English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Modeling the evolution of beliefs using an attentional focus mechanism

Markovic, D., Gläscher, J., Bossaerts, P., O'Doherty, J., & Kiebel, S. J. (2015). Modeling the evolution of beliefs using an attentional focus mechanism. PLoS Computational Biology, 11(10): e1004558. doi:10.1371/journal.pcbi.1004558.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0029-6F8D-B Version Permalink: http://hdl.handle.net/21.11116/0000-0003-1570-7
Genre: Journal Article

Files

show Files
hide Files
:
Markovic_2015.PDF (Publisher version), 4MB
Name:
Markovic_2015.PDF
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Markovic, Dimitrije1, 2, Author              
Gläscher, Jan3, 4, Author
Bossaerts, Peter4, 5, 6, Author
O'Doherty, John4, 6, 7, Author
Kiebel, Stefan J.1, 2, Author              
Affiliations:
1Department of Psychology, TU Dresden, Germany, ou_persistent22              
2Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_634549              
3Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Germany, ou_persistent22              
4Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA , ou_persistent22              
5Department of Finance, University of Utah, Salt Lake City, UT, USA, ou_persistent22              
6Computation and Neural Systems, California Institute of Technology, Pasadena, CA, USA , ou_persistent22              
7Institute of Neuroscience, Trinity College Dublin, United Kingdom, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: For making decisions in everyday life we often have first to infer the set of environmental features that are relevant for the current task. Here we investigated the computational mechanisms underlying the evolution of beliefs about the relevance of environmental features in a dynamical and noisy environment. For this purpose we designed a probabilistic Wisconsin card sorting task (WCST) with belief solicitation, in which subjects were presented with stimuli composed of multiple visual features. At each moment in time a particular feature was relevant for obtaining reward, and participants had to infer which feature was relevant and report their beliefs accordingly. To test the hypothesis that attentional focus modulates the belief update process, we derived and fitted several probabilistic and non-probabilistic behavioral models, which either incorporate a dynamical model of attentional focus, in the form of a hierarchical winner-take-all neuronal network, or a diffusive model, without attention-like features. We used Bayesian model selection to identify the most likely generative model of subjects’ behavior and found that attention-like features in the behavioral model are essential for explaining subjects’ responses. Furthermore, we demonstrate a method for integrating both connectionist and Bayesian models of decision making within a single framework that allowed us to infer hidden belief processes of human subjects.

Details

show
hide
Language(s): eng - English
 Dates: 2014-12-162015-09-012015-10-23
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1004558
PMID: 26495984
PMC: PMC4619749
Other: eCollection 2015
 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: 11 (10) Sequence Number: e1004558 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1