Distributional learning is error-driven: the role of surprise in the acquisition of phonetic categories

Abstract

Abstract Much previous research on distributional learning and phonetic categorization assumes that categories are either faithful reproductions or parametric summaries of experienced frequency distributions, acquired through a Hebbian learning process in which every experience contributes equally to the category representation. We suggest that category representations may instead be formed via error-driven predictive learning. Rather than passively storing tagged category exemplars or updating parametric summaries of token counts, learners actively anticipate upcoming events and update their beliefs in proportion to how surprising/unexpected these events turn out to be. As a result, rare category members exert a disproportionate influence on the category representation. We present evidence for this hypothesis from a distributional learning experiment on acquiring a novel phonetic category, and show that the results are well described by a classic error-driven learning model (Rescorla, R. A. & A. R. Wagner. 1972. A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In A. H. Black & W. F. Prokasy (eds.), Classical conditioning II: Current research and theory, 64–99. New York, NY: Appleton-Century-Crofts).