Integrating a Flexible Representation Machinery in a Model of Human Concept Learning

High-order human cognition involves processing of abstract and categorically represented knowledge. Traditionally, it has been considered that there is a single innate internal representation system for categorical knowledge. However, on the basis of the previous empirical and simulation studies, we view the representational system as a dynamic mechanism, capable of selecting a representation scheme that meets situational characteristics, including complexities of category structure. The present paper introduces a framework for a cognitive model that integrates robust and flexible internal representation machinery. A set of three simulation studies were conducted. The results showed that SUPERSET, our new model, successfully exhibited cognitive behaviors that are consistent with rule-(Simulation 1A), prototype-(Simulation IB), and exemplar-like (Simulation 1C) internal representation schemes.