Identifying Latent Processes in Multi-Modal Physiological Signals for Team Performance: An Exploratory Structural Equation Modeling Approach

This is the authors' abstract. We don't add key points for this paper.

Teams operating in complex, dynamic environments rely on coordinated interdependent action for mission success. Underlying these team interactions are latent processes that are commonly measured through self-report measures, but little is known about how physiological signals contribute to these latent processes within teaming environments. This study examined how physiological signals from 5-person teams load onto latent factors using exploratory factor analysis (EFA). Results revealed a two-factor structure: inter-beat interval (IBI), respiratory rate, and pupil diameter loaded onto an “arousal” factor, while oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) loaded onto a “cognitive engagement” factor. A structural equation model (SEM) then evaluated whether these factors predicted team and aggregated individual performance ratings. Arousal significantly predicted both outcomes, whereas cognitive engagement was non-significant. These findings suggest that respiratory and neural activity contribute most distinctly to their respective latent factors, though only arousal was a reliable indicator of team and aggregated individual performance.