When Eyes Wander Around: Mind-Wandering as Revealed by Eye Movement Analysis with Hidden Markov Models
Mind-wandering has been shown to largely influence our learning efficiency, especially in the digital and distracting era nowadays. Detecting mind-wandering thus becomes imperative in educational scenarios. Here, we used a wearable eye-tracker to record eye movements during the sustained attention t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/22/7569 |
| _version_ | 1797508508167438336 |
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| author | Hsing-Hao Lee Zih-Ling Chen Su-Ling Yeh Janet Huiwen Hsiao An-Yeu (Andy) Wu |
| author_facet | Hsing-Hao Lee Zih-Ling Chen Su-Ling Yeh Janet Huiwen Hsiao An-Yeu (Andy) Wu |
| author_sort | Hsing-Hao Lee |
| collection | DOAJ |
| description | Mind-wandering has been shown to largely influence our learning efficiency, especially in the digital and distracting era nowadays. Detecting mind-wandering thus becomes imperative in educational scenarios. Here, we used a wearable eye-tracker to record eye movements during the sustained attention to response task. Eye movement analysis with hidden Markov models (EMHMM), which takes both spatial and temporal eye-movement information into account, was used to examine if participants’ eye movement patterns can differentiate between the states of focused attention and mind-wandering. Two representative eye movement patterns were discovered through clustering using EMHMM: centralized and distributed patterns. Results showed that participants with the centralized pattern had better performance on detecting targets and rated themselves as more focused than those with the distributed pattern. This study indicates that distinct eye movement patterns are associated with different attentional states (focused attention vs. mind-wandering) and demonstrates a novel approach in using EMHMM to study attention. Moreover, this study provides a potential approach to capture the mind-wandering state in the classroom without interrupting the ongoing learning behavior. |
| first_indexed | 2024-03-10T05:04:56Z |
| format | Article |
| id | doaj.art-4fc898115f8d4809aba7e61b86c8f4bc |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:04:56Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4fc898115f8d4809aba7e61b86c8f4bc2023-11-23T01:25:49ZengMDPI AGSensors1424-82202021-11-012122756910.3390/s21227569When Eyes Wander Around: Mind-Wandering as Revealed by Eye Movement Analysis with Hidden Markov ModelsHsing-Hao Lee0Zih-Ling Chen1Su-Ling Yeh2Janet Huiwen Hsiao3An-Yeu (Andy) Wu4Department of Psychology, College of Science, National Taiwan University, Taipei City 10617, TaiwanGraduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei City 10051, TaiwanDepartment of Psychology, College of Science, National Taiwan University, Taipei City 10617, TaiwanDepartment of Psychology, The University of Hong Kong, Pok Fu Lam, Hong KongGraduate Institute of Electronics Engineering, National Taiwan University, Taipei City 10617, TaiwanMind-wandering has been shown to largely influence our learning efficiency, especially in the digital and distracting era nowadays. Detecting mind-wandering thus becomes imperative in educational scenarios. Here, we used a wearable eye-tracker to record eye movements during the sustained attention to response task. Eye movement analysis with hidden Markov models (EMHMM), which takes both spatial and temporal eye-movement information into account, was used to examine if participants’ eye movement patterns can differentiate between the states of focused attention and mind-wandering. Two representative eye movement patterns were discovered through clustering using EMHMM: centralized and distributed patterns. Results showed that participants with the centralized pattern had better performance on detecting targets and rated themselves as more focused than those with the distributed pattern. This study indicates that distinct eye movement patterns are associated with different attentional states (focused attention vs. mind-wandering) and demonstrates a novel approach in using EMHMM to study attention. Moreover, this study provides a potential approach to capture the mind-wandering state in the classroom without interrupting the ongoing learning behavior.https://www.mdpi.com/1424-8220/21/22/7569mind-wanderingsustained attentioneye movement analysis with hidden Markov models (EMHMM)fixationlearning |
| spellingShingle | Hsing-Hao Lee Zih-Ling Chen Su-Ling Yeh Janet Huiwen Hsiao An-Yeu (Andy) Wu When Eyes Wander Around: Mind-Wandering as Revealed by Eye Movement Analysis with Hidden Markov Models Sensors mind-wandering sustained attention eye movement analysis with hidden Markov models (EMHMM) fixation learning |
| title | When Eyes Wander Around: Mind-Wandering as Revealed by Eye Movement Analysis with Hidden Markov Models |
| title_full | When Eyes Wander Around: Mind-Wandering as Revealed by Eye Movement Analysis with Hidden Markov Models |
| title_fullStr | When Eyes Wander Around: Mind-Wandering as Revealed by Eye Movement Analysis with Hidden Markov Models |
| title_full_unstemmed | When Eyes Wander Around: Mind-Wandering as Revealed by Eye Movement Analysis with Hidden Markov Models |
| title_short | When Eyes Wander Around: Mind-Wandering as Revealed by Eye Movement Analysis with Hidden Markov Models |
| title_sort | when eyes wander around mind wandering as revealed by eye movement analysis with hidden markov models |
| topic | mind-wandering sustained attention eye movement analysis with hidden Markov models (EMHMM) fixation learning |
| url | https://www.mdpi.com/1424-8220/21/22/7569 |
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