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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.