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  The feedback-related negativity codes components of abstract inference during reward-based decision-making

Reiter, A., Koch, S., Schröger, E., Hinrichs, H., Heinze, H.-J., Deserno, L., et al. (2016). The feedback-related negativity codes components of abstract inference during reward-based decision-making. Journal of Cognitive Neuroscience, 28(8), 1127-1138. doi:10.1162/jocn_a_00957.

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 Creators:
Reiter, Andrea1, 2, 3, Author           
Koch, Stefan4, Author
Schröger, Erich2, Author
Hinrichs, Hermann5, 6, Author
Heinze, Hans-Jochen5, 6, Author
Deserno, Lorenz1, 4, 6, Author           
Schlagenhauf, Florian1, 4, Author           
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_634549              
2University of Leipzig, Germany, ou_persistent22              
3TU Dresden, Germany, ou_persistent22              
4Charité University Medicine Berlin, Germany, ou_persistent22              
5Leibniz Institute for Neurobiology, Magdeburg, Germany, ou_persistent22              
6Otto von Guericke University Magdeburg, Germany, ou_persistent22              

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 Abstract: Behavioral control is influenced not only by learning from the choices made and the rewards obtained but also by “what might have happened,” that is, inference about unchosen options and their fictive outcomes. Substantial progress has been made in understanding the neural signatures of direct learning from choices that are actually made and their associated rewards via reward prediction errors (RPEs). However, electrophysiological correlates of abstract inference in decision-making are less clear. One seminal theory suggests that the so-called feedback-related negativity (FRN), an ERP peaking 200–300 msec after a feedback stimulus at frontocentral sites of the scalp, codes RPEs. Hitherto, the FRN has been predominantly related to a so-called “model-free” RPE: The difference between the observed outcome and what had been expected. Here, by means of computational modeling of choice behavior, we show that individuals employ abstract, “double-update” inference on the task structure by concurrently tracking values of chosen stimuli (associated with observed outcomes) and unchosen stimuli (linked to fictive outcomes). In a parametric analysis, model-free RPEs as well as their modification because of abstract inference were regressed against single-trial FRN amplitudes. We demonstrate that components related to abstract inference uniquely explain variance in the FRN beyond model-free RPEs. These findings advance our understanding of the FRN and its role in behavioral adaptation. This might further the investigation of disturbed abstract inference, as proposed, for example, for psychiatric disorders, and its underlying neural correlates.

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Language(s): eng - English
 Dates: 2016-06-222016-08
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1162/jocn_a_00957
PMID: 27031567
Other: Epub 2016
 Degree: -

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Title: Journal of Cognitive Neuroscience
Source Genre: Journal
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Publ. Info: Cambridge, MA : MIT Press Journals
Pages: - Volume / Issue: 28 (8) Sequence Number: - Start / End Page: 1127 - 1138 Identifier: ISSN: 0898-929X
CoNE: https://pure.mpg.de/cone/journals/resource/991042752752726