English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Impaired adaptation of learning to contingency volatility in internalizing psychopathology

Gagne, C., Zika, O., Dayan, P., & Bishop, S. (2020). Impaired adaptation of learning to contingency volatility in internalizing psychopathology. eLife, 9, 1-51. doi:10.7554/eLife.61387.

Item is

Basic

show hide
Genre: Journal Article

Files

show Files

Locators

show
hide
Locator:
https://elifesciences.org/articles/61387 (Publisher version)
Description:
-

Creators

show
hide
 Creators:
Gagne, C1, 2, Author              
Zika, O, Author
Dayan, P1, 2, Author              
Bishop, SJ, Author
Affiliations:
1Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3017468              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: Using a contingency volatility manipulation, we tested the hypothesis that difficulty adapting probabilistic decision-making to second-order uncertainty might reflect a core deficit that cuts across anxiety and depression and holds regardless of whether outcomes are aversive or involve reward gain or loss. We used bifactor modeling of internalizing symptoms to separate symptom variance common to both anxiety and depression from that unique to each. Across two experiments, we modeled performance on a probabilistic decision-making under volatility task using a hierarchical Bayesian framework. Elevated scores on the common internalizing factor, with high loadings across anxiety and depression items, were linked to impoverished adjustment of learning to volatility regardless of whether outcomes involved reward gain, electrical stimulation, or reward loss. In particular, high common factor scores were linked to dampened learning following better-than-expected outcomes in volatile environments. No such relationships were observed for anxiety- or depression-specific symptom factors.

Details

show
hide
Language(s):
 Dates: 2020-12
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.7554/eLife.61387
eDoc: e61387
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: eLife
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Cambridge : eLife Sciences Publications
Pages: - Volume / Issue: 9 Sequence Number: - Start / End Page: 1 - 51 Identifier: ISSN: 2050-084X
CoNE: https://pure.mpg.de/cone/journals/resource/2050-084X