Effects of emotional responses on learning in decision-making: A computational approach

  • Katahira Kentaro
    ERATO, Okanoya Emotional Information Project, Japan Science Technology Agency RIKEN, Brain Science Institute Graduate School of Arts and Sciences, The University of Tokyo
  • Fujimura Tomomi
    ERATO, Okanoya Emotional Information Project, Japan Science Technology Agency RIKEN, Brain Science Institute
  • Matsuda Yoshi-Taka
    ERATO, Okanoya Emotional Information Project, Japan Science Technology Agency RIKEN, Brain Science Institute
  • Okanoya Kazuo
    ERATO, Okanoya Emotional Information Project, Japan Science Technology Agency RIKEN, Brain Science Institute Graduate School of Arts and Sciences, The University of Tokyo
  • Okada Masato
    ERATO, Okanoya Emotional Information Project, Japan Science Technology Agency RIKEN, Brain Science Institute Graduate School of Frontier Sciences, The University of Tokyo

Bibliographic Information

Other Title
  • 意思決定における学習過程に情動反応が及ぼす効果
  • 第20回大会・優秀発表賞 意思決定における学習過程に情動反応が及ぼす効果 : 計算論的アプローチによる検討
  • ダイ20カイ タイカイ ・ ユウシュウ ハッピョウショウ イシ ケッテイ ニ オケル ガクシュウ カテイ ニ ジョウドウ ハンノウ ガ オヨボス コウカ : ケイサンロンテキ アプローチ ニ ヨル ケントウ
  • ―計算論的アプローチによる検討―

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Abstract

We investigated how emotional responses reflected in autonomic nervous system activities and facial muscles activities are related to learning in decision-making. Based on the conventional Q-learning model, we constructed novel learning models that incorporate the trial-to-trial variability in the physiological responses. In our models, the variables reflecting the physiological activities can modulate two important parameters of the model: (1) the learning rate, which determines the degree of update in response to the current choice outcome, and (2) the reward value, which quantifies the valence of the current outcome. We applied the models to the data from two types of decision-making task; one used emotional pictures as decision outcomes, and another used monetary reward. The valence of the outcomes was stochastically contingent on participants' choices. We demonstrated that proposed models that incorporated physiological measures including skin conductance, corrugator muscle activity and orbicular muscle activity, improved the prediction of the model, mainly for the emotional picture task. Our results suggest that some emotional responses are related to the subsequent choice behavior.

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