Informed inferences of unknown feature values in categorization.

Many current computational models of object categorization either include no explicit provisions for dealing with incomplete stimulus information (e.g. Kruschke, Psychological Review 99:22-44, 1992) or take approaches that are at odds with evidence from other fields (e.g. Verguts, Ameel, & Storms, Memory & Cognition 32:379-389, 2004). In two experiments centered around the inverse base-rate effect, we demonstrate that people not only make highly informed inferences about the values of unknown features, but also subsequently use the inferred values to come to a categorization decision. The inferences appear to be based on immediately available information about the particular stimulus under consideration, as well as on higher-level inferences about the stimulus class as a whole. Implications for future modeling efforts are discussed.