Language Evolution and Computation Bibliography

The ability of humans to communicate via language is a complex, adapted phenotype, which undoubtedly has a recently evolved genetic component. However, the evolutionary dynamics of language-associated alleles are poorly understood. To improve our knowledge of such systems, a population-genetics model for language-associated genes is developed. (The model is general and applicable to social interactions other than communication.) When an allele arises that potentially improves the ability of individuals to communicate, it will experience positive frequency-dependent selection because its fitness will depend on how many other individuals communicate the same way. Consequently, new and rare alleles are selected against, posing a problem for the evolutionary origin of language. However, the model shows that if individuals form language-based cliques, then novel language-associated alleles can sweep through a population. Thus, the origin of language ability can be sufficiently explained by Darwinian processes operating on genetic diversity in a finite population of human ancestors.

We investigate a model of language evolution, based on population game dynamics with learning. Specifically, we examine the case of two genetic variants of universal grammar (UG), the heart of the human language faculty, assuming each admits two possible grammars. The dynamics are driven by a communication game. We prove using dynamical systems techniques that if the payoff matrix obeys certain constraints, then the two UGs are stable against invasion by each other, that is, they are evolutionarily stable. These constraints are independent of the learning process. Intuitively, if a mutation in UG results in grammars that are incompatible with the established languages, then it will die out because individuals with the mutation will be unable to communicate and therefore unable to realize any potential benefit of the mutation. An example for which the proofs do not apply shows that compatible mutations may or may not be able to invade, depending on the population's history and the learning process. These results suggest that the genetic history of language is constrained by the need for compatibility and that mutations in the language faculty may have died out or taken over depending more on historical accident than on any simple notion of relative fitness.

In order to learn grammar from a finite amount of evidence, children must begin with in-built expectations of what is grammatical. They clearly are not born, however, with fully developed grammars. Thus early language development involves refinement of the grammar hypothesis until a target grammar is learnt. Here we address the question of how much evidence is required for this refinement process, by considering two standard learning algorithms and a third algorithm which is presumably as efficient as a child for some value of its memory capacity. We reformulate this algorithm in the context of Chomsky's 'principles and parameters' and show that it is possible to bound the amount of evidence required to almost certainly speak almost grammatically.

Universal grammar (UG) is a list of innate constraints that specify the set of grammars that can be learned by the child during primary language acquisition. UG of the human brain has been shaped by evolution. Evolution requires variation. Hence, we have to postulate and study variation of UG. We investigate evolutionary dynamics and language acquisition in the context of multiple UGs. We provide examples for competitive exclusion and stable coexistence of different UGs. More specific UGs admit fewer candidate grammars, and less specific UGs admit more candidate grammars. We will analyze conditions for more specific UGs to outcompete less specific UGs and vice versa. An interesting finding is that less specific UGs can resist invasion by more specific UGs if learning is more accurate. In other words, accurate learning stabilizes UGs that admit large numbers of candidate grammars.

The lexical matrix is an integral part of the human language system. It provides the link between word form and word meaning. A simple lexical matrix is also at the center of any animal communication system, where it defines the associations between form and meaning of animal signals. We study the evolution and population dynamics of the lexical matrix. We assume that children learn the lexical matrix of their parents. This learning process is subject to mistakes: (i) children may not acquire all lexical items of their parents (incomplete learning); and (ii) children might acquire associations between word forms and word meanings that differ from their parents' lexical items (incorrect learning). We derive an analytic framework that deals with incomplete learning. We calculate the maximum error rate that is compatible with a population maintaining a coherent lexical matrix of a given size. We calculate the equilibrium distribution of the number of lexical items known to individuals. Our analytic investigations are supplemented by numerical simulations that describe both incomplete and incorrect learning, and other extensions.