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Theories of categorization typically assume that categories are represented by some set of features that describe the properties of category members. However this view of category representation is incomplete. This dissertation lays out a framework for category representation, following Markman and Stilwell (2001), that creates a taxonomy of categories based on different components of relational structures. Relational categories are categories of entire relational systems while, role-governed categories, are represented as the roles in these systems. Lastly, thematic-relation categories group entities together that play complementary roles within a system. Four experiments are presented in support of this framework. They contrast thematic-relation categorization with role-governed categorization. Thematic-relation categorization entails categorizing objects together that play different roles within a domain, while role-governed categorization entails categorizing two entities that play the same role across domains. When the two are put in direct conflict, people prefer to form a thematic-relation category because within-domain connections are easier to find than across-domain connections. The purpose of the four experiments is to examine ways to boost the preference for role-governed categorization, thus revealing underlying processes. Here, role-governed categorization is facilitated in two ways. Experiment 1 re-frames the question of category formation as novel word extension. Natural role-governed categories have labels while thematic-relation categories do not. This pattern is reflected in the measured behavior as novel labels are extended across members of role-governed categories more readily than across members of thematic-relation categories. By claiming relational structures are critical to category representation, the framework described in this dissertation predicts that role-governed categorization and analogical reasoning share underlying mechanisms. Experiments 2-4 examine how making an analogy between the members of role-governed categories facilitates forming such categories. When making an analogy, people align the relational representations of a pair of domains, putting entities into correspondence by role, ignoring featural dissimilarities. When analogical comparison is induced, the rate of role-governed categorization is shown to double as compared to a baseline with no such analogical processes. The thesis concludes by outlining several future lines of research generated by unifying the fields of analogy and concept learning.