Relationship between Executive Functions and Categorization

A multiple-systems theory of category learning proposes that an explicit, verbally-mediated system learns rule-defined (RD) categories categorization rules, and a procedural, nonverbal system learns new non-rule-defined (NRD) categories for which there is no easily verbalizable rule. Executive Function (EF) is a broad term that brings together cognitive processes like selective attention, inhibition, and reasoning, plays a key role in the explicit category rule learning or learning RD categories. The multiple-systems theory further hypothesizes that EF does not predict performance in learning NRD categories. The proposed study intends to examine the relationship between EF and category learning through Structural Equation Modeling (SEM) analysis. The SEM procedure will be significantly optimized if Bayesian estimation was incorporated. Bayesian analysis not only facilitate with the creation of more optimized categorization models, it is also highly transferable knowledge that can be applied in any data-driven research that involve inferential statistics. The purpose of this visit is to learn Bayesian statistics through modeling this projects’ data, and investigate the EF and category relationship through interpreting model results.

Faculty Supervisor:

John Paul Minda

Student:

Partner:

University of New South Wales

Discipline:

Sociology

Sector:

Education

University:

Western University

Program: