| Project/Area Number |
16K00212
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Cognitive science
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| Research Institution | Tohoku Fukushi University |
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Principal Investigator |
Yousuke Kawachi 東北福祉大学, 総合福祉学部, 准教授 (20565775)
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| Co-Investigator(Kenkyū-buntansha) |
成 烈完 東北福祉大学, 感性福祉研究所, 准教授 (30358816)
姜 東植 琉球大学, 工学部, 准教授 (00315459)
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | 情動知能 / 一般知能 / 実行機能 / 実験心理学的計測 / 脳機能計測 / 脳構造計測 / 機械学習 / 認知機能 / 脳計測 |
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| Outline of Final Research Achievements |
Although both measurements target emotional intelligence (EI), previous studies indicated that questionnaire- and task-EI were not correlated with each other. Here we devised new EI tasks and stimuli and combined brain measures with psychological measures to comprehensively understand various aspects of EI. (1) We devised emotional tasks and stimuli (faces, sentences, and animations) and the corresponding emotional responses were quantified. (2) We explored the relationships among EI, general intelligence (GI) and executive functions (EF), showing that questionnaire-EI was correlated with questionnaire-EF but not with GI and task-EF. (3) We used a measure of regional gray matter volume (rGMV) to examine the relationship between EI and personality. Results showed that the rGMV associated with EI mainly overlapped with that of personality. (4) We devised new support vector machine classifiers related to brain structural and functional data for EI as a target to estimate individual EI.
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| Academic Significance and Societal Importance of the Research Achievements |
国内では質問紙法によって情動知能の測定がなされることがほとんどであるだけに、新たな情動喚起刺激および行動課題を考案したことが本研究の特色であり、これは情動知能を包括的に理解するステップとなる。また情動知能を一般知能や実行機能という社会適応を支えうる他の主要な心的機能との関係の中で、その機能的意味を捉え直す先駆的な試みも実施された。そして、脳機能・構造データを入力、情動知能データを標的とした機械学習を実施して作成した分類器は、集団の平均的傾向ではなく個人の情動知能を予測する手段となり、教育的サポートや精神障碍の診断補助等のニーズにこたえることが期待される。
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