Eye movement analysis with hidden Markov models (EMHMM) with co-clustering

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

34 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)2473–2486
Journal / PublicationBehavior Research Methods
Volume53
Issue number6
Online published30 Apr 2021
Publication statusPublished - Dec 2021

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Abstract

The eye movement analysis with hidden Markov models (EMHMM) method provides quantitative measures of individual differences in eye-movement pattern. However, it is limited to tasks where stimuli have the same feature layout (e.g., faces). Here we proposed to combine EMHMM with the data mining technique co-clustering to discover participant groups with consistent eye-movement patterns across stimuli for tasks involving stimuli with different feature layouts. Through applying this method to eye movements in scene perception, we discovered explorative (switching between the foreground and background information or different regions of interest) and focused (mainly looking at the foreground with less switching) eye-movement patterns among Asian participants. Higher similarity to the explorative pattern predicted better foreground object recognition performance, whereas higher similarity to the focused pattern was associated with better feature integration in the flanker task. These results have important implications for using eye tracking as a window into individual differences in cognitive abilities and styles. Thus, EMHMM with co-clustering provides quantitative assessments on eye-movement patterns across stimuli and tasks. It can be applied to many other real-life visual tasks, making a significant impact on the use of eye tracking to study cognitive behavior across disciplines.

Research Area(s)

  • Co-clustering, EMHMM, Eye movements, Hidden Markov model, Scene perception

Citation Format(s)

Eye movement analysis with hidden Markov models (EMHMM) with co-clustering. / Hsiao, Janet H.; Lan, Hui; Zheng, Yueyuan et al.
In: Behavior Research Methods, Vol. 53, No. 6, 12.2021, p. 2473–2486.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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