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Nada Attar, Matthew Schneps, Marc Pomplun; Pupil Size as a Measure of Working Memory Load During a Complex Visual Search Task. Journal of Vision 2013;13(9):160. doi: https://doi.org/10.1167/13.9.160.
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© ARVO (1962-2015); The Authors (2016-present)
An observer’s pupil dilates and constricts in response to changes in variables such as ambient luminance, emotional stimulus content, and working memory load. Although it is difficult to measure working memory load during an ongoing task, it is still possible to use pupil size as an indicator for this purpose to benefit many fields of research. One of the important domains in which pupillometry has been used to study cognitive processing is visual search. Porter, Troscianko, and Gilchrist (QJEP 2007) found that pupil size statistically increased over the course of the search, and they attributed this finding to accumulating working memory load. However, other factors, e.g., arousal and effort, likely affected pupil size as well and added noise to the data and some uncertainty to the conclusions. In the present study, we used a search task designed to increase working memory load by observers loading their memory with a number of targets and their different locations in the stimuli. We then analyzed their eye movements to examine their strategy in searching and investigated the interaction between eye movements and pupil size. The experiment interspersed Gabor patch search displays with intermittent blank screens showing only a central fixation marker, thought to induce a low, stable level of arousal and cognitive effort. Observers were to report the number of targets in each of four circles of search items in the stimuli. Consequently, differences in mean pupil size between successive fixation screens should mainly reflect changes in working memory load that occurred during the search interval between the fixation screens. The results show that (1) intermittent fixation screens greatly enhance pupil-based memory load estimation in complex search tasks; (2) eye-movements and strategy during search tasks can be valuable for enhancing pupil-based memory load estimation.
Meeting abstract presented at VSS 2013
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