A popular view holds that learning and generalizing concepts depends on two fundamentally distinct modes of representation: rules and similarityto -exemplars. Through a combination of experiments and formal analysis, I show how a Bayesian framework offers a unifying account of both rule-based and similarity-based generalization. Bayes explains the specific workings of these two modes -- which rules are abstracted, how similarity is measured -- as well as why generalization appears rule-based or similarity-based in different situations. I conclude that the distinction between rules and similarity in concept learning may be useful at the level of heuristic algorithms, but is not computationally fundamental. 1 Introduction In domains ranging from reasoning to language acquisition, a broad view is emerging of cognition as a hybrid of two distinct modes of computation, one based on applying abstract rules and the other based on assessing similarity to stored exemplars [6]. Much s...