Predicting Attention Allocation in Dual-Task Scenarios Using Multidimensional Eye-Tracking Metrics.
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| Title: | Predicting Attention Allocation in Dual-Task Scenarios Using Multidimensional Eye-Tracking Metrics. |
|---|---|
| Authors: | Qiu, Xuyi (AUTHOR), Gao, Ying (AUTHOR), Wang, Kangning (AUTHOR), Yang, Xizhong (AUTHOR), Wei, Wei (AUTHOR), Qiu, Shuang (AUTHOR) |
| Source: | International Journal of Human-Computer Interaction. Jul2026, Vol. 42 Issue 14, p11517-11535. 19p. |
| Subjects: | Dual-task paradigm, Eye tracking, Cognitive ability, Convolutional neural networks, Selectivity (Psychology), Computer multitasking, Eye movements, Multivariate analysis |
| Abstract: | Attention allocation is essential for multitasking, as it reflects the distribution of limited cognitive resources across tasks. Traditional eye-tracking metrics, such as fixation and glance ratios, provide limited insight into complex attentional processes. This study employed a comprehensive set of eye-tracking measures, including pupil diameter, eye movement amplitude, velocity, and duration, to assess attentional dynamics. We designed a dual-task scenario with varied task difficulty, elicited different levels of attention allocation. Analysis of eye position data revealed significant correlations between self-reported attention and multiple eye metrics. We proposed a CNN-based attention allocation classification network that outperformed conventional pattern recognition methods, demonstrating the approach's effectiveness. These findings demonstrate that combining multidimensional eye-tracking metrics enables a more comprehensive assessment of attention allocation. This approach enhances understanding of cognitive resource distribution in dual-task scenarios and provides a promising method for monitoring attentional states in complex environments. [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.) | |
| Database: | Psychology and Behavioral Sciences Collection |
| FullText | Text: Availability: 0 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 195177704 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Predicting Attention Allocation in Dual-Task Scenarios Using Multidimensional Eye-Tracking Metrics. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Qiu%2C+Xuyi%22">Qiu, Xuyi</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gao%2C+Ying%22">Gao, Ying</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Kangning%22">Wang, Kangning</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Xizhong%22">Yang, Xizhong</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wei%2C+Wei%22">Wei, Wei</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qiu%2C+Shuang%22">Qiu, Shuang</searchLink> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. Jul2026, Vol. 42 Issue 14, p11517-11535. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Dual-task+paradigm%22">Dual-task paradigm</searchLink><br /><searchLink fieldCode="DE" term="%22Eye+tracking%22">Eye tracking</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+ability%22">Cognitive ability</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Selectivity+%28Psychology%29%22">Selectivity (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+multitasking%22">Computer multitasking</searchLink><br /><searchLink fieldCode="DE" term="%22Eye+movements%22">Eye movements</searchLink><br /><searchLink fieldCode="DE" term="%22Multivariate+analysis%22">Multivariate analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Attention allocation is essential for multitasking, as it reflects the distribution of limited cognitive resources across tasks. Traditional eye-tracking metrics, such as fixation and glance ratios, provide limited insight into complex attentional processes. This study employed a comprehensive set of eye-tracking measures, including pupil diameter, eye movement amplitude, velocity, and duration, to assess attentional dynamics. We designed a dual-task scenario with varied task difficulty, elicited different levels of attention allocation. Analysis of eye position data revealed significant correlations between self-reported attention and multiple eye metrics. We proposed a CNN-based attention allocation classification network that outperformed conventional pattern recognition methods, demonstrating the approach's effectiveness. These findings demonstrate that combining multidimensional eye-tracking metrics enables a more comprehensive assessment of attention allocation. This approach enhances understanding of cognitive resource distribution in dual-task scenarios and provides a promising method for monitoring attentional states in complex environments. [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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=195177704 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/10447318.2025.2594142 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 11517 Subjects: – SubjectFull: Dual-task paradigm Type: general – SubjectFull: Eye tracking Type: general – SubjectFull: Cognitive ability Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Selectivity (Psychology) Type: general – SubjectFull: Computer multitasking Type: general – SubjectFull: Eye movements Type: general – SubjectFull: Multivariate analysis Type: general Titles: – TitleFull: Predicting Attention Allocation in Dual-Task Scenarios Using Multidimensional Eye-Tracking Metrics. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Qiu, Xuyi – PersonEntity: Name: NameFull: Gao, Ying – PersonEntity: Name: NameFull: Wang, Kangning – PersonEntity: Name: NameFull: Yang, Xizhong – PersonEntity: Name: NameFull: Wei, Wei – PersonEntity: Name: NameFull: Qiu, Shuang IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10447318 Numbering: – Type: volume Value: 42 – Type: issue Value: 14 Titles: – TitleFull: International Journal of Human-Computer Interaction Type: main |
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