Lecturer: Judith Degen, Stanford University
Pragmatics was once thought of as the wastebasket of linguistics: as the caricature went, phenomena that were too complex to handle in the semantics were pushed to the mushy pragmatics, where they were dispatched with hand-wavy just-so stories. Recent developments in cognitive science have led pragmatics to a new period of maturation, facilitated by two important factors: a) the novel application of mathematical modeling techniques, and b) access to rich experimental data. Advances in probabilistic and game-theoretic models that treat pragmatic inference as a problem of social reasoning under uncertainty have yielded testable quantitative predictions about the outcome of many different kinds of pragmatic inference. The phenomena that these types of models have been successfully applied to include scalar implicature, ad hoc Quantity implicatures, M-implicatures, gradable adjectives, and hyperbole, among many others (for a review, see Goodman & Frank, 2016).
The course will introduce students to models of pragmatics that employ probabilistic inference to explain both utterance interpretation and production choices for a variety of phenomena. The basics of fitting experimental data to probabilistic cognitive models will be explained on the basis of case studies of increasing complexity from the recent literature. Students will learn to modify and build their own computational models within the Rational Speech Act framework using the probabilistic programming language WebPPL.