Proceedings of the Annual Meeting of the Cognitive Science Society

Probabilistic prediction is a central process in language com-prehension. Properties of probability distributions over predic-tions are often difficult to study in natural language. To obtainprecise control over these distributions, we created artificiallanguages consisting of sequences of shapes. The languageswere constructed to vary the uncertainty of the probability dis-tribution over predictions as well as the probability of the pre-dicted item. Participants were exposed to the languages in aself-paced presentation paradigm, which provides a measureof processing difficulty at each element of a sequence. Therewas a robust pattern of graded predictability: shapes were pro-cessed faster the more predictable they were, as in natural lan-guage. Processing times were also affected by the uncertainty(entropy) over predictions at the point at which those predic-tions were made; this effect was less consistent, however.