Does It MultiMatch? What Scanpath Comparison Tells us About Task Performance in Teams

Abstract

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.

The Effect of Time Pressure on Visual Search Eye Tracking Metrics in Virtual Reality

“…There are several future directions to take based on our preliminary findings. First, we can explore the effect of time pressure on visual search using a more comprehensive set of metrics (El Iskandarani et al, 2023). For instance, how does time affect the mean fixation duration i.e., the time spent looking at each object, accuracy, and task performance?…”

Quantifying Visual Attention of Teams During Workload Transitions Using AOI-Based Cross-Recurrence Metrics

“…To date, the focus has been on the percentage of cross-recurrent fixations and similarities between the teammates’ trajectories. While these analyses are needed, there is a need to also explore the percentage of identical scanpath segments over time and the average duration the teammates are in synch, especially within the context of workload transitions (El Iskandarani et al, 2023).…”

Can Real-Time Gaze Sharing Help Team Collaboration? A Preliminary Examination of its Effectiveness with Pairs

“…Understanding what teammates are looking at and when can provide insight into how the performance results came about which cannot be obtained through other means such as surveys, questionnaires, or debriefing participants at the conclusion of a study. Several eye tracking metrics have been used to measure team performance such as pupillometry metrics (Daggett et al, 2017), gaze overlap (Pietinen et al, 2010), scanpath similarity (El Iskandarani et al, 2023), and cross-recurrence metrics (Atweh et al, 2023;Cherubini et al, 2010). However, there has been limited work looking at gaze sharing, i.e., how to visualize eye tracking measures in real-time between teammates.…”