Language Evolution and Computation Bibliography

Memory is essential to many cognitive tasks including language. Apart from empirical studies of memory effects on language acquisition and use, there lack sufficient evolutionary explorations on whether a high level of memory capacity is prerequisite for language and whether language origin could influence memory capacity. In line with evolutionary theories that natural selection refined language-related cognitive abilities, we advocated a coevolution scenario between language and memory capacity, which incorporated the genetic transmission of individual memory capacity, cultural transmission of idiolects, and natural and cultural selections on individual reproduction and language teaching. To illustrate the coevolution dynamics, we adopted a multi-agent computational model simulating the emergence of lexical items and simple syntax through iterated communications. Simulations showed that: along with the origin of a communal language, an initially-low memory capacity for acquired linguistic knowledge was boosted; and such coherent increase in linguistic understandability and memory capacities reflected a language-memory coevolution; and such coevolution stopped till memory capacities became sufficient for language communications. Statistical analyses revealed that the coevolution was realized mainly by natural selection based on individual communicative success in cultural transmissions. This work elaborated the biology-culture parallelism of language evolution, demonstrated the driving force of culturally-constituted factors for natural selection of individual cognitive abilities, and suggested that the degree difference in language-related cognitive abilities between humans and nonhuman animals could result from a coevolution with language.

We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.

Studying language evolution has become resurgent in modern scientific research. In this revival field, approaches from a number of disciplines other than linguistics, including (paleo)anthropology and archaeology, animal behaviors, genetics, neuroscience, computer simulation, and psychological experimentation, have been adopted, and a wide scope of topics have been examined in one way or another, covering not only world languages, but also human behaviors, brains and cultural products, as well as nonhuman primates and other species remote to humans. In this paper, together with a survey of recent findings based on these many approaches, we evaluate how this multidisciplinary perspective yields important insights into a comprehensive understanding of language, its evolution, and human cognition.

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.

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.

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.