Institute for Mathematical Behavioral Sciences

A classic argument in cognitive science holds that certain aspects of language structure cannot be learned from the impoverished linguistic input that children receive - and that children therefore must rely in part on innate knowledge of language. I will argue for another possibility: that children succeed at learning language because of a domain-general bias favoring simple interpretations. I will show that a Bayesian learner with such a bias toward simple hypotheses, given realistic child-directed speech, can acquire aspects of linguistic knowledge that have been held to be necessarily innate. This work represents a collaboration with Amy Perfors, Joshua Tenenbaum, Stephani Foraker, and Naveen Khetarpal.