A Hybrid Model of Categorization
Category learning is often modeled as either an exemplar-based or a rule-based process. This paper shows that both strategies can be combined in a cognitive architecture that was developed to model other task domains. Variations on the exemplar-based random walk (EBRW) model of Nosofsky and Palmeri (1997b) and the rule-plus-exception (RULEX) rule-based model of Nosofsky, Palmeri, and McKinley (1994) were implemented in the ACT-R cognitive architecture. The architecture allows the two strategies to be mixed to produce classification behavior. The combined system reproduces latency, learning, and generalization data from three category-learning experiments—Nosofsky and Palmeri (1997b), Nosofsky et al., and Erickson and Kruschke (1998). It is concluded that EBRW and ACT-R have different but equivalent means of incorporating similarity and practice. In addition, ACT-R brings a theory of strategy selection that enables the exemplar and the rule-based strategies to be mixed.