Establishing Workload Manipulations Utilizing a Simulated Environment

  • Julian AbichIV
  • Lauren Reinerman-Jones
  • Grant Taylor
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8022)


Research seeking to improve the measurement of workload requires the use of established task load manipulations to impose varying levels of demand on human operators. The present study sought to establish task load manipulations for research utilizing realistically complex task environments that elicit distinct levels of workload (i.e. low, medium, and high). A repeated measures design was used to test the effects of various demand manipulations on performance and subjective workload ratings using the NASA-Task Load Index (TLX) and Instantaneous Self-Assessment technique (ISA). This experiment successfully identified task demand manipulations that can be used to investigate operator workload within realistically complex environments. Results revealed that the event rate manipulations had the most consistent impact on performance and subjective workload ratings in both tasks, with each eliciting distinct levels of workload.


Workload simulated environments complex systems signal detection change blindness 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Green, D.M., Swets, J.A.: Signal detection theory and psychophysics. Wiley, New York (1966)Google Scholar
  2. 2.
    Hart, S.G.: Nasa-Task Load Index (NASA-TLX); 20 Years Later. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 50(9), 904–908 (2006)CrossRefGoogle Scholar
  3. 3.
    Hart, S.G., Staveland, L.E.: Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In: Hancock, P.A., Meshkati, N. (eds.) Human Mental Workload, pp. 139–184. Elsevier Science Publishers, Holland (1988)CrossRefGoogle Scholar
  4. 4.
    Heeger, D.: Signal Detection Theory. Standford University, Department of Psychology (1997)Google Scholar
  5. 5.
    Hollingsworth, A., Henderson, J.M.: Accurate visual memory for previously attended objects in natural scenes. Journal of Experimental Psychology: Human Perception and Performance, 113–136 (2002)Google Scholar
  6. 6.
    Johannsen, G.: Workload and workload measurement. In: Moray, N. (ed.) Mental Workload, pp. 3–12. Plenum Press, New York (1979)CrossRefGoogle Scholar
  7. 7.
    Kahneman, D.: Attention and effort. Prentice Hall, Englewood Cliffs (1973)Google Scholar
  8. 8.
    Knowles, W.B.: Operator loading tasks. Human Factors 5(2), 155–161 (1963)Google Scholar
  9. 9.
    Meshkati, N., Hancock, P., Rahimi, M., Dawes, S.M.: Techniques in mental workload assessment. In: Wilson, J.R., Corlett, E.N. (eds.) Evaluation of Human Work: A Practical Ergonomics Methodology, 2nd edn., pp. 749–782. Taylor & Francis, Bristol (1995)Google Scholar
  10. 10.
    Moray, N.: Where is capacity limited? A Survey and A Model Acta Psychologica 27, 84–92 (1967)CrossRefGoogle Scholar
  11. 11.
    Reinerman-Jones, L., Barber, D., Lackey, S., Nicholson, D.: Developing methods for utilizing physiological measures. In: Applied Human Factors and Ergonomics, Miami, FL (2010)Google Scholar
  12. 12.
    Rensink, R.A.: How much of a scene is seen? The role of attention in scene perception. In: Investigative Ophthalmology and Visual Science, vol. 38 (1997)Google Scholar
  13. 13.
    Rensink, R.A.: Change detection. Annual Review of Psychology 53, 245–277 (2002)CrossRefGoogle Scholar
  14. 14.
    See, J.E., Howe, S.R., Warm, J.S., Dember, W.N.: Meta-analysis of the sensitivity decrement in vigilance. Psychological Bulletin 117(2), 230–249 (1995)CrossRefGoogle Scholar
  15. 15.
    Simons, D.J., Rensink, R.A.: Change blindness: Past, present, and future. Trends in Cognitive Sciences 9(1), 16–20 (2005)CrossRefGoogle Scholar
  16. 16.
    Rensink, R.A., Regan, J.K.O., Clark, J.J.: To see or not to see: The need for attention to perceive changes in scenes. Psychological Science 8(5), 368–373 (1997) CrossRefGoogle Scholar
  17. 17.
    Taylor, G.S.: Comparing types of adaptive automation within a multi-tasking environment. University of Central Florida, Orlando (2012) (Dissertation)Google Scholar
  18. 18.
    Tattersall, A.J., Foord, P.S.: An experimental evaluation of instantaneous self-assessment as a measure of workload. Ergonomics 39(5), 740–748 (1996)CrossRefGoogle Scholar
  19. 19.
    Tollner, A.M.: Individual and Team Susceptibility to Change Blindness. University of Cincinnati, Cincinnati (2006) (Dissertation)Google Scholar
  20. 20.
    Veltman, J.A., Gaillard, A.W.K.: Physiological indices of workload in a simulated flight task. Biological Psychology 42, 323–342 (1996)CrossRefGoogle Scholar
  21. 21.
    Wickens, C.D., Hollands, J.G.: Engineering psy-chology and human performance, 3rd edn. Prentice Hall, Upper Saddle River (2000)Google Scholar
  22. 22.
    Wickens, C.: Multiple resources and performance prediction. Theoretical Issues in Ergonomic Science 3(2), 159–177 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Julian AbichIV
    • 1
  • Lauren Reinerman-Jones
    • 1
  • Grant Taylor
    • 1
  1. 1.Institute for Simulation & Training (IST), Applied Cognition in Virtual Immersive Training Environments Laboratory (ACTIVE Lab)University of Central Florida (UCF)USA

Personalised recommendations