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A Bayesian Reformulation of the Extended Drift-Diffusion Model in Perceptual Decision Making

Perceptual decision making can be described as a process of accumulating evidence to a bound which has been formalized within drift-diffusion models (DDMs). Recently, an equivalent Bayesian model has been proposed. In contrast to standard DDMs, this Bayesian model directly links information in the stimulus to the decision process. Here, we extend this Bayesian model further and allow inter-trial variability of two parameters following the extended version of the DDM. We derive parameter distributions for the Bayesian model and show that they lead to predictions that are qualitatively equivalent to those made by the extended drift-diffusion model (eDDM). Further, we demonstrate the usefulness of the extended Bayesian model (eBM) for the analysis of concrete behavioral data. Specifically, using Bayesian model selection, we find evidence that including additional inter-trial parameter variability provides for a better model, when the model is constrained by trial-wise stimulus features. This result is remarkable because it was derived using just 200 trials per condition, which is typically thought to be insufficient for identifying variability parameters in DDMs. In sum, we present a Bayesian analysis, which provides for a novel and promising analysis of perceptual decision making experiments.

Identiferoai:union.ndltd.org:DRESDEN/oai:qucosa.de:bsz:14-qucosa-230313
Date10 November 2017
CreatorsFard, Pouyan R., Park, Hame, Warkentin, Andrej, Kiebel, Stefan J., Bitzer, Sebastian
ContributorsFrontiers ,
PublisherSaechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden
Source SetsHochschulschriftenserver (HSSS) der SLUB Dresden
LanguageEnglish
Detected LanguageEnglish
Typedoc-type:article
Formatapplication/pdf
SourceFrontiers in Computational Neuroscience (2017), 11, ISSN: 1662-5188. DOI: 10.3389/fncom.2017.00029. Artikelnr.: 29

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