Modeling the Evolution of Beliefs Using an Attentional Focus Mechanism.
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.
Department of Psychology, Technical University Dresden, Dresden, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Dimitrije
D
Marković
Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, California, United States of America.
Jan
J
Gläscher
Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, California, United States of America; Department of Finance, University of Utah, Salt Lake City, United States of America; Computation and Neural Systems, California Institute of Technology, Pasadena, California, United States of America.
Peter
P
Bossaerts
Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, California, United States of America; Computation and Neural Systems, California Institute of Technology, Pasadena, California, United States of America; Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland.
John
J
O'Doherty
Department of Psychology, Technical University Dresden, Dresden, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Stefan J
SJ
Kiebel
D001288:Attention / Q000502:physiology*; D002755:Choice Behavior / Q000502:physiology; D003469:Culture; D003657:Decision Making / Q000502:physiology*; D003661:Decision Support Techniques; D004777:Environment; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D014796:Visual Perception / Q000502:physiology*