The goal of this dissertation was to study the effects of state and trait anxiety on explicit and implicit category learning. It was hypothesized that participants with higher state anxiety scores would require more trials to learn the explicit rule learning task compared to participants with lower state anxiety scores. On the other hand, high state anxiety participants were expected to excel in the implicit rule learning task relative to participants with low state anxiety scores. The hypotheses were informed by two theories, COVIS and ACT. The ACT theory states that there are three major mechanisms of executive functions that worsen with increasing anxiety. The COVIS theory states that explicit and implicit category learning rely on separate structures of the brain and, therefore, differently affected by anxiety. In experiment 1, participants completed implicit and explicit category learning tasks in either the control condition or the pressure condition. In the pressure manipulation group, participants completed a mortality salience writing task and were told they had a partner relying on their success in learning the categorization rule for both to receive a reward to induce anxiety. While the control participants completed a neutral writing task and were offered a reward solely based on their performance. In experiment 2, the study design was same as experiment 1 except for the addition of neuroimaging during category learning. Manipulating pressure during category learning replicated earlier research showing worsened performance in explicit rule learning under pressure, but no effect for implicit rule learning. In general, there was evidence that category learning was better in participants with high state anxiety scores, contradicting predictions based on ACT theory.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1267 |
| Date | 01 January 2020 |
| Creators | Patel, Pooja |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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