Contrasting Exemplar and Prototype Models in a Natural-Science Category Domain

AbstractA classic issue in the cognitive-science of human category learning involves the contrast between exemplar and prototype models. However, experimental tests to distinguish the models have relied almost solely on use of artificial categories composed of simplified stimuli. Here we contrast the models’ predictions in a natural-science category domain – geologic rock types. Previous work used a set of complementary methods, including multidimensional scaling and direct dimension ratings, to derive a high-dimensional feature space in which the rock stimuli are embedded. The present work compares the category-learning predictions of exemplar and prototype models that make reference to this derived feature space. The experiments include conditions that should be favorable to prototype abstraction, including large-size categories and delayed transfer testing. Nevertheless, the results of qualitative and quantitative model comparisons point toward the exemplar model as providing a better account of the observed results. Limitations and directions of future work are discussed.

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