Eye movement analysis with hidden Markov models (EMHMM) with co-clustering
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
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Detail(s)
Original language | English |
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Pages (from-to) | 2473–2486 |
Journal / Publication | Behavior Research Methods |
Volume | 53 |
Issue number | 6 |
Online published | 30 Apr 2021 |
Publication status | Published - Dec 2021 |
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DOI | DOI |
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Attachment(s) | Documents
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Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85105391726&origin=recordpage |
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(3bce60ae-db52-4b16-bf77-6169f7869e29).html |
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.
In: Behavior Research Methods, Vol. 53, No. 6, 12.2021, p. 2473–2486.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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