Data Sheet 1_Physiological activation regimes reveal distinct autonomic mechanisms underlying operational performance under cognitive workload and stress.pdf

HRV-performance associations were assessed using Spearman correlations with permutation-based inference and Benjamini–Hochberg correction, while performance differences were evaluated using non-parametric tests. HRV-performance correlations varied across activation ranges and contexts; none passed FDR correction, so these should be considered exploratory. In WO, associations involved LF in the Easy range, SDNN, SD2, and LF in the Medium range, and SDNN, LF/HF, and Sample Entropy in the Hard range.

In SO, associations in the Medium range extended to vagal, global variability, and non-linear complexity indices, while Hard-range associations mainly involved vagal and LF-related measures. Omission rate was lower at Medium than Easy activation in WO, providing partial support for an intermediate activation advantage, whereas no consistent performance optimum emerged across contexts. Between-condition comparisons revealed a robust speed-accuracy trade-off, with WO producing faster but less accurate responses and SO producing slower but more accurate responses.

Sensitivity analysis further indicated that differences in exercise windows affected HRV metrics in a metric-dependent manner, with greater deviations generally observed for frequency-domain measures. The findings suggest that HRV-performance relationships are context- and activation-dependent rather than captured by a single universal marker. The results support the potential of regime-based, multimodal HRV monitoring for adaptive human-machine systems, while highlighting the need for standardized recording durations, independent autonomic markers, and validation across individuals and real operational environments.</p>