| Project/Area Number |
18H03318
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Review Section |
Basic Section 61060:Kansei informatics-related
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| Research Institution | The University of Tokyo |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
上田 一貴 東京大学, 大学院工学系研究科(工学部), 講師 (10403594)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥17,940,000 (Direct Cost: ¥13,800,000、Indirect Cost: ¥4,140,000)
Fiscal Year 2020: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2019: ¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2018: ¥12,870,000 (Direct Cost: ¥9,900,000、Indirect Cost: ¥2,970,000)
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| Keywords | 感情 / 新奇性 / 不確かさ / 情報理論 / ベイズ / P300 / 数理モデル / 慣れ / 自由エネルギー / 事象関連電位 / 覚醒ポテンシャル / 複雑さ / 数理モデリング / 期待効果 |
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| Outline of Final Research Achievements |
We proposed a general model of emotion dimensions (i.e., arousal and valence) associated with novelty using Bayesian perception model and the information gain. We modelled the arousal as a information gain represented as Kullback-Leibler divergence of Bayesian posterior from the prior. From the analysis of the proposed model, we derived the interaction effect between the prediction errors and the uncertainties on arousal and verified the model prediction by an experiment using subjective reports of surprise and ERP P300. In addition, we formulated the emotional valence as a function of information gain as the summation of the reward system and the aversive system. Furthermore, we modelled the habituation to novelty by applying the proposed information gain model and elucidated the effect of predictability on the habituation speed.
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| Academic Significance and Societal Importance of the Research Achievements |
感情次元を数理的にモデル化し実験的に検証した研究は世界的に類を見ない．したがって，本研究の成果は感情の数理という新たな研究領域の科学的基盤を提出したと言える．本研究で対象とした新奇性に対する感情は，新しさに対する社会的受容や普及のために重要である．本研究で提案したモデルは，新奇性の受容性の条件を不確実性と予測誤差を用いて一般的に明らかにした．さらに，提案モデルの応用から，新奇性に対する慣れや飽きの条件も不確実性と予測誤差を用いて明らかにした．提案モデルの応用可能性は非常に幅広く，たとえば，新しい製品やサービスの受容性と普及の条件を理解し、予測・シミュレーションする際の基礎理論となり得る．
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