Deutsch
 
Benutzerhandbuch Datenschutzhinweis Impressum Kontakt
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

Modeling the evolution of beliefs using an attentional focus mechanism

MPG-Autoren
/persons/resource/persons98534

Markovic,  Dimitrije
Department of Psychology, TU Dresden, Germany;
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

/persons/resource/persons19770

Kiebel,  Stefan J.
Department of Psychology, TU Dresden, Germany;
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)

Markovic_2015.PDF
(Verlagsversion), 4MB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

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.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0029-6F8D-B
Zusammenfassung
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.