Category learning is an important cognitive process that facilitates decision-making. Over the past few decades it has become clear that there are multiple systems underlying category learning, each with unique advantages. In my research I focused on rule-based and information-integration category structures, which rely on explicit and procedural systems, respectively. One key difference between these two systems is that learning in the procedural system appears to be specific only to the trained context, while learning in the explicit system appears to be generalizable. A study my colleagues and I conducted (Casale, Roeder, & Ashby, 2012) demonstrated that category knowledge could be transferred in rule-based tasks, but not in information-integration tasks. The purpose of this dissertation was to further explore the stimulus-specific nature of procedural category learning and the generalizability of explicit category learning. The results of these experiments showed that learning is not specific to just one or a small group of stimuli in explicit category learning, even when explicit learning becomes automatic knowledge. These results also showed that procedural category learning is highly specific to trained stimuli, even when category knowledge becomes automatic. Finally, these results cannot be accounted for by the effects of either sensory adaptation or perceptual learning.