Date of Graduation

5-2016

Document Type

Thesis

Degree Name

Bachelor of Arts

Degree Level

Undergraduate

Department

Psychological Science

Advisor

Parks, Nathan

Reader

Chapman, Kate

Second Reader

Shew, Woodrow

Third Reader

Rulli, Richard

Abstract

Extensive literature in the cognitive neurosciences has been dedicated to understanding the neural processes involved in object category learning. However, much remains to be learned regarding the mechanisms by which high-level visual patterns are extracted from a crowded visual scene, segregated into discrete object categories, and represented in the cortical visual hierarchy. Here, we used event-related potentials (ERPs) to investigate the neural underpinnings of visual object category extraction in a cluttered visual environment. Electroencephalography (EEG) was continuously recorded while subjects were given a hybrid of an object category learning and visual search task. In this hybrid task, a peripheral array of four dot patterns was flashed. In 50% of trials, one position of the array contained a distortion of a prototype dot pattern. The remaining trials consisted entirely of randomly generated dot patterns. After hundreds of trials, observers learned to detect the dot pattern object category via correct or incorrect feedback given on each trial. We assessed improvements in dot pattern detection (d’) in conjunction with three component ERPs, N250, P3, and FRN, to examine the neural mechanisms of visual object category formation. Our results revealed a sequence of effects during the course of learning to detect the pattern. First, FRN amplitude was greatest at the beginning of learning (low d’) and then decreased over time. Following the FRN effect, a P3 effect developed as learning continued. Finally, an N250 effect appeared at the peak of learning (where d’ peaked), following the beginning of the P3 effect. The underlying neural mechanisms of these components suggest a correlational relationship between these components and contribute to how the brain learns to represent a novel object.

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