In this dissertation, two exemplar-based models of categorization, the General Context Model (GCM) and the Exemplar Based Random Walk model (EBRW), were used to describe between-group categorization differences in artificial and natural language categories. Prior research has shown that political Conservatives in avoidance mode are more exclusive categorizers of natural language category members than Conservatives in approach mode, but this effect was absent for Liberals (Rock & Janoff-Bulman, 2010). In Experiment 1, experimenter-generated stimuli were used to show that the EBRW could account for between-group differences in categorization decisions. In Experiment 2, the data collected by Rock and Janoff-Bulman were used to develop techniques allowing the GCM to account for between-group differences in natural language categorization decisions. Experiment 3 extends these methods to allow the EBRW to account for between-group differences in natural language categorization decisions. Across these experiments, the models identify between-group differences in determining similarity, bias to give an "in-the-category" decision, and the amount of information required to make a categorization decision. Techniques for modeling natural language categorization decisions are discussed.