Categorization behavior can be fruitfully analyzed in terms of the trade-off between as high as possible faithfulness in the transmission of information about samples of the classes to be categorized, and as low as possible transmission costs for that same information. The kinds of categorization behaviors we associate with conceptual atoms, prototypes, and exemplars emerge naturally as a result of this trade-off, in the presence of certain natural constraints on the probabilistic distribution of samples, and the ways in which we measure faithfulness. Beyond the general structure of categorization in these circumstances, the same information-centered perspective can shed light on other, more concrete properties of human categorization performance, such as the results of experiments on supervised categorization in Smith and Minda (1998).