State anxiety biases estimates of uncertainty during reward learning in volatile environments

Abstract

Previous research established that clinical anxiety impairs decision making and that high trait anxiety interferes with learning rates. Less understood are the effects of temporary anxious states on learning and decision making in healthy populations. Here we follow proposals that anxious states in healthy individuals elicit a pattern of aberrant behavioural, neural, and physiological responses comparable with those found in anxiety disorders, particularly when processing uncertainty in unstable environments. In our study, both a state anxious and a control group learned probabilistic stimulus-outcome mappings in a volatile task environment while we recorded their electrophysiological (EEG) signals. By using a hierarchical Bayesian model, we assessed the effect of state anxiety on Bayesian belief updating with a focus on uncertainty estimates. State anxiety was associated with an underestimation of environmental and informational uncertainty, and an increase in uncertainty about volatility estimates. Anxious individuals deemed their beliefs about reward contingencies to be more precise and to require less updating, ultimately leading to impaired reward-based learning. We interpret this pattern as evidence that state anxious individuals are less tolerant to informational uncertainty about the contingencies governing their environment and more uncertain about the level of stability of the world itself. Further, we tracked the neural representation of belief update signals in the trial-bytrial EEG amplitudes. In control participants, both lower-level precision-weighted prediction errors (pwPEs) about the reward outcomes and higher-level volatility-pwPEs were represented in the ERP signals with an anterior distribution. A different pattern emerged under state anxiety, where a neural representation of pwPEs was only found for updates about volatility. Expanding previous computational work on trait anxiety, our findings establish that temporary anxious states in healthy individuals impair reward-based learning in volatile environments, primarily through changes in uncertainty estimates and potentially a degradation of the neuronal representation of hierarchically-related pwPEs, considered to play a central role in current Bayesian accounts of perceptual inference and learning.

Information Theoretic Characterization of Uncertainty Distinguishes Surprise From Accuracy Signals in the Brain

“…Therefore, one could probe a patient's response to confidence in the hippocampus to determine if it deviates from a healthy range. Both repetitive actions and negative outlooks (expecting the worst) may increase confidence, and therefore minimize (unpleasant) surprise in OCD and anxiety patients (Hein et al, 2019 ), respectively; but increasing confidence would also erroneously minimize information gain (Kwisthout et al, 2017 ) and therefore accuracy. While these strategies are maladaptive, they are not irrational; framing them in the context of aberrant computations offers a way to identify the specific sub-process causing distress (Parr et al, 2018 ).…”