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Emotion-Based Reinforcement Learning

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Ahn, Woo-Young; Rass, Olga; Shin, Yong-Wook; Busemeyer, Jerome R.; Brown, Joshua W.; O’Donnell, Brian F.

Issue Date
The Cognitive Science Society
Building Bridges Across Cognitive Sciences Around the World - Proceedings of the 34th Annual Meeting of the Cognitive Science Society, CogSci 2012, pp.78-83
Studies have shown that counterfactual reasoning can shape human decisions. However, there is a gap in the literature between counterfactual choices in description-based and experience-based paradigms. While studies using description-based paradigms suggest participants maximize expected subjective emotion, studies using experience-based paradigms assume that participants learn the values of options and select what maximizes expected utility. In this study, we used computational modeling to test 1) whether participants make emotion-based decisions in experience-based paradigms, and 2) whether the impact of regret depends on its degree of unexpectedness as suggested by the current regret theory. The results suggest that 1) participants make emotion-based choices even in experience-based paradigms, and 2) the impact of regret is greater when it is expected than when it is unexpected. These results challenge the current theory of regret and suggest that reinforcement learning models may need to use counterfactual value functions when full information is provided.
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  • College of Social Sciences
  • Department of Psychology
Research Area Addiction, computational neuroscience, decision neuroscience, 계산 신경과학, 의사결정 신경과학, 중독


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