Monitoring interfaces are crucial for dynamic, highstakes 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.

翻译：监控界面对于动态且高风险的任务至关重要，其中用户注意力的有效分配不可或缺。视觉高亮能有效引导注意力，但也可能引入非预期的干扰。为探究此问题，我们研究了在无人机监控任务中，视觉高亮如何影响用户的注视行为，重点关注其何时、持续多久以及吸引多少注意力。我们发现，与非高亮区域相比，高亮区域展现出独特的时间特性，并使用归一化显著度（NS）指标进行量化。高亮能引发即时响应，NS迅速达到峰值，但这种注意力转移以减少其他区域的搜索努力为代价，可能影响情境感知。为预测这些动态变化并支持界面设计，我们开发了高亮信息显著度模型（HISM），该模型能提供随时间推移的NS细粒度预测。这些预测使得评估高亮效果成为可能，并为未来监控界面设计（尤其是时间敏感型任务）中高亮的最佳时机与部署提供依据。