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Predicting Multitasking Performance: An EEG- and Eye Movement-Based Dynamic Bayesian Network.

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Title: Predicting Multitasking Performance: An EEG- and Eye Movement-Based Dynamic Bayesian Network.
Authors: Li, Jiaxin1 (AUTHOR), Huang, Shuai1 (AUTHOR), Kim, Ji-Eun1 (AUTHOR) jikim@uw.edu
Source: International Journal of Human-Computer Interaction. Nov2025, Vol. 41 Issue 22, p14068-14078. 11p.
Subjects: Computer multitasking, Electroencephalography, Bayesian analysis, Eye movements, Physiology
Abstract: This study proposes a probabilistic model using neural and physiological responses to continuously predict performance during multitasking. Though multitasking performance is known to be time-varying depending on task demand, there is limited research modeling temporal changes in multitasking performance. We applied a dynamic Bayesian network (DBN) to predict multitasking performance while recording participants' eye movements and electroencephalogram (EEG) band power during the Multi-Attribute Task Battery II task. Our DBN model including multimodal eye movements and EEG band power significantly outperformed DBN models that rely on single physiological responses at an alpha of 0.01. We also found our DBN had significantly higher accuracy than linear regression, decision trees, random forests, and deep neural networks at an alpha of 0.001 based on Wilcoxon's signed-rank tests. Our predictive modeling of multitasking performance has the potential to allow individuals and organizations to anticipate the occurrence and mitigate the impact of multitasking errors. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Jiaxin%22">Li, Jiaxin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Shuai%22">Huang, Shuai</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kim%2C+Ji-Eun%22">Kim, Ji-Eun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jikim@uw.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. Nov2025, Vol. 41 Issue 22, p14068-14078. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Computer+multitasking%22">Computer multitasking</searchLink><br /><searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Eye+movements%22">Eye movements</searchLink><br /><searchLink fieldCode="DE" term="%22Physiology%22">Physiology</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: This study proposes a probabilistic model using neural and physiological responses to continuously predict performance during multitasking. Though multitasking performance is known to be time-varying depending on task demand, there is limited research modeling temporal changes in multitasking performance. We applied a dynamic Bayesian network (DBN) to predict multitasking performance while recording participants' eye movements and electroencephalogram (EEG) band power during the Multi-Attribute Task Battery II task. Our DBN model including multimodal eye movements and EEG band power significantly outperformed DBN models that rely on single physiological responses at an alpha of 0.01. We also found our DBN had significantly higher accuracy than linear regression, decision trees, random forests, and deep neural networks at an alpha of 0.001 based on Wilcoxon's signed-rank tests. Our predictive modeling of multitasking performance has the potential to allow individuals and organizations to anticipate the occurrence and mitigate the impact of multitasking errors. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/10447318.2025.2480882
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        Text: English
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      – SubjectFull: Computer multitasking
        Type: general
      – SubjectFull: Electroencephalography
        Type: general
      – SubjectFull: Bayesian analysis
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      – SubjectFull: Eye movements
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              Text: Nov2025
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