Participants learned to classify a set of rock images into geologically-defined science categories. We then investigated the nature of their category-based memory representations by collecting old-new recognition data in a subsequent transfer phase. An exemplar model provided better qualitative accounts of the old-new recognition data than did a prototype or clustering model. However, to account for the variability in recognition probabilities among the old training items themselves, a hybrid-similarity exemplar model was needed that took account of distinctive features present in the items. The study is among the first to use computational models for making detailed quantitative predictions of old-new recognition probabilities for individual items embedded in complex, high-dimensional similarity spaces.

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.