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Two experiments explored the role of people's prior knowledge or intuitive theories on category learning by manipulating the labels associated with the category. Learning differed dramatically when categories of children's drawings were meaningfully labeled (e.g., \u201cdone by creative children\u201d) compared to when they were labeled in a neutral manner. When categories are meaningfully labeled, people bring intuitive theories to the learning context. Learning then involves a process in which people search for evidence in the data that supports abstract features or hypotheses that have been activated by the intuitive theories. In contrast, when categories are labeled in a neutral manner, people search for simple features that distinguish one category from another. Importantly, the final study suggests that learning involves an <jats:italic>interaction<\/jats:italic> of people's intuitive theories with data, in which theories and data mutually influence each other. The results strongly suggest that straight\u2010forward, relatively modular ways of incorporating prior knowledge into models of category learning are inadequate. More telling, the results suggest that standard models may have fundamental limitations. We outline a speculative model of learning in which the interaction of theory and data is <jats:italic>tightly<\/jats:italic> coupled. 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