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  Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception

Rohe, T., & Noppeney, U. (2015). Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception. PLoS Biology, 13(2), 1-18. doi:10.1371/journal.pbio.1002073.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002A-4764-1 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-8788-D
Genre: Journal Article

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Rohe, T1, 2, Author              
Noppeney, U1, 2, Author              
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1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              
2Research Group Cognitive Neuroimaging, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497804              

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 Abstract: To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the “causal inference problem.” Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI), and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain. At the bottom of the hierarchy, in auditory and visual areas, location is represented on the basis that the two signals are generated by independent sources (= segregation). At the next stage, in posterior intraparietal sulcus, location is estimated under the assumption that the two signals are from a common source (= forced fusion). Only at the top of the hierarchy, in anterior intraparietal sulcus, the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference. Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex. It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition. Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world.

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 Dates: 2015-02
 Publication Status: Published online
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 Identifiers: DOI: 10.1371/journal.pbio.1002073
eDoc: e1002073
BibTex Citekey: RoheN2015
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Title: PLoS Biology
Source Genre: Journal
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Pages: - Volume / Issue: 13 (2) Sequence Number: - Start / End Page: 1 - 18 Identifier: -