Proceedings of the Annual Meeting of the Cognitive Science Society

Between prototype and exemplar models of categorization lie adaptive models, which represent categories using a varyingnumber of reference points. They regulate the amount of abstraction they make depending on the category structure. Moti-vated by ecological considerations, we investigate whether adopting such adaptive strategies could improve generalizationin realistic environments. We compare performance of four adaptive models: RMC, SUSTAIN, REX, VAM with that ofprototype and exemplar models on three artificial and three natural category structures. Both the exemplar model withadapted sensitivity parameter and VAM perform well on category structures requiring different amount of abstraction. Ourresults confirm the importance of the link between abstraction and generalization.