Computational Psycholinguistics

Instructor(s): T. Florian Jaeger and Roger Levy

Over the last two decades, cognitive science has undergone a paradigm shift towards probabilistic models of the brain and cognition. Many aspect of human cognition -- ranging across memory, categorization, generalization, concept learning, vision, and motor planning -- are now understood in terms of rational use of available information in the light of uncertainty. Building on a long traditional of computational models for language, such rational models have also been proposed for language production, comprehension, and acquisition. This class provides an overview to this newly emerging field of computational psycholinguistics, combining insights and methods from linguistic theory, natural language processing, machine learning, psycholinguistics, and cognitive science into the study of how we understand and produce language. The goal of this class is to provide students with an overview of this field and a toolset that will allow them to start their own research in computational psycholinguistics.


  1. Familiarity with basic syntax (phrase structure, alternations; no specific framework required). Any undergraduate level introduction to syntax or linguistic theory will suffice.
  2. An introductory class in either psycholinguistics or computational linguistics. A background in variationist linguistics/sociolinguistics may also suffice. Students who have never worked with and have not been trained for work with quantitative data should consult with the instructors prior to enrolling in the class.
  3. Bare essentials of probability and information theory (e.g., via an introduction to statistics or probability theory, or by reading Goldsmith (2007) or Chapter 2 of Manning and Schuetze (1999)). All definitions are provided in class, but students who have not taken a probability or statistics class should carefully go through one of the introductory readings mentioned above.