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Enhancing Intuitive Decision Making through Implicit Learning

  • Joseph Cohn
  • Peter Squire
  • Ivy Estabrooke
  • Elizabeth O’Neill
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8027)

Abstract

Today’s military missions pose complex time-constrained challenges, such as detecting IED emplacements while in a moving vehicle or detecting anomalous civilian behaviors indicative of impending danger. These challenges are compounded by recent doctrinal requirements that require younger and less-experienced Warfighters to make ever-more complex decisions. Current understanding of decision making, which is based on concepts developed around theories of analytic decision making (Newell and Simon, 1972), cannot effectively address these new challenges since they are based on the notion of enabling experts to apply their expertise to addressing new problems. Yet, there are actually two types of recognized decision making processes, analytical and intuitive, which appear to be mediated by different processes or systems (Ross et al, 2004; Evans, 2008; Kahneman & Klein, 2009). Analytical decision making is mediated by processes that reflect a sequential, step-by-step, methodical, and time-consuming process. To be effective, analytic decision making appears to require domain expertise. In contrast, intuitive decision making relies upon a more holistic approach to processing information at a subconscious level (Luu et al, 2010). The thesis of this paper is that unlike analytic decision making, effective intuitive decision making does not require domain expertise but, rather, can be enhanced through training methods and technologies. This paper will explore ways in which the results from a range of studies at the behavioral, cognitive and neurophysiological levels can be leveraged to provide a comprehensive approach to understanding and enabling more effective intuitive decision-making for these non-experts.

Keywords

Cognitive Modeling Perception Emotion and Interaction Intuition Decision Making Implicit Learning 

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References

  1. 1.
    Anderson, J.R.: ACT: A simple theory of complex cognition. American Psychologist 51, 355–365 (1996)CrossRefGoogle Scholar
  2. 2.
    Anderson, J.R., Carter, C.S., Fincham, J.M., Qin, Y., Ravizza, S.M., Rosenberg-Lee, M.: Using fMRI to Test Models of Complex Cognition. Cognitive Science 32, 1323–1348 (2008)CrossRefGoogle Scholar
  3. 3.
    Bowers, K.S., Regehr, G., Balthazard, C.G., Parker, K.: Intuition in the context of discovery. Cog. Psyc. 22, 72–110 (1990)CrossRefGoogle Scholar
  4. 4.
    Ericsson, K.A., Krampe, R.T., Tesch-Romer, C.: The role of deliberate practice in the acquisition of expert performance. Psychological Review 700, 379–384 (1993)Google Scholar
  5. 5.
    Evans, J.: Dual-processing accounts of reasoning, judgment, and social cognition. Ann. Rev. Psyc. 59, 255–278 (2008)CrossRefGoogle Scholar
  6. 6.
    French, P.A., Runger, D.: Implicit Learning. Current Directions in Psychological Science 12, 13–18 (2003)CrossRefGoogle Scholar
  7. 7.
    Hodgkinson, G., Langan-Fox, J., Sadler-Smith, E.: Intuition: A fundamental bridging construct in the behavioral sciences. British Journal of Psychology 99(1), 1–27 (2008)CrossRefGoogle Scholar
  8. 8.
    Jung-Beeman, M., Bowden, E.M., Haberman, J., Frymiare, J.L., Arambel-Liu, S., Greenblatt, R., et al.: Neural activity when people solve verbal problems with insight. PLoS Biology 2, 500–510 (2004)CrossRefGoogle Scholar
  9. 9.
    Kahneman, D., Klein, G.: Conditions for intuitive expertise: A failure to disagree. Am. Psyc. 64(6), 515–526 (2009)CrossRefGoogle Scholar
  10. 10.
    Lieberman, M.D.: Intuition: A social cognitive neuroscience approach. Psychological Bulletin 126(1), 109–137 (2000)CrossRefGoogle Scholar
  11. 11.
    Lieberman, M.D.: Social cognitive neuroscience: A review of core processes. Annual Review of Psychology 58, 259–289 (2007)CrossRefGoogle Scholar
  12. 12.
    Luu, P., Geyer, A., Wheeler, T., Campbell, G., Tucker, D., Cohn, J.: The Neural Dynamics and Temporal Course of Intuitive Decisions. Public Library of Science (2010) (in Press)Google Scholar
  13. 13.
    Mitchell, T.M., Hutchinson, R., Niculescu, R.S., Pereira, F., Wang, X., Just, M., Newman, S.: Learning to Decode Cognitive States from Brain Images. Machine Learning 57, 145–175 (2004)zbMATHCrossRefGoogle Scholar
  14. 14.
    Newell, A., Simon, H.A.: Human problem solving. Prentice-Hall, Englewood Cliffs (1972)Google Scholar
  15. 15.
    Oser, R.L., Cannon-Bowers, J.A., Salas, E., Dwyer, D.J.: Enhancing human performance in technology-rich environments: Guidelines for scenario based training. In: Salas, E. (ed.) Human Technology Interaction in Complex Systems, vol. 9, pp. 175–202. JAI Press (1999)Google Scholar
  16. 16.
    Parasuraman, R., Sheridan, T.B., Wickens, C.D.: A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans 30, 286–297 (2000)CrossRefGoogle Scholar
  17. 17.
    Ross, K., Klein, G., Thunholm, P., Schmitt, J., Baxter, H.C.: The Recognition-Primed Decision Model Mil Rev., p. 6–10 (2004)Google Scholar
  18. 18.
    Reber, A.S.: Implicit learning and tacit knowledge. Journal of Experimental Psychology: General 118, 219–235 (1989)CrossRefGoogle Scholar
  19. 19.
    Shinkareva, S.V., Mason, R.A., Malave, V.L., Wang, W., Mitchell, T.M., Just, M.A.: Using fMRI brain activation to identify cognitive states associated with perception of tools and dwellings. PLoS ONE 3, e1394 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Joseph Cohn
    • 1
  • Peter Squire
    • 1
  • Ivy Estabrooke
    • 1
  • Elizabeth O’Neill
    • 1
  1. 1.Office of Naval ResearchUSA

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