Language Evolution and Computation Bibliography

The evolution of human language allowed the efficient propagation of nongenetic information, thus creating a new form of evolutionary change. Language development in children offers the opportunity of exploring the emergence of such complex communication system and provides a window to understanding the transition from protolanguage to language. Here we present the first analysis of the emergence of syntax in terms of complex networks. A previously unreported, sharp transition is shown to occur around two years of age from a (pre-syntactic) tree-like structure to a scale-free, small world syntax network. The observed combinatorial patterns provide valuable data to understand the nature of the cognitive processes involved in the acquisition of syntax, introducing a new ingredient to understand the possible biological endowment of human beings which results in the emergence of complex language. We explore this problem by using a minimal, data-driven model that is able to capture several statistical traits, but some key features related to the emergence of syntactic complexity display important divergences.

Human language is the key evolutionary innovation that makes humans different from other species. And yet, the fabric of language is tangled and all levels of description (from semantics to syntax) involve multiple layers of complexity. Recent work indicates that the global traits displayed by such levels can be analyzed in terms of networks of connected words. Here, we review the state of the art on language webs and their potential relevance to cognitive science. The emergence of syntax through language acquisition is used as a case study to illustrate how the approach can shed light into relevant questions concerning language organization and its evolution.

Language development in children provides a window to understand the transition from protolanguage to language. Here we present the first analysis of the emergence of syntax in terms of complex networks. A previously unreported, sharp transition is shown to occur around two years of age from a (pre-syntactic) tree-like structure to a scale-free, small world syntax network. The development of these networks thus reveals a nonlinear dynamical pattern where the global topology of syntax graphs shifts from a hierarchical, tree-like pattern, to a scale-free organization. Such change seems difficult to be explained under a self-organization framework. Instead, it actually supports the presence of some underlying innate component, as early suggested by some authors.

Several important recent advances in various sciences (particularly biology and physics) are based on complex network analysis, which provides tools for characterizing statistical properties of networks and explaining how they may arise. This article examines the relevance of this trend for the study of human languages. We review some early efforts to build up language networks, characterize their properties, and show in which direction models are being developed to explain them. These insights are relevant, both for studying fundamental unsolved puzzles in cognitive science, in particular the origins and evolution of language, but also for recent data-driven statistical approaches to natural language.