Advertisement

Applying Real Time Physiological Measures of Cognitive Load to Improve Training

  • Joseph T. Coyne
  • Carryl Baldwin
  • Anna Cole
  • Ciara Sibley
  • Daniel M. Roberts
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5638)

Abstract

This paper discusses how the fields of augmented cognition and neuroergonomics can be expanded into training. Several classification algorithms based upon EEG data and occular data are discussed in terms of their ability to classify operator state in real time. These indices have been shown to enhance operator performance within adaptive automation paradigms. Learning is different from performing a task that one is familiar with. According to cognitive load theory (CLT), learning is essentially the act of organizing information from working memory into long term memory. However, our working memory system has a bottleneck in this process, such that when training exceeds working memory capacity, learning is hindered. This paper discusses how CLT can be combined with multiple resource theory to create a model of adaptive training. This new paradigm hypothesizes that a system that can monitor working memory capacity in real time and adjust training difficulty can improve learning.

Keywords

Cognitive Load Instructional Design Work Memory Capacity Cognitive Load Theory Mental Workload 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Parasuraman, R.: Neuroergonomics: Research and Practice. Theoretical Issues in Ergonomics Science 4, 5–20 (2003)CrossRefGoogle Scholar
  2. 2.
    Parasuraman, R., Wilson, G.F.: Putting the Brain to Work: Neuroergonomics Past, Present, and Future. Human Factors 50, 468–474 (2008)CrossRefPubMedGoogle Scholar
  3. 3.
    Wickens, C.D.: Processing resources in attention. In: Parasuraman, R., Daives, D.R. (eds.) Varieties of attention, pp. 63–102. Academic Press, New York (1984)Google Scholar
  4. 4.
    Baddley, A.D.: Working Memory. Oxford University Press, Oxford (1986)Google Scholar
  5. 5.
    Parasuraman, R., Caggiano, D.: Neural and genetic assays of mental workload. In: McBride, D., Schmorrow, D.D. (eds.) Quantifying Human Information Processing Rowman and Littlefield, Lanham, MD (2005)Google Scholar
  6. 6.
    Conway, A.R.A., Engle, R.W.: Individual Differences in Working Memory Capacity: More Evidence for a General Capacity Theory. Memory 4(6), 577–590 (1996)CrossRefPubMedGoogle Scholar
  7. 7.
    Brumback, C.R., Low, K.A., Gratton, G., Fabiani, M.: Putting Things into Perspective: Individual Differences in Working-Memory Span and the Integration of Information. Experimental Psychology 52(1), 21–30 (2005)CrossRefPubMedGoogle Scholar
  8. 8.
    Unsworth, N., Engle, R.W.: The Nature of Individual Differences in Working Memory Capacity: Active Maintenance in Primary Memory and Controlled Search From Secondary Memory. Psychological Review 114(1), 104–132 (2007)CrossRefPubMedGoogle Scholar
  9. 9.
    Cantor, J., Engle, R.W.: Working-memory capacity as long-term memory activation: An individual-differences approach. Journal of Experimental Psychology: Learning, Memory, and Cognition 19(5), 1101–1114 (1993)PubMedGoogle Scholar
  10. 10.
    Sobel, K.V., Gerrie, M.P., Poole, B.J., Kane, M.J.: Individual differences in working memory capacity and visual search: The roles of top-down and bottom-up processing. Psychonomic Bulletin & Review 14(5), 840–845 (2007)CrossRefGoogle Scholar
  11. 11.
    Graf, S., Lin, T., Kinshuk: The relationship between learning styles and cognitive traits–Getting additional information for improving student modelling. Computers in Human Behavior 24(2), 122–137 (2008)CrossRefGoogle Scholar
  12. 12.
    Pope, A.T., Bogart, E.H., Bartolome, D.S.: Biocybernetic system evaluates indices of operator engagement in automated task. Biological Psychology 40(1), 187–195 (1995)CrossRefPubMedGoogle Scholar
  13. 13.
    Mikulka, P.J., Scerbo, M.W., Freeman, F.G.: Effects of a biocybernetic system on vigilance performance. Human Factors 44, 654–664 (2002)CrossRefPubMedGoogle Scholar
  14. 14.
    Freeman, F.G., Mikulka, P.J., Scerbo, M.W., Prinzel, L.J., Clouatre, K.: Evaluation of a psychophysiologically controlled adaptive automation system, using performance on a tracking task. Applied Psychophysiology and Biofeedback 25(2), 103–115 (2000)CrossRefPubMedGoogle Scholar
  15. 15.
    DuRousseau, D.R., Mannucci, M.A.: eXecutive Load Index (XLI): Spaital-frequency EEG tracks moment-to moment changes in high-order attentional resourcesFoundations of Augmented Cognition, pp. 245–251. Lawrence Erlbaum Associates, Mahwah (2005)Google Scholar
  16. 16.
    Berka, C., Levendowski, D., Lumicao, M.N., Yau, A., Davis, G., Zivkovic, V.T., Olmstead, R.E., Tremoulet, P., Craven, P.L.: EEG Correlates of Task Engagement and Mental Workload in Vigilance, Learning, and Memory Tasks. Aviation, Space, and Environmental Medicine 78, B231–B244 (2007)Google Scholar
  17. 17.
    Wilson, G.F., Russell, C.A.: Real-Time Assessment of Mental Workload Using Physiological Measures and Artifiicial Neural Networks. Human Factors 45, 635–643 (2003)CrossRefPubMedGoogle Scholar
  18. 18.
    Wilson, G.F., Russell, C.A.: Operator Functional State Classification Using Multiple Psychophysiological Features in an Air Traffic Control Task. Human Factors 45(3), 381–389 (2003)CrossRefPubMedGoogle Scholar
  19. 19.
    Di Nocera, F., Terenzi, M., Camilli, M.: Another look at scanpath: Distance to nearest neighbour as a measure of mental workload. In: de Waard, D., Brookhuis, K.A., Toffetti, A. (eds.) Developments in human factors in transportation, design, and evaluation, pp. 295–303. Shaker, Maastricht (2006)Google Scholar
  20. 20.
    Marshall, S.P., Pleydell-Pearce, C.W., Dickson, B.T.: Integrating psychophysiological measures of cognitive workload and eye movements to detect strategy shifts. In: Proceedings of the 36th Annual Hawaii International Conference on System Sciences, vol. 6 (2003)Google Scholar
  21. 21.
    Marshall, S.P.: The Index of Cognitive Activity: measuring cognitive workload. In: Proceedings of the 2002 IEEE 7th Conference on Human Factors and Power Plants, pp. 7-5–7-9 (2002)Google Scholar
  22. 22.
    van Gog, T., Kester, L., Nievelstein, F., Giesbers, B., Pass, F.: Uncovering cognitive processes: Different techniques that can contribute to cognitive load research and instruction. Computers in Human Behavior (25), 325–331 (2009)Google Scholar
  23. 23.
    Marshall, S.P.: Identifying Cognitive State from Eye Metrics. Aviation, Space, and Environmental Medicine 78(5, Section II), B165–B175 (2007)Google Scholar
  24. 24.
    Sweller, J.: Cognitive load during problem solving: Effects on learning. Cognitive Science: A Multidisciplinary Journal 12(2), 257–285 (1988)CrossRefGoogle Scholar
  25. 25.
    Sweller, J.: Discussion of Emerging Topics in Cognitive Load Research: Using Learner and Information Characteristics in the Design of Powerful Learning Environments. Applied Cognitive Psychology 20(3), 353–357 (2006)CrossRefGoogle Scholar
  26. 26.
    Miller, G.A.: The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review 63, 81–97 (1956)CrossRefPubMedGoogle Scholar
  27. 27.
    Kahneman, D.: Attention and effort. Prentice Hall, Englewood Cliffs (1973)Google Scholar
  28. 28.
    Paas, F., Renkl, A., Sweller, J.: Cognitive load theory and instructional design: Recent developments. Educational Psychologist 38(1), 1–4 (2003)CrossRefGoogle Scholar
  29. 29.
    DeLeeuw, K.E., Mayer, R.E.: A comparison of three measures of cognitive load: Evidence for separable measures of intrinsic, extraneous, and germane load. Journal of Educational Psychology 100(1), 223–234 (2008)CrossRefGoogle Scholar
  30. 30.
    Shiffrin, R.M., Schneider, W.: Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory. Psychological Review 84(2), 127–190 (1977)Google Scholar
  31. 31.
    Marcus, N., Cooper, M., Sweller, J.: Understanding instructions. Journal of Educational Psychology 88(1), 49–63 (1996)CrossRefGoogle Scholar
  32. 32.
    Ayres, P.: Impact of Reducing Intrinsic Cognitive Load on Learning in a Mathematical Domain. Applied Cognitive Psychology 20(3), 287–298 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Joseph T. Coyne
    • 1
  • Carryl Baldwin
    • 2
  • Anna Cole
    • 3
  • Ciara Sibley
    • 2
    • 3
  • Daniel M. Roberts
    • 2
  1. 1.Naval Research LaboratoryWashingtonUSA
  2. 2.Department of PsychologyGeorge Mason UniversityFairfaxUSA
  3. 3.Strategic Analysis IncorporatedArlingtonUSA

Personalised recommendations