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2 Research has shown that learning a concept via standard supervised classification leads to a focus on diagnostic features whereas learning by inferring missing features promotes the acquisition of within-category information. Accordingly, we predicted that classification learning would produce a deficit in people’s ability to draw novel contrasts—distinctions that were not part of training—as compared to feature inference learning. Two experiments confirmed that classification learners they were at a disadvantage at making novel distinctions. Eye movement data indicated that this conceptual inflexibility was due to (a) a narrower attention profile that reduces the encoding of many category features and (b) learned inattention that inhibits the reallocation of attention to newly relevant information. Implications of these costs of supervised classification learning for views of conceptual structure are discussed. 3 The Costs of Supervised Classification: The Effect of Learning Task on Conceptual Flexibility Two assumptions have guided the study of concept learning ever since Hull (1920). The first is that category learning amounts to learning to provide a common label for sets of objects. This assumption is explicit in the field’s standard supervised classification task, in which people label a series of visually presented stimuli. Learning is supervised, as experimenters wait for a response and correct the learner after every trial. Additional stimuli are often tested to assess generalization patterns. Experimenters vary the structure of the categories being learned (i.e., the distribution of features within and between categories) to establish the success of one model over another (e.g., that a model that abstracts th