Depression: A Decision-Theoretic Analysis

Quentin J M Huys, Nathaniel D. Daw, Peter Dayan

Research output: Contribution to journalArticle

Abstract

The manifold symptoms of depression are common and often transient features of healthy life that are likely to be adaptive in difficult circumstances. It is when these symptoms enter a seemingly self-propelling spiral that the maladaptive features of a disorder emerge. We examine this malignant transformation from the perspective of the computational neuroscience of decision making, investigating how dysfunction of the brain's mechanisms of evaluation might lie at its heart. We start by considering the behavioral implications of pessimistic evaluations of decision variables. We then provide a selective review of work suggesting how such pessimism might arise via specific failures of the mechanisms of evaluation or state estimation. Finally, we analyze ways that miscalibration between the subject and environment may be self-perpetuating. We employ the formal framework of Bayesian decision theory as a foundation for this study, showing how most of the problems arise from one of its broad algorithmic facets, namely model-based reasoning.

Original languageEnglish (US)
Pages (from-to)1-23
Number of pages23
JournalAnnual Review of Neuroscience
Volume38
DOIs
StatePublished - Jul 8 2015

Fingerprint

Decision Support Techniques
Depression
Decision Theory
Neurosciences
Decision Making
Brain

Keywords

  • Decision theory
  • Depression
  • Model-based control
  • Model-free control
  • Reinforcement learning

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Depression : A Decision-Theoretic Analysis. / Huys, Quentin J M; Daw, Nathaniel D.; Dayan, Peter.

In: Annual Review of Neuroscience, Vol. 38, 08.07.2015, p. 1-23.

Research output: Contribution to journalArticle

Huys, Quentin J M ; Daw, Nathaniel D. ; Dayan, Peter. / Depression : A Decision-Theoretic Analysis. In: Annual Review of Neuroscience. 2015 ; Vol. 38. pp. 1-23.
@article{086402f7ffad4b01a114f265bd55d5c5,
title = "Depression: A Decision-Theoretic Analysis",
abstract = "The manifold symptoms of depression are common and often transient features of healthy life that are likely to be adaptive in difficult circumstances. It is when these symptoms enter a seemingly self-propelling spiral that the maladaptive features of a disorder emerge. We examine this malignant transformation from the perspective of the computational neuroscience of decision making, investigating how dysfunction of the brain's mechanisms of evaluation might lie at its heart. We start by considering the behavioral implications of pessimistic evaluations of decision variables. We then provide a selective review of work suggesting how such pessimism might arise via specific failures of the mechanisms of evaluation or state estimation. Finally, we analyze ways that miscalibration between the subject and environment may be self-perpetuating. We employ the formal framework of Bayesian decision theory as a foundation for this study, showing how most of the problems arise from one of its broad algorithmic facets, namely model-based reasoning.",
keywords = "Decision theory, Depression, Model-based control, Model-free control, Reinforcement learning",
author = "Huys, {Quentin J M} and Daw, {Nathaniel D.} and Peter Dayan",
year = "2015",
month = "7",
day = "8",
doi = "10.1146/annurev-neuro-071714-033928",
language = "English (US)",
volume = "38",
pages = "1--23",
journal = "Annual Review of Neuroscience",
issn = "0147-006X",
publisher = "Annual Reviews Inc.",

}

TY - JOUR

T1 - Depression

T2 - A Decision-Theoretic Analysis

AU - Huys, Quentin J M

AU - Daw, Nathaniel D.

AU - Dayan, Peter

PY - 2015/7/8

Y1 - 2015/7/8

N2 - The manifold symptoms of depression are common and often transient features of healthy life that are likely to be adaptive in difficult circumstances. It is when these symptoms enter a seemingly self-propelling spiral that the maladaptive features of a disorder emerge. We examine this malignant transformation from the perspective of the computational neuroscience of decision making, investigating how dysfunction of the brain's mechanisms of evaluation might lie at its heart. We start by considering the behavioral implications of pessimistic evaluations of decision variables. We then provide a selective review of work suggesting how such pessimism might arise via specific failures of the mechanisms of evaluation or state estimation. Finally, we analyze ways that miscalibration between the subject and environment may be self-perpetuating. We employ the formal framework of Bayesian decision theory as a foundation for this study, showing how most of the problems arise from one of its broad algorithmic facets, namely model-based reasoning.

AB - The manifold symptoms of depression are common and often transient features of healthy life that are likely to be adaptive in difficult circumstances. It is when these symptoms enter a seemingly self-propelling spiral that the maladaptive features of a disorder emerge. We examine this malignant transformation from the perspective of the computational neuroscience of decision making, investigating how dysfunction of the brain's mechanisms of evaluation might lie at its heart. We start by considering the behavioral implications of pessimistic evaluations of decision variables. We then provide a selective review of work suggesting how such pessimism might arise via specific failures of the mechanisms of evaluation or state estimation. Finally, we analyze ways that miscalibration between the subject and environment may be self-perpetuating. We employ the formal framework of Bayesian decision theory as a foundation for this study, showing how most of the problems arise from one of its broad algorithmic facets, namely model-based reasoning.

KW - Decision theory

KW - Depression

KW - Model-based control

KW - Model-free control

KW - Reinforcement learning

UR - http://www.scopus.com/inward/record.url?scp=84929597054&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84929597054&partnerID=8YFLogxK

U2 - 10.1146/annurev-neuro-071714-033928

DO - 10.1146/annurev-neuro-071714-033928

M3 - Article

VL - 38

SP - 1

EP - 23

JO - Annual Review of Neuroscience

JF - Annual Review of Neuroscience

SN - 0147-006X

ER -