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  A bayesian attractor model for perceptual decision making

Bitzer, S., Bruineberg, J., & Kiebel, S. J. (2015). A bayesian attractor model for perceptual decision making. PLoS Computational Biology, 11(8): e1004442. doi:10.1371/journal.pcbi.1004442.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0029-77D6-F Version Permalink: http://hdl.handle.net/21.11116/0000-0003-789D-6
Genre: Journal Article

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 Creators:
Bitzer, Sebastian1, 2, Author              
Bruineberg, Jelle1, 3, Author              
Kiebel, Stefan J.1, 2, 4, Author              
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2Department of Psychology, TU Dresden, Germany, ou_persistent22              
3Department of Philosophy, Institute for Logic, Language and Computation, University of Amsterdam, the Netherlands, ou_persistent22              
4Biomagnetic Center, Jena University Hospital, Germany, ou_persistent22              

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 Abstract: Even for simple perceptual decisions, the mechanisms that the brain employs are still under debate. Although current consensus states that the brain accumulates evidence extracted from noisy sensory information, open questions remain about how this simple model relates to other perceptual phenomena such as flexibility in decisions, decision-dependent modulation of sensory gain, or confidence about a decision. We propose a novel approach of how perceptual decisions are made by combining two influential formalisms into a new model. Specifically, we embed an attractor model of decision making into a probabilistic framework that models decision making as Bayesian inference. We show that the new model can explain decision making behaviour by fitting it to experimental data. In addition, the new model combines for the first time three important features: First, the model can update decisions in response to switches in the underlying stimulus. Second, the probabilistic formulation accounts for top-down effects that may explain recent experimental findings of decision-related gain modulation of sensory neurons. Finally, the model computes an explicit measure of confidence which we relate to recent experimental evidence for confidence computations in perceptual decision tasks.

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Language(s): eng - English
 Dates: 2014-11-282015-07-092015-08-12
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1004442
PMID: 26267143
PMC: PMC4534380
Other: eCollection 2015
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 11 (8) Sequence Number: e1004442 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1