From UG to Universals: Linguistic adaptation through iterated learning

Abstract

What constitutes linguistic evidence for Universal Grammar (UG)? The principal approach to this question equates UG on the one hand with language universals on the other. Parsimonious and general characterizations of linguistic variation are assumed to uncover features of UG. This paper reviews a recently developed evolutionary approach to language that casts doubt on this assumption: the Iterated Learning Model (ILM). We treat UG as a model of our prior learning bias, and consider how languages may adapt in response to this bias. By dealing directly with populations of linguistic agents, the ILM allows us to study the adaptive landscape that particular learning biases result in. The key result from this work is that the relationship between UG and language structure is non-trivial.