Classifying mental states from eye movements during scene viewing

Journal of Experimental Psychology. Human Perception and Performance
Omid KardanJohn M Henderson

Abstract

How eye movements reflect underlying cognitive processes during scene viewing has been a topic of considerable theoretical interest. In this study, we used eye-movement features and their distributions over time to successfully classify mental states as indexed by the behavioral task performed by participants. We recorded eye movements from 72 participants performing 3 scene-viewing tasks: visual search, scene memorization, and aesthetic preference. To classify these tasks, we used statistical features (mean, standard deviation, and skewness) of fixation durations and saccade amplitudes, as well as the total number of fixations. The same set of visual stimuli was used in all tasks to exclude the possibility that different salient scene features influenced eye movements across tasks. All of the tested classification algorithms were successful in predicting the task within a single participant. The linear discriminant algorithm was also successful in predicting the task for each participant when the training data came from other participants, suggesting some generalizability across participants. The number of fixations contributed most to task classification; however, the remaining features and, in particular, their covariance pr...Continue Reading

Citations

Apr 7, 2019·Cognitive Research: Principles and Implications·R Austin HicklinJoAnn Buscaglia
Jan 9, 2019·Frontiers in Psychology·Kimberly G SmithJulius Fridriksson
Sep 14, 2017·Scientific Reports·Omid KardanAmanda Woodward
Feb 12, 2019·Cognitive Computation·W Joseph MacInnesMichael D Dodd
Nov 19, 2019·Vision·Carrick C Williams, Monica S Castelhano
Jun 17, 2020·Learning & Memory·Michelle M RameyJohn M Henderson
Jun 7, 2020·International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology·Benjamin T Carter, Steven G Luke

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