The neuropsychological theory known as COVIS (COmpetition between Verbal and Implicit Systems) postulates that distinct brain systems compete during category learning. The explicit system involves conscious hypothesis testing about verbalizable rules, while the implicit system relies on procedural learning of rules that are difficult to verbalize. Specifically from a behavioral approach, COVIS has been supported through demonstrating empirical dissociations between explicit and implicit learning tasks. The current studies were designed to gain deeper understanding of implicit category learning through the implementation of a subjective measure of awareness, Meta d', which until now has not been validated within a COVIS framework. Meta d' is a measure of metacognitive accuracy. This is the ability to assess the accuracy of one's own performance. These three experiments evaluated the use of Meta d' as a valid predictor of task performance within a two-structure perceptual categorization task. Experiment 1 focuses on using Meta d' to parse out dissociations between awareness and performance through the phenomenon of Blind Sight and Blind Insight. Experiment 2 and 3 utilize a motor response mapping disruption to observe predicted decrements to the implicit learning system. Experiment 3 utilizes functional Near Infrared Spectroscopy (fNIRS) to measure hemodynamic changes in the Prefrontal Cortex as a function of category structure. Across the 3 experiments, Meta d' in conjunction with decision bound model fits were used to make accurate predictions about the differences in performance throughout implicit and explicit categorization tasks. These collective results indicate that metacognitive accuracy, an implicit structure, was highly sensitive to a whether a person is using the correct rule strategies through the task.
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Doctor of Philosophy (Ph.D.)
College of Sciences
Psychology; Human Factors and Cognitive Psychology
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Zlatkin, Audrey, "Subjective Measures of Implicit Categorization Learning" (2019). Electronic Theses and Dissertations, 2004-2019. 6605.