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…

A well-known challenge for language learners is that the input is typically produced by a variety of speakers, each with distinct vocal characteristics (Liberman, Harris, Hoffman, & Griffith, 1957). Accordingly, many studies have indicated that talker variability leads to processing costs for learners across the lifespan (Jusczyk & Pisoni,…

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…