Display Design Shapes How Eye Tracking, Workload, and Situation Awareness Predict Team Performance

Understanding how cognitive and physiological measures predict team performance is crucial for optimizing human-machine displays. While eye tracking and workload metrics are commonly used in individual performance modeling, their predictive power in team settings remains underexplored, particularly for different display designs. This study examines whether predictors of team performance vary across gaze sharing visualization techniques. Gaze sharing is a means of communication used to exchange visual information between teammates as it allows partners to view each other’s gaze points on their respective displays. Thirty-five UAV teams (70 participants) completed a multitasking simulation under three display design conditions: (1) no gaze sharing, (2) gaze sharing with a fixation dot, and (3) gaze sharing with a fixation trail. Team performance was modeled using Principal Component Regression with eye tracking metrics, SAGAT-TSA scores, and NASA-TLX dimensions. Results indicate that different gaze sharing displays have different predictors of performance. Eye tracking metrics alone significantly predicted team performance only in the trail condition. Adding workload measures strengthened predictions in both gaze sharing conditions but remained non-significant in the control no gaze sharing condition. These findings suggest that team performance prediction is not one-size-fits-all; instead, the effectiveness of eye tracking and workload measures depends on display being used. Gaze sharing changes how teammates coordinate, and in doing so, may alter the reliability of standard cognitive and physiological predictors as we know it.