Interaction of knowledge-driven and data-driven processing in category learning

Abstract

The present paper argues that category learning is both a data-driven and a knowledge-driven process. This is described in a generic model that distinguishes between categorical knowledge, conceptual knowledge, and implicit cognitive theories. The model assumes that each of these knowledge aspects may affect the process of category learning by affecting the way similarities between objects are perceived. This central assumption of the model is tested in two experiments. The first experiment shows that the presence or absence of prior categorical and conceptual knowledge affects the psychological stimulus space by changing the saliency of the stimulus dimensions. The second experiment uses these weights to predict the distribution of errors over the stimuli and the number of trials to criterion in category learning by other participants under the same knowledge conditions. We conclude that prior categorical and conceptual knowledge affect category learning by mediation of similarity perception, and discuss the implications of these results.