Computational Modeling of Personality-Moderated Autonomic Recovery: Simulating Extraversion Effects on Heart Rate Variability After Driving Stress

This study presents a computational simulation examining how extraversion-linked differences in vagal tone shape post-driving autonomic recovery. Prior simulation research demonstrates that computationally derived parameter sweeps yield testable physiological predictions prior to empirical data collection (Banks & Carson, 1984). Using parameters drawn from published literature, RMSSD trajectories were modeled across resting baseline, high-demand driving, and passive recovery phases for 70 virtual participants (35 high-extraversion, 35 low-extraversion; seed = 42). High-extraversion individuals demonstrated faster and more complete heart rate variability (HRV) recovery following simulated driving stress, achieving near-complete recovery ( M  = 97.9%) compared to low-extraversion participants ( M  = 84.4%), with group differences emerging reliably across 1,000 Monte Carlo simulations (recovery completeness: 100% of runs significant; recovery rate: 91.0%). Sensitivity analyses confirmed robustness across recovery time-constant variants. Findings yield testable predictions for empirical validation and suggest that autonomic recovery profiles may inform personalized driver-monitoring and rest-break systems.