Proceedings of the Annual Meeting of the Cognitive Science Society

How people represent categories—and how those representations change over time—is a basic question about human cognition. Previous research has suggested that people categorize objects by comparing them to category prototypes in early stages of learning but use strategies that consider the individual exemplars within each category in later stages. However, many category learning experiments do not accurately reflect the environmental statistics of the real world, where the probability that we encounter an object changes over time. Our goal in this study was to introduce memory constraints by presenting each stimulus at intervals corresponding to the power-law function of memory decay. Since the exemplar model relies on the individual’s ability to store and retrieve previously seen exemplars, we hypothesized that adding memory constraints that better reflect real environments would favor the exemplar model more early on compared with later. Confirming our hypothesis, the results illustrate that under realistic environmental statistics with memory constraints, the exemplar model’s advantage over the the prototype model decreases over time.