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Eye movement analysis with switching hidden Markov models

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.

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Notes

  1. 1.

    Note that there are other hierarchical HMMs for other purposes. For example, please see Camci and Chinnam (2006) and Hariri, Shirmohammadi, and Pakravan (2008) for more details.

  2. 2.

    Since we were interested in the differences in the transitions between the two sides, we forced all HMMs to use the same set of ROIs that covered each face.

  3. 3.

    Greenhouse–Geisser correction was applied whenever the assumption of sphericity was not met.

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Acknowledgments

We are grateful to the Research Grant Council of Hong Kong (project 17609117 to J.H.H. and CityU 110513 to A.B.C.) and to JST.CREST (to S.S.). A.B.C. and J.H.H. contributed equally to this article. We thank the editor and two anonymous reviewers for the helpful comments.

Open Practices Statement

The code (Matlab Toolbox EMSHMM) and data of the study are available to the research community for noncommercial use at http://visal.cs.cityu.edu.hk/research/emshmm/. The experiment reported here was not preregistered.

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Correspondence to Antoni B. Chan or Janet H. Hsiao.

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Chuk, T., Chan, A.B., Shimojo, S. et al. Eye movement analysis with switching hidden Markov models. Behav Res (2019). https://doi.org/10.3758/s13428-019-01298-y

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Keywords

  • Hidden Markov model
  • Eye movement
  • Preference decision making
  • EMHMM