Date of Graduation

5-2019

Document Type

Thesis

Degree Name

Master of Arts in Psychology (MA)

Degree Level

Graduate

Department

Psychological Science

Advisor/Mentor

Lamm, Connie

Committee Member

Levine, William H.

Second Committee Member

Shew, Woodrow L.

Keywords

category learning; electroencephalography; neuroplasticity; object representation; perceptual learning; vision

Abstract

In perceptual learning studies, participants engage in extensive training in the discrimination of visual stimuli in order to modulate perceptual performance. Much of the literature in perceptual learning has looked at the induction of the reorganization of low-level representations in V1. However, much remains to be understood about the mechanisms behind how the adult brain (an expert in visual object categorization) extracts high-level visual objects from the environment and categorically represents them in the cortical visual hierarchy. Here, I used event-related potentials (ERPs) to investigate the neural mechanisms involved in object representation formation during a hybrid visual search and prototype distortion category learning task. EEG was continuously recorded while participants performed the hybrid task, in which a peripheral array of four dot patterns was briefly flashed on a computer screen. In half of the trials, one of the four dot patterns of the array contained the target, a distorted prototype pattern. The remaining trials contained only randomly generated patterns. After hundreds of trials, participants learned to discriminate the target pattern through corrective feedback. A multilevel modeling approach was used to examine the predictive relationship between behavioral performance over time and two ERP components, the N1 and the N250. The N1 is an early sensory component related to changes in visual attention and discrimination (Hopf et al., 2002; Vogel & Luck, 2000). The N250 is a component related to category learning and expertise (Krigolson et al., 2009; Scott et al., 2008; Tanaka et al., 2006). Results indicated that while N1 amplitudes did not change with improved performance, increasingly negative N250 amplitudes did develop over time and were predictive of improvements in pattern detection accuracy.

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