Computing the Uncontrollable: Insights from Computational Modelling of Learning and Choice in Depression

被引:0
|
作者
Henry W. Chase
机构
[1] University of Pittsburgh School of Medicine,Department of Psychiatry, Western Psychiatric Institute and Clinic
关键词
Depression; Learned helplessness; Depressive realism; Computational modelling; Reinforcement learning; Instrumental contingencies;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
页码:28 / 37
页数:9
相关论文
共 50 条
  • [41] Editorial: Computational modelling of cardiovascular hemodynamics and machine learning
    Bourantas, Christos
    Torii, Ryo
    Karabasov, Sergey
    Krams, Rob
    FRONTIERS IN CARDIOVASCULAR MEDICINE, 2024, 11
  • [42] Deep learning: new computational modelling techniques for genomics
    Gökcen Eraslan
    Žiga Avsec
    Julien Gagneur
    Fabian J. Theis
    Nature Reviews Genetics, 2019, 20 : 389 - 403
  • [43] Deep learning: new computational modelling techniques for genomics
    Eraslan, Gokcen
    Avsec, Ziga
    Gagneur, Julien
    Theis, Fabian J.
    NATURE REVIEWS GENETICS, 2019, 20 (07) : 389 - 403
  • [44] Reassessing determinants of urban energy intensity in China: insights from controllable and uncontrollable factors
    Xiao B.
    Guo X.
    Si F.
    Environmental Science and Pollution Research, 2024, 31 (26) : 38367 - 38384
  • [45] Activation of O2and NO in heme-copper oxidases - mechanistic insights from computational modelling
    Blomberg, Margareta R. A.
    CHEMICAL SOCIETY REVIEWS, 2020, 49 (20) : 7301 - 7330
  • [46] Insights from computational modelling: Characterising Midget and Parasol Retinal Ganglion Cells using Electrical Stimulation
    Song, Xiaoyu
    Wu, Donglin
    Qiu, Shirong
    Liu, Zhengyang
    Zhou, Feng
    Ma, Saidong
    Li, Liming
    Guo, Tianruo
    2021 14TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI 2021), 2021,
  • [47] A Free-Choice Social Learning Network for Computational Thinking
    Jamil, Hasan M.
    2018 IEEE 18TH INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES (ICALT 2018), 2018, : 69 - 71
  • [48] Computational modelling of valvular heart disease: haemodynamic insights and clinical implications
    Seman, Michael
    Stephens, Andrew F.
    Kaye, David M.
    Gregory, Shaun D.
    Stub, Dion
    FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, 2024, 12
  • [49] Computing for all?: Examining critical biases in computational tools for learning
    Litts, Breanne K.
    Searle, Kristin A.
    Brayboy, Bryan M. J.
    Kafai, Yasmin B.
    BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY, 2021, 52 (02) : 842 - 857
  • [50] ENHANCING WELDING QUALITY THROUGH PREDICTIVE MODELLING - INSIGHTS FROM MACHINE LEARNING TECHNIQUES
    Kalita, Kanak
    Ghadai, Ranjan Kumar
    Cep, Robert
    Jangir, Pradeep
    MM SCIENCE JOURNAL, 2024, 2024 : 7900 - 7905