Qin Yinuo, Lee Richard T, Zhang Weijia, Sun Xiaoxiao, Sajda Paul
Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA.
Department of Electrical Engineering, Columbia University, New York, NY 10027, USA.
iScience. 2025 Apr 17;28(5):112429. doi: 10.1016/j.isci.2025.112429. eCollection 2025 May 16.
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 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 insights provide a quantitative framework for understanding multi-human team dynamics, paving the way for deeper insights into team dynamics and performance.
在协作环境中，深入理解多人团队协作动态对于优化绩效至关重要。然而，个体的行为和生理指标之间的关系以及它们对团队整体绩效的综合影响仍未得到充分理解。为了探究这一点，我们设计了一个虚拟现实（VR）中的三人协作感觉运动任务，并引入了一个可预测性指标来考察团队动态和绩效。我们的研究结果揭示了团队绩效与基于其他团队成员的行为和生理数据对团队成员未来行动的可预测性之间存在紧密联系。与传统观念中高绩效团队高度同步不同，我们的结果表明团队成员之间的生理和行为同步与团队绩效的相关性有限。这些见解为理解多人团队动态提供了一个定量框架，为更深入洞察团队动态和绩效铺平了道路。
