Abstract
Virtual reality (VR) systems are increasingly using physiology to improve human training. However, these systems do not account for the complex intra-individual variability in physiology and human performance across multiple timescales and psychophysiological demands. To fill this gap, we propose a theory of multilevel variability where tractable neurobiological mechanisms generate complex variability in performance over time and in response to heterogeneous sources. Based on this theory, we also present a study that examines changes in cardiovascular activity and performance during a stressful shooting task in VR. We examined physiology and performance at three important levels of analysis: task-to-task, block-to-block, session-to-session. Findings indicated joint patterns of physiology and performance that notably varied by the level of analysis. At the task level, higher task difficulty worsened performance but did not change cardiovascular activation. At the block level, there were nonlinear changes in performance and heart rate variability. At the session level, performance improved while blood pressure decreased and heart rate variability increased across days. Of all the physiological metrics, only heart rate variability was correlated with marksmanship performance. Findings are consistent with our multilevel theory and highlight the need for VR and other affective computing systems to assess physiology across multiple timescales.
Highlights
1.1 BackgroundTHE last two decades have witnessed a surge of research on affective computing systems that aim to detect human emotion and stress based on physiological signals [1], [2], [3]
For all hypotheses pertaining to marksmanship, we examined concomitant changes in response time (RT) metrics to clarify the underlying cognitive factors driving shifts in shooting performance
Paralleling the decrease in marksmanship, the high difficulty condition was associated with an increase in the coefficient of variation, a metric reflecting RT variability (Fixed effect of Difficulty: B = .56, SE = .07, 95% CI [.42, .70], p < .05)
Summary
THE last two decades have witnessed a surge of research on affective computing systems that aim to detect human emotion and stress based on physiological signals [1], [2], [3]. In addition to simulating real-world stressors, VR may enhance training because it can be integrated into closed-loop systems that adapt to the user [12], [13] Cardiovascular (CV) responses in particular have shown promise as affordable and unobtrusive indicators of performance-relevant states within VR [20], [21], [22], [23], [24], [25]
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