Author ORCID Identifier
https://orcid.org/0000-0002-2847-3083
Date of Award
2024
Document Type
Thesis (Ph.D.)
Department or Program
Cognitive Neuroscience
First Advisor
Peter U. Tse
Second Advisor
Patrick Cavanagh
Abstract
At any waking moment, we are bombarded with more sensory information than we can fully process. Attention is necessary to deal with the dynamic world we live in. One fundamental function of vision and attention is to keep track of moving objects, but what are the targets of attention during tracking?
One of the first theories of attentional tracking predicted that targets would be selected at early processing stages. By employing the double-drift illusion, which dissociates physical and perceived positions of moving objects, we investigated which of these positions is selected for tracking. Contrary to earlier theories and in line with newer findings, targets were selected rather late in visual processing, at least after the construction of illusory percepts, for both covert (Chapter One) and overt tracking (Chapter Two).
Furthermore, capacity and speed limits to attentional tracking are hemifield specific. Brain activity in many areas is known to covary with the number of tracked targets, but it was previously unknown whether this effect would also show hemifield bias. Only targets presented in the contralateral hemifield influenced activity in earlier visual areas, while both contralateral and ipsilateral targets affected activity in parietal and frontal areas associated with attention (Chapter Three). Due to the hemifield specific nature of the capacity limit, we conjecture that it should emerge where load dependent activity is strongly contralateral.
Overall, the studies presented in this dissertation illuminate two different aspects of attentional tracking. While selection happens late in the visual hierarchy, capacity and speed limits appear to emerge early in visual processing.
Recommended Citation
Maechler, Marvin R., "Target Selection and Enhancement During Attentional Tracking" (2024). Dartmouth College Ph.D Dissertations. 184.
https://digitalcommons.dartmouth.edu/dissertations/184