Language Evolution and Computation Bibliography

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.

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.

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.

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.

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

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.

We describe the reconstruction of a phylogeny for a set of taxa, with a character-based cladistics approach, in a declarative knowledge representation formalism, and show how to use computational methods of answer set programming to generate conjectures about the evolution of the given taxa. We have applied this computational method in two domains: to historical analysis of languages, and to historical analysis of parasite-host systems. In particular, using this method, we have computed some plausible phylogenies for Chinese dialects, for Indo-European language groups, and for Alcataenia species. Some of these plausible phylogenies are different from the ones computed by other software. Using this method, we can easily describe domain specific information (e.g. temporal and geographical constraints), and thus prevent the reconstruction of some phylogenies that are not plausible.

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.

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.

1. Introduction Part 1 -- Language Emergence
2. Speech and language - a human trait defined by molecular genetics -- King Chow
3. Conceptual complexity and the brain: understanding language origins -- P. Thomas Schoenemann
4. The emergence of grammar from perspective -- Brian MacWhinney
5. Polygenesis of linguistic strategies: a scenario for the emergence of languages -- Christophe Coupe Jand ean-Marie Hombert

Part 2 -- Language Acquisition
6. Multiple-cue integration in language acquisition: a connectionist model of speech segmentation and rule-like behavior -- Morten Christiansen, Christopher M. Conway and Suzanne Curtin
7. Unsupervised lexical learning as inductive inference via compression -- Chunyu Kit
8. The origin of linguistic irregularity -- Charles Yang

Part 3 -- Language Change
9. The language organism: the Leiden theory of language evolution -- George van Driem
10. Taxonomy, typology, and historical linguistics -- Merritt Ruhlen
11. Modeling language evolution -- Felipe Cucker, Steve Smale and Ding-Xuan Zhou

Part 4 -- Language and Complexity
12. Language and complexity -- Murray Gell-Mann
13. Language acquisition as a complex adaptive system -- John H. Holland
14. How many meanings does a word have? Meaning estimation in Chinese and English -- Charles Lin and Kathleen Ahrens
15. Typology and complexity -- Randy LaPolla
16. Creoles and complexity -- Bernard Comrie

Research into the emergence and evolution of human language has received unprecedented attention during the past 15 years. Efforts to better understand the processes of language emergence and evolution have proceeded in two main directions: from the top-down (linguists) and from the bottom-up (cognitive scientists). Language can be viewed as an invading process that has had profound impact on the human phenotype at all levels, from the structure of the brain to modes of cultural interaction. In our view, the most effective way to form a connection between the two efforts (essential if theories for language evolution are to reflect the constraints imposed on language by the brain) lies in computational modelling, an approach that enables numerous hypotheses to be explored and tested against objective criteria and which suggest productive paths for empirical researchers to then follow. Here, with the aim of promoting the cross-fertilization of ideas across disciplines, we review some of the recent research that has made use of computational methods in three principal areas of research into language evolution: language emergence, language change, and language death.

It has been observed that borrowing within a group of genetically related languages often causes the lexical similarities among them to be skewed. Consequently, it has been proposed that borrowing can sometimes be inferred from such skewing. However, heterogeneity in the rate of lexical replacement, as well as borrowing from other languages, can also give rise to skewed lexical similarities. It is important, therefore, to determine to what degree skewing is a statistically significant indicator of borrowing. Here, we describe a statistical hypothesis test for detecting language contact based on skewing of linguistic characters of arbitrary type. Significant probabilities of correct detection of contact are maintained for various contact scenarios, with low false alarm probability. Our experiments show that the test is fairly robust to substantial heterogeneity in the retention rate, both across characters and across lineages, suggesting that the method can provide an objective criterion against which claims of significant skewing due to contact can be tested, pointing the way for more detailed analysis.

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.

The study of language evolution has revitalized recently due to converging interests from many disciplines. Computational modeling is one such fruitful area. Various aspects of language evolution have been studied using mathematical modeling and simulation. In this paper we discuss several computational studies in language change and language emergence.

Human language may have started from a consistent set of mappings between meanings and signals. These mappings, referred to as the early vocabulary, are considered to be the results of conventions established among the agents of a population. In this study, we report simulation models for investigating how such conventions can be reached. We propose that convention is essentially the product of self-organization of the population through interactions among the agents; and that cultural selection is another mechanism that speeds up the establishment of convention. Whereas earlier studies emphasized either one or the other of these two mechanisms, our focus is to integrate them into one hybrid model. The combination of these two complementary mechanisms, i.e. self-organization and cultural selection, provides a plausible explanation for cultural evolution which progresses with high transmission rate. Furthermore, we observe that as the vocabulary tends to convergence there is a uniform tendency to exhibit a sharp phase transition.