Syntactic Adaptation in Language Comprehension

In this paper we investigate the manner in which the human language comprehension system adapts to shifts in probability distributions over syntactic structures, given experimentally controlled experience with those structures. We replicate a classic reading experiment, and present a model of the behavioral data that implements a form of Bayesian belief update over the course of the experiment.