Physiologically-Informed Predictability of a Teammate's Future Actions Forecasts Team Performance
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915122780307456 |
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| author | Qin, Yinuo Lee, Richard T. Zhang, Weijia Sun, Xiaoxiao Sajda, Paul |
| author_facet | Qin, Yinuo Lee, Richard T. Zhang, Weijia Sun, Xiaoxiao Sajda, Paul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15328 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physiologically-Informed Predictability of a Teammate's Future Actions Forecasts Team Performance Qin, Yinuo Lee, Richard T. Zhang, Weijia Sun, Xiaoxiao Sajda, Paul Neurons and Cognition Machine Learning 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. |
| title | Physiologically-Informed Predictability of a Teammate's Future Actions Forecasts Team Performance |
| topic | Neurons and Cognition Machine Learning |
| url | https://arxiv.org/abs/2501.15328 |