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動的かつ不確実な環境下での適応的知覚と行動に関する研究

Research Project

Project/Area Number 19F19809
Research Category

Grant-in-Aid for JSPS Fellows

Allocation TypeSingle-year Grants
Section外国
Review Section Basic Section 61030:Intelligent informatics-related
Research InstitutionInstitute of Physical and Chemical Research

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)

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
Project Period (FY) 2019-11-08 – 2022-03-31
Project Status Completed (Fiscal Year 2021)
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)
Keywordsuncertainty / bayesian inference / closed-loop control
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.

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.

Research Progress Status

令和3年度が最終年度であるため、記入しない。

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。

Report

(3 results)
  • 2021 Annual Research Report
  • 2020 Annual Research Report
  • 2019 Annual Research Report
  • Research Products

    (11 results)

All 2021 2020 2019

All Journal Article (8 results) (of which Int'l Joint Research: 6 results,  Peer Reviewed: 6 results,  Open Access: 3 results) Presentation (3 results) (of which Int'l Joint Research: 3 results)

  • [Journal Article] The Emperor's New Markov Blankets2021

    • Author(s)
      Bruineberg Jelle、Dolega Krzysztof、Dewhurst Joe、Baltieri Manuel
    • Journal Title

      Behavioral and Brain Sciences

      Volume: - Pages: 1-63

    • DOI

      10.1017/s0140525x21002351

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed / Int'l Joint Research
  • [Journal Article] Active inference through whiskers2021

    • Author(s)
      Mannella Francesco、Maggiore Federico、Baltieri Manuel、Pezzulo Giovanni
    • Journal Title

      Neural Networks

      Volume: 144 Pages: 428-437

    • DOI

      10.1016/j.neunet.2021.08.037

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed / Int'l Joint Research
  • [Journal Article] Embodied skillful performance: where the action is2021

    • Author(s)
      Hipolito Ines、Baltieri Manuel、Friston Karl、Ramstead Maxwell J. D.
    • Journal Title

      Synthese

      Volume: 199 Issue: 1-2 Pages: 4457-4481

    • DOI

      10.1007/s11229-020-02986-5

    • Related Report
      2021 Annual Research Report 2020 Annual Research Report
    • Peer Reviewed / Int'l Joint Research
  • [Journal Article] Predictions in the eye of the beholder: an active inference account of Watt governors2020

    • Author(s)
      Baltieri Manuel、Buckley Christopher L.、Bruineberg Jelle
    • Journal Title

      Artificial Life Conference Proceedings

      Volume: - Pages: 121-129

    • DOI

      10.1162/isal_a_00288

    • Related Report
      2020 Annual Research Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] A Bayesian perspective on classical control2020

    • Author(s)
      Baltieri Manuel
    • Journal Title

      2020 International Joint Conference on Neural Networks (IJCNN)

      Volume: - Pages: 1-8

    • DOI

      10.1109/ijcnn48605.2020.9206617

    • Related Report
      2020 Annual Research Report
    • Peer Reviewed
  • [Journal Article] Scaling Active Inference2020

    • Author(s)
      Tschantz Alexander、Baltieri Manuel、Seth Anil. K.、Buckley Christopher L.
    • Journal Title

      2020 International Joint Conference on Neural Networks (IJCNN)

      Volume: - Pages: 1-8

    • DOI

      10.1109/ijcnn48605.2020.9207382

    • Related Report
      2020 Annual Research Report
    • Peer Reviewed / Int'l Joint Research
  • [Journal Article] A Bayesian perspective on classical control2020

    • Author(s)
      Manuel Baltieri
    • Journal Title

      arXiv.org

      Volume: 2004.10288

    • Related Report
      2019 Annual Research Report
    • Open Access
  • [Journal Article] Scaling active inference2019

    • Author(s)
      Alexander Tschantz, Manuel Baltieri, Anil. K. Seth, Christopher L. Buckley
    • Journal Title

      arXiv.org

      Volume: 1911.10601

    • Related Report
      2019 Annual Research Report
    • Open Access / Int'l Joint Research
  • [Presentation] Predictions in the eye of the beholder: an active inference account of Watt governors.2020

    • Author(s)
      Manuel Baltieri
    • Organizer
      ALife 2020
    • Related Report
      2020 Annual Research Report
    • Int'l Joint Research
  • [Presentation] A Bayesian perspective on classical control2020

    • Author(s)
      Manuel Baltieri
    • Organizer
      IJCNN 2020
    • Related Report
      2020 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Active inference for cognitive science and artificial intelligence - open questions and new challenges2020

    • Author(s)
      Manuel Baltieri
    • Organizer
      CHAIN Academic Seminars, Hokkaido University, Sapporo, Japan
    • Related Report
      2019 Annual Research Report
    • Int'l Joint Research

URL: 

Published: 2019-11-29   Modified: 2024-03-26  

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