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ObjectiveThe goal of this study was to assess how different real-time gaze sharing visualization techniques affect eye tracking metrics, workload, team situation awareness (TSA), and team performance. BackgroundGaze sharing is a real-time visualization technique that allows teams to know where their team members are looking on a shared display. Gaze sharing visualization techniques are a promising means to improve collaborative performance on simple tasks; however, there needs to be validation of gaze sharing with more complex and dynamic tasks. MethodsThis study evaluated the effect of gaze sharing on eye tracking metrics, workload, team SA, and team performance in a simulated unmanned aerial vehicle (UAV) command-and-control task. Thirty-five teams of two performed UAV tasks under three conditions: one with no gaze sharing and two with gaze sharing. Gaze sharing was presented using a fixation dot (i.e., a translucent colored dot) and a fixation trail (i.e., a trail of the most recent fixations). ResultsThe results showed that the fixation trail significantly reduced saccadic activity, lowered workload, supported team SA at all levels, and improved performance compared to no gaze sharing; however, the fixation dot had the opposite effect on performance and SA. In fact, having no gaze sharing outperformed the fixation dot. Participants also preferred the fixation trail for its visibility and ability to track and monitor the history of their partner’s gaze. ConclusionThe results showed that gaze sharing has the potential to support collaboration, but its effectiveness depends highly on the design and context of use. ApplicationThe findings suggest that gaze sharing visualization techniques, like the fixation trail, have the potential to improve teamwork in complex UAV tasks and could have broader applicability in a variety of collaborative settings. 

Complex and dynamic domains rely on operators to collaborate on multiple tasks and cope with changes in task demands. Gaze sharing is a means of communication used to exchange visual information by allowing teammates to view each other’s gaze points on their displays. Existing work on gaze sharing focuses on relatively simple task-specific domains and no work-to-date addresses how to use gaze sharing in data-rich environments. For this study, nine pairs of participants completed a UAV search and rescue command-and-control task with three visualization techniques: no gaze sharing, gaze sharing using the real-time dot, and gaze sharing using the real-time fixation trail. Our preliminary results show that performance scores using the real-time fixation trail were statistically significantly higher than when no gaze sharing was present. This suggests that the real-time fixation trail is a promising tool to better understand operators’ strategies and could form the basis of an adaptive display. 

Cross-recurrence quantification analysis (CRQA) metrics may offer a means to provide information about the quality of collaboration in real-time. The goal of the present work is to use Area of Interest (AOI) based CRQA metrics to analyze the eye-tracking data of 10 pairs who participated in a shared unmanned aerial vehicle (UAV) command and control task. We are interested in how teams respond to workload transitions and how it affects AOI-based CRQA metrics. The results showed that as workload increased, team members spent a longer time on the same task which may indicate that they are coordinating together on a task, or they are not adapting and getting “trapped” in certain tasks. The findings suggest that CRQA AOI-based metrics are sensitive to workload changes and validate these metrics in unraveling the visual puzzle of how workload impacts scanpath patterns which contribute to quantifying the adaptation process of pairs over time. This also has the potential to inform the design of real-time technology in the future. 

Teamwork and collaboration form the cornerstones of organizational performance and success. It is important to understand how the attention allocation of team members is linked to performance. One approach to studying attention allocation in a team context is to compare the scanpath similarity of two people working in teams and to explore the link between scanpath similarity and team performance. In this study, participants were recruited to work in pairs on an unmanned aerial vehicle (UAV) task that included low and high workload conditions. An eye tracker was used to collect the eye movements of both participants in each team. The scanpaths of two teammates were compared in low and high workload conditions using MultiMatch, an established scanpath comparison algorithm. The obtained scanpath similarity values were correlated with performance measures of response time and accuracy. Several MultiMatch measures showed significant strong correlations across multiple dimensions, providing insight into team behavior and attention allocation. The results suggested that the more similar each team member’s scanpath is, the better their performance. Additional research and consideration of experimental variables will be necessary to further understand how best to use MultiMatch for scanpath similarity assessment in complex domains.