Course Description: A growing consensus sees language processing, as well as cognition more broadly, as a probabilistic process that follows principles of rational inference. Computational modeling is therefore increasingly necessary to formalize theories of how language is represented and processed. Moreover, advances in computing power, the development of new statistical methods, and the creation of large linguistic datasets are allowing us to apply these models on an increasingly large scale. This course will introduce students to key methods and findings in the study of computational psycholinguistics. The course will cover psycholinguistic phenomena at a variety of levels, from phonemes to sentences, from the perspectives of both language acquisition and adult language processing. It will introduce students to probabilistic modeling, with a focus on programming in R, statistical language models, and Bayesian inference. No previous programming experience is required.
Instructor(s): Prof. Emily Morgan