selecting the category that contains the most similar exemplars. Decision bound theory suggests that to learn a category, one must either learn the regions of a stimulus space associated with particular responses or the boundaries (the decision bounds) that divide these response regions. Categorization of a novel stimulus is then accomplished by determining which response region it is contained within. Formal models of categorization Computational models of categorization have been developed to test theories about how humans represent and use category information. To accomplish this, categorization models can be fit to experimental data to see how well the predictions afforded by the model line up with human performance. Based on the model's success at explaining the data, theorists are able to draw conclusions about the accuracy of their theories and their theory's relevance to human category representations. To effectively capture how humans represent and use category information, categorization models generally operate under variations of the same three basic assumptions. First, the model must make some kind of assumption about the internal representation of the stimulus (e.g., representing the perception of a stimulus as a point in a multi-dimensional space). Second, the model must make an assumption about the specific information that needs to be accessed in order to formulate a response (e.g., exemplar models require the collection of all available exemplars for each category). Third, the model must make an assumption about how a response is selected given the available information. Though all categorization models make these three assumptions, they distinguish themselves by the ways in which they represent and transform an input into a response representation. The internal knowledge structures of various categorization models reflect the specific representation(s) they use to perform these transformations. Typical representations employed by models include exemplars, prototypes, and rules. Exemplar models store all distinct instances of stimuli with their corresponding category labels in memory. Categorization of subsequent stimuli is determined by the stimulus' collective similarity to all known exemplars. Prototype models store a summary representation of all instances in a category. Categorization of subsequent stimuli is determined by selecting the category whose prototype is most similar to the stimulus. Rule-based models define categories by storing summary lists of the necessary and sufficient features required for category membership. Boundary models can be considered as atypical rule models, as they do not define categories based on their content. Rather, boundary models define the edges (boundaries) between