How Nature Meets Nurture: Universal Grammar and Statistical Learning

Abstract

Evidence of children's sensitivity to statistical features of their input in language acquisition is often used to argue against learning mechanisms driven by innate knowledge. At the same time, evidence of children acquiring knowledge that is richer than the input supports arguments in favor of such mechanisms. This tension can be resolved by separating the inferential and deductive components of the language learning mechanism. Universal Grammar provides representations that support deductions about sentences that fall outside of experience. In addition, these representations define the evidence that learners use to infer a particular grammar. The input is compared with the expected evidence to drive statistical inference. In support of this model, we review evidence of (a) children's sensitivity to the environment, (b)mismatches between input andintake, (c) the need for learning mechanisms beyond innate representations, and (d) the deductive consequences of children's acquired syntactic representations.