Recurrence Quantification Analysis of Eye Gaze Dynamics During Team Collaboration

Shared visual attention between team members facilitates collaborative problem solving (CPS), but little is known about how team-level eye gaze dynamics influence the quality and successfulness of CPS. To better understand the role of shared visual attention during CPS, we collected eye gaze data from 279 individuals solving computer-based physics puzzles while in teams of three. We converted eye gaze into discrete screen locations and quantified team-level gaze dynamics using recurrence quantification analysis (RQA). Specifically, we used a centroid-based auto-RQA approach, a pairwise team member cross-RQAs approach, and a multi-dimensional RQA approach to quantify team-level eye gaze dynamics from the eye gaze data of team members. We find that teams differing in composition based on prior task knowledge, gender, and race show few differences in team-level eye gaze dynamics. We also find that RQA metrics of team-level eye gaze dynamics were predictive of task success (all ps < .001). However, the same metrics showed different patterns of feature importance depending on predictive model and RQA type, suggesting some redundancy in task-relevant information. These findings signify that team-level eye gaze dynamics play an important role in CPS and that different forms of RQA pick up on unique aspects of shared attention between team-members.