Eye movement analysis with switching hidden Markov models
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 1026–1043 |
Journal / Publication | Behavior Research Methods |
Volume | 52 |
Issue number | 3 |
Online published | 11 Nov 2019 |
Publication status | Published - Jun 2020 |
Link(s)
Abstract
Here we propose the eye movement analysis with switching hidden Markov model (EMSHMM) approach to analyzing eye movement data in cognitive tasks involving cognitive state changes. We used a switching hidden Markov model (SHMM) to capture a participant's cognitive state transitions during the task, with eye movement patterns during each cognitive state being summarized using a regular HMM. We applied EMSHMM to a face preference decision-making task with two pre-assumed cognitive states-exploration and preference-biased periods-and we discovered two common eye movement patterns through clustering the cognitive state transitions. One pattern showed both a later transition from the exploration to the preference-biased cognitive state and a stronger tendency to look at the preferred stimulus at the end, and was associated with higher decision inference accuracy at the end; the other pattern entered the preference-biased cognitive state earlier, leading to earlier above-chance inference accuracy in a trial but lower inference accuracy at the end. This finding was not revealed by any other method. As compared with our previous HMM method, which assumes no cognitive state change (i.e., EMHMM), EMSHMM captured eye movement behavior in the task better, resulting in higher decision inference accuracy. Thus, EMSHMM reveals and provides quantitative measures of individual differences in cognitive behavior/style, making a significant impact on the use of eyetracking to study cognitive behavior across disciplines.
Research Area(s)
- Hidden Markov model, Eye movement, Preference decision making, EMHMM, INDIVIDUAL-DIFFERENCES, BEHAVIOR, TASK, INDECISIVENESS, RECOGNITION, PERSONALITY, PREFERENCE, ATTENTION, PATTERNS
Citation Format(s)
Eye movement analysis with switching hidden Markov models. / Chuk, Tim; Chan, Antoni B.; Shimojo, Shinsuke et al.
In: Behavior Research Methods, Vol. 52, No. 3, 06.2020, p. 1026–1043.
In: Behavior Research Methods, Vol. 52, No. 3, 06.2020, p. 1026–1043.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review