Spatiotemporal neural characterization of prediction error valence and surprise during reward learning in humans

Reward learning depends on accurate reward associations with potential choices. These associations can be attained with reinforcement learning mechanisms using a reward prediction error (RPE) signal (the difference between actual and expected rewards) for updating future reward expectations. Despite...

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Main Authors: Fouragnan, Elsa, Queirazza, Filippo, Retzler, Chris, Mullinger, Karen J., Philiastides, Marios G.
Format: Article
Published: Nature Publishing Group 2017
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Online Access:https://eprints.nottingham.ac.uk/43507/
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author Fouragnan, Elsa
Queirazza, Filippo
Retzler, Chris
Mullinger, Karen J.
Philiastides, Marios G.
author_facet Fouragnan, Elsa
Queirazza, Filippo
Retzler, Chris
Mullinger, Karen J.
Philiastides, Marios G.
author_sort Fouragnan, Elsa
building Nottingham Research Data Repository
collection Online Access
description Reward learning depends on accurate reward associations with potential choices. These associations can be attained with reinforcement learning mechanisms using a reward prediction error (RPE) signal (the difference between actual and expected rewards) for updating future reward expectations. Despite an extensive body of literature on the influence of RPE on learning, little has been done to investigate the potentially separate contributions of RPE valence (positive or negative) and surprise (absolute degree of deviation from expectations). Here, we coupled single-trial electroencephalography with simultaneously acquired fMRI, during a probabilistic reversal-learning task, to offer evidence of temporally overlapping but largely distinct spatial representations of RPE valence and surprise. Electrophysiological variability in RPE valence correlated with activity in regions of the human reward network promoting approach or avoidance learning. Electrophysiological variability in RPE surprise correlated primarily with activity in regions of the human attentional network controlling the speed of learning. Crucially, despite the largely separate spatial extend of these representations our EEG-informed fMRI approach uniquely revealed a linear superposition of the two RPE components in a smaller network encompassing visuo mnemonic and reward areas. Activity in this network was further predictive of stimulus value updating indicating a comparable contribution of both signals to reward learning.
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spelling nottingham-435072020-05-04T18:54:36Z https://eprints.nottingham.ac.uk/43507/ Spatiotemporal neural characterization of prediction error valence and surprise during reward learning in humans Fouragnan, Elsa Queirazza, Filippo Retzler, Chris Mullinger, Karen J. Philiastides, Marios G. Reward learning depends on accurate reward associations with potential choices. These associations can be attained with reinforcement learning mechanisms using a reward prediction error (RPE) signal (the difference between actual and expected rewards) for updating future reward expectations. Despite an extensive body of literature on the influence of RPE on learning, little has been done to investigate the potentially separate contributions of RPE valence (positive or negative) and surprise (absolute degree of deviation from expectations). Here, we coupled single-trial electroencephalography with simultaneously acquired fMRI, during a probabilistic reversal-learning task, to offer evidence of temporally overlapping but largely distinct spatial representations of RPE valence and surprise. Electrophysiological variability in RPE valence correlated with activity in regions of the human reward network promoting approach or avoidance learning. Electrophysiological variability in RPE surprise correlated primarily with activity in regions of the human attentional network controlling the speed of learning. Crucially, despite the largely separate spatial extend of these representations our EEG-informed fMRI approach uniquely revealed a linear superposition of the two RPE components in a smaller network encompassing visuo mnemonic and reward areas. Activity in this network was further predictive of stimulus value updating indicating a comparable contribution of both signals to reward learning. Nature Publishing Group 2017-07-06 Article PeerReviewed Fouragnan, Elsa, Queirazza, Filippo, Retzler, Chris, Mullinger, Karen J. and Philiastides, Marios G. (2017) Spatiotemporal neural characterization of prediction error valence and surprise during reward learning in humans. Scientific Reports, 7 . 4762/1-4762/18. ISSN 2045-2322 Reward learning prediction error valence surprise EEG fMRI http://www.nature.com/articles/s41598-017-04507-w doi:10.1038/s41598-017-04507-w doi:10.1038/s41598-017-04507-w
spellingShingle Reward
learning
prediction error
valence
surprise
EEG
fMRI
Fouragnan, Elsa
Queirazza, Filippo
Retzler, Chris
Mullinger, Karen J.
Philiastides, Marios G.
Spatiotemporal neural characterization of prediction error valence and surprise during reward learning in humans
title Spatiotemporal neural characterization of prediction error valence and surprise during reward learning in humans
title_full Spatiotemporal neural characterization of prediction error valence and surprise during reward learning in humans
title_fullStr Spatiotemporal neural characterization of prediction error valence and surprise during reward learning in humans
title_full_unstemmed Spatiotemporal neural characterization of prediction error valence and surprise during reward learning in humans
title_short Spatiotemporal neural characterization of prediction error valence and surprise during reward learning in humans
title_sort spatiotemporal neural characterization of prediction error valence and surprise during reward learning in humans
topic Reward
learning
prediction error
valence
surprise
EEG
fMRI
url https://eprints.nottingham.ac.uk/43507/
https://eprints.nottingham.ac.uk/43507/
https://eprints.nottingham.ac.uk/43507/