Team Heart Rate Dynamics and Performance in a Simulation-Based Cooperative Task: A Multidimensional Recurrence Quantification Analysis (MdRQA) Approach
Ryan Thomas Villarreal, Gregory Funke, Michael Tolston, Denny YuObjective
This study examined whether multidimensional recurrence quantification analysis (MdRQA) metrics derived from team heart rate data predict team performance and team workload under varied team and task demands.
Background
Assessing team performance in operational environments is challenging. Traditional approaches require task interruptions or ground-truth data that are often unavailable. Physiological sensing offers a less-intrusive approach, yet most applications aggregate individual-level data or rely on simple pairwise metrics that do not represent how coupling is organized across the whole team. MdRQA instead embeds multivariate time series into a shared phase space, but evidence linking MdRQA metrics to team outcomes remains limited and mixed.
Method
Twenty triads completed a cooperative virtual reality choice reaction time task across four conditions manipulating interdependence and time pressure. MdRQA metrics (percent determinism, average diagonal line length, diagonal line entropy) were extracted from heart rate interbeat interval data. Linear mixed-effects models tested whether these metrics predicted self-reported team workload and team performance outcomes including sensitivity and response bias from signal detection theory.
Results
Longer sustained coupling predicted increased sensitivity and fewer errors, while greater entropy predicted poorer performance. Higher determinism and lower entropy predicted a more liberal response bias. MdRQA metrics did not predict perceived workload beyond condition effects.
Conclusion
MdRQA metrics reflect joint physiological dynamics and predict performance outcomes in triads beyond the effects of task demands alone.
Application
Continuously computed physiological coupling metrics may serve as less-intrusive, real-time proxies for team performance in operational environments where traditional assessment is infeasible.