Wonnacott, E.
University of Oxford, UK
Successful language acquisition involves generalization, but learners must balance this against the acquisition of lexical constraints. For example, native English speakers know that certain noun-adjective combinations are impermissible (e.g. strong winds, high winds, strong breezes, *high breezes). Another example is the restrictions imposed by verb sub-categorization, (e.g. I gave/sent/threw the ball to him; I gave/sent/threw him the ball; I donated/carried/pushed the ball to him; * I donated/carried/pushed him the ball). How do children learn these exceptions? (Baker, 1979). The current work addressed this question via a series of Artificial Language Learning experiments with 6 year olds. The results demonstrated that children are sensitive to distributional statistics in their input language and use this information to make inferences about the extent to which generalization is appropriate (cf. Braine, 1971; Wonnacott, Newport & Tanenhaus, 2008). In particular, there was evidence that children assessed whether the choice of linguistic structures depended upon the particular words with which they had occurred, and this affected their learning of arbitrary exceptions. The results are interpreted in terms of a rational Bayesian perspective on statistical learning (Perfors, Tenenbaum & Wonnacott, 2010).