Data-driven learning uncontroversially plays a role in human language acquisition--how large a role is a matter of much debate. The success of artificial neural networks in NLP in recent years calls for a re-evaluation of our understanding of the possibilities for learning grammar from data alone. This dissertation argues the case for using…

Language is one of the hallmarks of intelligence, demanding explanation in a theory of human cognition. However, language presents unique practical challenges for quantitative empirical research, making many linguistic theories difficult to test at naturalistic scales. Artificial neural network language models (LMs) provide a new tool for studying…

From limited evidence, children track the regularities of their language impressively fast and they infer generalized rules that apply to novel instances. This study investigated what drives the inductive leap from memorizing specific items and statistical regularities to extracting abstract rules. We propose an innovative entropy model that…

We study the learnability problem concerning the dative alternations in English (Baker, 1979; Pinker, 1989). We consider how first language learners productively apply the double-object and to-dative constructions ("give the book to library"/"give the library the book"), while excluding negative exceptions ("donate the…

In the past 15 years, a substantial body of evidence has confirmed that a powerful distributional learning mechanism is present in infants, children, adults and (at least to some degree) in nonhuman animals as well. The present article briefly reviews this literature and then examines some of the fundamental questions that must be addressed for…

Previous work demonstrated that 9-month-olds who were familiarized with 3-syllable strings consistent with both a broader (AAB or ABA) and narrower (AA"di" or A"di"A) generalization made only the latter. Because the narrower generalization is a subset of the broader one, any example that is consistent with the broader generalization but not the…