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  Temporal dynamics of prediction error processing reward-based decision making

Philiastides, M. G., Biele, G., Vavatzanidis, N., Kazzer, P., & Heekeren, H. R. (2010). Temporal dynamics of prediction error processing reward-based decision making. NeuroImage, 53(1), 221-232. doi:10.1016/j.neuroimage.2010.05.052.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0012-2E40-D Version Permalink: http://hdl.handle.net/21.11116/0000-0002-B38B-7
Genre: Journal Article

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 Creators:
Philiastides, Marios G.1, 2, Author
Biele, Guido1, 2, 3, Author
Vavatzanidis, Niki1, Author              
Kazzer, Philipp1, Author
Heekeren, Hauke R.1, 2, 3, Author              
Affiliations:
1Max Planck Institute for Human Development, Berlin, Germany, ou_persistent22              
2MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634548              
3Department of Education & Psychology, Freie Universität Berlin, Germany, ou_persistent22              

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Free keywords: Decision making; Reward; Reinforcement learning; Prediction error; Single-trial; Model; EEG
 Abstract: Adaptive decision making depends on the accurate representation of rewards associated with potential choices. These representations can be acquired with reinforcement learning (RL) mechanisms, which use the prediction error (PE, the difference between expected and received rewards) as a learning signal to update reward expectations. While EEG experiments have highlighted the role of feedback-related potentials during performance monitoring, important questions about the temporal sequence of feedback processing and the specific function of feedback-related potentials during reward-based decision making remain. Here, we hypothesized that feedback processing starts with a qualitative evaluation of outcome-valence, which is subsequently complemented by a quantitative representation of PE magnitude. Results of a model-based single-trial analysis of EEG data collected during a reversal learning task showed that around 220 ms after feedback outcomes are initially evaluated categorically with respect to their valence (positive vs. negative). Around 300 ms, and parallel to the maintained valence-evaluation, the brain also represents quantitative information about PE magnitude, thus providing the complete information needed to update reward expectations and to guide adaptive decision making. Importantly, our single-trial EEG analysis based on PEs from an RL model showed that the feedback-related potentials do not merely reflect error awareness, but rather quantitative information crucial for learning reward contingencies.

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Language(s): eng - English
 Dates: 2010-05-062010-02-262010-05-192010-05-252010-10-15
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1016/j.neuroimage.2010.05.052
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Title: NeuroImage
Source Genre: Journal
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Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 53 (1) Sequence Number: - Start / End Page: 221 - 232 Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166