Undergraduate Honors Theses

Date of Award

Winter 4-30-2022

Program

Psychology

Supervisor

Dr. John Paul Minda

Abstract

Category learning is an essential part of human cognition that impact our everyday experiences. The way in which humans assign categories to objects have been investigated by various theories and models. Among the most extensively studied theories is COVIS which suggests that a verbal system deals with learning explicit rules to determine category membership whereas the implicit system is procedurally based and uses a non-verbalizable, gradual, and automatic learning of similarity or family resemblance among category members (Ashby et al., 1998; Ashby & Maddox, 2011). Furthermore, a theoretical model of analytic vs. holistic thinking was proposed by Nisbett et al., (2001) to explain cognitive differences observed between individuals. The holistic thinking strategy is similar to the implicit system in COVIS, whereas the analytic strategy is similar to the explicit system in COVIS (Nisbett et al., 2001). The aim of the present study was to investigate the difference between the analytic and holistic thinking in category learning using an entirely online task. This study successfully gathered data on an entirely online categorization task and showed expected trends in learning the optimal strategy to accurately categorize three different category learning tasks. The results show that the correlation between task Type and AHS scores for the family resemblance task (Type IV) is significant, and the rule-based task (Type II) is trending in the predicted direction.

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