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.
Additional InformationThis is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Received: December 16, 2014; Accepted: September 1, 2015; Published: October 23, 2015. This work was supported by the US-German Collaboration in Computational Neuroscience of NSF (1207573, to JO) and BMBF (Förderkennzeichen: 01GQ1205, to SJK). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors have declared that no competing interests exist. We thank Sebastian Bitzer and Daniel McNamee for helpful discussions and comments on earlier versions of the manuscript. We also thank the Center of Information Services and High Performance Computing (ZIH) at Technische Universität Dresden for providing the computer resources. Author Contributions: Conceived and designed the experiments: JG JO PB. Performed the experiments: JG. Analyzed the data: DM SJK. Wrote the paper: DM SJK.
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