Understanding and Predicting Temporal Visual Attention Influenced by Dynamic Highlights in Monitoring Task

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Abstract
Monitoring interfaces are crucial for dynamic, high-stakes tasks where effective user attention is essential. Visual highlights can guide attention effectively, but may also introduce unintended disruptions. To investigate this, we examined how visual highlights affect users’ gaze behavior in a drone monitoring task, focusing on when, how long, and how much attention they draw. We found that highlighted areas exhibit distinct temporal characteristics compared to nonhighlighted ones, quantified using normalized saliency (NS) metrics. We found that highlights elicited immediate responses, with NS peaking quickly, but this shift came at the cost of reduced search efforts elsewhere, potentially impacting situational awareness. To predict these dynamic changes and support interface design, we developed the Highlight-Informed Saliency Model, which provides granular predictions of NS over time. These predictions enable evaluations of highlight effectiveness and inform the optimal timing and deployment of highlights in future monitoring interface designs, particularly for time-sensitive tasks.
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  • Deutsche Forschungsgemeinschaft (389792660, TRR 248)

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