Multimodal analysis of mechanisms of empathic behavior
| Project/Area Number |
15K01845
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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 |
Basic / Social brain science
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| Research Institution | Meiji University |
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Principal Investigator |
Mukai Hideo 明治大学, 理工学部, 専任講師 (20534358)
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| Project Period (FY) |
2015-10-21 – 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 2017: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2015: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Keywords | 行動解析 / 機械学習 / 共感行動 / 情動 / 共感 / 大脳辺縁系 / 帯状回 / 神経回路 |
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| Outline of Final Research Achievements |
In current study we developed a method of analysis for empathic behavior in rodents using machine learning, which has achieved remarkable development in recent years. For machine learning technique, the semantic segmentation neural network with convolutional neural network (CNN) structure was adopted. We obtained the result that animals demonstrated the action of "consolation" between two individuals, strongly suggesting the occurrence of empathy. The behavior could have a great significance as a new mode of empathic behavior. We also found that sniffing (smell seeking behavior) is a frequent mode of behavior in addition to freezing (immobile) and rearing (rising) behavior intermittently seen during empathic behavior. The above results contribute to the elucidation of empathic behavior and also have important significance as an application example of machine learning to neuroscience.
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| Academic Significance and Societal Importance of the Research Achievements |
共感情動は近年注目されている情動のモードであり、対人関係等の困難などが社会的な問題となっている中、その実態を明らかにすることは社会的に重要な課題であると考えられる。
本研究では2つの動物個体が寄り添う行動(consolation(慰め行動))が新たに共感の発生を強く示唆する行動として捉えられた。また、新たな行動モードの検出にも成功した。本研究は近年発展著しい機械学習（人工知能）の先端的な手法である画像検出（Semantic Segmentation)の適用例になっており、今後の神経科学その他画像を用いる科学研究への適用の基盤となる重要な成果を挙げた。
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Report
(5 results)
Research Products
(2 results)
