Previous work in object categorization has shown that people tend to optimize their allocation of attention to object features, and suggests that attentional optimization may best be explained in terms of cost-benefit tradeoffs. In support of this idea, we found that implementing a cost for accessing information about object features in a category learning task facilitates both attentional optimization and category acquisition, contrary to the predictions of existing models.