Statistical Learning of Linguistic Structure

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It is a commonplace observation that children, in contrast to non-human primates or sophisticated computers, are able to learn a language in what seems to be an effortless fashion. Yet, the basis of this ability remains a point of contentious debate. Some theorists emphasize the importance of nature, focusing on the exquisitely abstract and subtle generalizations that form part of a child's linguistic knowledge. Others stress the importance of nurture, highlighting the remarkable ability of child language learners to detect and exploit subtle statistical properties of the language input they receive. While there is no incompatibility between these two perspectives on language learning, there has to date been little systematic investigation of the relative contributions of nature and nurture in an empirically rich domain. With support from the National Science Foundation, Drs. Robert Frank, William Badecker, and Donald Mathis will explore this middle ground, focusing on a diverse and crosslinguistically variable set of phenomena in the domain of sentence structure (syntax). This research project will use computational simulations of artificial neural networks to probe the precise character of the generalizations that emerge when statistical learning is applied to such complex syntactic data. These generalizations will be compared to patterns of human linguistic behavior via a parallel set of psycholinguistic studies, allowing the identification of contexts in which human linguistic knowledge is a close reflection of statistically-induced patterns and contexts in which it bears the stamp of a learner's innate nature.The broader impacts of this project include an understanding of factors that might distinguish between the processes normal and abnormal language development and point to possible lines of intervention, as well as a potential characterization of certain types of language breakdown. The interdisciplinary character of this work, encompassing ideas from linguistics, psycholinguistics, and computer science, will enhance the integration of research and education both through the unique research opportunities the project will provide for undergraduate and graduate students as well as through the development of innovative courses on the mental structure of language that will incorporate a laboratory component to be made publicly available on the web.