Understanding Individual Differences in Eye Movement Pattern During Scene Perception through Co-Clustering of Hidden Markov Models

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationCOGSCI'19 - Creativity Cognition Computation
Subtitle of host publicationProceedings of the 41st Annual Meeting of the Cognitive Science Society
Place of PublicationMontreal
Pages3283
Publication statusPublished - Jul 2019

Conference

Title41st Annual Meeting of the Cognitive Science Society (CogSci 2019)
PlaceCanada
CityMontreal
Period24 - 27 July 2019

Abstract

Here we combined the Eye Movement analysis with Hidden Markov Models (EMHMM) method with the data mining technique co-clustering to discover participant groups with consistent eye movement patterns across stimuli during scene perception. We discovered explorative (switching between foreground and background information) and focused (mainly on foreground) eye movement strategy groups among Asian participants. In contrast to previous research suggesting a cultural difference where Asians adopted explorative and Caucasians used focused eye movement strategies, we found that explorative patterns were associated with better foreground object recognition performance whereas focused patterns were associated with better feature integration in the flanker task and higher preference rating of the scenes. In addition, images with a salient foreground object relative to the background induced larger individual differences in eye movements. Thus, eye movements in scene perception not only contribute to scene recognition performance, but also reflects individual differences in cognitive ability and scene preference.

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Citation Format(s)

Understanding Individual Differences in Eye Movement Pattern During Scene Perception through Co-Clustering of Hidden Markov Models. / Hsiao, Janet; Chan, Kin Yan; Du, Yuefeng et al.
COGSCI'19 - Creativity Cognition Computation: Proceedings of the 41st Annual Meeting of the Cognitive Science Society. Montreal, 2019. p. 3283.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review