Overview

Categorical knowledge representation has a profound impact on decision making. Yet, relatively little is known about how different types of representations are acquired, or how useful different representations are for decision making in general. The proposed research aims to fill this void by characterizing the factors contributing to the development and generalizability of different types of category representations. More specifically, we will test the following novel hypotheses: (1) The training methodology and the structure of the categories themselves are critical factors in determining the nature of the category representation. (2) Distinct psychological representations depend upon different computational processes and are mediated by different brain networks. (3) Different category representations vary in the extent to which they can be generalized to support cognition. To achieve these objectives, we will utilize a combination of experimental, neuroimaging, and computational techniques that will enable investigation of the impact of different types of category representations on decision making at the aggregate and individual participant levels.

Goals of Research

A current challenge facing the field of categorization, and cognitive science in general, is how best to integrate the findings and understand the generalizability of different types of knowledge representations. The proposed research utilizes a combination of behavioral, neuroimaging, and computational modeling techniques to address these challenges. Specifically, we will investigate (1) the factors influencing the development of different types of category representations; (2) the psychological functions and brain networks supporting category representations; and (3) the utility of different types of category representations for supporting performance in new tasks and/or with new stimuli. The proposed research will build upon existing work and develop innovative techniques for advancing research in the field.