August 2009
Volume 9, Issue 8
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2009
Promoting optimal decision making by reducing unexplained variability in outcome
Author Affiliations
  • C. Shawn Green
    Department of Psychology, University of Minnesota
  • Charlie Benson
    Department of Psychology, University of Minnesota
  • Daniel Kersten
    Department of Psychology, University of Minnesota
  • Schrater Paul
    Department of Psychology, University of Minnesota, and Department of Computer Science, University of Minnesota
Journal of Vision August 2009, Vol.9, 836. doi:10.1167/9.8.836
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      C. Shawn Green, Charlie Benson, Daniel Kersten, Schrater Paul; Promoting optimal decision making by reducing unexplained variability in outcome. Journal of Vision 2009;9(8):836. doi: 10.1167/9.8.836.

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Abstract

Human behavior in binary choice tasks is notoriously suboptimal. Given repeated choices between two options, one with a higher probability of being the correct option than the other, the obvious optimal solution is to choose only the higher probability option. Interestingly, this optimal strategy is rarely observed. The more typical finding is that subjects sample the options in proportion to their respective probabilities of being correct - a tendency known as probability matching. While standard models in the field posit that subjects in decision making tasks simply collect outcome statistics and base their decisions upon those statistics, we propose that individuals have a natural propensity to not just simply learn the outcome statistics, but instead attempt to build a causal model that can reduce unexplained variability in outcome. Only when this unexplained outcome variability is sufficiently reduced will behavior approach optimal. We tested this hypothesis by comparing subject performance in various conditions that had identical outcome statistics, but differed in the degree to which they fostered the creation of compelling causal models that could explain the statistics. As predicted, subject behavior was significantly nearer to optimal in the condition where a natural causal model existed than in conditions where no such model could be formed.

Green, C. S. Benson, C. Kersten, D. Paul, S. (2009). Promoting optimal decision making by reducing unexplained variability in outcome [Abstract]. Journal of Vision, 9(8):836, 836a, http://journalofvision.org/9/8/836/, doi:10.1167/9.8.836. [CrossRef]
Footnotes
 This work was supported by ONR N 00014-07-1-0937.
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