Abstract
In order to survive in combat zones, an individual soldier must be proficient in the following skills: Pattern recognition and Behavior cue detection. Although, current military training requirements are inadequate for developing pattern recognition and behavior detection, research shows that Simulation-based Training, via virtual environments (VE’s) can improve pattern recognition and behavior detection skills. However, the use of VEs for visually dependent tasks may also increase simulator sickness in some individuals. This experiment compared a virtual version of Kim’s game (i.e., an observational game to increase memory and performance) to a control group to assess the role of simulator sickness on performance. Participants were randomly assigned to either the Kim’s game or control condition and completed a pre-test, training vignette, and post-test. During the experiment, participants recorded their level of simulator sickness using a questionnaire developed by Kennedy et al. (International Journal of Aviation Psychology 3:203–220, 1993 [1]). The data analysis revealed that the Kim’s game group reported higher levels of simulator sickness symptoms which had a negative effect on performance (i.e., detection accuracy and false positive detection). The results also indicated that there was a positive correlation in the control group between disorientation and detection accuracy. This implies that the control group may have become familiar with the experimental task, suggesting that simulator sickness did not negatively impact their performance. The following paper discusses the influence of simulator sickness on performance and offers new ways to reduce simulator sickness for behavior cue detection training.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kennedy, R.S., Lane, N.E., Berbaum, K.S., Lilienthal, M.G.: Simulator sickness questionnaire: an enhanced method for quantifying simulator sickness. Int. J. Aviat. Psychol. 3(3), 203–220 (1993)
Department of Defense: Defense Budget Priorities and Choices Fiscal Year 2014. DOD (2013)
Fischer, S.C., Geiwetz, J.: Training Strategies for Tactical Pattern Recognition. United States Army Research Institute for the Behavioral and Social Sciences, Alexandria, Virginia (1996)
Spain, R.D., Priest, H.A., Murphy, J.S.: Current trends in adaptive training with military applications: an introduction. Mil. Psychol. 24(2), 87 (2012)
Haque, S., Srinivasan, S.: A meta-analysis of the training effectiveness of virtual reality surgical simulators. IEEE Trans. Inf Technol. Biomed. 10, 51–58 (2006)
Vogel-Walcutt, J.J., Gebrim, J.B., Bowers, C., Carper, T.M., Nicholson, D.: Cognitive load theory vs. constructivist approaches: which best leads to efficient, deep learning? J. Comput. Assist. Learn. 27, 133–145 (2011)
Nicholson, D., Schatz, S., Bowers, C.: Developing Adaptive, Intelligent Scenarios to Support Enhanced Operations Training (2008)
Schatz, S., Reitz, E.A., Nicholson, D., Fautua, D.: Expanding combat hunter: the science and metrics of border hunter. In: The Interservice/Industry Training, Simulation & Education Conference (I/ITSEC), p. 10147. NTSA, Arlington, VA (2010)
Colombo, G., Dolletski-Lazar, R., Coxe, M., Tarr, R.: Setting the stage: preparation for advanced combat profiling training. In: Schatz, S., Nicholson, D., Fautua, D., Reitz, E. (ed.) The Interservice/Industry Training, Simulation & Education Conference (I/ITSEC). Orlando, FL (2012)
Kolasinski, E.M.: Simulator Sickness in Virtual Environments. Army Research Institute for the Behavioral and Social Sciences, Alexandria, VA (1995)
Henderson, J.M.: Human gaze control during real-world scene perception. Trends Cogn. Sci. 7(11), 498–504 (2003)
Kennedy, R.S., Stanney, K.M., Compton, D.E., Jones, M.B.: Computerized methods to evaluate virtual environment aftereffects. In: Proceedings of DSC’99. Paris (1999)
McGee, M.K.: Using psychophysics to measure negative side effects in immersive virtual environments. In: Proceedings of the 42nd Human Factors and Ergonomics Society, pp. 1501–1505 (1998)
Acknowledgments
This research was sponsored by the U.S. Army Research Laboratory—Human Research Engineering Directorate Simulation and Training Technology Center (ARL HRED STTC), in collaboration with the Institute for Simulation and Training at the University of Central Florida. This work is supported in part by ARL HRED STTC contract W911NF-14-2-0021. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of ARL HRED STTC or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this paper
Cite this paper
Maraj, C.S., Badillo-Urquiola, K.A., Lackey, S.J., Hudson, I.L. (2017). Behavior Cue Detection Training: Understanding the Impact of Simulator Sickness on Performance. In: Kantola, J., Barath, T., Nazir, S., Andre, T. (eds) Advances in Human Factors, Business Management, Training and Education. Advances in Intelligent Systems and Computing, vol 498. Springer, Cham. https://doi.org/10.1007/978-3-319-42070-7_60
Download citation
DOI: https://doi.org/10.1007/978-3-319-42070-7_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42069-1
Online ISBN: 978-3-319-42070-7
eBook Packages: EngineeringEngineering (R0)