Representation of visual and non-visual object attributes in the brain and artificial neural networks
| Project/Area Number |
17H01756
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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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| Research Field |
Cognitive science
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| Research Institution | National Institute for Physiological Sciences |
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Principal Investigator |
Goda Naokazu 生理学研究所, システム脳科学研究領域, 助教 (30373195)
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| Co-Investigator(Kenkyū-buntansha) |
蒲池 みゆき 工学院大学, 情報学部(情報工学部), 教授 (70395101)
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| Project Period (FY) |
2017-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥17,680,000 (Direct Cost: ¥13,600,000、Indirect Cost: ¥4,080,000)
Fiscal Year 2021: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
Fiscal Year 2020: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
Fiscal Year 2019: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2018: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2017: ¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
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| Keywords | 視覚 / 多感覚 / 質感 / 深層学習 / 視触覚 / VR / 物体認識 / fMRI / 物体 |
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| Outline of Final Research Achievements |
Our brain effortlessly recognizes not only visual but also non-visual attributes of objects and environments just by looking at them. To delve into this intriguing neural processing, we analyzed the relationship between the psychological/neural representations of non-visual attributes of objects and the image features derived from deep convolutional neural networks (DNN). We discovered DNN features valuable for recognizing non-visual attributes and developed prototype image synthesis techniques that manipulate non-visual impressions using those features. Additionally, we created a virtual reality environment capable of manipulating visual and non-visual features, revealing new visual-somatosensory interactions within the environment. These techniques contribute to advancing our understanding of the brain's multisensory nature.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究は、未だ解明されていない脳の情報処理を理解するために、現在成功を収めている深層畳込みニューラルネットワーク(DNN)を利用する試みである。脳神経科学研究だけでなくDNNの研究開発においても、複数の感覚（視覚や触覚など）情報を統合的に扱うアプローチはまだこれからのテーマであり、本研究で得られた知見や技術は、これら両分野の研究の進展に貢献することが期待される。
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Report
(6 results)
Research Products
(11 results)
