English
 
Help Privacy Policy Disclaimer
  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. (2021). Dissecting the links between reward and loss, decision-making, and self-reported affect using a computational approach. PLoS Computational Biology, 17(1), 1-27. doi:10.1371/journal.pcbi.1008555.

Item is

Files

show Files

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 risk-taking 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 accommodate the fact that ongoing (e.g., within-task) experience of rewarding and punishing decision outcomes may alter future decisions and affective states. To date, the interplay between affect, ongoing reward and punisher experience, and decision-making has received little detailed investigation. Here, we examined the relationships between reward and loss experience, affect, and decision-making in humans using a novel judgement bias task analysed with a novel computational model. We demonstrated the influence of within-task favourability on decision-making, with more risk-averse/'pessimistic' decisions following more positive previous outcomes and a greater current average earning rate. Additionally, individuals reporting more negative affect tended to exhibit greater risk-seeking decision-making, and, based on our model, estimated time more poorly. We also found that individuals reported more positive affective valence during periods of the task when prediction errors and offered decision outcomes were more positive. Our results thus provide new evidence that (short-term) within-task rewarding and punishing experiences determine both future decision-making and subjectively experienced affective states.

Details

show
hide
Language(s):
 Dates: 2020-032021-01
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1371/journal.pcbi.1008555
eDoc: e1008555
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: PLoS Computational Biology
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 17 (1) Sequence Number: - Start / End Page: 1 - 27 Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1