2026-10-06T06:51:31Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34054482 2026-10-02T05:35:48Z category_15 portal_316 item_type_3 month_year_10_2026

Data Sheet 1_Physiological activation regimes reveal distinct autonomic mechanisms underlying operational performance under cognitive workload and stress.pdf Ludovica Di Pompeo (25162500) Michele Tritto (25162503) Francesco Romano (2983326) David Perpetuni (25162506) Daniela Cardone (486813) Arcangelo Merla (298915) Neuroscience cognitive workload heart rate variability (HRV) mental stress operational performance physiological activation <p>Human performance in safety-critical environments depends on the interaction between mental workload, stress, autonomic regulation, and behavior. This study investigated whether associations between heart rate variability (HRV) and performance varied across physiological activation ranges and operational contexts during a multi-task flight simulation implemented through the AeroStim platform. Forty-two healthy participants (25 ± 5 years) performed either a Workload-Only condition (WO; n = 20), comprising Tracking and System Monitoring tasks, or a Stress-Oriented condition (SO; n = 22), in which additional stress-inducing tasks were concurrently administered. ECG was continuously recorded using an EqVital wearable T-shirt. Linear and non-linear HRV indices were extracted and normalized within participants using Min-Max scaling. Gaussian Mixture Modeling of normalized SDNN identified three relative, subject-specific activation ranges (Easy, Medium, and Hard), with classification showing high stability across alternative normalization procedures. 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> 2026-10-02T05:35:48Z Dataset Dataset 10.3389/fnrgo.2026.1946611.s001 https://figshare.com/articles/dataset/Data_Sheet_1_Physiological_activation_regimes_reveal_distinct_autonomic_mechanisms_underlying_operational_performance_under_cognitive_workload_and_stress_pdf/34054482 CC BY 4.0