Physiologically-Informed Predictability of a Teammate's Future Actions Forecasts Team Performance
Yinuo Qin, Richard T. Lee, Weijia Zhang, Xiaoxiao Sun, Paul Sajda

TL;DR
This study introduces a new framework for predicting team performance in collaborative tasks by analyzing behavioral and physiological data, revealing that synchronization is less critical than previously thought.
Contribution
It presents a novel predictability metric and demonstrates that physiological and behavioral synchronization are weak indicators of team success.
Findings
Strong link between predictability of actions and team performance
Synchronization among team members has limited correlation with performance
Proposes a new quantitative approach to studying team dynamics
Abstract
In collaborative environments, a deep understanding of multi-human teaming dynamics is essential for optimizing performance. However, the relationship between individuals' behavioral and physiological markers and their combined influence on overall team performance remains poorly understood. To explore this, we designed a triadic human collaborative sensorimotor task in virtual reality (VR) and introduced a novel predictability metric to examine team dynamics and performance. Our findings reveal a strong connection between team performance and the predictability of a team member's future actions based on other team members' behavioral and physiological data. Contrary to conventional wisdom that high-performing teams are highly synchronized, our results suggest that physiological and behavioral synchronizations among team members have a limited correlation with team performance. These…
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Taxonomy
TopicsTeam Dynamics and Performance
