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  Predicting speech from a cortical hierarchy of event-based timescales

Schmitt, L., Erb, J., Tune, S., Rysop, A., Hartwigsen, G., & Obleser, J. (2021). Predicting speech from a cortical hierarchy of event-based timescales. Science Advances, 7(49): eabi6070. doi:10.1126/sciadv.abi6070.

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 Creators:
Schmitt, Lea1, 2, Author
Erb, Julia1, 2, Author           
Tune, Sarah1, 2, Author
Rysop, Anna3, Author           
Hartwigsen, Gesa3, Author           
Obleser, Jonas1, 2, Author           
Affiliations:
1Department of Psychology, University of Lübeck, Germany, ou_persistent22              
2Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Germany, ou_persistent22              
3Lise Meitner Research Group Cognition and Plasticity, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_3025665              

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 Abstract: How do predictions in the brain incorporate the temporal unfolding of context in our natural environment? We here provide evidence for a neural coding scheme that sparsely updates contextual representations at the boundary of events. This yields a hierarchical, multilayered organization of predictive language comprehension. Training artificial neural networks to predict the next word in a story at five stacked time scales and then using model-based functional magnetic resonance imaging, we observe an event-based “surprisal hierarchy” evolving along a temporoparietal pathway. Along this hierarchy, surprisal at any given time scale gated bottom-up and top-down connectivity to neighboring time scales. In contrast, surprisal derived from continuously updated context influenced temporoparietal activity only at short time scales. Representing context in the form of increasingly coarse events constitutes a network architecture for making predictions that is both computationally efficient and contextually diverse.

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Language(s): eng - English
 Dates: 2021-10-162021-12-03
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1126/sciadv.abi6070
Other: epub 2021
PMID: 34860554
PMC: PMC8641937
 Degree: -

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Grant ID : OB 352/2-1; HA 6314/4-1
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Funding organization : German Research Foundation (DFG)
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Grant ID : ERC-CoG-2014–646696
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Funding organization : European Research Council

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Title: Science Advances
  Other : Sci. Adv.
Source Genre: Journal
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Publ. Info: Washington : AAAS
Pages: - Volume / Issue: 7 (49) Sequence Number: eabi6070 Start / End Page: - Identifier: ISSN: 2375-2548
CoNE: https://pure.mpg.de/cone/journals/resource/2375-2548