The Acquisition of Gramar in an Evolving Population of Language Agents

Human language acquisition, and in particular the acquisition of grammar, is a partially-canalized, strongly-biased but robust and efficient procedure. For example, children prefer to induce compositional rules (e.g. Wanner and Gleitman, 1982) despite peripheral use of non-compositional constructions, such as idioms, in every attested human language. And, most parameters of grammatical variation set during language acquisition appear to have default values retained in the absence of robust counter-evidence (e.g.Bickerton, 1984; Lightfoot, 1989). A variety of explanations have been offered for the emergence of a partially-innate language acquisition device (LAD) with such properties, such as exaption of a spandrel (Gould, 1987), biological saltation (Chomsky, 1972) or genetic assimilation (Pinker and Bloom, 1990). But none provide a coherent account of both the emergence and maintenance of a LAD in an evolving population. The account offered here is that an embryonic LAD emerged via exaption of general-purpose (Bayesian) learning mechanisms (e.g. Staddon, 1983) to a specifically-linguistic mental representation capable of expressing mappings from the `language of thought' to `realizable' encodings of propositions expressed in the language of thought. However, the selective pressure favouring such an exaption, and its subsequent maintenance and refinement, is only coherent given a coevolutionary scenario in which a (proto)language supporting successful communication within a population had already itself evolved on a historical timescale (e.g. Hurford, 1987; Kirby, 1998; Steels, 1997) and continued to coevolve with the LAD (e.g. Briscoe, 1997, in press). This account is supported by the results of a number of computational simulations of evolving populations of software agents acquiring and communicating with coevolving structured languages. The model behind the simulations suggests a new dynamic framework forthe study of communication systems in general, and human language in particular, which both incorporates the insights gained from formalizing a language as static well-formed stringset (Chomsky, 1957) and extends them by embedding this model in an evolving population of distributed language agents. The practical implication of this framework for natural language processing is that development of static hand-coded systems should be replaced by development of autonomous software agents capable of adapting to their linguistic environment