Featural selective attention, exemplar representation, and the inverse base-rate effect

Selective attention plays a central role in theories of category learning and representation. In exemplar theory, selective attention has typically been formalized as operating uniformly across entire stimulus dimensions. Selective featural attention operating within dimensions has been recognized as a conceptual possibility, but relatively little research has focused on evaluating it. In the present research, we explored the usefulness of selective featural attention in the context of exemplar representation. We report the results of embedding the feature-to-category relations typically associated with the inverse base-rate effect—a classic and paradoxical category-learning result— perceptual category-learning task using a category structure with three multivalued feature dimensions. An exemplar model incorporating featural selective attention accurately accounted for the inverse base-rate effect that occurred but failed to do so with only dimensional attention.