New paper accepted to the IEEE Transactions on Human Machine Systems by our PhD student Zekun Wu. This paper is part of a research project within the Center for Perspicuous Computing.
Understanding and Predicting Temporal Visual Attention Influenced by Dynamic Highlights in Monitoring Task
Zekun Wu, Anna Maria Feit
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 non-highlighted ones, quantified using normalized saliency (NS) metrics. 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 (HISM), 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.
Link to article:
https://ieeexplore.ieee.org/document/11197128
https://arxiv.org/abs/2510.08777
Link to Dataset and Code: coming soon.
Comments are closed.