Multilevel Longitudinal Analysis of Shooting Performance as a Function of Stress and Cardiovascular Responses

Derek Spangler, Sazedul Alam, Saad Rahman, Joshua Crone, Ryan Robucci, Nilanjan Banerjee, Scott Kerick, Justin Brooks

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
JournalIEEE Transactions on Affective Computing
DOIs
StateAccepted/In press - 2020

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction

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