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. |
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| 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] |
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| Database: | Psychology and Behavioral Sciences Collection |
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