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)


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.


Cognitive Modeling Perception Emotion and Interaction Intuition Decision Making Implicit Learning 


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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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