In time-critical detection tasks, such as drone monitoring, a key condition for users to effectively leverage AI assistance is to find an appropriate trade-off between making fast decisions and verifying AI suggestions, which we refer to as appropriate user reliance. However, assessing such reliance is often oversimplified by focusing solely on task outcomes, potentially overlooking whether users properly verify AI messages. We collected eye-tracking data from an AI-assisted monitoring task and developed a gaze-based reliance model: RelEYEance, to assess the extent of user reliance on AI-suggested alarms. We found that gaze patterns related to verification behaviors distinguish between appropriate reliance, over-reliance, and under-reliance, influencing task performance. We validated our model in a second user study, showing it can reliably detect users' over- and under-reliance at run-time, which could be used e.g. for issuing intervention messages. The results demonstrate the potential for real-time human-AI reliance assessment, facilitating adaptive reliance calibration.

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.

This study examines the role of visual highlights in guiding user attention in drone monitoring tasks, employing a simulated interface for observation. The experiment results show that such highlights can significantly expedite the visual attention on the corresponding area. Based on this observation, we leverage both the temporal and spatial information in the highlight to develop a new saliency model: the highlight-informed saliency model (HISM), to infer the visual attention change in the highlight condition. Our findings show the effectiveness of visual highlights in enhancing user attention and demonstrate the potential of incorporating these cues into saliency prediction models.

Visual highlighting can guide user attention in complex interfaces. However, its effectiveness under limited attentional capacities is underexplored. This paper examines the joint impact of visual highlighting (permanent and dynamic) and dual-task-induced cognitive load on gaze behaviour. Our analysis, using eye-movement data from 27 participants viewing 150 unique webpages reveals that while participants' ability to attend to UI elements decreases with increasing cognitive load, dynamic adaptations (i.e., highlighting) remain attention-grabbing. The presence of these factors significantly alters what people attend to and thus what is salient. Accordingly, we show that state-of-the-art saliency models increase their performance when accounting for different cognitive loads. Our empirical insights, along with our openly available dataset, enhance our understanding of attentional processes in UIs under varying cognitive (and perceptual) loads and open the door for new models that can predict user attention while multitasking.