Language Evolution and Computation Bibliography

We study the dynamics of the Naming Game as an opinion formation model on social networks. This agent-based model captures the essential features of the agreement dynamics by means of a memory-based negotiation process. Our study focuses on the impact of dominance of certain opinions over others in pursuit of faster agreement on social networks. We propose two models to incorporate dominance of the opinions. We observe that both these models lead to faster agreement among the agents on an opinion as compared to the base case reported in the literature. We perform extensive simulations on computer-generated networks as well as on a real online social network (Facebook) and in both cases the dominance based models converge significantly faster than the base case.

In the real world we observe a proliferation of regional dialects and jargons. Most of the research on naming conventions focuses on how to explain the process that allows a single naming convention to establish itself. This paper presents a different approach that aims to investigate why different conventions may emerge and co-exist for a certain amount of time. The naming game is an abstraction of lexical acquisition dynamics, in which n agents try to find an agreement on the names to give to objects. To understand how different heterogeneous conventions emerge, I discuss a naming game model that takes into account experimental data on human and animal learning.

Based on three evolutionary computational models that respectively simulate lexical, categorical and syntactic evolutions, we explore the effect of power-law distributed social popularity on language origin and change. Simulation results reveal a critical scaling degree (λ ≈ 1.0) in power-law distributions that helps accelerate the diffusion of linguistic conventions and preserve high linguistic understandability in population. Other scaling degrees (λ = 0.0 or λ >; 1.0), however, tend to delay such diffusion process and affect linguistic understandability. Apart from the conventionalization nature of language communications in these models, increase in population size could also contribute to select the critical scaling degree, since this scaling degree can accommodate the influence of population size on linguistic understandability and many power-laws in real-world systems have their scaling degrees around this critical value.

There are two competing hypotheses for the origin of the Indo-European language family. The conventional view places the homeland in the Pontic steppes about 6000 years ago. An alternative hypothesis claims that the languages spread from Anatolia with the expansion of farming 8000 to 9500 years ago. We used Bayesian phylogeographic approaches, together with basic vocabulary data from 103 ancient and contemporary Indo-European languages, to explicitly model the expansion of the family and test these hypotheses. We found decisive support for an Anatolian origin over a steppe origin. Both the inferred timing and root location of the Indo-European language trees fit with an agricultural expansion from Anatolia beginning 8000 to 9500 years ago. These results highlight the critical role that phylogeographic inference can play in resolving debates about human prehistory.

One of the most astonishing features of human language is its capacity to convey information efficiently in context. Many theories provide informal accounts of communicative inference, yet there have been few successes in making precise, quantitative predictions about pragmatic reasoning. We examined judgments about simple referential communication games, modeling behavior in these games by assuming that speakers attempt to be informative and that listeners use Bayesian inference to recover speakers intended referents. Our model provides a close, parameter-free fit to human judgments, suggesting that the use of information-theoretic tools to predict pragmatic reasoning may lead to more effective formal models of communication.

Languages vary in their systems of kinship categories, but the scope of possible variation appears to be constrained. Previous accounts of kin classification have often emphasized constraints that are specific to the domain of kinship and are not derived from general principles. Here, we propose an account that is founded on two domain-general principles: Good systems of categories are simple, and they enable informative communication. We show computationally that kin classification systems in the world's languages achieve a near-optimal trade-off between these two competing principles. We also show that our account explains several specific constraints on kin classification proposed previously. Because the principles of simplicity and informativeness are also relevant to other semantic domains, the trade-off between them may provide a domain-general foundation for variation in category systems across languages.

Recently, researchers studying the origins of language have gone into the field to study how songbirds learn to sing and into nurseries to observe the vocalizations and gestures of children for hints of how language may have emerged. Now researchers are testing their hypotheses under experimental conditions. The experiments, observations, and even some theorizing were on the agenda at the Evolang9 conference in Kyoto and a follow-up forum in Tokyo last month. By design, these meetings bring diverse views together to unravel questions not likely to be answered by work within one discipline.

Languages of the world display many structural similarities. We test the hypothesis that some of these structural properties may arise from biases operating during language acquisition that shape languages over time. Specifically, we investigate whether language learners are biased toward linguistic systems that strike an efficient balance between robust information transfer, on the one hand, and effort or resource demands, on the other hand, thereby increasing the communicative utility of the acquired language. In two experiments, we expose learners to miniature artificial languages designed in such a way that they do not use their formal devices (case marking) efficiently to facilitate robust information transfer. We find that learners restructure such languages in ways that facilitate efficient information transfer compared with the input language. These systematic changes introduced by the learners follow typologically frequent patterns, supporting the hypothesis that some of the structural similarities found in natural languages are shaped by biases toward communicatively efficient linguistic systems.

Cognition materializes in an interpersonal space. The emergence of complex behaviors requires the coordination of actions among individuals according to a shared set of rules. Despite the central role of other individuals in shaping one's mind, most cognitive studies focus on processes that occur within a single individual. We call for a shift from a single-brain to a multi-brain frame of reference. We argue that in many cases the neural processes in one brain are coupled to the neural processes in another brain via the transmission of a signal through the environment. Brain-to-brain coupling constrains and shapes the actions of each individual in a social network, leading to complex joint behaviors that could not have emerged in isolation.

We used individual-based stochastic models to examine how social structure influences the diversity of socially learned behaviour within a non-human population. For continuous behavioural variables we modelled three forms of dyadic social learning, averaging the behavioural value of the two individuals, random transfer of information from one individual to the other, and directional transfer from the individual with highest behavioural value to the other. Learning had potential error. We also examined the transfer of categorical behaviour between individuals with random directionality and two forms of error, the adoption of a randomly chosen existing behavioural category or the innovation of a new type of behaviour. In populations without social structuring the diversity of culturally transmitted behaviour increased with learning error and population size. When the populations were structured socially either by making individuals members of permanent social units or by giving them overlapping ranges, behavioural diversity increased with network modularity under all scenarios, although the proportional increase varied considerably between continuous and categorical behaviour, with transmission mechanism, and population size. Although functions of the form e(c)¹(m)⁻(c)² + (c)³(Log(N)) predicted the mean increase in diversity with modularity (m) and population size (N), behavioural diversity could be highly unpredictable both between simulations with the same set of parameters, and within runs. Errors in social learning and social structuring generally promote behavioural diversity. Consequently, social learning may be considered to produce culture in populations whose social structure is sufficiently modular.

Humans are animals that specialize in thinking and knowing, and our extraordinary cognitive abilities have transformed every aspect of our lives. In contrast to our chimpanzee cousins and Stone Age ancestors, we are complex political, economic, scientific and artistic creatures, living in a vast range of habitats, many of which are our own creation. Research on the evolution of human cognition asks what types of thinking make us such peculiar animals, and how they have been generated by evolutionary processes. New research in this field looks deeper into the evolutionary history of human cognition, and adopts a more multi-disciplinary approach than earlier ‘Evolutionary Psychology’. It is informed by comparisons between humans and a range of primate and non-primate species, and integrates findings from anthropology, archaeology, economics, evolutionary biology, neuroscience, philosophy and psychology. Using these methods, recent research reveals profound commonalities, as well striking differences, between human and non-human minds, and suggests that the evolution of human cognition has been much more gradual and incremental than previously assumed. It accords crucial roles to cultural evolution, techno-social co-evolution and gene–culture co-evolution. These have produced domain-general developmental processes with extraordinary power—power that makes human cognition, and human lives, unique.

Abstract The Paridae family (chickadees, tits and titmice) is an interesting avian group in that species vary in important aspects of their social structure and many species have large and complex vocal repertoires. For this reason, parids represent an important set of species for ...

The movements we make with our hands both reflect our mental processes and help to shape them. Our actions and gestures can affect our mental representations of actions and objects. In this paper, we explore the relationship between action, gesture and thought in both humans and non-human primates and discuss its role in the evolution of language. Human gesture (specifically representational gesture) may provide a unique link between action and mental representation. It is kinaesthetically close to action and is, at the same time, symbolic. Non-human primates use gesture frequently to communicate, and do so flexibly. However, their gestures mainly resemble incomplete actions and lack the representational elements that characterize much of human gesture. Differences in the mirror neuron system provide a potential explanation for non-human primates' lack of representational gestures; the monkey mirror system does not respond to representational gestures, while the human system does. In humans, gesture grounds mental representation in action, but there is no evidence for this link in other primates. We argue that gesture played an important role in the transition to symbolic thought and language in human evolution, following a cognitive leap that allowed gesture to incorporate representational elements.

Artificial grammar learning (AGL) provides a useful tool for exploring rule learning strategies linked to general purpose pattern perception. To be able to directly compare performance of humans with other species with different memory capacities, we developed an AGL task in the visual domain. Presenting entire visual patterns simultaneously instead of sequentially minimizes the amount of required working memory. This approach allowed us to evaluate performance levels of two bird species, kea (Nestor notabilis) and pigeons (Columba livia), in direct comparison to human participants. After being trained to discriminate between two types of visual patterns generated by rules at different levels of computational complexity and presented on a computer screen, birds and humans received further training with a series of novel stimuli that followed the same rules, but differed in various visual features from the training stimuli. Most avian and all human subjects continued to perform well above chance during this initial generalization phase, suggesting that they were able to generalize learned rules to novel stimuli. However, detailed testing with stimuli that violated the intended rules regarding the exact number of stimulus elements indicates that neither bird species was able to successfully acquire the intended pattern rule. Our data suggest that, in contrast to humans, these birds were unable to master a simple rule above the finite-state level, even with simultaneous item presentation and despite intensive training.

This is a reply to commentaries on a target article in this volume reviewing models for the cultural evolution of language. Many commentaries amplify positions taken in this article but they also cover novel issues in social evolution and biological evolution, which are briefly ...

The recent conceptual achievement that the cortical motor system plays a crucial role not only in motor control but also in higher cognitive functions has given a new perspective also on the involvement of motor cortex in language perception and production. In particular, there is evidence that the matching mechanism based on mirror neurons can be involved in both pho-nological recognition and retrieval of meaning, especially for action word categories, thus suggesting a contribution of an action–perception mechanism to the automatic comprehension of semantics. Furthermore, a compari-son of the anatomo-functional properties of the frontal motor cortex among different primates and their communicative modalities indicates that the combination of the voluntary control of the gestural communication systems and of the vocal apparatus has been the critical factor in the transition from a gestural-based communication into a predominantly speech-based system. Finally, considering that the monkey and human premotor-parietal motor system, plus the prefrontal cortex, are involved in the sequential motor organization of actions and in the hierarchical combination of motor elements, we propose that elements of such motor organization have been exploited in other domains, including some aspects of the syntactic structure of language.

We analyze the occurrence frequencies of over 15 million words recorded in millions of books published during the past two centuries in seven different languages. For all languages and chronological subsets of the data we confirm that two scaling regimes characterize the word frequency distributions, with only the more common words obeying the classic Zipf law. Using corpora of unprecedented size, we test the allometric scaling relation between the corpus size and the vocabulary size of growing languages to demonstrate a decreasing marginal need for new words, a feature that is likely related to the underlying correlations between words. We calculate the annual growth fluctuations of word use which has a decreasing trend as the corpus size increases, indicating a slowdown in linguistic evolution following language expansion. This cooling pattern forms the basis of a third statistical regularity, which unlike the Zipf and the Heaps law, is dynamical in nature.

We analyze the dynamic properties of 10,000,000 words recorded in English, Spanish and Hebrew over the period 1800-2008 in order to gain insight into the coevolution of language and culture. We report language independent patterns useful as benchmarks for theoretical models of language evolution. A significantly decreasing (increasing) trend in the birth (death) rate of words indicates a recent shift in the selection laws governing word use. For new words, we observe a peak in the growth-rate fluctuations around 40 years after introduction, consistent with the typical entry time into standard dictionaries and the human generational timescale. Pronounced changes in the dynamics of language during periods of war shows that word correlations, occurring across time and between words, are largely influenced by coevolutionary social, technological, and political factors. We quantify cultural memory by analyzing the long-term correlations in the use of individual words using detrended fluctuation analysis.

In contrast with animal communication systems, diversity is characteristic of almost every aspect of human language. Languages variously employ tones, clicks, or manual signs to signal differences in meaning; some languages lack the noun-verb distinction (e.g., Straits Salish), whereas others have a proliferation of fine-grained syntactic categories (e.g., Tzeltal); and some languages do without morphology (e.g., Mandarin), while others pack a whole sentence into a single word (e.g., Cayuga). A challenge for evolutionary biology is to reconcile the diversity of languages with the high degree of biological uniformity of their speakers. Here, we model processes of language change and geographical dispersion and find a consistent pressure for flexible learning, irrespective of the language being spoken. This pressure arises because flexible learners can best cope with the observed high rates of linguistic change associated with divergent cultural evolution following human migration. Thus, rather than genetic adaptations for specific aspects of language, such as recursion, the coevolution of genes and fast-changing linguistic structure provides the biological basis for linguistic diversity. Only biological adaptations for flexible learning combined with cultural evolution can explain how each child has the potential to learn any human language.

Language is the best example of a cultural evolutionary system, able to retain a phylogenetic signal over many thousands of years. The temporal stability (conservatism) of basic vocabulary is relatively well understood, but the stability of the structural properties of language (phonology, morphology, syntax) is still unclear. Here we report an extensive Bayesian phylogenetic investigation of the structural stability of numerous features across many language families and we introduce a novel method for analyzing the relationships between the stability profiles of language families. We found that there is a strong universal component across language families, suggesting the existence of universal linguistic, cognitive and genetic constraints. Against this background, however, each language family has a distinct stability profile, and these profiles cluster by geographic area and likely deep genealogical relationships. These stability profiles seem to show, for example, the ancient historical relationships between the Siberian and American language families, presumed to be separated by at least 12,000 years, and possible connections between the Eurasian families. We also found preliminary support for the punctuated evolution of structural features of language across families, types of features and geographic areas. Thus, such higher-level properties of language seen as an evolutionary system might allow the investigation of ancient connections between languages and shed light on the peopling of the world.

Language change takes place primarily via diffusion of linguistic variants in a population of individuals. Identifying selective pressures on this process is important not only to construe and predict changes, but also to inform theories of evolutionary dynamics of socio-cultural factors. In this paper, we advocate the Price equation from evolutionary biology and the Polya-urn dynamics from contagion studies as efficient ways to discover selective pressures. Using the Price equation to process the simulation results of a computer model that follows the Polya-urn dynamics, we analyze theoretically a variety of factors that could affect language change, including variant prestige, transmission error, individual influence and preference, and social structure. Among these factors, variant prestige is identified as the sole selective pressure, whereas others help modulate the degree of diffusion only if variant prestige is involved. This multidisciplinary study discerns the primary and complementary roles of linguistic, individual learning, and socio-cultural factors in language change, and offers insight into empirical studies of language change.

The warp ikat method of making decorated textiles is one of the most geographically widespread in southeast Asia, being used by Austronesian peoples in Indonesia, Malaysia and the Philippines, and Daic peoples on the Asian mainland. In this study a dataset consisting of the decorative characters of 36 of these warp ikat weaving traditions is investigated using Bayesian and Neighbornet techniques, and the results are used to construct a phylogenetic tree and taxonomy for warp ikat weaving in southeast Asia. The results and analysis show that these diverse traditions have a common ancestor amongst neolithic cultures the Asian mainland, and parallels exist between the patterns of textile weaving descent and linguistic phylogeny for the Austronesian group. Ancestral state analysis is used to reconstruct some of the features of the ancestral weaving tradition. The widely held theory that weaving motifs originated in the late Bronze Age Dong-Son culture is shown to be inconsistent with the data.

The advent of humanoid robots has enabled a new approach to investigating the acquisition of language, and we report on the development of robots able to acquire rudimentary linguistic skills. Our work focuses on early stages analogous to some characteristics of a human child of about 6 to 14 months, the transition from babbling to first word forms. We investigate one mechanism among many that may contribute to this process, a key factor being the sensitivity of learners to the statistical distribution of linguistic elements. As well as being necessary for learning word meanings, the acquisition of anchor word forms facilitates the segmentation of an acoustic stream through other mechanisms. In our experiments some salient one-syllable word forms are learnt by a humanoid robot in real-time interactions with naive participants. Words emerge from random syllabic babble through a learning process based on a dialogue between the robot and the human participant, whose speech is perceived by the robot as a stream of phonemes. Numerous ways of representing the speech as syllabic segments are possible. Furthermore, the pronunciation of many words in spontaneous speech is variable. However, in line with research elsewhere, we observe that salient content words are more likely than function words to have consistent canonical representations; thus their relative frequency increases, as does their influence on the learner. Variable pronunciation may contribute to early word form acquisition. The importance of contingent interaction in real-time between teacher and learner is reflected by a reinforcement process, with variable success. The examination of individual cases may be more informative than group results. Nevertheless, word forms are usually produced by the robot after a few minutes of dialogue, employing a simple, real-time, frequency dependent mechanism. This work shows the potential of human-robot interaction systems in studies of the dynamics of early language acquisition.

Many patterns displayed by the distribution of human linguistic groups are similar to the ecological organization described for biological species. It remains a challenge to identify simple and meaningful processes that describe these patterns. The population size distribution of human linguistic groups, for example, is well fitted by a log-normal distribution that may arise from stochastic demographic processes. As we show in this contribution, the distribution of the area size of home ranges of those groups also agrees with a log-normal function. Further, size and area are significantly correlated: the number of speakers and the area spanned by linguistic groups follow the allometric relation , with an exponent varying accross different world regions. The empirical evidence presented leads to the hypothesis that the distributions of and , and their mutual dependence, rely on demographic dynamics and on the result of conflicts over territory due to group growth. To substantiate this point, we introduce a two-variable stochastic multiplicative model whose analytical solution recovers the empirical observations. Applied to different world regions, the model reveals that the retreat in home range is sublinear with respect to the decrease in population size, and that the population-area exponent grows with the typical strength of conflicts. While the shape of the population size and area distributions, and their allometric relation, seem unavoidable outcomes of demography and inter-group contact, the precise value of could give insight on the cultural organization of those human groups in the last thousand years.

Language is a key adaptation of our species, yet we do not know when it evolved. Here, we use data on language phonemic diversity to estimate a minimum date for the origin of language. We take advantage of the fact that phonemic diversity evolves slowly and use it as a clock to calculate how long the oldest African languages would have to have been around in order to accumulate the number of phonemes they possess today. We use a natural experiment, the colonization of Southeast Asia and Andaman Islands, to estimate the rate at which phonemic diversity increases through time. Using this rate, we estimate that present-day languages date back to the Middle Stone Age in Africa. Our analysis is consistent with the archaeological evidence suggesting that complex human behavior evolved during the Middle Stone Age in Africa, and does not support the view that language is a recent adaptation that has sparked the dispersal of humans out of Africa. While some of our assumptions require testing and our results rely at present on a single case-study, our analysis constitutes the first estimate of when language evolved that is directly based on linguistic data.

Background

Recent advances in automated assessment of basic vocabulary lists allow the construction of linguistic phylogenies useful for tracing dynamics of human population expansions, reconstructing ancestral cultures, and modeling transition rates of cultural traits over time.

Methods

Here we investigate the Tupi expansion, a widely-dispersed language family in lowland South America, with a distance-based phylogeny based on 40-word vocabulary lists from 48 languages. We coded 11 cultural traits across the diverse Tupi family including traditional warfare patterns, post-marital residence, corporate structure, community size, paternity beliefs, sibling terminology, presence of canoes, tattooing, shamanism, men's houses, and lip plugs.

Results/Discussion

The linguistic phylogeny supports a Tupi homeland in west-central Brazil with subsequent major expansions across much of lowland South America. Consistently, ancestral reconstructions of cultural traits over the linguistic phylogeny suggest that social complexity has tended to decline through time, most notably in the independent emergence of several nomadic hunter-gatherer societies. Estimated rates of cultural change across the Tupi expansion are on the order of only a few changes per 10,000 years, in accord with previous cultural phylogenetic results in other language families around the world, and indicate a conservative nature to much of human culture.

This article adopts the category game model, which simulates the origins and evolution of linguistic categories in a group of artificial agents, to evaluate the effect of social structure on linguistic categorization. Based on the simulation results in a number of typical networks, we examine the isolating and collective effects of some structural features, including average degree, shortcuts, and level of centrality, on the categorization process. This study extends the previous simulations mainly on lexical evolution, and illustrates a general framework to systematically explore the effect of social structure on language evolution.

Language in social media is rich with linguistic innovations, most strikingly in the new words and spellings that constantly enter the lexicon. Despite assertions about the power of social media to connect people across the world, we find that many of these neologisms are restricted to geographically compact areas. Even for words that become ubiquituous, their growth in popularity is often geographical, spreading from city to city. Thus, social media text offers a unique opportunity to study the diffusion of lexical change. In this paper, we show how an autoregressive model of word frequencies in social media can be used to induce a network of linguistic influence between American cities. By comparing the induced network with the geographical and demographic characteristics of each city, we can measure the factors that drive the spread of lexical innovation.

Dynamics of average length of words in Russian and English is analysed in the article. Words belonging to the diachronic text corpus Google Books Ngram and dated back to the last two centuries are studied. It was found out that average word length slightly increased in the 19th century, and then it was growing rapidly most of the 20th century and started decreasing over the period from the end of the 20th - to the beginning of the 21th century. Words which contributed mostly to increase or decrease of word average length were identified. At that, content words and functional words are analysed separately. Long content words contribute mostly to word average length of word. As it was shown, these words reflect the main tendencies of social development and thus, are used frequently. Change of frequency of personal pronouns also contributes significantly to change of average word length. The other parameters connected with average length of word were also analysed.

Complex acoustic signals in many animal species are characterized by a syntax that governs how different notes are combined, but the importance of syntax to the communicative function of signals is not well understood. Mated pairs of yellow-naped amazons, Amazona auropalliata, produce coordinated vocal duets that are used for territory maintenance and defense. These duets follow rules that specify the ordering of notes within duets, such as a strict alternation of sex-specific notes and a defined progression of note types through each duet. These syntactical rules may function to define sex-specific roles, improve coordination, and allow individuals to combine calls into meaningful sequences. As a first step toward understanding the functional significance of syntax, we conducted two separate audio playback experiments in which we presented nesting pairs with normal duets and duets with broken syntax (i.e., one of the syntactic rules was broken). In Experiment One, we reversed the order of female and male notes within note pairs while retaining the typical progression of note types through a duet. In Experiment Two we reversed the order of note types across a whole duet while retaining the typical female–male ordering within note pairs. We hypothesized that duets with broken syntax would be less-effective signals than duets with normal syntax and predicted that pairs would respond less to broken syntax than to normal duets. Contrary to predictions, we did not observe differences in response between treatments for any variables except latency to approach the speaker. After we combined data across experiments post hoc, we observed longer latencies to approach the speakers after playbacks of broken syntax duets, suggesting that pairs could differentiate between playbacks. These responses suggest that breaking one rule of duet syntax at a time does not result in detectable loss of signal efficacy in the context of territorial intrusions.

Cross-situational learning has recently gained attention as a plausible candidate for the mechanism that underlies the learning of word-meaning mappings. In a recent study, Blythe and colleagues have studied how many trials are theoretically required to learn a human-sized lexicon using cross-situational learning. They show that the level of referential uncertainty exposed to learners could be relatively large. However, one of the assumptions they made in designing their mathematical model is questionable. Although they rightfully assumed that words are distributed according to Zipf's law, they applied a uniform distribution of meanings. In this article, Zipf's law is also applied to the distribution of meanings, and it is shown that under this condition, cross-situational learning can only be plausible when referential uncertainty is sufficiently small. It is concluded that cross-situational learning is a plausible learning mechanism but needs to be guided by heuristics that aid word learners with reducing referential uncertainty.

In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language-learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners’ input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized learning biases. The test case is an experiment (Culbertson, Smolensky, & Legendre, 2012) targeting the learning of word-order patterns in the nominal domain. The model identifies internal biases of the experimental participants, providing evidence that learners impose (possibly arbitrary) properties on the grammars they learn, potentially resulting in the cross-linguistic regularities known as typological universals. Learners exposed to mixtures of artificial grammars tended to shift those mixtures in certain ways rather than others; the model reveals how learners’ inferences are systematically affected by specific prior biases. These biases are in line with a typological generalization—Greenberg's Universal 18—which bans a particular word-order pattern relating nouns, adjectives, and numerals.

We investigate the dynamics of a broad class of stochastic copying processes on a network that includes examples from population genetics (spatially structured Wright-Fisher models), ecology (Hubbell-type models), linguistics (the utterance selection model), and opinion dynamics (the voter model) as special cases. These models all have absorbing states of fixation where all the nodes are in the same state. Earlier studies of these models showed that the mean time when this occurs can be made to grow as different powers of the network size by varying the degree distribution of the network. Here we demonstrate that this effect can also arise if one varies the asymmetry of the copying dynamics while holding the degree distribution constant. In particular, we show that the mean time to fixation can be accelerated even on homogeneous networks when certain nodes are very much more likely to be copied from than copied to. We further show that there is a complex interplay between degree distribution and asymmetry when they may covary, and that the results are robust to correlations in the network or the initial condition.

For 20 years many authors have attempted to model language competition. Some models involve two different languages, others include also a bilingual population. The issues are to understand one language extinction or to determine in which parameter range coexistence is possible. A key parameter is the prestige of one language compare to the other. If this parameter remains constant, coexistence is not sustainable. However, prestige may vary with time. In this article, thanks to the viability theory concepts and tools, we study a set of prestige variations which would allow language coexistence in presence of a bilingual population. Among this set, we emphasise slow viable evolutions with the lowest prestige variations that guarantee coexistence.

Thirty authors of different disciplines, ranging from cognitive science and linguistics to mathematics and physics, address the topic of language origin and evolution. Language dynamics is investigated through an interdisciplinary effort, involving field and synthetic experiments, modelling and comparison of the theoretical predictions with empirical data. The result consists in new insights that significantly contribute to the ongoing debate on the origin and the evolution of language. In this Topical Issue the state of the art of this novel and fertile approach is reported by major experts of the field.

The problem of how young learners acquire the meaning of words is fundamental to language development and cognition. A host of computational models exist which demonstrate various mechanisms in which words and their meanings can be transferred between a teacher and learner. However these models often assume that the learner can easily distinguish between the referents of words, and do not show if the learning mechanisms still function when there is perceptual ambiguity about the referent of a word. This paper presents two models that acquire meaning-word mappings in a continuous semantic space. The first model is a cross-situational learning model in which the learner induces word-meaning mappings through statistical learning from repeated exposures. The second model is a social model, in which the learner and teacher engage in a dyadic learning interaction to transfer word-meaning mappings. We show how cross-situational learning, despite there being no information to the learner as to the exact referent of a word during learning, still can learn successfully. However, social learning outperforms cross-situational strategies both in speed of acquisition and performance. The results suggest that cross-situational learning is efficient for situations where referential ambiguity is limited, but in more complex situations social learning is the more optimal strategy.

We review the task of aligning simple models for language dynamics with relevant empirical data, motivated by the fact that this is rarely attempted in practice despite an abundance of abstract models. We propose that one way to meet this challenge is through the careful construction of null models. We argue in particular that rejection of a null model must have important consequences for theories about language dynamics if modeling is truly to be worthwhile. Our main claim is that the stochastic process of neutral evolution (also known as genetic drift or random copying) is a viable null model for language dynamics. We survey empirical evidence in favor and against neutral evolution as a mechanism behind historical language changes, highlighting the theoretical implications in each case.

Human language is unparalleled in both its expressive capacity and its diversity. What accounts for the enormous diversity of human languages [13]? Recent evidence suggests that the structure of languages may be shaped by the social and demographic environment in which the languages are learned and used. In an analysis of over 2000 languages Lupyan and Dale [25] demonstrated that socio-demographic variables, such as population size, significantly predicted the complexity of inflectional morphology. Languages spoken by smaller populations tend to employ more complex inflectional systems. Languages spoken by larger populations tend to avoid complex morphological paradigms, employing lexical constructions instead. This relationship may exist because of how language learning takes place in these different social contexts [44, 45]. In a smaller population, a tightly-knit social group combined with exclusive or almost exclusive language acquisition by infants permits accumulation of complex inflectional forms. In larger populations, adult language learning and more extensive cross-group interactions produce pressures that lead to morphological simplification. In the current paper, we explore this learning-based hypothesis in two ways. First, we develop an agent-based simulation that serves as a simple existence proof: As adult interaction increases, languages lose inflections. Second, we carry out a correlational study showing that English-speaking adults who had more interaction with non-native speakers as children showed a relative preference for over-regularized (i.e. morphologically simpler) forms. The results of the simulation and experiment lend support to the linguistic niche hypothesis: Languages may vary in the ways they do in part due to different social environments in which they are learned and used. In short, languages adapt to the learning constraints and biases of their learners.

This paper reviews how the structure of form and meaning spaces influences the nature and the dynamics of the form-meaning mappings in language. In general, in a structured form or meaning space, not all forms and meanings are equivalent: some forms and some meanings are more easily confused with each other than with other forms or meanings. We first give a formalization of this idea, and explore how it influences robust form-meaning mappings. It is shown that some fundamental properties of human language, such as discreteness and combinatorial structure as well as universals of sound systems of human languages follow from optimal communication in structured form and meaning spaces. We also argue that some properties of human language follow less from these fundamental issues, and more from cognitive constraints.

We then show that it is possible to experimentally investigate the relative contribution of functional constraints and of cognitive constraints. We illustrate this with an example of one of our own experiments, in which experimental participants have to learn a set of complex form-meaning mappings that have been produced by a previous generation of participants. Theoretically predicted properties appear in the sets of signals that emerge in this iterated learning experiment.

The recent growth of Experimental Semiotics (ES) offers us a new option to investigate human communication. We briefly introduce ES, presenting results from three themes of research which emerged within it. Then we illustrate the contribution ES can make to the investigation of human communication systems, particularly in comparison with the other existing options. This comparison highlights how ES can provide an engine of discovery for understanding human communication. In fact, in complementing the other options, ES offers us unique opportunities to test assumptions about communicative behavior, both through the experimenters' planned manipulations and through the unexpected behaviors humans exhibit in experimental settings. We provide three examples of such opportunities, one from each of the three research themes we present.

We investigate the directed and weighted complex network of free word associations in which players write a word in response to another word given as input. We analyze in details two large datasets resulting from two very different experiments: On the one hand the massive multiplayer web-based Word Association Game known as Human Brain Cloud, and on the other hand the South Florida Free Association Norms experiment. In both cases, the networks of associations exhibit quite robust properties like the small world property, a slight assortativity and a strong asymmetry between in-degree and out-degree distributions. A particularly interesting result concerns the existence of a characteristic scale for the word association process, arguably related to specific conceptual contexts for each word. After mapping, the Human Brain Cloud network onto the WordNet semantics network, we point out the basic cognitive mechanisms underlying word associations when they are represented as paths in an underlying semantic network. We derive in particular an expression describing the growth of the HBC graph and we highlight the existence of a characteristic scale for the word association process.

The paper investigates the quantitative distribution of language types across languages of the world. The studies are based on three large-scale typological data bases: The World Color Survey, the Automated Similarity Judgment Project data base, and the World Atlas of Language Structures. The main finding is that a surprisingly large and varied collection of linguistic typologies show power law behavior. The bulk of the paper deals with the statistical validation of these findings.

It is widely known that color names across the world's languages tend to be organized into a neat hierarchy with a small set of 'basic names' featuring in a comparatively fixed order across linguistic societies. However, to date, the basic names have only been defined through a set of linguistic principles. There is no statistical definition that quantitatively separates the basic names from the rest of the color words across languages. Here we present a rigorous statistical analysis of the World Color Survey database hosting color word information from 110 non-industrialized languages. The central result is that those names for which a population of individuals show a larger overall agreement across languages turn out to be the basic ones exactly reproducing the color name hierarchy and, thereby, providing, for the first time, an empirical definition of the basic color names.

Linguistic meaning is a convention. This article investigates how such conventions can arise for color categories in populations of simulated 'agents'. The method uses concepts from evolutionary game theory: A language game where agents assign names to color patches and is played repeatedly by members of a population. The evolutionary dynamics employed make minimal assumptions about agents' perceptions and learning processes. Through various simulations it is shown that under different kinds of reasonable conditions involving outcomes of individual games, the evolutionary dynamics push populations to stationary equilibria, which can be interpreted as achieving shared population meaning systems. Optimal population agreement for meaning is characterized through a mathematical formula, and the simulations presented reveal that for a wide variety of situations, optimality is achieved.

In this position paper we discuss how language influences the mind by comparing robots that have language with robots that do not have language. Robots with language respond more adaptively to objects belonging to different categories and requiring different behaviors compared to robots without language, and it is possible to show that categories of objects are represented differently in the neural network which controls the behavior of the two types of robots. By exposing the robots to sounds which co-vary systematically with specific aspects of their experience, the robots can distinguish nouns from verbs and can respond appropriately to simple noun-verb sentences. Robots can also be used to show that, while all animals develop a mental (neural) model of their environment which incorporates the co-variations among different aspects of their experiences, human beings develop a more analytical and modular model because specific sounds co-vary with different aspects of their experiences and this may explain why human beings have a more articulated and creative behavioral repertoire.

During the last decade, much attention has been paid to language competition in the complex systems community, that is, how the fractions of speakers of several competing languages evolve in time. In this paper, we review recent advances in this direction and focus on three aspects. First, we consider the shift from two-state models to three-state models that include the possibility of bilingual individuals. The understanding of the role played by bilingualism is essential in sociolinguistics. In particular, the question addressed is whether bilingualism facilitates the coexistence of languages. Second, we will analyze the effect of social interaction networks and physical barriers. Finally, we will show how to analyze the issue of bilingualism from a game theoretical perspective.

Evolutionary game theory is used to form a finite partition of a continuous hue circle in which perceptually similar hues are each represented by an icon chip and the circle by a finite but game dynamically determined number of icon chips. On the basis of such icon chip structures, a color categorization for both an individual learner and a population of learners is then evolved. These results remove limitations of some particular previous color categorization simulation work which assumed a fixed number of color stimuli and a maximal number of predefined color categories. These simulations are extended to demonstrate that learners need neither to share the same icon chip structures, nor do these structures have to be fully developed for a population of learners to produce a stable color categorization system. Additionally, when a naive learner is introduced into a population with a stable color categorization, the game dynamics result in the learner's adopting the existing categorization. All results are shown to hold while the underlying icon chip structures evolve continuously in response to novel stimuli. The usefulness of the approach as well as some of the potential implications of the results for human learning of color categories are discussed.

configure a pattern on a board to communicate with each other. Distinct from related studies, players in this game have no explicit game scores or tasks to optimize. Any dynamics occurring in this game are therefore ad-hoc and on-going processes. There were three major findings in this paper. (i) The subjects mainly interacted in two modes: a dynamic mode where players proceed through the game without assigning any meanings to the pattern, and a metaphoric mode, where players process with narrative reflection. (ii) Subjects spontaneously switch between the two modes, but this switching is suppressed when playing alone. (iii) A transition diagram of the board pattern can be used to label the two modes, e.g. linearity of the diagram is correlated with the metaphoric mode. One of the main features of grammar is to display subjects' intentionality in a systematic way. We argue that the switching between the two modes observed in our experiment can be taken as a grammatical aspect that emerged in the process. These modes express the speaker's perspective in the same manner as grammatical elements do in natural language. The switching behavior should be seen as a process that embodies a player's intention using the medium (in this case, the patterns in the wall game), and a player's exploration of the medium is a necessary step before generating a grammar structure.

The question how a shared vocabulary can arise in a multi-agent population despite the fact that each agent autonomously invents and acquires words has been solved. The solution is based on alignment: Agents score all associations between words and meanings in their lexicons and update these preference scores based on communicative success. A positive feedback loop between success and use thus arises which causes the spontaneous self-organization of a shared lexicon. The same approach has been proposed for explaining how a population can arrive at a shared grammar, in which we get the same problem of variation because each agent invents and acquires their own grammatical constructions. However, a problem arises if constructions reuse parts that can also exist on their own. This happens particularly when frequent usage patterns, which are based on compositional rules, are stored as such. The problem is how to maintain systematicity. This paper identifies this problem and proposes a solution in the form of multilevel alignment. Multilevel alignment means that the updating of preference scores is not restricted to the constructions that were used in the utterance but also downward and upward in the subsumption hierarchy.

We introduce a theory of sequential causal inference in which learners in a chain estimate a structural model from their upstream “teacher” and then pass samples from the model to their downstream “student”. It extends the population dynamics of genetic drift, recasting Kimura's selectively neutral theory as a special case of a generalized drift process using structured populations with memory. We examine the diffusion and fixation properties of several drift processes and propose applications to learning, inference, and evolution. We also demonstrate how the organization of drift process space controls fidelity, facilitates innovations, and leads to information loss in sequential learning with and without memory.

Joint attention (JA) is important to many social, communicative activities, including language, and humans exhibit a considerably high level of JA compared with non-human primates. We propose a coevolutionary hypothesis to explain this degree-difference in JA: once JA started to aid linguistic comprehension, along with language evolution, communicative success (CS) during cultural transmission could enhance the levels of JA among language users. We illustrate this hypothesis via a multi-agent computational model, where JA boils down to a genetically transmitted ability to obtain non-linguistic cues aiding comprehension. The simulation results and statistical analysis show that: (i) the level of JA is correlated with the understandability of the emergent language; and (ii) CS can boost an initially low level of JA and ratchet it up to a stable high level. This coevolutionary perspective helps explain the degree-difference in many language-related competences between humans and non-human primates, and reflects the importance of biological evolution, individual learning and cultural transmission to language evolution.

Communication involves a pair of behaviours—a signal and a response—that are functionally interdependent. Consequently, the emergence of communication involves a chicken-and-egg problem: if signals and responses are dependent on one another, then how does such a relationship emerge in the first place? The empirical literature suggests two solutions to this problem: ritualization and sensory manipulation; and instances of ritualization appear to be more common. However, it is not clear from a theoretical perspective why this should be the case, nor if there are any other routes to communication. Here, we develop an analytical model to examine how communication can emerge. We show that: (i) a state of non-interaction is evolutionarily stable, and so communication will not necessarily emerge even when it is in both parties' interest; (ii) the conditions for sensory manipulation are more stringent than for ritualization, and hence ritualization is likely to be more common; and (iii) communication can arise by a third route, when the intention to communicate can itself be communicated, but this may be limited to humans. More generally, our results demonstrate the utility of a functional approach to communication.

The expansion of Bantu languages represents one of the most momentous events in the history of Africa. While it is well accepted that Bantu languages spread from their homeland (Cameroon/Nigeria) approximately 5000 years ago (ya), there is no consensus about the timing and geographical routes underlying this expansion. Two main models of Bantu expansion have been suggested: The ‘early-split’ model claims that the most recent ancestor of Eastern languages expanded north of the rainforest towards the Great Lakes region approximately 4000 ya, while the ‘late-split’ model proposes that Eastern languages diversified from Western languages south of the rainforest approximately 2000 ya. Furthermore, it is unclear whether the language dispersal was coupled with the movement of people, raising the question of language shift versus demic diffusion. We use a novel approach taking into account both the spatial and temporal predictions of the two models and formally test these predictions with linguistic and genetic data. Our results show evidence for a demic diffusion in the genetic data, which is confirmed by the correlations between genetic and linguistic distances. While there is little support for the early-split model, the late-split model shows a relatively good fit to the data. Our analyses demonstrate that subsequent contact among languages/populations strongly affected the signal of the initial migration via isolation by distance.

Sharing a public language facilitates particularly efficient forms of joint perception and action by giving interlocutors refined tools for directing attention and aligning conceptual models and action. We hypothesized that interlocutors who flexibly align their linguistic practices ...

Formal models of cultural evolution have illustrated circumstances under which behavioral traits that have no inherent advantage over others can undergo positive selection pressure. One situation in which this may occur is when the behavior functions as a social marker, and there is pressure to identify oneself as a member of a particular group. Our aim in the current study was to determine whether participants organized into subpopulations could effectively exploit variation in a completely novel behavior to advertise themselves as belonging to a particular subpopulation, such that discrimination between in-group and out-group members was possible and subpopulations exhibited increasing distinctiveness. Eighty participants took part, organized into four subpopulations, each composed of five four-member generations. They each completed a tower-building task, used in previous experimental studies of cultural evolution. An incentive payment structure was imposed with the aim of motivating participants to advertise themselves as belonging to a particular subpopulation and to distinguish in-group members from members of other subpopulations. The first generation were exposed to photographs of randomly assigned “seed” towers, and later generations were exposed to photographs of the towers built by the members of the previous generation of their own subpopulation. Participants were able to discriminate towers built by in-group members of the same generation, from towers built by out-group members. Over generations, tower designs evolved such that they were increasingly identifiable as belonging to a particular subpopulation. Arbitrary traits which had no prior advantage became associated with group membership, providing empirical support for theoretical models.

Duality of Patterning, one of Hockett's (1960) proposed design features unique to human language, refers in part to the arrangements of a relatively small stock of distinguishable meaningless sounds which are combined to create a potentially infinite set of morphemes. Literature regarding the emergence of this design feature is less abundant than that exploring other levels of structure as focus is more often given to the emergence of syntax. In an effort to explore where combinatorial structure of meaningless elements arises the results of two pilot experiments are presented within which we observe human participants modifying a small lexicon of visual symbols through a process of iterated learning. As this lexicon evolves there is evidence that it becomes simpler and more learnable, more easily transmitted. I argue that these features are a consequence of spontaneous emergence of combinatorial, sub-lexical structure in the lexicon, that the pattern of emergence is more complex than the most widely espoused explanation suggests, and I propose ways in which future work can build on what we learn from these pilot experiments to confirm this hypothesis.

The concept of duality of patterning (henceforth DP) has recently begun to undergo new scrutiny. In particular, the fact that Al-Sayyid Bedouin Sign Language (ABSL) does not appear to exhibit a layer of meaningless units (Sandler et al. 2011) casts doubt on the universality of DP as a defining feature of natural language. Why, then, do the vast majority of the world's languages exhibit DP? Two hypotheses have been suggested. The first is that DP is a necessary solution to the problem of conveying a large number of meanings; the second is that DP arises as a consequence of conventionalization. We tested these hypotheses in an experimental-semiotics study. Our results supported the hypothesis based on conventionalization but were inconclusive with regard to the hypothesis based on the number of meanings. At the same time, the task of measuring DP in an experimental-semiotics study presented interesting challenges, suggesting that the concept of DP may need some overhauling.

Syntax is widely considered the feature that most decisively sets human language apart from other natural communication systems. Animal vocalisations are generally considered to be holistic with few examples of utterances meaning something other than the sum of their parts. Previously, we have shown that male putty-nosed monkeys produce call series consisting of two call types in response to different events. They can also be combined into short sequences that convey a different message from those conveyed by either call type alone. Here, we investigate whether ‘pyow–hack’ sequences are compositional in that the individual calls contribute to their overall meaning. However, the monkeys behaved as if they perceived the sequence as an idiomatic expression rather than decoding the sequence. Nonetheless, while this communication system lacks the generative power of syntax it enables callers to increase the number of messages that can be conveyed by a small and innate call repertoire.

When established communication systems cannot be used, people rapidly create novel systems to modify the mental state of another agent according to their intentions. However, there are dramatic inter-individual differences in the implementation of this human competence for communicative innovation. Here we characterize psychological sources of inter-individual variability in the ability to build a shared communication system from scratch. We consider two potential sources of variability in communicative skills. Cognitive traits of two individuals could independently influence their joint ability to establish a communication system. Another possibility is that the overlap between those individual traits influences the communicative performance of a dyad. We assess these possibilities by quantifying the relationship between cognitive traits and behavior of communicating dyads. Cognitive traits were assessed with psychometric scores quantifying cooperative attitudes and fluid intelligence. Competence for implementing successful communicative innovations was assessed by using a non-verbal communicative task. Individual capacities influence communicative success when communicative innovations are generated. Dyadic similarities and individual traits modulate the type of communicative strategy chosen. The ability to establish novel communicative actions was influenced by a combination of the communicator's ability to understand intentions and the addressee's ability to recognize patterns. Communicative pairs with comparable systemizing abilities or behavioral inhibition were more likely to explore the search space of possible communicative strategies by systematically adding new communicative behaviors to those already available. No individual psychometric measure seemed predominantly responsible for communicative success. These findings support the notion that the human ability for fast communicative innovations represents a special type of complex collaborative activity.

In human speech, a finite set of basic sounds is combined into a (potentially) unlimited set of well-formed morphemes. Hockett (1960) placed this phenomenon under the term ‘duality of patterning’ and included it as one of the basic design features of human language. Of the thirteen basic design features Hockett proposed, duality of patterning is the least studied and it is still unclear how it evolved in language. Recent work shedding light on this is summarized in this paper and experimental data is presented. This data shows that combinatorial structure can emerge in an artificial whistled language through cultural transmission as an adaptation to human cognitive biases and learning. In this work the method of experimental iterated learning (Kirby et al. 2008) is used, in which a participant is trained on the reproductions of the utterances the previous participant learned. Participants learn and recall a system of sounds that are produced with a slide whistle. Transmission from participant to participant causes the whistle systems to change and become more learnable and more structured. These findings follow from qualitative observations, quantitative measures and a follow-up experiment that tests how well participants can learn the emerged whistled languages by generalizing from a few examples.

One of the major stumbling blocks to understanding evolution is the difficulty in reconciling the emergence of complexity with the apparently undirected forces that drive evolutionary processes. This difficulty was originally framed as the "Watch and Watchmaker" argument and more recently revived by proponents of "intelligent design." Undergraduates in particular often attribute purpose and forethought as the driving force behind biological phenomena, and have difficulty understanding evolutionary processes. To demonstrate that complexity can arise solely through mutations that fix in populations via natural selection or drift, we can use analogies where processes can be observed across short time frames and where the key data are accessible to those without specialized biological knowledge. The evolution of language provides such an example. Processes of natural selection, mutation, genetic drift, acquisition of new functions, punctuated equilibria, and lateral gene transfer can be illustrated using examples of changing spellings, neologism, and acquisition of words from other languages. The examples presented in this article are readily accessible, and demonstrate to students that languages have dynamically increased in complexity, simply driven by the usage patterns of their speakers.

In multi-agent systems, the study of language and communication is an active field of research. In this paper we present the application of evolutionary strategies to the self-emergence of a common lexicon in a population of agents. By modeling the vocabulary or lexicon of each agent as an association matrix or look-up table that maps the meanings (i.e. the objects encountered by the agents or the states of the environment itself) into symbols or signals we check whether it is possible for the population to converge in an autonomous, decentralized way to a common lexicon, so that the communication efficiency of the entire population is optimal. We have conducted several experiments aimed at testing whether it is possible to converge with evolutionary strategies to an optimal Saussurean communication system. We have organized our experiments alongside two main lines: first, we have investigated the effect of the population size on the convergence results. Second, and foremost, we have also investigated the effect of the lexicon size on the convergence results. To analyze the convergence of the population of agents we have defined the population's consensus when all the agents (i.e. 100% of the population) share the same association matrix or lexicon. As a general conclusion we have shown that evolutionary strategies are powerful enough optimizers to guarantee the convergence to lexicon consensus in a population of autonomous agents.

Mirror neurons for manipulation fire both when the animal manipulates an object in a specific way and when it sees another animal (or the experimenter) perform an action that is more or less similar. Such neurons were originally found in macaque monkeys, in the ventral premotor cortex, area F5 and later also in the inferior parietal lobule. Recent neuroimaging data indicate that the adult human brain is endowed with a “mirror neuron system,” putatively containing mirror neurons and other neurons, for matching the observation and execution of actions. Mirror neurons may serve action recognition in monkeys as well as humans, whereas their putative role in imitation and language may be realized in human but not in monkey. This article shows the important role of computational models in providing sufficient and causal explanations for the observed phenomena involving mirror systems and the learning processes which form them, and underlines the need for additional circuitry to lift up the monkey mirror neuron circuit to sustain the posited cognitive functions attributed to the human mirror neuron system.

Recent studies have taken advantage of newly available, large-scale, cross-linguistic data and new statistical techniques to look at the relationship between language structure and social structure. These ‘nomothetic’ approaches contrast with more traditional approaches and a tension is observed between proponents of each method. We review some nomothetic studies and point out some challenges that must be overcome. However, we argue that nomothetic approaches can contribute to our understanding of the links between social structure and language structure if they address these challenges and are taken as part of a body of mutually supporting evidence. Nomothetic studies are a powerful tool for generating hypotheses that can go on to be corroborated and tested with experimental and theoretical approaches. These studies are highlighting the effect of interaction on language.

What are the driving forces of cultural macroevolution, the evolution of cultural traits that characterize societies or populations? This question has engaged anthropologists for more than a century, with little consensus regarding the answer. We develop and fit autologistic models, built upon both spatial and linguistic neighbor graphs, for 44 cultural traits of 172 societies in the Western North American Indian (WNAI) database. For each trait, we compare models including or excluding one or both neighbor graphs, and for the majority of traits we find strong evidence in favor of a model which uses both spatial and linguistic neighbors to predict a trait’s distribution. Our results run counter to the assertion that cultural trait distributions can be explained largely by the transmission of traits from parent to daughter populations and are thus best analyzed with phylogenies. In contrast, we show that vertical and horizontal transmission pathways can be incorporated in a single model, that both transmission modes may indeed operate on the same trait, and that for most traits in the WNAI database, accounting for only one mode of transmission would result in a loss of information.

Language is a complex system, and those who study it often seek simpler models to explain linguistic features. Two such models are examined here, the extended phenotype proposed by Richard Dawkins and the affordance proposed by James J. Gibson. These models are placed in the context of current research programs in language evolution and distributed cognition, and are related to one another through examples from parasitology and animal communication. The concept of an extended affordance is introduced as the environmental complement of the extended phenotype. In primate signaling systems, both phenotype and affordance are proposed to be not only extended but also distributed. The distributed phenotype and affordance are then related to the problem of body–world interaction in human language.

This chapter studies three strategies giving rise to color categories and descriptions for them: a strategy for basic hue terms (“yellow”,“blue”,“green”, etc.), for brightness terms (“shiny”,“dull”, etc.) and for graded membership terms (as in:“very blue” or “slightly blue”). ...

Robotic swarms offer flexibility, robustness, and scalability. For successful operation they need appropriate communication strategies that should be dynamically adaptable to possibly changing environmental requirements. In this paper we try to achieve this through evolving communication on-the-fly. As a test case we use a scenario where robots need to cooperate to gather energy and the necessity to cooperate is scalable. We implement an evolutionary algorithm that works during the actual operation of the robots (on-line), where evolutionary operators are performed by the robots themselves (on-board) and robots exchange genomes with other robots for reproduction (distributed). We perform experiments with different cooperation pressures and observe that communication strategies can be successfully adapted to the particular demands of the environment.

The lexicon is the central locus of association between form and meaning. The prior sections in this chapter focus on the lexicon as it figures in the cognitive systems of individuals. The lexicon can also be viewed at the level of language communities, as shared intellectual property that supports mech- anisms of information transmission amongst individuals. This viewpoint is foreshadowed by Hawkins (this volume), and sketched for linguistic systems in general in Hruschka et al. (2009). Here, I consider the relationship between the lexical systems of individuals and lexical systems at the community level. The dynamics of these systems over time, rooted in their relationship to each other, can inform our understanding of the lexicon, and of the entries and relationships that comprise it. Tackling problems in lexical dynamics, in the light of experimental findings and synchronic statistics, provides laboratory phonology both with fresh lines of empirical evidence and with fresh arenas for theoretical prediction.

A fascinating, far-reaching study of how our species' innate capacity for culture altered the course of our social and evolutionary history. A unique trait of the human species is that our personalities, lifestyles, and worldviews are shaped by an accident of birth—namely, the ...

Many of the world's languages display a preferred ordering of subject, object and verb, known as that language's basic word order. There are six logically possible basic word orders, and while each occurs in at least one known language, not all are found equally frequently. Some are extremely rare, while others are used by almost half the world's languages. This highly non-uniform cross-linguistic distribution of basic orders is a fundamental explanatory target for linguistics. This thesis tackles this problem from a psychological perspective. It constitutes an advance over previously proposed explanations in that it is compatible not only with the distributions observed today, but with what is known of broad trends in the word order change which happen over hundreds of years. There are two largely independent components of the explanation given in this thesis, which is necessary to be compatible with both synchronic and diachronic evidence. The first component is focused on the structures which the human mind uses to represent the meanings of sentences. While mental representations of meaning are not inherently serial (hence ordered) like spoken language, we can think of the different components in these representations as being ordered in a different sense, based on some components being more accessible to cognitive processing than others. This thesis develops the idea that the word order used most often in the earliest human languages, which are taken to rely on a direct interface between mental representations and motor control systems, were determined by a "word order of the language of thought". The second component is focused on the functional adequacy of different word orders for high speed, reliable communication. The driving idea here is that human language represents a rational solution to the problem of communication. The mathematical formalism of information theory is used to determine the gold standard for solutions to this problem, and this is used to derive a ranking of word orders by functionality. This thesis develops a novel perspective on word order functionality in which cross-linguistic preferences are ultimately a reflection of statistical properties of the events which languages describe.

Language is one of the most prominent examples of complex systems. It is one of the basic tools of humanity, and yet many of its aspects still puzzle a broad range of scientists. The phenomena underlying the emergence and evolution of language, as well as cultural change, have been subject to increased interest from the physics community over the past two decades. We begin this work by discussing a number of mathematical and computational models of language dynamics.