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.
Additional Information
© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Published 11 November 2019; Issue Date June 2020. 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.Attached Files
Published - 13428_2021_Article_1541.pdf
Files
Name | Size | Download all |
---|---|---|
md5:747134043b624c1b4b1267003db4bb0d
|
1.5 MB | Preview Download |
Additional details
- PMCID
- PMC8613150
- Eprint ID
- 99883
- Resolver ID
- CaltechAUTHORS:20191118-074052352
- 17609117
- Research Grants Council of Hong Kong
- CityU 110513
- Research Grants Council of Hong Kong
- Japan Science and Technology Agency
- Created
-
2019-11-18Created from EPrint's datestamp field
- Updated
-
2023-07-14Created from EPrint's last_modified field
- Caltech groups
- Division of Biology and Biological Engineering