English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Dissecting the links between reward and loss, decision-making, and self-reported affect using a computational approach

Neville, V., Dayan, P., Gilchrist, I., Paul, E., & Mendl, M. (submitted). Dissecting the links between reward and loss, decision-making, and self-reported affect using a computational approach.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/21.11116/0000-0005-EB70-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-EB71-3
Genre: Paper

Files

show Files

Locators

show
hide
Locator:
https://psyarxiv.com/ndc7h/ (Any fulltext)
Description:
-

Creators

show
hide
 Creators:
Neville, V, Author
Dayan, P1, 2, Author              
Gilchrist, ID, Author
Paul, ES, Author
Mendl, M, 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, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: Links between affective states and disorders, and risk-taking and reward sensitivity, are often characterised using summary statistics from serial decision-making tasks. However, our understanding of these links, and the utility of decision-making as a marker of affect, needs to consider that within-task experience of rewarding and punishing decision outcomes may alter future decisions and affective states. We investigated this issue by examining relationships between reward and loss experience, affect, and decision-making in humans using a novel judgement bias task analysed with a novel computational model. Findings included that participants made more pessimistic decisions when recent outcomes were unpredictable, and this effect was greatest in those reporting negative affect. More positive affect was reported when recent and offered decision outcomes were positive, and average earning rate was high. Short-term reward and loss experience can thus influence both future affect and decision-making, and computational modelling may reveal new decision-making markers of affect.

Details

show
hide
Language(s):
 Dates: 2020-03
 Publication Status: Submitted
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.31234/osf.io/ndc7h
 Degree: -

Event

show

Legal Case

show

Project information

show

Source

show