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Switching Competitors Reduces Win-Stay but Not Lose-Shift Behaviour: The Role of Outcome-Action Association Strength on Reinforcement Learning

Author

Listed:
  • Vincent Srihaput

    (School of Psychology, University of Sussex, Falmer BN1 9QH, UK)

  • Kaylee Craplewe

    (Department of Psychology, University of Alberta, Edmonton, AB T6G 2E9, Canada)

  • Benjamin James Dyson

    (School of Psychology, University of Sussex, Falmer BN1 9QH, UK
    Department of Psychology, University of Alberta, Edmonton, AB T6G 2E9, Canada
    Department of Psychology, Ryerson University, Toronto, ON M5B 2K3, Canada)

Abstract

Predictability is a hallmark of poor-quality decision-making during competition. One source of predictability is the strong association between current outcome and future action, as dictated by the reinforcement learning principles of win–stay and lose–shift. We tested the idea that predictability could be reduced during competition by weakening the associations between outcome and action. To do this, participants completed a competitive zero-sum game in which the opponent from the current trial was either replayed (opponent repeat) thereby strengthening the association, or, replaced (opponent change) by a different competitor thereby weakening the association. We observed that win–stay behavior was reduced during opponent change trials but lose–shiftbehavior remained reliably predictable. Consistent with the group data, the number of individuals who exhibited predictable behavior following wins decreased for opponent change relative to opponent repeat trials. Our data show that future actions are more under internal control following positive relative to negative outcomes, and that externally breaking the bonds between outcome and action via opponent association also allows us to become less prone to exploitation.

Suggested Citation

  • Vincent Srihaput & Kaylee Craplewe & Benjamin James Dyson, 2020. "Switching Competitors Reduces Win-Stay but Not Lose-Shift Behaviour: The Role of Outcome-Action Association Strength on Reinforcement Learning," Games, MDPI, vol. 11(3), pages 1-10, July.
  • Handle: RePEc:gam:jgames:v:11:y:2020:i:3:p:25-:d:381811
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    References listed on IDEAS

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