Open Access
Open access
volume 249 pages 118895

State anxiety alters the neural oscillatory correlates of predictions and prediction errors during reward-based learning

Publication typeJournal Article
Publication date2022-04-01
scimago Q1
wos Q1
SJR2.074
CiteScore10.8
Impact factor4.5
ISSN10538119, 10959572
Neurology
Cognitive Neuroscience
Abstract
Anxiety influences how the brain estimates and responds to uncertainty. The consequences of these processes on behaviour have been described in theoretical and empirical studies, yet the associated neural correlates remain unclear. Rhythm-based accounts of Bayesian predictive coding propose that predictions in generative models of perception are represented in alpha (8-12 Hz) and beta oscillations (13-30 Hz). Updates to predictions are driven by prediction errors weighted by precision (inverse variance) encoded in gamma oscillations (>30 Hz) and associated with the suppression of beta activity. We tested whether state anxiety alters the neural oscillatory activity associated with predictions and precision-weighted prediction errors (pwPE) during learning. Healthy human participants performed a probabilistic reward-based learning task in a volatile environment. In our previous work, we described learning behaviour in this task using a hierarchical Bayesian model, revealing more precise (biased) beliefs about the tendency of the reward contingency in state anxiety, consistent with reduced learning in this group. The model provided trajectories of predictions and pwPEs for the current study, allowing us to assess their parametric effects on the time-frequency representations of EEG data. Using convolution modelling for oscillatory responses, we found that, relative to a control group, state anxiety increased beta activity in frontal and sensorimotor regions during processing of pwPE, and in fronto-parietal regions during encoding of predictions. No effects of state anxiety on gamma modulation were found. Our findings expand prior evidence on the oscillatory representations of predictions and pwPEs into the reward-based learning domain. The results suggest that state anxiety modulates beta-band oscillatory correlates of pwPE and predictions in generative models, providing insights into the neural processes associated with biased belief updating and poorer learning.
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Hein T., Herrojo Ruiz M. State anxiety alters the neural oscillatory correlates of predictions and prediction errors during reward-based learning // NeuroImage. 2022. Vol. 249. p. 118895.
GOST all authors (up to 50) Copy
Hein T., Herrojo Ruiz M. State anxiety alters the neural oscillatory correlates of predictions and prediction errors during reward-based learning // NeuroImage. 2022. Vol. 249. p. 118895.
RIS |
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RIS Copy
TY - JOUR
DO - 10.1016/j.neuroimage.2022.118895
UR - https://doi.org/10.1016/j.neuroimage.2022.118895
TI - State anxiety alters the neural oscillatory correlates of predictions and prediction errors during reward-based learning
T2 - NeuroImage
AU - Hein, Thomas
AU - Herrojo Ruiz, Maria
PY - 2022
DA - 2022/04/01
PB - Elsevier
SP - 118895
VL - 249
PMID - 35017125
SN - 1053-8119
SN - 1095-9572
ER -
BibTex
Cite this
BibTex (up to 50 authors) Copy
@article{2022_Hein,
author = {Thomas Hein and Maria Herrojo Ruiz},
title = {State anxiety alters the neural oscillatory correlates of predictions and prediction errors during reward-based learning},
journal = {NeuroImage},
year = {2022},
volume = {249},
publisher = {Elsevier},
month = {apr},
url = {https://doi.org/10.1016/j.neuroimage.2022.118895},
pages = {118895},
doi = {10.1016/j.neuroimage.2022.118895}
}