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By extracting communication sequences from audio data collected during two separate five-person mission-planning tasks, interaction patterns in team communication were analyzed using a recurrence-based, nonlinear dynamics approach. These methods, previously successful in detecting pattern change in a three-person team task, were evaluated for their applicability to larger team settings, and…
By extracting communication sequences from audio data collected during two separate five-person mission-planning tasks, interaction patterns in team communication were analyzed using a recurrence-based, nonlinear dynamics approach. These methods, previously successful in detecting pattern change in a three-person team task, were evaluated for their applicability to larger team settings, and their ability to detect pattern change when team members switched roles or locations partway through the study (Study 1) or change in patterns over time (Study 2). Both traditional interaction variables (Talking Time, Co-Talking Time, and Sequence Length of Interactions) and dynamic interaction variables (Recurrence Rate, Determinism, and Pattern Information) were explored as indicators and predictors of changes in team structure and performance. Results from these analyses provided support that both traditional and dynamic interaction variables reflect some changes in team structure and performance. However, changes in communication patterns were not detected. Because simultaneous conversations are possible in larger teams, but not detectable through our communication sequence methods, team pattern changes may not be visible in communication sequences for larger teams. This suggests that these methods may not be applicable for larger teams, or in situations where simultaneous conversations may occur. Further research is needed to continue to explore the applicability of recurrence-based nonlinear dynamics in the analysis of team communication.
