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.

It is generally difficult to define reasonable parameters and interpret their values in mathematical models of social phenomena. Rather than directly fitting abstract parameters against empirical data, we should define some concrete parameters to denote the sociocultural factors relevant for particular phenomena, and compute the values of these parameters based upon the corresponding empirical data. Taking the example of modeling studies of language competition, we propose a language diffusion principle and two language inheritance principles to compute two critical parameters, namely the impacts and inheritance rates of competing languages, in our language competition model derived from the Lotka–Volterra competition model in evolutionary biology. These principles assign explicit sociolinguistic meanings to those parameters and calculate their values from the relevant data of population censuses and language surveys. Using four examples of language competition, we illustrate that our language competition model with thus-estimated parameter values can reliably replicate and predict the dynamics of language competition, and it is especially useful in cases lacking direct competition data.

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.

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.

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.

This paper proposes a coevolutionary scenario on the origins of compositionality and word order regularity in human language, and illustrates it using a multi-agent, behavioral model. The model traces a bottom-up process of syntactic development; artificial agents, by iterating local orders among lexical items, gradually build up basic constituent word order(s) in sentences. These results show that structural features of language (e.g. syntactic categories and word orders) could have coevolved with lexical items, as a consequence of general learning mechanisms (e.g. pattern extraction and sequential learning) initially not language-specific.

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.

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.

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.

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.

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.

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.

We conduct an evolutionary simulation to explore the coevolution of language and a language-related ability, intentionality sharing. Our simulation shows that during the evolution of a simple informative language, communicative success helps optimize the level of intentionality sharing in the population. This study illustrates a selective role of language communications on language-related abilities, and assists the discussion of the uniqueness of language-related abilities based on comparative studies.

The majority of the extant languages have one of three dominant basic word orders: SVO, SOV or VSO. Various hypotheses have been proposed to explain this word order bias, including the existence of a universal grammar, the learnability imposed by cognitive constraints, the descent of modern languages from an ancestral protolanguage, and the constraints from functional principles. We run simulations using a multi-agent computational model to study this bias. Following a local order approach, the model simulates individual language processing mechanisms in production and comprehension. The simulation results demonstrate that the semantic structures that a language encodes can constrain the global syntax, and that local syntax can help trigger bias towards the global order SOV/SVO (or VOS/OVS).

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

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

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

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

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

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

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

Compositionality and regularity are universals in human languages; in most languages, complex expressions are determined by their structures and their components' meanings. Based on a multi-agent computational model, the coevolution of compositionality and one type of regularity, word order, is traced during the emergence of compositional language out of holistic signals. The model modifies some questionable aspects in the Iterated Learning Model and Fluid Construction Grammar by considering the conventionalization in horizontal transmission and the gradual formation of syntactic categories which mirror the semantic categories. The model also implements a bottom-up syntactic developmental process, i.e., the global orders for regulating multiple arguments are gradually formed from simple local orders between two categories.

The emergence of a compositional language with a simple grammar and the effects of individuals popularity on the phylogeny of language are studied based on a multi-agent computational model. In this model, a bottom-up syntactic development is traced, in which the global syntax in sentences is gradually formed from local sequential information. Assuming that the popularity of individuals follows a power-law distribution, we demonstrate that a common language can emerge efficiently only for certain power-law distributions and that these distributions could also be formed as a result of the language phylogeny.

The majority of extant languages have one of three basic word orders: SVO, SOV or VSO. Various hypotheses have been put forward to explain aspects of this bias, including the existence of a universal grammar, learnability imposed by non-linguistic-specific cognitive constraints, and the descent of the extant languages from a common ancestral proto-language. Here, we adopt a multi-agent model for language emergence that simulates the coevolution of a lexicon and syntax from a holistic signaling system. The syntax evolves through a process of categorization; local syntactic rules are constructed that assign a relative order (e.g., S before V) to the elements of the two categories to which each rule applies. We demonstrate that local syntax encoding the relative position of S and O are the most stable, allowing the coexistence of the global word order pairs SOV/SVO and VOS/OVS. The structure of the semantic space that the language encodes further constrains the global syntax that is stable.

Whether simple syntax (in the form of simple word order) can emerge during the emergence of lexicon is studied from a simulation perspective; a multiagent computational model is adopted to trace a lexicon-syntax coevolution through iterative communications. Several factors that may affect this self-organizing process are discussed. An indirect meaning transference is simulated to study the effect of nonlinguistic information in listener's comprehension. Besides the theoretical and empirical argumentations, this computational model, following the Emergentism, demonstrates an adaptation of syntax from some domain-general abilities, which provides an argumentation against the Innatism.

In this paper, after a brief review of current computational models on language emergence, a multi-agent model is introduced to simulate the emergence of a compositional language from a holistic signaling system, through iterative interactions among heterogeneous agents. A coevolution of lexicon and syntax (in the form of simple word order) is tracked during communications with indirect meaning transference, in which the listener's comprehension is based on interactions of linguistic and nonlinguistic information, and the feedback is not a direct meaning check. In this model, homonymous and synonymous rules emerge inevitably, and a sufficiently developed communication system is available only when a homonym-avoidance mechanism is adopted. In addition, certain degrees of heterogeneity regarding agent's natural characteristics and linguistic behaviors do not significantly affect language emergence. Finally, based on theories of complex networks, a preliminary study of social structure's influence on language emergence is given, and a coevolution of the emergence of language and that of simple social structure is implemented.

Evolutionary computation is used to explore the emergence of language, focusing particularly on the intrinsic relationship between the lexicon and syntax, and the exogenous relationship between language use and cultural development. A multi-agent model traces a coevolution of the lexicon and syntax, and demonstrates that linguistic and some distance constraint on communications can trigger and maintain cultural heterogeneity. This model also traces an optimization process using evolutionary mechanisms based on local information. Certain mechanisms in this model, such as recurrent pattern extraction, strength-based competition and indirect feedback, can be generalized to study robot learning, optimization and other evolutionary phenomena.

In this paper, a multi-agent computational model is proposed to simulate the coevolution of social structure and compositional protolanguage from a holistic signaling system through iterative interactions within a heterogeneous population. We implement an indirect meaning transference based on both linguistic and nonlinguistic information in communications, together with a feedback without direct meaning check. The emergent social structure, triggered by two locally selective strategies, friendship and popularity, has small-world characteristics. The influence of these selective strategies on the emergent language and the emergent social structure are discussed.