Predicting Navigation Performance with Psychophysiological Responses to Threat in a Virtual Environment

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8021)


The present study examined the physiological responses collected during a route-learning and subsequent navigation task in a novel virtual environment. Additionally, participants were subjected to varying levels of environmental threat during the route-learning phase of the experiment to assess the impact of threat on consolidating route and survey knowledge of the directed path through the virtual environment. Physiological response measures were then utilized to develop multiple linear regression (MLR) and artificial neural network (ANN) models for prediction of performance on the navigation task. Comparisons of predictive abilities between the developed models were performed to determine optimal model parameters. The ANN models were determined to better predict navigation performance based on psychophysiological responses gleaned during the initial tour through the city. The selected models were able to predict navigation performance with better than 80% accuracy. Applications of the models toward improved human-computer interaction and psychophysiologically-based adaptive systems are discussed.


Psychophysiology Threat Simulation Navigation Route-Learning 


  1. 1.
    Parasuraman, R., Wilson, G.F.: Putting the brain to work: Neuroergonomics past, present, and future. Human Factors 50, 468–474 (2008)CrossRefGoogle Scholar
  2. 2.
    Parsons, T.D., Courtney, C.: Neurocognitive and Psychophysiological Interfaces for Adaptive Virtual Environments. In: Röcker, C., Ziefle, M. (eds.) Human Centered Design of E-Health Technologies, pp. 208–233. IGI Global, Hershey (2011)Google Scholar
  3. 3.
    Parsons, T.D., Rizzo, A.A., Courtney, C., Dawson, M.: Psychophysiology to Assess Impact of Varying Levels of Simulation Fidelity in a Threat Environment. Advances in Human-Computer Interaction 5, 1–9 (2012)CrossRefGoogle Scholar
  4. 4.
    Parsons, T.D.: Neuropsychological Assessment Using Virtual Environments: Enhanced Assessment Technology for Improved Ecological Validity. In: Brahnam, S., Jain, L.C. (eds.) Advanced Computational Intelligence Paradigms in Healthcare 6. SCI, vol. 337, pp. 271–289. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Rizzo, A.A., Pair, J., Graap, K., Treskunov, A., Parsons, T.D.: User-Centered Design Driven Development of a VR Therapy Application for Iraq War Combat-Related Post Traumatic Stress Disorder. In: Proceedings of the 2006 International Conference on Disability, Virtual Reality and Associated Technology, pp. 113–122 (2006)Google Scholar
  6. 6.
    Parsons, T.D., Rizzo, A.A.: Initial Validation of a Virtual Environment for Assessment of Memory Functioning: Virtual Reality Cognitive Performance Assessment Test. Cyberpsychology and Behavior 11, 17–25 (2008)CrossRefGoogle Scholar
  7. 7.
    Nadolne, M.J., Stringer, A.Y.: Ecologic validity in neuropsychological assessment: Prediction of wayfinding. Journal of the International Neurophychological Society 7, 675–682 (2001)CrossRefGoogle Scholar
  8. 8.
    Waller, D., Hunt, E., Knapp, D.: The transfer of spatial knowledge in virtual environment training. Presence 7, 129–143 (1998)CrossRefGoogle Scholar
  9. 9.
    Golledge, R.G.: Cognition of physical and built environments. In: Garling, G., Evans, G.W. (eds.) Environment, Cognition and Action: An Integrated Approach, pp. 35–62. Oxford University Press, NY (1991)Google Scholar
  10. 10.
    Thorndyke, P.W., Hayes-Roth, B.: Differences in spatial knowledge acquired from maps and navigation. Cognitive Psychology 14, 560–589 (1982)CrossRefGoogle Scholar
  11. 11.
    Hahm, J., Lee, K., Lim, S.L., Kim, S.Y., Kim, H.T., Lee, J.H.: A study of active navigation and object recognition in virtual environments. Annual Review of CyberTherapy and Telemedicine 4, 67–72 (2006)Google Scholar
  12. 12.
    Ramloll, R., Mowat, D.: Wayfinding in virtual environments using an interactive spatial cognitive map. In: IV 2001 Proceedings, pp. 574–583. IEEE Press, London (2001)Google Scholar
  13. 13.
    Walker, B.N., Lindsay, J.: The effect of a speech discrimination task on navigation in a virtual environment. In: Proceedings of the Human Factors and Ergonomics Society 50th Annual Meeting, pp. 1536–1541 (2006)Google Scholar
  14. 14.
    Meilinger, T., Knauff, M., Bulthoff, H.H.: Working memory in wayfinding: A dual task experiment in a virtual city. Cognitive Science 32, 755–770 (2008)CrossRefGoogle Scholar
  15. 15.
    Parsons, T.D., Iyer, A., Cosand, L., Courtney, C., Rizzo, A.A.: Neurocognitive and psychophysiological analysis of human performance within virtual reality environments. Studies in Health Technology and Informatics 142, 247–252 (2009)Google Scholar
  16. 16.
    Allanson, J., Fairclough, S.H.: A research agenda for physiological computing. Interacting with Computers 16, 857–878 (2004)CrossRefGoogle Scholar
  17. 17.
    Parsons, T.D., Cosand, L., Courtney, C., Iyer, A., Rizzo, A.A.: Neurocognitive Workload Assessment using the Virtual Reality Cognitive Performance Assessment Test. In: Harris, D. (ed.) EPCE 2009. LNCS (LNAI), vol. 5639, pp. 243–252. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  18. 18.
    Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology: Heart rate variability: Standards of measurement, physiological interpretation and clinical use. Circulation, 93, 1043–1065 (1996)Google Scholar
  19. 19.
    Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996)zbMATHGoogle Scholar
  20. 20.
    Parsons, T.D., Rizzo, A.A., Buckwalter, J.G.: Backpropagation and regression: Comparative utility for neuropsychologists. Journal of Clinical and Experimental Neuropsychology 26, 95–104 (2004)CrossRefGoogle Scholar
  21. 21.
    Saltelli, A.: Global sensitivity analysis: An introduction. In: Proceedings of the 4th International Conference on Sensitivity Analysis of Model Output, Santa Fe, New, Mexico, pp. 27–43 (2005)Google Scholar
  22. 22.
    Chen, H., Kocauglu, D.F.: A sensitivity analysis algorithm for hierarchical decision models. European Journal of Operational Research 185, 266–288 (2003)CrossRefGoogle Scholar
  23. 23.
    Winebrake, J.J., Creswick, B.P.: The future of hydrogen fueling systems for transportation: An application of perspective-based scenario analysis using the analytic hierarchy process. Technological Forecasting and Social Chance 70, 359–384 (2003)CrossRefGoogle Scholar
  24. 24.
    Gevins, A., Smith, M.E., Leong, H., McEvoy, L., Whitfield, S., Du, R., Rush, G.: Monitoring working memory load during compter-based tasks with EEG pattern recognition methods. Human Factors 40, 79–91 (1998)CrossRefGoogle Scholar
  25. 25.
    Wilson, G.F., Russell, C.A.: Real-time assessment of mental workload using psychophysiological measures and artificial neural networks. Human Factors 45(4), 635–675 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of PsychologyUniversity of Southern CaliforniaUSA
  2. 2.Institute for Creative TechnologiesUniversity of Southern CaliforniaUSA
  3. 3.Department of PsychologyUniversity of ArizonaUSA
  4. 4.Department of PsychologyUniversity of North TexasUSA

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