Header

UZH-Logo

Maintenance Infos

Depression: a decision-theoretic analysis


Huys, Quentin J M; Daw, Nathaniel D; Dayan, Peter (2015). Depression: a decision-theoretic analysis. Annual Review of Neuroscience, 38:1-23.

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.

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.

Statistics

Citations

15 citations in Web of Science®
16 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

3 downloads since deposited on 19 Nov 2015
0 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Institute of Biomedical Engineering
Dewey Decimal Classification:170 Ethics
610 Medicine & health
Language:English
Date:8 July 2015
Deposited On:19 Nov 2015 09:26
Last Modified:05 Apr 2016 19:31
Publisher:Annual Reviews
ISSN:0147-006X
Publisher DOI:https://doi.org/10.1146/annurev-neuro-071714-033928
PubMed ID:25705929

Download

Preview Icon on Download
Filetype: PDF - Registered users only
Size: 539kB
View at publisher