title: Modeling a flexible representation machinery of human concept learning.
abstract: It is widely acknowledged that categorically organized abstract knowledge plays a significant role in high-order human cognition. Yet, there are many unknown issues about the nature of how categories are internally represented in our mind. Traditionally, it has been considered that there is a single innate internal representation system for categorical knowledge, such as Exemplars, Prototypes, or Rules. However, results of recent empirical and computational studies collectively suggest that the human internal representation system is apparently capable of exhibiting behaviors consistent with various types of internal representation schemes. We, then, hypothesized that humans' 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. Three simulation studies were conducted. The results showed that SUPERSET, our new model, successfully exhibited cognitive behaviors that are consistent with three main theories of the human internal representation system. Furthermore, a simulation study on social cognitive behaviors showed that the model was capable of acquiring knowledge with high commonality, even for a category structure with numerous valid conceptualizations.
authors.affiliation: Chiba University, Department of Cognitive and Information Science, Chiba, Japan. matsuka@cogsci.L.chiba-u.ac.jp
authors.forename: Toshihiko
authors.initials: T
authors.lastname: Matsuka
authors.forename: Yasuaki
authors.initials: Y
authors.lastname: Sakamoto
authors.forename: Arieta
authors.initials: A
authors.lastname: Chouchourelou
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003210:Concept Formation / Q000502:physiology*; D005793:Generalization, Psychological; D006801:Humans; D019359:Knowledge; D007858:Learning / Q000502:physiology*; D008960:Models, Psychological