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

NeuroImage ◽  
2022 ◽  
pp. 118895
Author(s):  
Thomas P Hein ◽  
Maria Herrojo Ruiz
Keyword(s):  
State Anxiety ◽  
Prediction Errors

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

2021 ◽  
Author(s):  
Thomas P Hein ◽  
Maria Herrojo Ruiz
Keyword(s):  
State Anxiety ◽  
Generative Models ◽  
Reward Learning ◽  
Beta Activity ◽  
Prediction Errors ◽  
Beta Oscillations ◽  
Healthy Human ◽  
Time Frequency ◽  
Alpha Beta ◽  
Human Participants

AbstractAnxiety 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-beta oscillations (8-30 Hz), while updates to predictions are driven by prediction errors weighted by precision (inverse variance; pwPE) and encoded in gamma oscillations (>30 Hz). 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 alpha-beta activity in frontal and sensorimotor regions during processing pwPE, and in fronto-parietal regions during encoding 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 oscillatory correlates of pwPE and predictions in generative models, providing insights into a potential mechanism explaining biased belief updating and poorer reward learning.Significance StatementLearning plays a central role in clinical and subclinical anxiety. This study tests whether a temporarily-induced state of anxiety in healthy human participants alters the neural oscillatory patterns associated with predicting and learning from rewards. We found that precision-weighted prediction errors were associated with increases in alpha-beta oscillations in our state anxious group. This finding suggested that anxiety states may inhibit encoding of relevant signals conveying the discrepancy between the predicted and observed reward. State anxiety also increased alpha-beta activity during processing predictions, indicating a stronger reliance on prior beliefs about the reward tendency. The results identify the alteration in alpha-beta oscillations as a candidate mechanism explaining misestimation of uncertainty and maladaptive learning in anxiety.

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

2019 ◽  
Author(s):  
Thomas P Hein ◽  
Lilian A Weber ◽  
Jan de Fockert ◽  
Maria Herrojo Ruiz
Keyword(s):  
Decision Making ◽  
Trait Anxiety ◽  
State Anxiety ◽  
Neural Representation ◽  
Control Group ◽  
Prediction Errors ◽  
Healthy Individuals ◽  
Perceptual Inference ◽  
Uncertainty Estimates ◽  
Informational Uncertainty

AbstractPrevious 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-by-trial 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.

Above and beyond the content: Feelings influence mental simulations

2020 ◽  
Vol 43 ◽  
Author(s):  
Kellen Mrkva ◽  
Luca Cian ◽  
Leaf Van Boven
Keyword(s):  
Mental Representation ◽  
Mental Simulation ◽  
Past Experience ◽  
Prediction Errors

Abstract Gilead et al. present a rich account of abstraction. Though the account describes several elements which influence mental representation, it is worth also delineating how feelings, such as fluency and emotion, influence mental simulation. Additionally, though past experience can sometimes make simulations more accurate and worthwhile (as Gilead et al. suggest), many systematic prediction errors persist despite substantial experience.

Effects of Fear Induction on Heart Period Variability

1999 ◽  
Vol 13 (1) ◽  
pp. 18-26 ◽  
Author(s):  
Rudolf Stark ◽  
Alfons Hamm ◽  
Anne Schienle ◽  
Bertram Walter ◽  
Dieter Vaitl
Keyword(s):  
Blood Pressure ◽  
Trait Anxiety ◽  
State Anxiety ◽  
Self Report ◽  
Emotional States ◽  
Heart Period ◽  
Experimental Conditions ◽  
Heart Period Variability ◽  
Physiological Variables ◽  
Period Variability

Abstract The present study investigated the influence of contextual fear in comparison to relaxation on heart period variability (HPV), and analyzed differences in HPV between low and high anxious, nonclinical subjects. Fifty-three women participated in the study. Each subject underwent four experimental conditions (control, fear, relaxation, and a combined fear-relaxation condition), lasting 10 min each. Fear was provoked by an unpredictable aversive human scream. Relaxation should be induced with the aid of verbal instructions. To control for respiratory effects on HPV, breathing was paced at 0.2 Hz using an indirect light source. Besides physiological measures (HPV measures, ECG, respiration, forearm EMG, blood pressure), emotional states (pleasure, arousal, dominance, state anxiety) were assessed by subjects' self-reports. Since relaxation instructions did not have any effect neither on the subjective nor on the physiological variables, the present paper focuses on the comparison of the control and the fear condition. The scream reliably induced changes in both physiological and self-report measures. During the fear condition, subjects reported more arousal and state anxiety as well as less pleasure and dominance. Heart period decreased, while EMG and diastolic blood pressure showed a tendency to increase. HPV remained largely unaltered with the exception of the LF component, which slightly decreased under fear induction. Replicating previous findings, trait anxiety was negatively associated with HPV, but there were no treatment-specific differences between subjects with low and high trait anxiety.

Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

2016 ◽  
Vol 63 (6) ◽  
pp. 333-342
Author(s):  
Roberto Limongi ◽  
Angélica M. Silva
Keyword(s):  
Response Time ◽  
Short Term Memory ◽  
Estimation Error ◽  
Predictive Coding ◽  
Time Estimation ◽  
Prediction Errors ◽  
Memory Scanning ◽  
Short Term ◽  
Term Memory ◽  
Temporal Prediction

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

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