Proceedings of the Annual Meeting of the Cognitive Science Society

A rule-plus-exception model of category learning, RULEX (Nosofsky, Palmeri, & McKinley, 1992), and an exemplar-based connectionist model of category learning, A L C O V E (Kxuschke, 1992), were evaluated on their ability to predict the types of generalization patterns exhibited by h u m a n subjects. Although both models were able to predict the average transfer data extremely well, each model had difficulty predicting certain types of generalizations shown by individual subjects. In particular, RULEX accurately predicted the prominence of rule-based generalizations, whereas A L C O V E accurately predicted the prominence of similarity-based generalizations. A hybrid model, incorporating both rules and similarity to exemplars, might best account for category learning. Furthermore, a stochastic learning rule, such as that used in RULEX , might be crucial for captiiring the different types of generalizations patterns exhibited by h u m a n s .