ABSTRACT: Previous researchers have discovered perplexing inconsistencies in how people appear to utilize knowledge of category base rates when making category judgments. In particular, Medin and Edelson (1988) found an ``inverse base rate effect'' in which people tended to select a rare category when tested with a combination of conflicting cues, and Gluck and Bower (1988) reported apparent ``base rate neglect'' in which people tended to select a rare category when tested with a single symptom whose objective diagnosticity was equal for all categories. This article suggests that common principles underlie both effects: First, base rate information is learned and consistently applied to all training and testing cases. Second, the crucial effect of base rates is to cause frequent categories to be learned before rare categories, so that the common categories are encoded in terms of their typical features, and the rare categories are encoded by whichever features distinguish them relative to the already-learned, common categories. Four new experiments provide evidence consistent with those principles. The principles are formalized in a new connectionist model that shifts attention over stimulus features, based on the network's current knowledge. Quantitative fits to the empirical data are reported, and provide additional support for the principles.