Categorization, Information Selection and Stimulus Uncertainty

Although a common assumption in models of perceptual discrimination, most models of categorization do not explicitly account for uncertainty in stimulus measurement. Such uncertainty may arise from inherent perceptual noise or external measurement noise (e.g., a medical test that gives variable results). In this paper we explore how people decide to gather information from various stimulus properties when each sample or measurement is noisy. The participant’s goal is to correctly classify the given item. Across two experiments we find support for the idea that people take category structure into account when selecting information for a classification decision. In addition, we find some evidence that people are also sensitive to their own perceptual uncertainty when selecting information.