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A novel measure to determine viewing priority and its neural correlates in the human brain
Author(s) -
JanBernard C. Marsman,
Frans W. Cornelissen,
Michael Dörr,
Eleonora Vig,
Erhardt Barth,
Remco J. Renken
Publication year - 2016
Publication title -
journal of vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.126
H-Index - 113
ISSN - 1534-7362
DOI - 10.1167/16.6.3
Subject(s) - surprise , measure (data warehouse) , gaze , property (philosophy) , computer science , functional magnetic resonance imaging , natural (archaeology) , artificial intelligence , brain activity and meditation , eye movement , motion (physics) , computer vision , cognitive psychology , psychology , electroencephalography , neuroscience , communication , data mining , geography , philosophy , epistemology , archaeology
A key property of human visual behavior is the very frequent movement of our eyes to potentially relevant information in the environment. Observers thus continuously have to prioritize information for directing their eyes to. Research in this field has been hampered by a lack of appropriate measures and tools. Here, we propose and validate a novel measure of priority that takes advantage of the variability in the natural viewing behavior of individual observers. In short, our measure assumes that priority is low when observers' gaze behavior is inconsistent and high when it is very consistent. We calculated priority for gaze data obtained during an experiment in which participants viewed dynamic natural scenes while we simultaneously recorded their gaze position and brain activity using functional magnetic resonance imaging. Our priority measure shows only limited correlation with various saliency, surprise, and motion measures, indicating it is assessing a distinct property of visual behavior. Finally, we correlated our priority measure with the BOLD signal, thereby revealing activity in a select number of human occipital and parietal areas. This suggests the presence of a cortical network involved in computing and representing viewing priority. We conclude that our new analysis method allows for empirically establishing the priority of events in near-natural vision paradigms

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