| Project/Area Number |
19F19809
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Single-year Grants |
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| Section | 外国 |
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| Review Section |
Basic Section 61030:Intelligent informatics-related
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| Research Institution | Institute of Physical and Chemical Research |
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Principal Investigator |
豊泉 太郎 国立研究開発法人理化学研究所, 脳神経科学研究センター, チームリーダー (50547461)
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| Co-Investigator(Kenkyū-buntansha) |
BALTIERI MANUEL 国立研究開発法人理化学研究所, 脳神経科学研究センター, 外国人特別研究員
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| Project Period (FY) |
2019-11-08 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥2,100,000 (Direct Cost: ¥2,100,000)
Fiscal Year 2021: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2020: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2019: ¥300,000 (Direct Cost: ¥300,000)
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| Keywords | uncertainty / bayesian inference / closed-loop control |
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| Outline of Research at the Start |
We propose an interdisciplinary approach to the study of perceptual and motor processes in naturalistic conditions. This approach will integrate computational models including explicit accounts of unpredictable variations in the world and based on closed-loop, embodied theories of behaviour with the analysis and modelling of data from novel experimental set ups focused on dynamically changing environments.
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| Outline of Annual Research Achievements |
Uncertainty is quantified via Bayesian models of optimal behaviour, studying their relations to stochastic (thermo)dynamics in physics, symplectic structures in information geometry and goal-directed agents in optimal control/reinforcement learning via their consistent mathematical formulation.
The project includes mathematical analysis and computational models of decision making mechanisms under uncertainty. This uncertainty is quantified via Bayesian models of optimal behaviour, studying their relations to stochastic (thermo)dynamics in physics, symplectic structures in information geometry and goal-directed agents in optimal control/reinforcement learning via their consistent mathematical formulation.
Our work presents an analysis of an emergent framework in neuroscience, cognitive science and biology: the free energy principle. This framework is a popular way of describing behaviour and neural activity in terms of predictive models encoding information about the world. More recently, this mathematical framework has been proposed to account for models of individuality and origins of life, casting the definition of “individual” (or even “agent”) in terms of statistical separations from the outside world. Our paper provides an extensive review and analysis of mathematical assumptions, philosophical commitments and applications to neuroscience of this frameworks, identifying significant flaws that still remain at the moment.
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| Research Progress Status |
令和3年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
令和3年度が最終年度であるため、記入しない。
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