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  Perceptual decision making: Drift-diffusion model is equivalent to a Bayesian model

Bitzer, S., Park, H., Blankenburg, F., & Kiebel, S. J. (2014). Perceptual decision making: Drift-diffusion model is equivalent to a Bayesian model. Frontiers in Human Neuroscience, 8: 102. doi:10.3389/fnhum.2014.00102.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0015-825F-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-7FD1-3
Genre: Journal Article

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 Creators:
Bitzer, Sebastian1, Author              
Park, Hame1, Author              
Blankenburg, Felix2, 3, Author
Kiebel, Stefan J.1, 4, Author              
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, Germany, ou_634549              
2Bernstein Center for Computational Neuroscience, Berlin, Germany, ou_persistent22              
3Neurocomputation and Neuroimaging Unit, FU Berlin, Germany, ou_persistent22              
4Biomagnetic Center, Jena University Hospital, Germany, ou_persistent22              

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Free keywords: Perceptual decision making; Drift diffusion model; Bayesian models; Reaction time; Decision variable; Parameter fitting; Uncertainty
 Abstract: Behavioral data obtained with perceptual decision making experiments are typically analyzed with the drift-diffusion model. This parsimonious model accumulates noisy pieces of evidence toward a decision bound to explain the accuracy and reaction times of subjects. Recently, Bayesian models have been proposed to explain how the brain extracts information from noisy input as typically presented in perceptual decision making tasks. It has long been known that the drift-diffusion model is tightly linked with such functional Bayesian models but the precise relationship of the two mechanisms was never made explicit. Using a Bayesian model, we derived the equations which relate parameter values between these models. In practice we show that this equivalence is useful when fitting multi-subject data. We further show that the Bayesian model suggests different decision variables which all predict equal responses and discuss how these may be discriminated based on neural correlates of accumulated evidence. In addition, we discuss extensions to the Bayesian model which would be difficult to derive for the drift-diffusion model. We suggest that these and other extensions may be highly useful for deriving new experiments which test novel hypotheses.

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Language(s): eng - English
 Dates: 2013-12-042014-02-102014-02-26
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.3389/fnhum.2014.00102
PMID: 24616689
PMC: PMC3935359
 Degree: -

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Title: Frontiers in Human Neuroscience
  Abbreviation : Front Hum Neurosci
Source Genre: Journal
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Pages: - Volume / Issue: 8 Sequence Number: 102 Start / End Page: - Identifier: ISSN: 1662-5161
CoNE: https://pure.mpg.de/cone/journals/resource/1662-5161