Proceedings of the Annual Meeting of the Cognitive Science Society

A classic issue in the cognitive-science of human categorylearning has involved the contrast between exemplar and prototypemodels. However, experimental tests to distinguish the models haverelied almost solely on use of artificial categories composed ofsimplified stimuli. Here we contrast the predictions from the modelsin a real-world natural-science category domain – geologic rocktypes. Previous work in this domain used a set of complementarymethods, including multidimensional scaling and direct dimensionratings, to derive a high-dimensional feature space in which the rockstimuli are embedded. The present work compares the category-learning predictions of exemplar and prototype models that makereference to this derived feature space. The experiments includeconditions that should be favorable to prototype abstraction,including use of large-size categories, delayed transfer testing, andreal-world natural category structures. Nevertheless, the results ofthe qualitative and quantitative model comparisons point toward theexemplar model as providing a better account of the observedresults. Limitations and directions of future work are discussed.