Publication Date

2019

Document Type

Thesis

Committee Members

Assaf Harel, Ph.D. (Advisor); Joseph W. Houpt, Ph.D. (Committee Member); Ion Juvina, Ph.D. (Committee Member)

Degree Name

Master of Science (MS)

Abstract

Object recognition entails a complex interplay between top-down and bottom-up signals. Yet, limited research has investigated the mechanisms through which top-down processes, such as task context and behavioral goals impact the neural basis of visual object processing. Using electroencephalography (EEG), we studied the temporal dynamics of task and object processing to identify how early the impact of task can be observed. We recorded ERPs from participants as they viewed object images from four categories spanning animacy (Inanimate: roller-skate, motorbike; Animate: cow, butterfly) and size (Large: motorbike, cow; Small: roller-skate, butterfly) dimensions under four task conditions comprising conceptual (naturalness, size) and perceptual (color, tilt) dimensions. We did not find evidence of behavioral goals, as manipulated by the task context, modulating early visual object representations, as indexed by early visual ERPs (P1, N1, P2), in extrastriate cortex. Additional analyses revealed that task-related processing occurred predominately in later time windows (300-600ms) within frontoparietal regions. Irrespective of task, we also observed a variety of object category effects across early visual ERPs. These findings support previous neuroimaging results suggesting object representations in occipitotemporal cortex are organized based on their animacy and real-world size, and, importantly, these ERP results indicate these organizational principles can be observed in relatively early stages along the visual processing hierarchy. Taken together, this work adds to the body of psychological and neuroscientific research examining how and when top-down and bottom-up signals interact to form the basis of visual object processing, facilitating of high-level vision.

Page Count

91

Year Degree Awarded

2019


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