on its provided features. Classification learning is centered around learning between-category information and the diagnostic features of categories. In contrast, inference learning tasks participants with inferring the presence/value of a category feature based on a provided category label and/or the presence of other category features. Inference learning is centered on learning within-category information and the category's prototypical features. Category learning tasks can generally be divided into two categories, supervised and unsupervised learning. Supervised learning tasks provide learners with category labels. Learners then use information extracted from labeled example categories to classify stimuli into the appropriate category, which may involve the abstraction of a rule or concept relating observed object features to category labels. Unsupervised learning tasks do not provide learners with category labels. Learners must therefore recognize inherent structures in a data set and group stimuli together by similarity into classes. Unsupervised learning is thus a process of generating a classification structure. Tasks used to study category learning take various forms: Rule-based tasks present categories that participants can learn through explicit reasoning processes. In these kinds of tasks, classification of stimuli is accomplished via the use of an acquired rule (i.e., if stimulus is large on dimension x, respond A). Information-integration tasks require learners to synthesize perceptual information from multiple stimulus dimensions prior to making categorization decisions. Unlike rule-based tasks, information-integration tasks do not afford rules that are easily articulable. Reading an X-ray and trying to determine if a tumor is present can be thought of as a real-world instantiation of an information-integration task. Prototype distortion tasks require learners to generate a prototype for a category. Candidate exemplars for the category are then produced by randomly manipulating the features of the prototype, which learners must classify as either belonging to the category or not. Category learning theories Category learning researchers have proposed various theories for how humans learn categories. Prevailing theories of category learning include the prototype theory, the exemplar theory, and the decision bound theory. The prototype theory suggests that to learn a category, one must learn the category's prototype. Subsequent categorization of novel stimuli is then accomplished by selecting the category with the most similar prototype. The exemplar theory suggests that to learn a category, one must learn about the exemplars that belong to that category. Subsequent categorization of a novel stimulus is then accomplished by computing its similarity to the known exemplars of potentially relevant categories and