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Evaluating and predicting psychiatric disorders by reduction of dynamic behavioral characteristics using computational models

Research Project

Project/Area Number 18KT0021
Research Category

Grant-in-Aid for Scientific Research (B)

Allocation TypeMulti-year Fund
Section特設分野
Research Field Complex Systems Disease Theory
Research InstitutionNagoya University

Principal Investigator

Katahira Kentaro  名古屋大学, 情報学研究科, 准教授 (60569218)

Co-Investigator(Kenkyū-buntansha) 国里 愛彦  専修大学, 人間科学部, 准教授 (30613856)
山下 祐一  国立研究開発法人国立精神・神経医療研究センター, 神経研究所 疾病研究第七部, 室長 (40584131)
Project Period (FY) 2018-07-18 – 2021-03-31
Project Status Completed (Fiscal Year 2020)
Budget Amount *help
¥18,460,000 (Direct Cost: ¥14,200,000、Indirect Cost: ¥4,260,000)
Fiscal Year 2020: ¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2019: ¥6,760,000 (Direct Cost: ¥5,200,000、Indirect Cost: ¥1,560,000)
Fiscal Year 2018: ¥7,150,000 (Direct Cost: ¥5,500,000、Indirect Cost: ¥1,650,000)
Keywords計算論モデリング / 精神疾患 / オンライン実験・調査 / 予測 / 強化学習 / WEB実験・調査
Outline of Final Research Achievements

The characteristics of various psychiatric disorders have been investigated from behavioral data by using computational models that represent the computational processes behind behavior. However, previous studies have only described the correlation between the tendency of the symptoms and the characteristics of the behavior at that time, and have not sufficiently investigated whether the computational models are useful for predicting prognosis and treatment response. In the present study, we examined whether the features captured by computational models directly reflect the current state of the disease and whether they are useful in predicting prognosis. As a result, it was shown that parameters of some computational models were useful for predicting future symptoms of psychiatric disorders.

Academic Significance and Societal Importance of the Research Achievements

うつ病,統合失調症などの精神疾患には,発症を未然に予測することが有効な場合もあると考えられている。計算論的精神医学と呼ばれる新しい分野では,計算論モデルを用いて精神疾患との関連が検討されてきたが,そこでとらえられた行動の特徴が精神疾患の予測に役立つか否かは十分に検討されてこなかった。本研究はそれを検討し,簡便な行動課題のデータから推定した計算論モデルのパラメータが抑うつ傾向の変化の予測に役立つ可能性を示した。本研究の意義はこれまでの計算論的精神医学の研究を精神疾患における予測につなげるという応用の可能性を示したことと,その可能性をさらに検討するための研究の枠組みを提示したという意義がある。

Report

(4 results)
  • 2020 Annual Research Report   Final Research Report ( PDF )
  • 2019 Research-status Report
  • 2018 Research-status Report
  • Research Products

    (16 results)

All 2021 2020 2019 2018

All Journal Article (8 results) (of which Int'l Joint Research: 3 results,  Peer Reviewed: 8 results,  Open Access: 8 results) Presentation (6 results) (of which Int'l Joint Research: 5 results) Book (2 results)

  • [Journal Article] Revisiting the importance of model fitting for model-based fMRI: It does matter in computational psychiatry2021

    • Author(s)
      Katahira Kentaro, Toyama Asako
    • Journal Title

      PLOS Computational Biology

      Volume: 17 Issue: 2 Pages: e1008738-e1008738

    • DOI

      10.1371/journal.pcbi.1008738

    • Related Report
      2020 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Dissociation between asymmetric value updating and perseverance in human reinforcement learning2021

    • Author(s)
      Sugawara Michiyo, Katahira Kentaro
    • Journal Title

      Scientific Reports

      Volume: 11 Issue: 1 Pages: 3574-3574

    • DOI

      10.1038/s41598-020-80593-7

    • Related Report
      2020 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Retrospective surprise: A computational component for active inference2020

    • Author(s)
      Katahira Kentaro、Kunisato Yoshihiko、Okimura Tsukasa、Yamashita Yuichi
    • Journal Title

      Journal of Mathematical Psychology

      Volume: 96 Pages: 102347-102347

    • DOI

      10.1016/j.jmp.2020.102347

    • NAID

      120006884953

    • Related Report
      2019 Research-status Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] Cognitive biases and perseverance in reinforcement learning: Does your current choice behavior depend on past “choice outcome” or “choice <i>per se</i>” ?2019

    • Author(s)
      菅原 通代、片平 健太郎
    • Journal Title

      The Japanese Journal of Psychonomic Science

      Volume: 38 Issue: 1 Pages: 48-55

    • DOI

      10.14947/psychono.38.5

    • NAID

      130007760486

    • ISSN
      0287-7651, 2188-7977
    • Year and Date
      2019-09-30
    • Related Report
      2019 Research-status Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] The Effect of Reduced Learning Ability on Avoidance in Psychopathy: A Computational Approach2019

    • Author(s)
      Oba Takeyuki、Katahira Kentaro、Ohira Hideki
    • Journal Title

      Frontiers in Psychology

      Volume: 10 Pages: 1-15

    • DOI

      10.3389/fpsyg.2019.02432

    • Related Report
      2019 Research-status Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] Biases in estimating the balance between model-free and model-based learning systems due to model misspecification2019

    • Author(s)
      Toyama Asako, Katahira Kentaro, Ohira Hideki
    • Journal Title

      Journal of Mathematical Psychology

      Volume: 91 Pages: 88-102

    • DOI

      10.1016/j.jmp.2019.03.007

    • Related Report
      2018 Research-status Report
    • Peer Reviewed / Open Access
  • [Journal Article] Reinforcement learning with parsimonious computation and a forgetting process2019

    • Author(s)
      Toyama Asako, Katahira Kentaro, Ohira Hideki
    • Journal Title

      Frontiers in Human Neuroscience

      Volume: 13 Pages: 153-153

    • DOI

      10.3389/fnhum.2019.00153

    • Related Report
      2018 Research-status Report
    • Peer Reviewed / Open Access
  • [Journal Article] The statistical structures of reinforcement learning with asymmetric value updates2018

    • Author(s)
      Katahira Kentaro
    • Journal Title

      Journal of Mathematical Psychology

      Volume: 87 Pages: 31-45

    • DOI

      10.1016/j.jmp.2018.09.002

    • Related Report
      2018 Research-status Report
    • Peer Reviewed / Open Access
  • [Presentation] Attention bias to affective outcomes2020

    • Author(s)
      Asako Toyama, Kentaro Katahira Hideki Ohira
    • Organizer
      The 2020 SAS Annual Conference
    • Related Report
      2020 Annual Research Report
    • Int'l Joint Research
  • [Presentation] 行動データの計算論モデリングと認知行動療法への貢献の可能性2020

    • Author(s)
      片平 健太郎
    • Organizer
      日本認知・行動療法学会第46回大会
    • Related Report
      2020 Annual Research Report
  • [Presentation] The learning mechanism of shaping risk preference and relations with psychopathic traits2019

    • Author(s)
      Oba, T., Katahira, K., & Ohira, H.
    • Organizer
      The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM2019)
    • Related Report
      2019 Research-status Report
    • Int'l Joint Research
  • [Presentation] Forgetting Process in Model-Free and Model-Based Reinforcement Learning2019

    • Author(s)
      Toyama, A., Katahira, K., & Ohira, H.
    • Organizer
      The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM2019)
    • Related Report
      2019 Research-status Report
    • Int'l Joint Research
  • [Presentation] Validation of cognitive bias represented by reinforcement learning with asymmetric value updates2019

    • Author(s)
      Sugawara, M. & Katahira, K.
    • Organizer
      The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM2019)
    • Related Report
      2019 Research-status Report
    • Int'l Joint Research
  • [Presentation] Pseudo-Learning Rate Modulation by the Forgetting of Action Value when Environmental Volatility Changes2019

    • Author(s)
      Oshima, S. & Katahira, K.
    • Organizer
      The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM2019)
    • Related Report
      2019 Research-status Report
    • Int'l Joint Research
  • [Book] 計算論的精神医学2019

    • Author(s)
      国里 愛彦、片平 健太郎、沖村 宰、山下 祐一
    • Total Pages
      328
    • Publisher
      勁草書房
    • ISBN
      432625131X
    • Related Report
      2018 Research-status Report
  • [Book] 行動データの計算論モデリング2018

    • Author(s)
      片平健太郎
    • Total Pages
      224
    • Publisher
      株式会社オーム社
    • ISBN
      4274222616
    • Related Report
      2018 Research-status Report

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Published: 2018-07-20   Modified: 2022-01-27  

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