Classifying Mental States From Eye Movements During Scene Viewing.
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 pa...
| Publicado en: | Journal of Experimental Psychology. Human Perception & Performance Vol. 41; no. 6; pp. 1502 - 1515 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
| Publicado: |
American Psychological Association
Dec2015
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=111213694&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 111213694 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00961523 EPH jtl: Journal of Experimental Psychology. Human Perception & Performance issn: 00961523 maglogo: N pubinfo: dt: Dec2015 vid: 41 iid: 6 pid: 34 pub: American Psychological Association artinfo: ui: 111213694 10.1037/a0039673 ppf: 1502 ppct: 13 formats: tig: atl: Classifying Mental States From Eye Movements During Scene Viewing. aug: au: Kardan, Omid Berman, Marc G. Yourganov, Grigori Schmidt, Joseph Henderson, John M. affil: The University of Chicago University of South Carolina University of California, Davis su: Cognitive ability Aesthetics Eye movements Visual perception Memorization sug: subj: Cognitive ability Aesthetics Eye movements Visual perception Memorization keyword: classification eye movements linear discriminant multivariate analysis scene viewing classification eye movements linear discriminant multivariate analysis scene viewing ab: 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 provided important task-specific information. These results provide evidence on how participants perform different visual tasks. In the visual search task, for example, participants exhibited more variance and skewness in fixation durations and saccade amplitudes, but also showed heightened correlation between fixation durations and the variance in fixation durations. In summary, these results point to the possibility that eye-movement features and their distributional properties can be used to classify mental states both within and across individuals. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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