.

Present-day people derive pleasure from rhymes, rhythms and repetitive visual patterns, that is, from instances of similarity. Similarity is the basis for grouping items into categories and so setting up abstract general concepts such as ripeness or weight. In present times, such grouping by similarity is a source of pleasure; the current plethora of concepts and words denoting them derives partly from pleasure in forming them. Then the question arises: how far back in prehistory has this pleasure been a motivation? Both beads and handaxes suggest by their symmetry that hominins may have derived this pleasure-in-the-head or internal reward as far back as Acheulian time: at this time, the motivation to construct abstract general concepts and thus expand language may have already been present. The timing is open to question but the pattern of inference connecting symmetry to language is particularly direct.

Understanding language evolution in terms of cultural transmission across generations of language users raises the possibility that some of the processes that have shaped language evolution can also be observed in historical language change. In this paper, we explore how constraints on production may affect the cultural evolution of language by analyzing the emergence of the Romance languages from Latin. Specifically, we focus on the change from Latin's flexible but OV (Object-Verb) dominant word order with complex case marking to fixed SVO (Subject-Verb-Object) word order with little or no noun inflections in Romance Languages. We suggest that constraints on second language learners' ability to produce sentences may help explain this historical change. We conclude that historical data on linguistic change can provide a useful source of information relevant to investigating the cognitive constraints that affect the cultural evolution of language.

Among the many puzzling questions about language, two are salient: First, why are there any languages at all, evidently unique to the human lineage. Second, why are there so many languages? These are in fact the basic questions of origin and variation that so occupied Darwin and other evolutionary thinkers and comprise modern biologys explanatory core: why do we observe this particular array of living forms in the world and not others -- the key problem of reconciling the underlying unity of organisms with their apparent diversity, invariance and variation. Here we examine these two questions from the viewpoint of modern linguistics, biology, and dynamical system theory.

Human language is the result of a cascade of consequences from an initial mutation which provided a new ''representational'' capacity to some mirror neurons. This initial mutation has high evolvability. The mutation coincidentally allowed representations of the two substances of signs to meet in human brains, thus accounting directly for signs. Recursivity is a result of the self-organization triggered by the choatic system that emerged from this system of signs.

Humans acquire far more of their behaviour from conspecifics via culture than any other species. Our culture is larger because it accumulates, where other species' seem to stay approximately the same size (Tomasello, 1999). This chapter attempts to clarify the problem of cultural accumulation by distinguishing between the size of a culture that can be transmitted from one generation, and the extent of culture transmitted. A culture's size is determined largely by ecological constraints, and certainly homonins (and some other species) show adaptations to facilitate this. But the exponential accumulation hypothesised by (Tomasello, 1999) I claim cannot be accounted for this way, but rather is a consequence of increasing information value in semantic components. This process can be achieved through memetics --- semantics will be selected for which transmits the most information. Thus cultural evolution achieves compression of information, generating increased extent in culture even when maintaining a fixed size. I support my argument with evidence from simulations explaining the size of culture (ae and Bryson, 2007), and simulations demonstrating selection for increased extent Kirby (1999).

Language learning is an iterative process, with each learner learning from other learners. Analysis of this process of iterated learning with chains of Bayesian agents, each of whom learns from one agent and teaches the next, shows that it converges to a distribution over languages that reflects the inductive biases of the learners. However, if agents are taught by multiple members of the previous generation, who potentially speak different languages, then a single language quickly dominates the population. In this work, we consider a setting where agents learn from multiple teachers, but are allowed to learn multiple languages. We show that if agents have a sufficiently strong expectation that multiple languages are being spoken. we reproduce the effects of inductive biases on the outcome of iterated learning seen with chains of agents.

This paper questions the assumption that subject-verb (SV) structures are basic and primary and shows instead that these apparently simple structures are quite complex informationally, intonationally, semantically, and syntactically. In contrast, we point out that verb-subject (VS) structures, particularly those involving unaccusative verbs and sentence focus, are simpler and better candidates for primary structures from an evolutionary point of view. From this perspective, Agent-first (SV) structures, which have been mentioned as examples of protolinguistic ''fossils'' (e.g. Jackendoff 2002), are not as basic as previously thought.

For the last two decades, a major question for paleoanthropologists has been the origins of modernity and modern thinking. Explanations such as symbolic culture, fully syntactic language, or abstract reasoning are all too often proffered without clear or adequate operationalizations. It is purpose of the present paper to suggest both an evolutionary cognitive basis for one aspect of modern thinking and modern language, metaphors, and to offer a potential neurological substrate.In our attempt to trace the evolution of a more circumscribed component of modern cognition, we think the candidate trait should be shared, at least in part, by our closer nonhuman primates. The trait should also be evident early (ontogeny) in humans, and there should be some specifiable and demonstrable neurological substrate. Finally, there should be evidence that the trait unambiguously sets a foundation for modern thinking. We think this trait is numerosity, i.e., the ability to think about and reason with numbers.

This paper presents a preliminary description and analysis of prosodic features (amplitude, duration and rhythm) observed in Northern muriquis vocalizations. The northern muriqui (Brachyteles hypoxanthus) is an endangered primate species which lives in Atlantic forests of Minas Gerais and Espirito Santo, Brazil.

Individuals devote one third of their language time to mentioning unexpected events. We try to make sense of this universal behaviour within the Costly Signalling framework. By systematically using language to point to the unexpected, individuals send a signal that advertises their ability to anticipate danger. This shift in display behaviour, as compared with typical displays in primate species, may result from the use by hominins of artefacts to kill.

Language is a defining characteristic of the biological species Homo sapiens. But Chomskian Universal Grammar is not what is innate about language; Universal Grammar requires magical thinking about genes and genetics. Constraints of universal grammar are better explained in an evolutionary context by processes inherent in symbols, and by such processes as syntactic carpentry, metaphor, and grammaticalization. We present an evolutionary timeline for language, with biological evidence for the long-term evolution of the human capacity for language, and for the co-evolution of language and the brain.

There is currently considerable disagreement about the value of different theoretical accounts that have been employed to explain the evolution of communication. In this paper, we review some of the core tenets of the 'adaptationist' and the 'informational' account. We argue that the former has its strength mainly in explaining the evolution of signals and the maintenance of honest signaling, while the latter is indispensible for understanding the cognitive mechanisms underpinning signal usage, structure, and comprehension. Importantly, the informational account that incorporates linguistic concepts is necessary prerequisite to identify which design features of language are shared with nonhuman primates or other animals, and which ones constitute derived traits specific for the human lineage.

Because language doesn't fossilize, it is difficult to unambiguously time the evolutionary events leading to language in the human lineage using traditional paleontological data. Furthermore, techniques from historical linguistics are generally seen to have an insufficient time depth to tell us anything about the nature of pre- modern-human language. Thus hypotheses about early stages of language evolution have often been seen as untestable ''fairy tales''. However, the discovery of human-unique alleles, associated with different aspects of language, offers a way out of this impasse. If an allele has been subjected to powerful selection, reaching or nearing fixation, statistical techniques allow us to approximately date the timing of the selective sweep. This technique has been employed to date the selective sweep associated with FOXP2, our current best example of a gene associated with spoken language. Although the dates themselves are subject to considerable error, a series of different dates, for different language-associated genes, provides a powerful means of testing evolutionary models of language if they are explicit and span the complete time period between our separation from chimpanzees to the present. We illustrate the potential of this approach by deriving explicit timing predictions from four contrasting models of ''protolanguage.'' For example, models of musical protolanguage suggest that vocal control came early, while gestural protolanguage sees speech as a late addition. Donald's mimetic protolanguage argues that these should appear at the same time, and further suggests that this was associated with Homo erectus. Although there are too few language-associated genes currently known to resolve the issue now, recent progress in the genetic basis for dyslexia and autism offers considerable hope that a suite of such genes will soon be available, and we offer this theoretical framework both in anticipation of this time, and to spur those developing hypotheses of language evolution to make them explicit enough to be integrated within such a hypothesis-testing framework.

Slavic aspect has remained a mystery for centuries and continues to fascinate linguists. The genesis of this intricate grammar category is even a greater puzzle. This paper aims at computationally reconstructing the prerequisite for aspect -- the emergence of a system of markers for Aktionsarten. We present an experiment where artificial language users develop a conventional system as the consequence of their distributed choices in locally situated communicative acts.

This paper reviews findings on comparative primate and animal cognition. It suggests that although modern human linguistic, cognitive, and motor behaviors differ profoundly from those of great apes, primarily with respect to required mental constructional skills, an early hominin with ape-like capacities could have used non-innate, referential signals. To determine the most probable selective agents that may have motivated these first steps towards language evolution, it is necessary to look beyond the non-human primates to a wider range of animal species. When this is done, foraging adaptations emerge as the most probable selective agents for cooperative breeding and for the cognitive and behavioral suite that would eventually lead to language.

This paper adopts the category game model that simulates the coevolution of categories and their word labels to explore the effect of social structure on linguistic categorization. Instead of detailed social connections, we adopt social popularities, the probabilities with which individuals participate into language games, to denote quantitatively the general characteristics of social structures. The simulation results show that a certain degree of social scaling could accelerate the categorization process, while a much high degree of social scaling will greatly delay this process.

Cultural transmission is the primary medium of linguistic interactions. We propose an acquisition framework that involves the major forms of cultural transmissions, such as vertical, oblique and horizontal transmissions. By manipulating the ratios of these forms of transmission in the total number of transmission across generations of individuals, we analyze their roles in language evolution, based on a lexicon-syntax coevolution model. The simulation results indicate that all these forms of transmission collectively lead to the dynamic equilibrium of language evolution across generations.

When evolutionary biologists and epistemologists investigate the evolution of life, they deconstruct the problem into three research areas: they search for the units, levels and mechanisms of life's evolution. Here, it is investigated how a similar approach can be applied to evolutionary linguistics. A methodology is proposed that allows us to identify and further investigate the units, levels and mechanisms of language evolution.

Through a constructive study of grammaticalization as a potentially important process of language evolution, we have found two findings. One is that linguistic analogy, which applies linguistic rules extendedly, is a very critical for language acquisition and meaning change. The other is that inferences based on the recognition of similarity and contingency among particular meanings can realize unidirectional meaning change, a remarkable characteristic of grammaticalization. We discuss the significance of these findings in the context of the origin and the evolution of language, especially the role of linguistic analogy in creativity. Based on the discussion, a hypothetical scenario of the origin and the evolution of language is proposed.

In the early seventies, the bio-mathematician George Price developed a simple and concise mathematical description of evolutionary processes that abstracts away from the specific properties of biological evolution. In the talk I will argue argued that Price's framework is well-suited to model various aspects of the cultural evolution of language. The first part of the talk describes Price's approach in some detail. In the second part, case studies about its application to language evolution are presented.

A computational language game model is presented that shows how a population of language users can evolve from a brightness-based to a brightness+hue-based color term system. The shift is triggered by a change in the communication challenges posed by the environment, comparable to what happened in English during the Middle English period in response to the rise of dyeing and textile manufacturing c. 1150--1500. In a previous model that is able to explain such a shift, these two color categorization strategies were explicitly represented. This is not needed in our model. Instead, whether a population evolves a brightness-or a hue-based system is an emergent phenomenon that depends only on environmental factors. In this way, the model provides an explanation of how such a shift may come about without introducing additional mechanisms that would require further explanation.

It has been suggested that human language emerged as either a new, critical faculty to handle recursion, which linked two other existing systems in the brain, or as an exaptation of an existing mechanism, which had been used for a different purpose to that point. Of these two theories, the latter appears more parsimonious, but, somewhat surprisingly, has attracted less attention among researchers in the field. Navigation is a prime candidate for a task that may benefit from being able to handle recursion, and we give an account of the possible transition from navigation to language. In the described context, it appears plausible that the transition adding the crucial component of human language was promoted by kin selection. We show that once language is present among its speakers, it reinforces the mechanisms of kin selection, boosting such behaviour that benefit one's kin, and any such behaviour in turn boosts the use of language. The article also describes a mechanism through which language is used in lieu of kin markers to promote altruistic behaviour between potentially large communities of unrelated individuals.

Recent studies showed that three-year old children learned novel words better when the form and meaning of the words were sound symbolically related. This was the case for both children learning a language with a rich sound symbolic lexicon (Japanese) and that without (English). From this robust nature of sound symbolic facilitation, it was inferred that children's ability to use sound symbolism in word learning is the vestige of protolanguage consisting largely of sound symbolic words. We argued that sound symbolic protolanguage was able to refer to a wide range of information (not just auditory events). It had the added advantage that it was relatively easy to develop a shared open-class lexicon and it provided a stepping stone from a holophrastic protolanguage to a combinatoric protolanguage.

In this paper I consider the possibility that language is more strongly grounded in sensorimotor cognition than is normally assumed---a scenario which would be providential for language evolution theorists. I argue that the syntactic theory most compatible with this scenario, perhaps surprisingly, is generative grammar. I suggest that there may be a way of interpreting the syntactic structures posited in one theory of generative grammar (Minimalism) as descriptions of sensorimotor processing, and discuss the implications of this for models of language evolution.

In this paper we offer arguments for why modeling in the field of artificial language evolution can benefit from the use of real robots. We will propose that robotic experimental setups lead to more realistic and robust models, that real-word perception can provide the basis for richer semantics and that embodiment itself can be a driving force in language evolution. We will discuss these proposals by reviewing a variety of robotic experiments that have been carried out in our group and try to argue for the relevance of the approach.

The received view is that the first distinct word types were noun and verb (Heine \& Kuteva, 2002; Hurford, 2003a). Heine and Kuteva (2007) have suggested that the first words were noun-like entities. The present paper submits ten new arguments that support this claim. The arguments are novel implications of the reviewed evidence which is made to bear on the evolution of the linguistic predicate/argument (e.g. noun/verb) structure. The paper concludes that the evidence for noun-like entities antedating other word types is overwhelming.

This paper presents a model of lexical alignment in communication. The aim is to provide a reference model for simulating dialogs in naming game-related simulations of language evolution. We introduce a network model of alignment to shed light on the law-like dynamics of dialogs in contrast to their random counterpart. That way, the paper provides evidence on alignment to be used as reference data in building simulation models of dyadic conversations.

We examine the evolution of major grammatical forms and constructions as linguistic manifestations of human cognitive ability, based on historical data from English. We show that the complex linguistic system has arisen as more and more grammaticalized forms have accumulated. Word order and case go back to the earliest language. Tense, aspect, modality, gender, questions, negations, parataxis can be traced back to Proto-Indo-European, and they may go back further. Most crucial is the rise of embedding recursion and its product, the VO word order in Old English. This brought about the transition from the syntactic organization of the clause interwoven with discourse organization to the more strictly syntactic organization of the clause. With this transition, the periphrastic constructions of progressive, perfect and pluperfect, modal auxiliaries, periphrastic do and definite article arose due to speakers' desire to be more specific than was possible with the older forms. We also show the role of high-frequency words in the evolution of the grammatical forms.

The last decade has been a very productive one for our knowledge of our closest extinct relative, Homo neanderthalensis. A wide variety of studies has focused on various aspects of the Neandertal skeletal record and how to read it (e.g. Hublin 2009; Weaver 2009), on the chemical composition of their bones and how that might inform us on their diet (e.g. Richards and Trinkaus 2009), on their geographical distribution and their archaeological record (e.g. Roebroeks 2008) and, very importantly, on their genetic characteristics (e.g. Green et al. 2008; Briggs et al. 2009). Genetic studies indicate that modern humans and Neandertals shared a common ancestor only 500,000 to 700,000 years ago, which is also the picture emerging from studies of their physical remains (Hublin 2009). Building on the same Bauplan, two different hominin lineages emerged, in Africa the ancestors of modern humans, and in western Eurasia the Neandertals, who vanished from the record around 35,000 radiocarbon years ago. Integration of genetic data with the other lines of evidence promises to yield major breakthroughs in our understanding of the differences and similarities between these two groups of hominins in the very near future ...

The purpose of language is to encode information, so that it can be communicated. Both the producer and the comprehender of a communication want the encoding to be simple. However, they have competing concerns as well. The producer desires conciseness and the comprehender desires fidelity. This paper argues that the Minimum Description Length Principle (MDL) captures these two pressures on language. A genetic algorithm is used to evolve languages, that take the form of finite-state transducers, using MDL as a fitness metric. The languages that emerge are shown to have the ability to generalize beyond their initial training scope, suggesting that when selecting to satisfy MDL one is implicitly selecting for compositional languages.

The evolution of language capabilities is closely linked to the evolution of human brain structures. Human brain auditory cortices are anatomically and functionally asymmetrical. Studies at the microscopic level have found a thinner cortex and more widely spaced neuronal columns in the left (dominant) hemisphere, which reasonably correlate with its greater ability to discriminate speech sounds. The nature of these differences is consistent with a ''balloon model'' of brain growth, which states that as the brain white matter grows, it stretches the overlying cortex. Thus, the amount and duration of brain growth is an important factor in acquiring the ability to perceive speech. Humans have a much longer brain maturation time than any other primates (or animals). This ''extended maturation time'' allows language capabilities to evolve in the brain over time, rather than requiring them to be present at birth. The extended maturation time also must have a genetic basis, but not one specific to language, and the HAR1, G72 and FOXP2 genes might well be examples of genes which affect cortical and white matter growth. Finally, if this neuronal system can learn language without depending on specific language genes, then what could be the origin of universal grammar? Natural human grammars, like object-oriented software programs, are constrained to describe our experiential universe -- an idea mooted also by ''the early Wittgenstein'' and others. Insofar as humans mostly share the same experiential universe, our descriptions of it (our languages, some branches of mathematics) share many features; these common features can appear as a ''universal grammar.''

Segmentation and combination is a fundamental and ubiquitous feature of modern human languages. Here we explore its development in newly emergent language systems. Previous work has shown that manner and path are segmented and sequenced in the early stages of Nicaraguan Sign Language (NSL) but, interestingly, not in the gestures produced by Spanish speakers in the same community; gesturers conflate manner and path into a single unit. To explore the missing step between gesturers' conflated expressions and signers' sequenced expressions, we examined the gestures of homesigners: deaf children not exposed to a sign language who develop their own gesture systems to communicate with hearing family members. Seven Turkish child homesigners were asked to describe animated motion events. Homesigners resembled Spanish-speaking gesturers in that they often produced conflated manner+path gestures. However, the homesigners produced these conflated gestures along with a segmented manner or path gesture and, in this sense, also resembled NSL signers. A reanalysis of the original Nicaraguan data uncovered this same transitional form, primarily in the earliest form of NSL. These findings point to an intermediate stage that may bridge the transition from conflated forms that have no segmentation to sequenced forms that are fully segmented.

In this study, we tested the circumstances under which cultural evolution might lead to regularisation, even in the absence of an explicit learning bottleneck. We used an artificial language experiment to evaluate the degree of structure preservation and the extent of a bias for regularisation during learning, using languages which differed both in their initial levels of regularity and their frequency distributions. The differential reproduction of regular and irregular linguistic items, which may signal the existence of a systematicity bias, is apparent only in languages with skewed distributions: in uniformly distributed languages, reproduction fidelity is high in all cases. Regularisation does happen despite the lack of an explicit bottleneck, and is most significant in infrequent items from an otherwise highly regular language.

How can we explain the enormous amount of creativity and flexibility in spatial language use? In this paper we detail computational experiments that try to capture the essence of this puzzle. We hypothesize that flexible semantics which allow agents to conceptualize reality in many different ways are key to this issue. We will introduce our particular semantic modeling approach as well as the coupling of conceptual structures to the language system. We will justify the approach and show how these systems play together in the evolution of spatial language using humanoid robots.

Evans \& Levinson (2009) argue that language diversity is more robust than linguistic homogeneity, and also suggest that explanations for recurring patterns in language are not the product of an innate, evolved language faculty. I examine various kinds of evidence in favour of a specialized language faculty, and argue against the claim that typologically distinct languages must have distinct parsing systems.

Recent iterated language learning studies have shown that artificial languages evolve over the generations towards regularity. This trend has been explained as a reflection of the learners' biases. We test whether this learning bias for regularity is affected by culturally acquired knowledge, specifically by familiarity and literacy. The results of non-iterated learning experiments with miniature artificial musical and spoken languages suggest that familiarity helps us learn and reproduce the signals of a language, but literacy is required for regularities to be faithfully replicated. This in turn indicates that, by modifying human learning biases, literacy may play a role in the evolution of linguistic structure.

Negated sentences in Dutch child language are analyzed. It is argued that, rather than an innate UG structure, the child's acquisition procedure explains a temporary rise and fall of negative concord. It is further suggested that natural preferences of the acquisition procedure are a substantive source for grammatical universals. This evades the assumption that the evolution of the human brain as such has already produced an innate repertoire of grammatical universals.

The art of ranking things in genera and species is of no small importance and very much assists our judgment as well as our memory. You know how much it matters in botany, not to mention animals and other substances, or again moral and notional entities as some call them. Order largely depends on it, and many good authors write in such a way that their whole account could be divided and subdivided according to a procedure related to genera and species. This helps one not merely to retain things, but also to find them. And those who have laid out all sorts of notions under certain headings or categories have done something very useful.Gottfried Wilhelm von Leibniz, New Essays on Human Understanding (Leibniz, 1704)

Pronouns form a particularly interesting part-of-speech for evolutionary linguistics because their development is often lagging behind with respect to other changes in their language. Many hypotheses on pronoun evolution exist -- both for explaining their initial resilience to change as well as for why they eventually cave in to evolutionary pressures -- but so far, no one has proposed a formal model yet that operationalizes these explanations in a unified theory. This paper therefore presents a computational model of pronoun evolution in a multi-agent population; and argues that pronoun evolution can best be understood as an interplay between the level of language strategies, which are the procedures for learning, expanding and aligning particular features of language, and the level of the specific language systems that instantiate these strategies in terms of concrete words, morphemes and grammatical structures. This claim is supported by a case study on Spanish pronouns, which are currently undergoing an evolution from a case- to a referential-based system, the latter of which there exist multiple variations (which are called leismo, laismo and loismo depending on the type of change).

According to recent developments in (computational) Construction Grammar, language processing occurs through the incremental buildup of meaning and form according to constructional specifications. If the number of available constructions becomes large however, this results in a search process that quickly becomes cognitively unfeasible without the aid of additional guiding principles. One of the main mechanisms the brain recruits (in all sorts of tasks) to optimize processing efficiency is priming. Priming in turn requires a specific organisation of the constructions. Processing efficiency thus must have been one of the main evolutionary pressures driving the organisation of linguistic constructions. In this paper we show how constructions can be organized in a constructional dependency network in which constructions are linked through semantic and syntactic categories. Using Fluid Construction Grammar, we show how such a network can be learned incrementally in a usage-based fashion, and how it can be used to guide processing by priming the suitable constructions.

This paper discusses problems associated with the ''moving target argument'' (cf. Christiansen \& Chater 2008, Chater et al. 2009, see also Deacon 1997: 329, Johansson 2005:190). According to this common argument, rapid language change renders biological adaptations to language unlikely. However, studies of rapid biological evolution, varying rates of language change and recent simulations pose problems for the underlying assumptions of the argument. A critique of these assumptions leads to a richer view of language-biology co-evolution.

Word co-occurrence networks are one of the most common linguistic networks studied in the past and they are known to exhibit several interesting topological characteristics. In this article, we investigate the global topological properties of word co-occurrence networks and, in particular, present a detailed study of their spectrum. Our experiments reveal certain universal trends found across the networks for seven different languages from three different language families, which are neither reported nor explained by any of the previous studies and models of word-cooccurrence networks. We hypothesize that since word co-occurrences are governed by syntactic properties of a language, the network has much constrained topology than that predicted by the previously proposed growth model. A deeper empirical and theoretical investigation into the evolution of these networks further suggests that they have a core-periphery structure, where the core hardly evolves with time and new words are only attached to the periphery of the network. These properties are fundamental to the nature of word co-occurrence across languages.

Communication processes rely on the production and interpretation of representations, thus an important issue is to understand what types of representations are involved during the emergence of interpretations. Here we present an experiment to evaluate conditions for the emergence of interpretations of different representation types. To design our experiment, we follow biological inspirations and a theoretical framework of representation processes. Our results show that different interpretations process can emerge depending on the adaptation cost of cognitive traits and on the availability of cognitive shortcuts.

There is disagreement about whether human political evolution has proceeded through a sequence of incremental increases in complexity, or whether larger, non-sequential increases have occurred. The extent to which societies have decreased in complexity is also unclear. These debates have continued largely in the absence of rigorous, quantitative tests. We evaluated six competing models of political evolution in Austronesian-speaking societies using phylogenetic methods. Here we show that in the best-fitting model political complexity rises and falls in a sequence of small steps. This is closely followed by another model in which increases are sequential but decreases can be either sequential or in bigger drops. The results indicate that large, non-sequential jumps in political complexity have not occurred during the evolutionary history of these societies. This suggests that, despite the numerous contingent pathways of human history, there are regularities in cultural evolution that can be detected using computational phylogenetic methods.

Language leaves no traces in the archaeological record, and many researchers have been doubtful about how much animal communication could reveal about the unique features of human communication. That began to change in the 1990s, when linguists, evolutionary biologists, psychologists, primatologists, and other scientists teamed up to test new hypotheses about how language arose. Since 1996, this interdisciplinary crowd has gathered every 2 years at Evolang, a meeting devoted to deciphering the evolutionary origins of language. Although some say the early Evolang gatherings suffered from too many hypotheses and too little testing, many think last month's meeting marks a turning point for the field. Participants flocked to hear a barrage of new data from animal and human studies. The new empiricism may help resolve one of the field's liveliest debates: whether the first human language consisted of gestures, similar to today's sign languages, or articulated speech. At the meeting, a new and unlikely seeming animal model for human language got star billing: songbirds. Their ability to learn and imitate their parents' melodious tunes has many parallels with the ability of human children to learn spoken language, researchers say.

Human language and social cognition are closely linked: advanced social cognition is necessary for children to acquire language, and language allows forms of social understanding (and, more broadly, culture) that would otherwise be impossible. Both `language' and `social cognition' are complex constructs, involving many independent cognitive mechanisms, and the comparative approach provides a powerful route to understanding the evolution of such mechanisms. We provide a broad comparative review of mechanisms underlying social intelligence in vertebrates, with the goal of determining which human mechanisms are broadly shared, which have evolved in parallel in other clades, and which, potentially, are uniquely developed in our species. We emphasize the importance of convergent evolution for testing hypotheses about neural mechanisms and their evolution.

The empirical evidence that human color categorization exhibits some universal patterns beyond superficial discrepancies across different cultures is a major breakthrough in cognitive science. As observed in the World Color Survey (WCS), indeed, any two groups of individuals develop quite different categorization patterns, but some universal properties can be identified by a statistical analysis over a large number of populations. Here, we reproduce the WCS in a numerical model in which different populations develop independently their own categorization systems by playing elementary language games. We find that a simple perceptual constraint shared by all humans, namely the human Just Noticeable Difference (JND), is sufficient to trigger the emergence of universal patterns that unconstrained cultural interaction fails to produce. We test the results of our experiment against real data by performing the same statistical analysis proposed to quantify the universal tendencies shown in the WCS [Kay P & Regier T. (2003) Proc. Natl. Acad. Sci. USA 100: 9085-9089], and obtain an excellent quantitative agreement. This work confirms that synthetic modeling has nowadays reached the maturity to contribute significantly to the ongoing debate in cognitive science.

Verbal communication is a joint activity; however, speech production and comprehension have primarily been analyzed as independent processes within the boundaries of individual brains. Here, we applied fMRI to record brain activity from both speakers and listeners during natural verbal communication. We used the speaker's spatiotemporal brain activity to model listeners' brain activity and found that the speaker's activity is spatially and temporally coupled with the listener's activity. This coupling vanishes when participants fail to communicate. Moreover, though on average the listener's brain activity mirrors the speaker's activity with a delay, we also find areas that exhibit predictive anticipatory responses. We connected the extent of neural coupling to a quantitative measure of story comprehension and find that the greater the anticipatory speaker-listener coupling, the greater the understanding. We argue that the observed alignment of production- and comprehension-based processes serves as a mechanism by which brains convey information.

Scaling laws are ubiquitous in nature, and they pervade neural, behavioral and linguistic activities. A scaling law suggests the existence of processes or patterns that are repeated across scales of analysis. Although the variables that express a scaling law can vary from one type of activity to the next, the recurrence of scaling laws across so many different systems has prompted a search for unifying principles. In biological systems, scaling laws can reflect adaptive processes of various types and are often linked to complex systems poised near critical points. The same is true for perception, memory, language and other cognitive phenomena. Findings of scaling laws in cognitive science are indicative of scaling invariance in cognitive mechanisms and multiplicative interactions among interdependent components of cognition.

The historical origins of natural language cannot be observed directly. We can, however, study systems that support language and we can also develop models that explore the plausibility of different hypotheses about how language emerged. More recently, evolutionary linguists have begun to conduct language evolution experiments in the laboratory, where the emergence of new languages used by human participants can be observed directly. This enables researchers to study both the cognitive capacities necessary for language and the ways in which languages themselves emerge. One theme that runs through this work is how individual-level behaviours result in population-level linguistic phenomena. A central challenge for the future will be to explore how different forms of information transmission affect this process.

Communication in nature is not restricted to the transmitter-receiver pair. Unintended listeners, or eavesdroppers, can intercept the signal and possibly utilize the received information to their benefit, which may confer a certain cost to the communicating pair. In this paper we explore (computationally and mathematically) such situations with the goal of uncovering their effect on language evolution. We find that in the presence of eavesdropping, languages exhibit a tendency to become more complex. On the other hand, if eavesdroppers belong to a different (competing) population, the languages used by the two populations tend to converge, if the cost of eavesdropping is sufficiently high; otherwise the languages synchronize. These findings are discussed in the context of animal communication and human language. In particular, the emergence of synonyms is predicted. We demonstrate that a small associated cost can suppress synonyms in the absence of eavesdropping, but that their likelihood increases strongly with the probability of eavesdropping.

Cultural evolution is a complex process that can happen at several levels. At the level of individuals in a population, each human bears a set of cultural traits that he or she can transmit to its offspring (vertical transmission) or to other members of his or her society (horizontal transmission). The relative frequency of a cultural trait in a population or society can thus increase or decrease with the relative reproductive success of its bearers (individual's level) or the relative success of transmission (called the idea's level). This article presents a mathematical model on the interplay between these two levels. The first aim of this article is to explore when cultural evolution is driven by the idea's level, when it is driven by the individual's level and when it is driven by both.

This paper presents an overview of the current state of historical linguistics in Australian languages. Australian languages have been important in theoretical debates about the nature of language change and the possibilities for reconstruction and classification in areas of intensive diffusion. Here are summarized the most important outstanding questions for Australian linguistic prehistory; I also present a case study of the Karnic subgroup of Pama-Nyungan, which illustrates the problems for classification in Australian languages and potential approaches using phylogenetic methods.

In this paper we outline two debates about the nature of human cultural history. The first focuses on the extent to which human history is tree-like (its shape), and the second on the unity of that history (its fabric). Proponents of cultural phylogenetics are often accused of assuming that human history has been both highly tree-like and consisting of tightly linked lineages. Critics have pointed out obvious exceptions to these assumptions. Instead of a priori dichotomous disputes about the validity of cultural phylogenetics, we suggest that the debate is better conceptualized as involving positions along continuous dimensions. The challenge for empirical research is, therefore, to determine where particular aspects of culture lie on these dimensions. We discuss the ability of current computational methods derived from evolutionary biology to address these questions. These methods are then used to compare the extent to which lexical evolution is tree-like in different parts of the world and to evaluate the coherence of cultural and linguistic lineages.

Linguists have traditionally represented patterns of divergence within a language family in terms of either a splits model, corresponding to a branching family tree structure, or the wave model, resulting in a (dialect) continuum. Recent phylogenetic analyses, however, have tended to assume the former as a viable idealization also for the latter. But the contrast matters, for it typically reflects different processes in the real world: speaker populations either separated by migrations, or expanding over continuous territory. Since history often leaves a complex of both patterns within the same language family, ideally we need a single model to capture both, and tease apart the respective contributions of each. The network type of phylogenetic method offers this, so we review recent applications to language data. Most have used lexical data, encoded as binary or multi-state characters. We look instead at continuous distance measures of divergence in phonetics. Our output networks combine branch- and continuum-like signals in ways that correspond well to known histories (illustrated for Germanic, and particularly English). We thus challenge the traditional insistence on shared innovations, setting out a new, principled explanation for why complex language histories can emerge correctly from distance measures, despite shared retentions and parallel innovations.

'Language shift' is the process whereby members of a community in which more than one language is spoken abandon their original vernacular language in favour of another. The historical shifts to English by Celtic language speakers of Britain and Ireland are particularly well-studied examples for which good census data exist for the most recent 100-120 years in many areas where Celtic languages were once the prevailing vernaculars. We model the dynamics of language shift as a competition process in which the numbers of speakers of each language (both monolingual and bilingual) vary as a function both of internal recruitment (as the net outcome of birth, death, immigration and emigration rates of native speakers), and of gains and losses owing to language shift. We examine two models: a basic model in which bilingualism is simply the transitional state for households moving between alternative monolingual states, and a diglossia model in which there is an additional demand for the endangered language as the preferred medium of communication in some restricted sociolinguistic domain, superimposed on the basic shift dynamics. Fitting our models to census data, we successfully reproduce the demographic trajectories of both languages over the past century. We estimate the rates of recruitment of new Scottish Gaelic speakers that would be required each year (for instance, through school education) to counteract the natural wastage as households with one or more Gaelic speakers fail to transmit the language to the next generation informally, for different rates of loss during informal intergenerational transmission.

We examine situations in which linguistic changes have probably been propagated via normal contact as opposed to via conquest, recent settlement and large-scale migration. We proceed then from two simplifying assumptions: first, that all linguistic variation is the result of either diffusion or independent innovation, and, second, that we may operationalize social contact as geographical distance. It is clear that both of these assumptions are imperfect, but they allow us to examine diffusion via the distribution of linguistic variation as a function of geographical distance. Several studies in quantitative linguistics have examined this relation, starting with Seguy (Seguy 1971 Rev. Linguist. Romane 35, 335-357), and virtually all report a sublinear growth in aggregate linguistic variation as a function of geographical distance. The literature from dialectology and historical linguistics has mostly traced the diffusion of individual features, however, so that it is sensible to ask what sort of dynamic in the diffusion of individual features is compatible with Seguy's curve. We examine some simulations of diffusion in an effort to shed light on this question.

Evolutionary approaches to cultural change are increasingly influential, and many scientists believe that a `grand synthesis' is now in sight. The papers in this Theme Issue, which derives from a symposium held by the AHRC Centre for the Evolution of Cultural Diversity (University College London) in December 2008, focus on how the phylogenetic tree-building and network-based techniques used to estimate descent relationships in biology can be adapted to reconstruct cultural histories, where some degree of inter-societal diffusion will almost inevitably be superimposed on any deeper signal of a historical branching process. The disciplines represented include the three most purely `cultural' fields from the four-fiel' model of anthropology (cultural anthropology, archaeology and linguistic anthropology). In this short introduction, some context is provided from the history of anthropology, and key issues raised by the papers are highlighted.

Sociolinguistic studies have demonstrated that centrally-connected and peripheral members of social networks can both propel and impede the spread of linguistic innovations. We use agent-based computer simulations to investigate the dynamic properties of these network roles in a large social influence network, in which diffusion is modeled as the probabilistic uptake of one of several competing variants by agents of unequal social standing. We find that highly-connected agents, structural equivalents of leaders in empirical studies, advance on-going change by spreading competing variants. Isolated agents, or loners, holding on to existing variants are safe-keepers of variants considered old or new depending on the current state of the rest of the population. Innovations spread following a variety of S-curves and stabilize as norms in the network only if two conditions are simultaneously satisfied: (1) the network comprises extremely highly connected and very isolated agents, and (2) agents are biased to pay proportionally more attention to better connected, or popular, neighbors. These findings reconcile competing models of individual network roles in the selection and propagation process of language change, and support Bloomfield's hypothesis that the spread of linguistic innovations in heterogeneous social networks depend upon communication density and relative prestige.

Deacon has suggested that one of the key factors of language evolution is not characterized by an increase in genetic contribution, often known as the Baldwin effect, but rather by a decrease. This process effectively increases linguistic learning capability by organizing a novel synergy of multiple lower-order functions previously irrelevant to the process of language acquisition. Deacon posits that this transition is not caused by natural selection. Rather, it is due to the relaxation of natural selection. While there are some cases in which relaxation caused by some external factors indeed induces the transition, we do not know what kind of relaxation has worked in language evolution. In this article, a genetic-algorithm-based computer simulation is used to investigate how the niche-constructing aspect of linguistic behavior may trigger the degradation of genetic predisposition related to language learning. The results show that agents initially increase their genetic predisposition for language learning—the Baldwin effect. They create a highly uniform sociolinguistic environment—a linguistic niche construction. This means that later generations constantly receive very similar inputs from adult agents, and subsequently the selective pressure to retain the genetic predisposition is relaxed.

In this this article we present a model of social learning of both language and skills, while assuming --insofar possible-- strict autonomy, virtual embodiment and situatedness. This model is built by integrating various previous models on language development and social learning, and it is this integration that, under the mentioned assumptions, provides novel challenges. The aim of the article is to investigate what socio-cognitive mechanisms agents should have in order to be able to transmit language from one generation to the next in such a way that it can be used as a medium to transmit internalised rules that represent skill-knowledge. The knowledge is about how to deal with the familiar poisonous food problem. Simulations reveal under what conditions regarding population structure, agents can successfully solve this problem. In addition to issues relating to perspective taking and mutual exclusivity, we show that agents need to coordinate interactions such that they can establish joint attention in order to form a scaffold for language learning, which in turn forms a scaffold for the learning of rule-based skills. Based on these findings we conclude by hypothesising that social learning at one level forms a scaffold to the social learning at another higher level, thus contributing to the accumulation of cultural knowledge.

Using our interdisciplinary research collaboration as a case study, we discuss the question of whether formal modeling and empirical approaches can be successfully integrated into a single line of research. We argue that to avoid an undesirable disconnect between the two, one needs considerable time and patience for a science-humanities collaboration to bear fruit. In our collaboration and, we believe, in science-humanities collaborations in general, certain shared goals are required for success, including: starting with simple models before moving to more complex models; the importance of continually comparing models with empirical data where possible; and a focus on explaining statistical patterns rather than accounting for single data points individually.

Abstract A fundamental characteristic of human speech is that it uses a limited set of basic building blocks (phonemes, syllables), that are put to use in many different combinations to mark differences in meaning. This article investigates the evolution of such combinatorial ...

Human speech has been investigated with computer models since the invention of digital computers, and models of the evolution of speech first appeared in the late 1960s and early 1970s. Speech science and computer models have a long shared history because speech is a physical signal and can be modeled accurately. This article gives a brief overview of the use of computer models in the study of the evolution of the vocal tract. We also present a critical case study of one model that has been used to study the vocal abilities of Neanderthals. We argue that this study contains subtle but fatal flaws which invalidate the conclusions drawn from the model, illustrating the dangers of applying computer models outside the area for which they have been developed. Future models need to make use of a broader database of anatomical and physiological data from other animals, especially nonhuman primates, to understand the path leading to modern Homo sapiens.

This article proposes an acquisition framework that involves horizontal, vertical, and oblique transmissions. Based on a lexicon-syntax coevolution model, it discusses the relative roles of these forms of cultural transmission on language origin and change. The simulation results not only reveal an integrated role of oblique transmission that combines the roles of horizontal and vertical transmissions in preserving linguistic understandability within and across generations of individuals, but also show that both horizontal and oblique transmissions are more necessary than vertical transmission for language evolution in a multiagent cultural environment.

Observations of alarm calling behavior in putty-nosed monkeys are suggestive of a link with human language evolution. However, as is often the case in studies of animal behavior and cognition, competing theories are underdetermined by the available data. We argue that computational modeling, and in particular the use of individual-based simulations, is an effective way to reduce the size of the pool of candidate explanations. Simulation achieves this both through the classification of evolutionary trajectories as either plausible or implausible, and by putting lower bounds on the cognitive complexity required to perform particular behaviors. A case is made for using both of these strategies to understand the extent to which the alarm calls of putty-nosed monkeys are likely to be a good model for human language evolution.

This methodological article serves as an introduction to this special issue, whose aim is to encourage more and better interaction between empirical studies and computer modeling with regard to the study of language evolution. We argue that research into the field of language evolution is so complex that computer modeling forms an essential tool to generate predictions based on some, possibly descriptive, theory in order to falsify that theory. Falsification should be carried out by comparing the generated predictions with empirical data. In order to improve the quality of the predictions and thus the reliability of the falsification process, we stress the importance of initializing the computer model with empirical data. The papers in this special issue provide some concrete examples and new proposals of applying the suggested methodology.

This article presents a dense database study of child language acquisition from a usage-based perspective and a new analysis of data from an earlier study on simulating language evolution. The new analysis is carried out to show how computer modeling studies can be designed to generate predictions (results) that can be compared quantitatively with empirical data obtained from the dense database studies. Although the comparison shows that the computer model in question is still far from realistic, the study illustrates how to carry out agent-based simulations of language evolution that allow quantitative verification of predictions with empirical data to validate theories on child language acquisition.

What are the ``design features'' of human language that need to be explained? Starting from R. Jackendoff's scenario for the evolution of language, we argue that it is the transitions between stages that pose the crucial challenges for accounts of the evolution of language. We review a number of formalisms for conceptualizations, sound, and the mapping between them, and describe and evaluate the differences between each of Jackendoff's stages in terms of these formalisms. We conclude from this discussion that the transitions to combinatorial phonology, compositional semantics and hierarchical phrase structure can be formally characterized. Modeling these transitions is a major challenge for language evolution research.

We study the viability and resilience of languages, using a simple dynamical model of two languages in competition. Assuming that public action can modify the prestige of a language in order to avoid language extinction, we analyze two cases: (i) the prestige can only take two values, (ii) it can take any value but its change at each time step is bounded. In both cases, we determine the viability kernel, that is, the set of states for which there exists an action policy maintaining the coexistence of the two languages, and we define such policies. We also study the resilience of the languages and identify configurations from where the system can return to the viability kernel (finite resilience), or where one of the languages is lead to disappear (zero resilience). Within our current framework, the maintenance of a bilingual society is shown to be possible by introducing the prestige of a language as a control variable.

Background

Zipf's law states that the relationship between the frequency of a word in a text and its rank (the most frequent word has rank , the 2nd most frequent word has rank) is approximately linear when plotted on a double logarithmic scale. It has been argued that the law is not a relevant or useful property of language because simple random texts - constructed by concatenating random characters including blanks behaving as word delimiters - exhibit a Zipf's law-like word rank distribution.

Methodology/Principal Findings

In this article, we examine the flaws of such putative good fits of random texts. We demonstrate - by means of three different statistical tests - that ranks derived from random texts and ranks derived from real texts are statistically inconsistent with the parameters employed to argue for such a good fit, even when the parameters are inferred from the target real text. Our findings are valid for both the simplest random texts composed of equally likely characters as well as more elaborate and realistic versions where character probabilities are borrowed from a real text.

Conclusions/Significance

The good fit of random texts to real Zipf's law-like rank distributions has not yet been established. Therefore, we suggest that Zipf's law might in fact be a fundamental law in natural languages.

We recently used computational phylogenetic methods on lexical data to test between two scenarios for the peopling of the Pacific. Our analyses of lexical data supported a pulse-pause scenario of Pacific settlement in which the Austronesian speakers originated in Taiwan around 5,200 years ago and rapidly spread through the Pacific in a series of expansion pulses and settlement pauses. We claimed that there was high congruence between traditional language subgroups and those observed in the language phylogenies, and that the estimated age of the Austronesian expansion at 5,200 years ago was consistent with the archaeological evidence. However, the congruence between the language phylogenies and the evidence from historical linguistics was not quantitatively assessed using tree comparison metrics. The robustness of the divergence time estimates to different calibration points was also not investigated exhaustively. Here we address these limitations by using a systematic tree comparison metric to calculate the similarity between the Bayesian phylogenetic trees and the subgroups proposed by historical linguistics, and by re-estimating the age of the Austronesian expansion using only the most robust calibrations. The results show that the Austronesian language phylogenies are highly congruent with the traditional subgroupings, and the date estimates are robust even when calculated using a restricted set of historical calibrations.

Background

Languages differ greatly both in their syntactic and morphological systems and in the social environments in which they exist. We challenge the view that language grammars are unrelated to social environments in which they are learned and used.

Methodology/Principal Findings

We conducted a statistical analysis of >2,000 languages using a combination of demographic sources and the World Atlas of Language Structures a database of structural language properties. We found strong relationships between linguistic factors related to morphological complexity, and demographic/socio-historical factors such as the number of language users, geographic spread, and degree of language contact. The analyses suggest that languages spoken by large groups have simpler inflectional morphology than languages spoken by smaller groups as measured on a variety of factors such as case systems and complexity of conjugations. Additionally, languages spoken by large groups are much more likely to use lexical strategies in place of inflectional morphology to encode evidentiality, negation, aspect, and possession. Our findings indicate that just as biological organisms are shaped by ecological niches, language structures appear to adapt to the environment (niche) in which they are being learned and used. As adults learn a language, features that are difficult for them to acquire, are less likely to be passed on to subsequent learners. Languages used for communication in large groups that include adult learners appear to have been subjected to such selection. Conversely, the morphological complexity common to languages used in small groups increases redundancy which may facilitate language learning by infants.

Conclusions/Significance

We hypothesize that language structures are subjected to different evolutionary pressures in different social environments. Just as biological organisms are shaped by ecological niches, language structures appear to adapt to the environment (niche) in which they are being learned and used. The proposed Linguistic Niche Hypothesis has implications for answering the broad question of why languages differ in the way they do and makes empirical predictions regarding language acquisition capacities of children versus adults.

This paper proposes a language acquisition framework that includes both intra-generational transmission among children and inter-generational transmission between adults and children. A multi-agent computational model that adopts this framework is designed to evaluate the relative roles of these forms of cultural transmission in language evolution. It is shown that intra-generational transmission helps accelerate the convergence of linguistic knowledge and introduce changes in the communal language, while inter-generational transmission helps preserve an initial language to a certain extent. Due to conventionalisation during transmission, both forms of transmission collectively achieve a dynamic equilibrium of language evolution: On short time-scales, good understandability is maintained among individuals across generations; in the long run, language change is inevitable.

We study the emergence of shared representations in a population of agents engaged in a supervised classification task, using a model called the classification game. We connect languages with tasks by treating the agents' classification hypothesis space as an information channel. We show that by learning through the classification game, agents can implicitly perform complexity regularisation, which improves generalisation. Improved generalisation also means that the languages that emerge are well adapted to the given task. The improved language-task fit springs from the interplay of two opposing forces: the dynamics of collective learning impose a preference for simple representations, while the intricacy of the classification task imposes a pressure towards representations that are more complex. The push-pull of these two forces results in the emergence of a shared representation that is simple but not too simple. Our agents use artificial neural networks to solve the classification tasks they face, and a simple counting algorithm to learn a language as a form-meaning mapping. We present several experiments to demonstrate that both compositional and holistic languages can emerge in our system. We also demonstrate that the agents avoid overfitting on noisy data, and can learn some very difficult tasks through interaction, which they are unable to learn individually. Further, when the agents use simple recurrent networks to solve temporal classification tasks, we see the emergence of a rudimentary grammar, which does not have to be explicitly learned.

Previous network analyses of several languages revealed a unique set of structural characteristics. One of these characteristicsathe presence of many smaller components (referred to as islands)awas further examined with a comparative analysis of the island constituents. The results showed that Spanish words in the islands tended to be phonologically and semantically similar to each other, but English words in the islands tended only to be phonologically similar to each other. The results of this analysis yielded hypotheses about language processing that can be tested with psycholinguistic experiments, and offer insight into cross-language differences in processing that have been previously observed.

During the last ten years several studies have appeared regarding language complexity. Research on this issue began soon after the burst of a new movement of interest and research in the study of complex networks, i.e., networks whose structure is irregular, complex and dynamically evolving in time. In the first years, network approach to language mostly focused on a very abstract and general overview of language complexity, and few of them studied how this complexity is actually embodied in humans or how it affects cognition. However research has slowly shifted from the language-oriented towards a more cognitive-oriented point of view. This review first offers a brief summary on the methodological and formal foundations of complex networks, then it attempts a general vision of research activity on language from a complex networks perspective, and specially highlights those efforts with cognitive-inspired aim.

The relationship between meanings of words and their sound shapes is to a large extent arbitrary, but it is well known that languages exhibit sound symbolism effects violating arbitrariness. Evidence for sound symbolism is typically anecdotal, however. Here we present a systematic approach. Using a selection of basic vocabulary in nearly one half of the worldas languages we find commonalities among sound shapes for words referring to same concepts. These are interpreted as due to sound symbolism. Studying the effects of sound symbolism cross-linguistically is of key importance for the understanding of language evolution.

Recent work on the emergence and evolution of human communication has focused on getting novel systems to evolve from scratch in the laboratory. Many of these studies have adopted an interactive construction approach, whereby pairs of participants repeatedly interact with one another to gradually develop their own communication system whilst engaged in some shared task. This paper describes four recent studies that take a different approach, showing how adaptive structure can emerge purely as a result of cultural transmission through single chains of learners. By removing elements of interactive communication and focusing only on the way in which language is repeatedly acquired by learners, we hope to gain a better understanding of how useful structural properties of language could have emerged without being intentionally designed or innovated.

Human communication is often thought about in terms of transmitted messages in a conventional code like a language. But communication requires a specialized interactive intelligence. Senders have to be able to perform recipient design, while receivers need to be able to do intention recognition, knowing that recipient design has taken place. To study this interactive intelligence in the lab, we developed a new task that taps directly into the underlying abilities to communicate in the absence of a conventional code. We show that subjects are remarkably successful communicators under these conditions, especially when senders get feedback from receivers. Signaling is accomplished by the manner in which an instrumental action is performed, such that instrumentally dysfunctional components of an action are used to convey communicative intentions. The findings have important implications for the nature of the human communicative infrastructure, and the task opens up a line of experimentation on human communication.

Although rapidity of fading has been long identified as one of the crucial design features of language, little is known about its effects on the design of communication systems. To investigate such effects, we performed an experiment in which pairs of participants developed novel communication systems using media that had different degrees of rapidity of fading. The results of the experiment suggest that rapidity of fading does not affect the pace with which communication systems emerge or the communicative effi cacy of the emerged systems. However, rapidity of fading seems to affect the design of these systems. In particular, communication systems implemented in the more rapidly fading medium exhibited a higher degree of combinatorial reuse of their forms than systems implemented in the medium that faded more slowly. These results suggest that the design of language might be constrained by subtle relations the presence of which can be ascertained only through direct experimental manipulation. Human communication systems crafted today in the laboratory can provide new insights into the design of natural languages.

This paper contrasts two influential theoretical accounts of language change and evolution a Iterated Learning and Social Coordination. The contrast is based on an experiment that compares drawings produced with Garrod et alas (2007) apictionarya task with those produced in an Iterated Learning version of the same task. The main finding is that Iterated Learning does not lead to the systematic simplification and increased symbolicity of graphical signs produced in the standard interactive version of the task. A second finding is that Iterated Learning leads to less conceptual and structural alignment between participants than observed for those in the interactive condition. The paper concludes with a comparison of the two accounts in relation to how each promotes signs that are effi cient, systematic and learnable.

Computational simulations have provided evidence that the use of linguistic cues as group markers plays an important role in the development of linguistic diversity shortcite (Nettle & Dunbar, 1997; Nettle, 1999). Other simulations, however, have contradicted these findings (Livingstone & Fyfe, 1999; Livingstone, 2002). Similar disagreements exist in sociolinguistics (e.g. Labov, 1963, 2001; Trudgill, 2004, 2008a; Baxter et al., 2009). This paper describes an experimental study in which participants played an anonymous economic game using an instant-messenger-style program and an artificial aalien languagea. The competitiveness of the game and the frequency with which players interacted were manipulated. Given frequent enough interaction with team-mates, players were able to use linguistic cues to identify themselves. In the most competitive condition, this led to divergence in the language, which did not occur in other conditions. This suggests that both frequency of interaction and a pressure to use language to mark identity play a significant role in encouraging linguistic divergence over short periods, but that neither is suffi cient on its own.

Communication is a fundamentally interactive phenomenon. Evolutionary biology recognises this fact in its definition of communication, in which signals are those actions that cause reactions, and where both action and reaction are designed for that reason. Where only one or the other is designed then the behaviours are classed as either cues or coercion. Since mutually dependent behaviours are unlikely to emerge simultaneously, the symmetry inherent in these definitions gives rise to a prediction that communication will only emerge if cues or coercive behaviours do so first. They will then be co-opted for communication. A range of case studies, from animal signalling, evolutionary robotics, comparative psychology, and evolutionary linguistics are used to test this prediction. The first three are found to be supportive. However in the Embodied Communication Game, a recent experimental approach to the emergence of communication between adult humans, communication emerges even when cues or coerced behaviours are not possible. This suggests that humans are exceptional in this regard. It is argued that the reason for this is the degree to which we are able and compelled to read and interpret the behaviour of others in intentional terms.

Arbitrariness and systematicity are two of languageas most fascinating properties. Although both are characterizations of the mappings between signals and meanings, their emergence and evolution in communication systems has generally been explored independently. We present an experiment in which both arbitrariness and systematicity are probed. Participants invent signs from scratch to refer to a set of items that share salient semantic features. Through interaction, the systematic re-use of arbitrary signal elements emerges.

In traditional generative linguistics sounds of a language are represented as bundle of binary valued features. The sounds used in a language are not randomly chosen from a universal repository of phonemes, but are known to be correlated in terms of the features they use. Discovery of these correlation patterns and organizational principles behind the structure of sound inventories has been one of the classic problems in phonology. In this work, we show that the amount of redundancy present in the sound inventory of a language, which is an information theoretic measure reflecting the ratio of the number of distinctive features used in the language to that of the minimum number of features required to distinguish between the sounds present in the language, lies within a very narrow range irrespective of the factors such as the size of the inventory, the language family and the typology. This is a hitherto unreported significant observation that points to a universal structural property of the sound inventories of human languages. This property might be an outcome of self-organization of the sound inventories through the processes of language acquisition and change, or of the way in which phonemes are represented in generative phonology.

The network characteristics based on the phonological similarities in the lexicons of several languages were examined. These languages differed widely in their history and linguistic structure, but commonalities in the network characteristics were observed. These networks were also found to be different from other networks studied in the literature. The properties of these networks suggest explanations for various aspects of linguistic processing and hint at deeper organization within the human language.

The mirror system provided a natural platform for the subsequent evolution of language. In nonhuman primates, the system provides for the understanding of biological action, and possibly for imitation, both prerequisites for language. I argue that language evolved from manual gestures, initially as a system of pantomime, but with gestures gradually `conventionalizing' to assume more symbolic form. The evolution of episodic memory and mental time travel, probably beginning with the genus Homo during the Pleistocene, created pressure for the system to `grammaticalize,' involving the increased vocabulary necessary to refer to episodes separated in time and place from the present, constructions such as tense to refer to time itself, and the generativity to construct future (and fictional) episodes. In parallel with grammaticalization, the language medium gradually incorporated facial and then vocal elements, culminating in autonomous speech (albeit accompanied still by manual gesture) in our own species, Homo sapiens.

Recent research suggests that language evolution is a process of cultural change, in which linguistic structures are shaped through repeated cycles of learning and use by domain-general mechanisms. This paper draws out the implications of this viewpoint for understanding the problem of language acquisition, which is cast in a new, and much more tractable, form. In essence, the child faces a problem of induction, where the objective is to coordinate with others (C-induction), rather than to model the structure of the natural world (N-induction). We argue that, of the two, C-induction is dramatically easier. More broadly, we argue that understanding the acquisition of any cultural form, whether linguistic or otherwise, during development, requires considering the corresponding question of how that cultural form arose through processes of cultural evolution. This perspective helps resolve the 'logical' problem of language acquisition and has far-reaching implications for evolutionary psychology.

The philosophical debate over innate ideas and their role in the acquisition of knowledge has a venerable history. It is thus surprising that very little attention was paid until early last century to the questions of how linguistic knowledge is acquired and what role, if any, ...

Previous research on categorical perception of pitch contours has mainly considered the contrast between tone language and non-tone language listeners. This study investigates not only the influence of tone language vs. non-tone language experience (German vs. Chinese), but also the influence of different tone inventories (Mandarin tones vs. Cantonese tones), on the categorical perception of pitch contours. The results show that the positions of the identification boundaries do not differ significantly across the 3 groups of listeners, i.e., Mandarin, Cantonese, and German, but that the boundary widths do differ significantly between tone language (Mandarin and Cantonese) listeners and non-tone language (German) listeners, with broader boundary widths for non-tone language listeners. In the discrimination tasks, the German listeners exhibit only psychophysical boundaries, whereas Chinese listeners exhibit linguistic boundaries, and these linguistic boundaries are further shaped by the different tone inventories.

Why and how have languages died out? We have devised a mathematical model to help us understand how languages go extinct. We use the model to ask whether language extinction can be prevented in the future and why it may have occurred in the past. A growing number of mathematical models of language dynamics have been developed to study the conditions for language coexistence and death, yet their phenomenological approach compromises their ability to influence language revitalization policy. In contrast, here we model the mechanisms underlying language competition and look at how these mechanisms are influenced by specific language revitalization interventions, namely, private interventions to raise the status of the language and thus promote language learning at home, public interventions to increase the use of the minority language, and explicit teaching of the minority language in schools. Our model reveals that it is possible to preserve a minority language but that continued long-term interventions will likely be necessary. We identify the parameters that determine which interventions work best under certain linguistic and societal circumstances. In this way the efficacy of interventions of various types can be identified and predicted. Although there are qualitative arguments for these parameter values (e.g., the responsiveness of children to learning a language as a function of the proportion of conversations heard in that language, the relative importance of conversations heard in the family and elsewhere, and the amplification of spoken to heard conversations of the high-status language because of the media), extensive quantitative data are lacking in this field. We propose a way to measure these parameters, allowing our model, as well as others models in the field, to be validated.

Menzerath-Altmann law is a general law of human language stating, for instance, that the longer a word, the shorter its syllables. With the metaphor that genomes are words and chromosomes are syllables, we examine if genomes also obey the law. We find that longer genomes tend to be made of smaller chromosomes in organisms from three different kingdoms: fungi, plants, and animals. Our findings suggest that genomes self-organize under principles similar to those of human language.

Human language is the key evolutionary innovation that makes humans different from other species. And yet, the fabric of language is tangled and all levels of description (from semantics to syntax) involve multiple layers of complexity. Recent work indicates that the global traits displayed by such levels can be analyzed in terms of networks of connected words. Here, we review the state of the art on language webs and their potential relevance to cognitive science. The emergence of syntax through language acquisition is used as a case study to illustrate how the approach can shed light into relevant questions concerning language organization and its evolution.

Is language a species-specific feature that distinguishes humans from other animals? While monkey and ape calls carry rich information that is potentially available to listeners, callers have little voluntary control over the structure of their calls and are hence unable to use ...

There are approximately 7000 languages spoken in the world today. This diversity reflects the legacy of thousands of years of cultural evolution. How far back we can trace this history depends largely on the rate at which the different components of language evolve. Rates of lexical evolution are widely thought to impose an upper limit of 6000-10 000 years on reliably identifying language relationships. In contrast, it has been argued that certain structural elements of language are much more stable. Just as biologists use highly conserved genes to uncover the deepest branches in the tree of life, highly stable linguistic features hold the promise of identifying deep relationships between the world's languages. Here, we present the first global network of languages based on this typological information. We evaluate the relative evolutionary rates of both typological and lexical features in the Austronesian and Indo-European language families. The first indications are that typological features evolve at similar rates to basic vocabulary but their evolution is substantially less tree-like. Our results suggest that, while rates of vocabulary change are correlated between the two language families, the rates of evolution of typological features and structural subtypes show no consistent relationship across families.

Scientists studying how languages change over time often make an analogy between biological and cultural evolution, with words or grammars behaving like traits subject to natural selection. Recent work has exploited this analogy by using models of biological evolution to explain the properties of languages and other cultural artefacts. However, the mechanisms of biological and cultural evolution are very different: biological traits are passed between generations by genes, while languages and concepts are transmitted through learning. Here we show that these different mechanisms can have the same results, demonstrating that the transmission of frequency distributions over variants of linguistic forms by Bayesian learners is equivalent to the Wright ``Fisher model of genetic drift. This simple learning mechanism thus provides a justification for the use of models of genetic drift in studying language evolution. In addition to providing an explicit connection between biological and cultural evolution, this allows us to define a neutral model that indicates how languages can change in the absence of selection at the level of linguistic variants. We demonstrate that this neutral model can account for three phenomena: the s-shaped curve of language change, the distribution of word frequencies, and the relationship between word frequencies and extinction rates.

To investigate the feasibility of using complex networks in the study of linguistic typology, this paper builds and explores 15 linguistic complex networks based on the dependency syntactic treebanks of 15 languages. The results show that it is possible to classify human languages by means of the following main parameters of complex networks: (a) average degree of the node, (b) cluster coefficients, (c) average path length, (d) network centralization, (e) diameter, (f) power exponent of degree distribution, and (g) the determination coefficient of power law distributions. The precision of this method is similar to the results achieved by means of modern word order typology. This paper tries to solve two problems of current linguistic typology. First, the language sample of a typological study is not real text; second, typological studies pay too much attention to local language structures in the course of choosing typological parameters. This study performs better in global typological features of language and not only enhances typological methods, but it is also valuable for developing the applications of complex networks in the humanities, social, and life sciences.

In this paper, we describe a digital scenario where we simulated the emergence of self-organized symbol-based communication among artificial creatures inhabiting a virtual world of unpredictable predatory events. In our experiment, creatures are autonomous agents that learn symbolic relations in an unsupervised manner, with no explicit feedback, and are able to engage in dynamical and autonomous communicative interactions with other creatures, even simultaneously. In order to synthesize a behavioral ecology and infer the minimum organizational constraints for the design of our creatures, we examined the well-studied case of communication in vervet monkeys. Our results show that the creatures, assuming the role of sign users and learners, behave collectively as a complex adaptive system, where self-organized communicative interactions play a major role in the emergence of symbol-based communication. We also strive in this paper for a careful use of the theoretical concepts involved, including the concepts of symbol and emergence, and we make use of a multi-level model for explaining the emergence of symbols in semiotic systems as a basis for the interpretation of inter-level relationships in the semiotic processes we are studying.

We simulate the evolution of a domain vocabulary in small communities. Empirical data show that human communicators can evolve graphical languages quickly in a constrained task (Pictionary), and that communities converge towards a common language. We propose that simulations of such cultural evolution incorporate properties of human memory (cue-based retrieval, learning, decay). A cognitive model is described that encodes abstract concepts with small sets of concrete, related concepts (directing), and that also decodes such signs (matching). Learning captures conventionalized signs. Relatedness of concepts is characterized by a mixture of shared and individual knowledge, which we sample from a text corpus. Simulations show vocabulary convergence of agent communities of varied structure, but idiosyncrasy in vocabularies of each dyad of models. Convergence is weakened when agents do not alternate between encoding and decoding, predicting the necessity of bi-directional communication. Convergence is improved by explicit feedback about communicative success. We hypothesize that humans seek out subtle clues to gauge success in order to guide their vocabulary acquisition.

A naive observer would be forgiven for assuming that the field of language evolution would, in terms of its scope and methodologies, look much like the field of evolutionary psychology, but with a particular emphasis on language. However, this is not the case. This editorial outlines some reasons why such a research agenda has not so far been pursued in any large-scale or systematic way, and briefly discusses one foundational aspect of that agenda, the question of evolutionary function. This background provides context for an introduction of the articles that appear in this special issue on the evolution of language.

Darwin saw similarities between the evolution of species and the evolution of languages, and it is now widely accepted that similarities between related languages can often be interpreted in terms of a bifurcating descent history ('phylogenesis'). Such interpretations are supported when the distributions of shared and unshared traits (for example, in terms of etymological roots for elements of basic vocabulary) are analysed using tree-building techniques and found to be well-explained by a phylogenetic model. In this article, we question the demographic assumption which is sometimes made when a tree-building approach has been taken to a set of cultures or languages, namely that the resulting tree is also representative of a bifurcating population history. Using historical census data relating to Gaelic- and English-speaking inhabitants of Sutherland (Highland Scotland), we have explored the dynamics of language death due to language shift, representing the extreme case of lack of congruence between the genetic and the culture-historical processes. Such cases highlight the important role of selective cultural migration (or shifting between branches) in determining the extinction rates of different languages on such trees.

Semantic maps have offered linguists an appealing and empirically rooted methodology for describing recurrent structural patterns in language development and the multifunctionality of grammatical categories. Although some researchers argue that semantic maps are universal and given, others provide evidence that there are no fixed or universal maps. This paper takes the position that semantic maps are a useful way to visualize the grammatical evolution of a language (particularly the evolution of semantic structuring) but that this grammatical evolution is a consequence of distributed processes whereby language users shape and reshape their language. So it is a challenge to find out what these processes are and whether they indeed generate the kind of semantic maps observed for human languages. This work takes a design stance towards the question of the emergence of linguistic structure and investigates how grammar can be formed in populations of autonomous artificial agents that play language games with each other about situations they perceive through a sensori-motor embodiment. The experiments reported here investigate whether semantic maps for case markers could emerge through grammaticalization processes without the need for a universal conceptual space.

Child language acquisition, one of Nature's most fascinating phenomena, is to a large extent still a puzzle. Experimental evidence seems to support the view that early language is highly formulaic, consisting for the most part of frozen items with limited productivity. Fairly ...

A model of language-group interaction as modified by deliberate language acquisition planning (LAP) is given. The behavior of the model while LAP measures are in force (First Regime or Regime 1) is distinguished from the longer term when natural interaction is re-established (Second Regime or Regime 2). Both regimes are subclassified into scenarios according to inherent long-term behavior, and an attempt is made to classify real-world examples of LAP and natural interaction in terms of these. Conclusions are drawn regarding the assumptions of LAP, and the use of the model in the allocation of resources to LAP initiatives.

We use Evolutionary Robotics to design robot controllers in which decision-making mechanisms to switch from solitary to social behavior are integrated with the mechanisms that underpin the sensory-motor repertoire of the robots. In particular, we study the evolution of behavioral and communicative skills in a categorization task. The individual decision-making structures are based on the integration over time of sensory information. The mechanisms for switching from solitary to social behavior and the ways in which the robots can affect each otheras behavior are not predetermined by the experimenter, but are aspects of our model designed by artificial evolution. Our results show that evolved robots manage to cooperate and collectively discriminate between different environments by developing a simple communication protocol based on sound signaling. Communication emerges in the absence of explicit selective pressure coded in the fitness function. The evolution of communication is neither trivial nor obvious; for a meaningful signaling system to evolve, evolution must produce both appropriate signals and appropriate reactions to signals. The use of communication proves to be adaptive for the group, even if, in principle, non-cooperating robots can be equally successful with cooperating robots.

This work investigates the conditions in which a population of embodied agents evolved for the ability to display coordinated/cooperative skills can develop an ability to communicate, whether and to what extent the evolved communication system can complexify during the course of the evolutionary process, and how the characteristics of such communication system varies evolutionarily. The analysis of the obtained results indicates that evolving robots develop a capacity to access/generate information which has a communicative value, an ability to produce different signals encoding useful regularities, and an ability to react appropriately to explicit and implicit signals. The analysis of the obtained results allows us to formulate detailed hypothesis on the evolution of communication for what concern aspects such us: (i) how communication can emerge from a population of initially non-communicating agents, (ii) how communication systems can complexify, (iii) how signals/meanings can originate and how they can be grounded in agentsa sensory-motor states.

Rarely do human behavioral scientists and scholars study language, music, and other forms of communication as strategiesaa means to some end. Some even deny that communication is the primary function of these phenomena. Here we draw upon selections of our earlier work to briefly define the strategy concept and sketch how decision theory, developed to explain the behavior of rational actors, is applied to evolved agents. Communication can then be interpreted as a strategy that advances the afitness interestsa of such agents. When this perspective is applied to agents with conflicts of interest, deception emerges as an important aspect of communication. We briefly review costly signaling, one solution to the problem of honest communication among agents with conflicts of interest. We also explore the subversion of cooperative signals by parasites and by plants defending themselves against herbivores, and we touch on biases in human gossip. Experiments with artificial embodied and communicating agents confirm that when there are conflicts of interest among agents, deception readily evolves. Finally, we consider signaling among super-organisms and the possible implications for understanding human music and language.

In this chapter we explore several language games of increasing complexity. We first consider the so-called Naming Game, possibly the simplest example of the complex processes leading progressively to the establishment of human-like languages. In this framework, a globally shared vocabulary emerges as a result of local adjustments of individual word-meaning association. The emergence of a common vocabulary only represents a first stage while it is interesting to investigate the emergence of higher forms of agreement, e.g., compositionality, categories, syntactic or grammatical structures. As an example in this direction we consider the so-called Category Game. Here one focuses on the process by which a population of individuals manages to categorize a single perceptually continuous channel. The problem of the emergence of a discrete shared set of categories out of a continuous perceptual channel is a notoriously difficult problem relevant for color categorization, vowels formation, etc. The central result here 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.

Statistical physics has proven to be a very fruitful framework to describe phenomena outside the realm of traditional physics. In social phenomena, the basic constituents are not particles but humans and every individual interacts with a limited number of peers, usually negligible compared to the total number of people in the system. In spite of that, human societies are characterized by stunning global regularities that naturally call for a statistical physics approach to social behavior, i.e., the attempt to understand regularities at large scale as collective effects of the interaction among single individuals, considered as relatively simple entities. This is the paradigm of Complex Systems: an assembly of many interacting (and simple) units whose collective behavior is not trivially deducible from the knowledge of the rules governing their mutual interactions. In this chapter we review the main theoretical concepts and tools that physics can borrow to socially-motivated problems. Despite their apparent diversity, most research lines in social dynamics are actually closely connected from the point of view of both the methodologies employed and, more importantly, of the general phenomenological questions, e.g., what are the fundamental interaction mechanisms leading to the emergence of consensus on an issue, a shared culture, a common language or a collective motion?

In this chapter we introduce the area of research that attempts to study the evolution of communication in embodied agents through adaptive techniques, such us artificial evolution. More specifically, we illustrate the theoretical assumptions behind this type of research, we present the methods that can be used to realize embodied and communicating artificial agents, and we discuss the main research challenges and the criteria for evaluating progresses in this field.

The evolution of communication requires the co-evolution of two abilities: the ability to send useful signals and the ability to react appropriately to perceived signals. This fact poses two related but distinct problems, which are often mixed up: (1) the phylogenetic problem regarding how can communication evolve if the two traits that are necessary for its emergence are complementary and seem to require each other for providing reproductive advantages; (2) the adaptive problem regarding how can communication systems that do not advantage both signallers and receivers in the same way emerge, given their altruistic character. Here we clarify the distinction, and provide some insights on how these problems can be solved in both real and artificial systems by reporting experiments on the evolution of artificial agents that have to evolve a simple food-call communication system. Our experiments show that (1) the phylogenetic problem can be solved thanks to the presence of producer biases that make agents spontaneously produce useful signals, an idea that is complementary to the well-known areceiver biasa hypothesis found in the biological literature, and (2) the adaptive problem can be solved by having agents communicate preferentially among kin, as predicted by kin selection theory. We discuss these results with respect to both the scientific understanding of the evolution of communication and the design of embodied and communicating artificial agents.

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

The evolution of human language allowed the efficient propagation of nongenetic information, thus creating a new form of evolutionary change. Language development in children offers the opportunity of exploring the emergence of such complex communication system and provides a window to understanding the transition from protolanguage to language. Here we present the first analysis of the emergence of syntax in terms of complex networks. A previously unreported, sharp transition is shown to occur around two years of age from a (pre-syntactic) tree-like structure to a scale-free, small world syntax network. The observed combinatorial patterns provide valuable data to understand the nature of the cognitive processes involved in the acquisition of syntax, introducing a new ingredient to understand the possible biological endowment of human beings which results in the emergence of complex language. We explore this problem by using a minimal, data-driven model that is able to capture several statistical traits, but some key features related to the emergence of syntactic complexity display important divergences.

In this chapter we summarize the progresses that have recently been made in the study of the emergence of communication in artificial embodied agents along different dimensions, including the understanding of the adaptive roles of communication, the expressive power and organization complexity of signalling systems, the stability, robustness, and evolvability of communication, and the knowledge gain obtained with such models. Finally, we briefly discuss what we think are the most important open challenges for future research in this area.

If artificial organisms are constructed with the goal to better understand the behaviour of real organisms, artificial organisms that resemble human beings should possess a communication system with the same properties of human language. This chapter tries to identify nine such properties and for each of them to describe what has been done and what has to be done. Human language: (1) is made up of signals which are arbitrarily connected to their meanings, (2) has syntax and, more generally, its signals are made up of smaller signals, (3) is culturally transmitted and culturally evolved, (4) is used to communicate with oneself and not only with others, (5) is particularly sophisticated for communicating information about the external environment, (6) uses displaced signals, (7) is intentional and requires recognition of intentions in others, (8) is the product of a complex nervous system, (9) influences human cognition. Communication presupposes a shared worldview which depends on the brain, body, and adaptive pattern of the organisms that want to communicate, and this represents a critical challenge also for communication between robots and us.

This chapter introduces the cultural approach towards the question how a symbolic communication system could form in a population of agents. This approach emphasises the role of communication, high level cognition, and social interaction. The chapter introduces briefly the main challenges for working out this approach and which methods could be used to address these challenges.

This chapter gives an overview of different experiments that have been performed to demonstrate how a symbolic communication system, including its underlying ontology, can arise in situated embodied interactions between autonomous agents. It gives some details of the Grounded Naming Game, which focuses on the formation of a system of proper names, the Spatial Language Game, which focuses on the formation of a lexicon for expressing spatial relations as well as perspective reversal, and an Event Description Game, which concerns the expression of the role of participants in events through an emergent case grammar. For each experiment, details are provided how the symbolic system emerges, how the interaction is grounded in the world through the embodiment of the agent and its sensori-motor processing, and how concepts are formed in tight interaction with the emerging language.

Computational and robotic research into symbolic communication systems requires sophisticated tools. This chapter introduces Babel, a tool framework that has been developed to engage in extensive repeatable multi-agent experiments including experiments with embodied robots. A brief example is presented of how experiments are configured in this framework.

In this chapter I briefly summarize views on adaptation and language, some relevant neurobiological and genetic facts, the presence or absence of recursion in animals, the possible role of genetic assimilation in language evolution, the prerequisites of language and the nature of the human adaptive suite, and the relative merits of proposed evolutionary scenarios for the origin of natural language. I highlight the special difficulty of this last major transition and a possible integrative modelling approach to the problem. Finally, I give a summary showing that the transition from early hominine societies with protolanguage to modern society with language indeed qualifies as a major transition.

Human language is not a monolithic whole. Rather, it is a mosaic of many components, such as the lexicon and syntax, the interactions among which give rise to many linguistic properties and universals. Here, we use a computational model to explore how one such universal, linguistic ambiguity arises and is resolved due to the interaction of the lexicon and syntax during linguistic communications. The simulation results illustrate the extent to which certain kinds of lexical ambiguity can be resolved with the help of syntactic knowledge. This work can inspire researchers to view language as a complex adaptive system, instead of focusing on the properties of individual sub-systems.

This book considers the evolution of the grammatical structure of words in the more general contexts of human evolution and the origins of language. The consensus in many fields is that language is well designed for its purpose, and became so either through natural selection or by virtue of non-biological constraints on how language must be structured. Andrew Carstairs-McCarthy argues that in certain crucial respects language is not optimally designed. This can be seen, he suggests, in the existence of not one but two kinds of grammatical organization - syntax and morphology - and in the morphological and morpho-phonological complexity which leads to numerous departures from the one-form-one-meaning principle.

Table of Contents
1. Design in Language and Design in Biology
2. Why There is Morphology: Traditional Accounts
3. A Cognitive-Articulatory Dilemma
4. Modes of Synonymy Avoidance
5. The Ancestors of Affixes
6. The Ancestors of Stem Alternants
7. Derivation, Compounding, and Lexical Storage
8. Morphological homonymy and Morphological Meanings
9. Conclusions

Language, more than anything else, is what makes us human. It appears that no communication system of equivalent power exists elsewhere in the animal kingdom. Any normal human child will learn a language based on rather sparse data in the surrounding world, while even the brightest chimpanzee, exposed to the same environment, will not. Why not How, and why, did language evolve in our species and not in others Since Darwin's theory of evolution, questions about the origin of language have generated a rapidly-growing scientific literature, stretched across a number of disciplines, much of it directed at specialist audiences. The diversity of perspectives from linguistics, anthropology, speech science, genetics, neuroscience and evolutionary biology can be bewildering. Tecumseh Fitch cuts through this vast literature, bringing together its most important insights to explore one of the biggest unsolved puzzles of human history.

- Explores a fascinating puzzle
- how did we humans develop the ability to speak?
- Unlike previous books, it combines insights from many different disciplines
- A useful glossary of terms helps readers from all backgrounds understand the concepts

Contents
Introduction; Part I. The Lay of the Land: 1. Language from a biological perspective; 2. Evolution; 3. Language; 4. Animal cognition and communication; Part II. Meet the Ancestors: 5. Meet the ancestors; 6. The last common ancestor; 7. The hominid fossil record; Part III. The Evolution of Speech: 8. The evolution of the human vocal tract; 9. The evolution of vocal control; 10. Modelling the evolution of speech; Part IV. Phylogenetic Models of Language Evolution: 11. Language evolution before Darwin; 12. Lexical protolanguage; 13. Gestural protolanguage; 14. Musical protolanguage; 15. Conclusions & prospects.

This field of research examines how embodied and situated agents, such as robots, evolve language and thus communicate with each other. This book is a comprehensive survey of the research in this emerging field.

The contributions explain the theoretical and methodological foundations of the field, and then illustrate the scientific and technological potentials and promising research directions. The book also provides descriptions of research experiments and related open software and hardware tools, allowing the reader to gain a practical knowledge of the topic.

The book will be of interest to scientists and undergraduate and graduate students in the areas of cognition, artificial life, artificial intelligence and linguistics.