用生理和行为数据预测队友动作，能更好预判团队表现。
Physiologically-Informed Predictability of a Teammate's Future Actions Forecasts Team Performance
- 通过队友的生理与行为数据预测其未来动作
- 预测准确率与团队绩效强相关，同步性影响不大
- 适合研究人机协作或团队效率优化的读者
在协作环境中，深入理解多主体团队动态对提升绩效至关重要。然而，个体行为与生理指标如何共同影响整体团队表现仍不清晰。为此，我们设计了一个虚拟现实中的三人协同感知运动任务，并提出一种新型可预测性度量方法来分析团队动态与绩效。结果表明，团队表现与基于其他成员行为和生理数据预测某成员未来动作的能力密切相关。与传统认为高绩效团队高度同步的观点相反，本研究发现成员间生理与行为同步性与团队绩效的相关性较弱。这些发现为多主体团队协作提供了新的量化分析框架，有助于深化对团队动态与绩效关系的理解。
原文摘要 · Abstract (English)
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 insights provide a new quantitative framework for understanding multi-human teaming, paving the way for deeper insights into team dynamics and performance.
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