Proceedings of the Annual Meeting of the Cognitive Science Society

Although a common assumption in models of perceptual dis-crimination, most models of categorization do not explicitlyaccount for uncertainty in stimulus measurement. Such un-certainty may arise from inherent perceptual noise or externalmeasurement noise (e.g., a medical test that gives variable re-sults). In this paper we explore how people decide to gatherinformation from various stimulus properties when each sam-ple or measurement is noisy. The participant’s goal is to cor-rectly classify the given item. Across two experiments we findsupport for the idea that people take category structure intoaccount when selecting information for a classification deci-sion. In addition, we find some evidence that people are alsosensitive to their own perceptual uncertainty when selectinginformation.