Proceedings of the Annual Meeting of the Cognitive Science Society

Different models of categorization are often treated as compet-ing accounts, but specific models are often used to understandindividual differences, by estimating individual-level param-eters. We develop an approach to understanding categoriza-tion that allows for individual differences both between andwithin models, using two prominent categorization models thatmake different theoretical assumptions: the Generalized Con-text Model (GCM) and General Recognition Theory (GRT).We develop a latent-mixture model for inferring whether anindividual uses the GCM or GRT, while simultaneously allow-ing for the use of special-case simpler strategies. The GCMsimple strategies involve attending to a single stimulus dimen-sion, while the GRT simple strategies involve using unidimen-sional decision bounds. Our model also allows for simple con-taminant strategies. We apply the model to four previouslypublished categorization experiments, finding large and inter-pretable individual differences in the use of both models andspecific strategies, depending on the nature of the stimuli andcategory structures.