PUBLICATIONS

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

Emanuel Rojas¹, Mengtao Lyu¹, Mengyao Li¹, Bethany Bracken², Nancy Cooke³, Phillip Desrochers², Jamie Gorman³, Lixiao Huang³, Molly Kilcullen4, Michael Rosen4, Matthew J Scalia³, Garima Arya Yadav³, Xiaoyun Yin³, Elmira Zahmat Doost³, and Shiwen Zhou³

Proceedings of the Human Factors and Ergonomics Society Annual Meeting, March 22-25, 2026

Abstract
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.

¹ Georgia Institute of Technology, Atlanta, USA
² Charles River Analytics, Cambridge, MA
² Cognitive Systems Engineering Lab (CSEL), The Ohio State University, Columbus, OH, USA
³Arizona State University, Tempe, USA
4Johns Hopkins University, Baltimore, MD, USA

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To learn more, contact Phillip Desrochers. Publication available from Sage Journals.

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