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

Abstract

Anxiety influences how the brain estimates and responds to uncertainty. These behavioural effects have been described within predictive coding and Bayesian inference frameworks, yet the associated neural correlates remain unclear. Recent work suggests 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; pwPE), and are encoded in gamma oscillations (>30 Hz) and associated with suppression of beta activity. We tested whether state anxiety alters the neural oscillatory activity associated with predictions and pwPE during learning. Healthy human participants performed a probabilistic reward-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 reward tendency 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-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 reward learning.