Alterations in the amplitude and burst rate of beta oscillations impair reward-dependent motor learning in anxiety
- School of Psychology, University of Birmingham, United Kingdom
- Department of Psychology, Goldsmiths University of London, United Kingdom
- Center for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Russian Federation
Figures
Figure 1
A novel paradigm for testing reward-based motor sequence learning.
(A) Schematic of the task. Participants performed sequence1 during 100 initial exploration trials, followed by 200 trials over two blocks of reward-based learning performing sequence2. During the …
Figure 2
Heart-rate variability (HRV) modulation by the anxiety manipulation.
(A) The average HRV measured as the coefficient of variation (CV) of the inter-beat-interval is displayed across the experimental blocks: initial resting state recording (Pre), initial exploration …
Figure 3
Temporal variability during initial exploration and during reward-based learning.
(A, B) Illustration of timing performance during initial exploration (A) and learning (B) blocks for one representative participant, s1. The x-axis represents the position of the inter-keystroke …
Figure 4 with 1 supplement
Effects of anxiety on behavioral variability and reward-based learning.
The score was computed as a 0–100 normalized measure of proximity between the norm of the pattern of differences in inter-keystroke intervals performed in each trial and the target norm. All of the b…
Figure 4—figure supplement 1
Mean learned solution in each group.
Mean learned solution in each group. On average, the learned performance was not significantly different between experimental and control groups, during either the first (A) or second (B) learning …
Figure 5 with 6 supplements
Two-level Hierarchical Gaussian Filter for continuous inputs.
(A) Schematic of the two-level HGF, which models how an agent infers a hidden state in the environment (a random variable), , as well as its rate of change over time (, environmental …
Figure 5—figure supplement 1
Trial-by-trial belief trajectories for simulated performances.
All belief trajectories were generated using prior values on the HGF parameters as shown in Table 1. We simulated performances in six agents by changing the trial-to-trial difference in IKI values …
Figure 5—figure supplement 2
Simulated trial-by-trial belief trajectories in an ideal learner.
Simulated trial-by-trialtrajectories of posterior means of belief distributions in an ideal learner with different values of (A, B) or (C, D). All trajectories were simulated with identical …
Figure 5—figure supplement 3
coefficients of the winning response model.
(A–C) Mean (and SEM) values of the coefficients that explain the performance measure in trial as a linear function of (i) a constant value () and (ii) the precision-weighted prediction …
Figure 5—figure supplement 4
Example in one control participant of the association between pwPEs and performance.
Example in one control participant of the association between pwPEs relating to volatility and subsequent changes in performance. (A, B) Illustration of the trajectory of pwPE relating to volatility …
Figure 5—figure supplement 5
Example in one anx1 participant of the association between pwPEs and performance.
Example in one anx1 participant of the association between pwPEs relating to volatility and subsequent changes in performance. This figure illustrates the effect of negative coefficients in the …
Figure 5—figure supplement 6
Grand-average trialwise residuals.
Grand-average trialwise residuals resulting from the difference between the observed responses and the responses predicted by the HGF. (A–C) The trialwise residuals in each control and experimental …
Figure 6 with 1 supplement
Computational modeling analysis.
Data shown as mean and ± SEM. (A) In the main experiment, anx1 participants underestimated the tendency for (meaning their expectation on reward in the current trial was lower; , purple bar at …
Figure 6—figure supplement 1
Correlation between HGF volatility estimates and the variance of the distribution of feedback scores.
Correlation between HGF volatility estimates and the variance in the distribution of feedback scores. Non-parametric rank correlation in the total population (N = 60) between the variance of the …
Figure 7 with 1 supplement
Anxiety during initial exploration prolongs the life-time of sensorimotor beta-band oscillation bursts.
(A) Illustration of the amplitude of beta oscillations (gray line) and the amplitude envelope (black line) for one representative subject and channel. (B) Schematic overview of the …
Figure 7—figure supplement 1
Sensorimotor beta power is modulated by anxiety during initial exploration.
(A) Topographical representation of the between-group difference (anx1–controls) in normalized beta-band power spectral density (PSD) in dB. A larger beta-band PSD increase was found in anx1 …
Figure 8 with 6 supplements
Time course of the beta power and burst rate during trials in the exploration block.
(A) The time representation of the beta power throughout trial performance shows two distinct time windows of increased power in participants affected by the anxiety manipulation: following sequence …
Figure 8—figure supplement 1
Post-movement increases in the beta-band amplitude and burst rate can be explained by state anxiety after matching participants on temporal variability.
(A–C) A separate control analysis was carried out to determine the influence of the anxiety manipulation alone on the beta PSD and burst rate properties, after controlling for changes in motor …
Figure 8—figure supplement 2
Post-movement increases in the beta-band amplitude and burst rate can be explained by state anxiety after matching participants on the sequence duration.
(A–C) Same as Figure 8—figure supplement 1 but after matching participants in the total duration of the sequence.
Figure 8—figure supplement 3
Post-movement increases in the beta-band amplitude and burst rate can be explained by state anxiety after matching participants on the variability of the total sequence duration.
(A–C) Same as Figure 8—figure supplement 1 but after matching participants on the variability of the total sequence duration.
Figure 8—figure supplement 4
Post-movement increases in the beta-band amplitude and burst rate can be explained by state anxiety after matching participants on the mean keystroke velocity.
(A–C) Same as Figure 8—figure supplement 1 but after matching participants on the mean keystroke velocity, related to loudness.
Figure 8—figure supplement 5
Changes in motor variability without concurrent changes in state anxiety only partially account for the observed alterations in post-movement beta amplitude and burst rate.
(A–C) Same as Figure 8—figure supplement 1, but in a control analysis performed to assess the effect of motor variability on beta PSD changes during exploration, independently of the anxiety …
Figure 8—figure supplement 6
Correlation between average beta power and the degree of task-related behavioral variability across trials during the exploration phase.
Non-parametric rank correlation in the total population (N = 60) between the mean beta power during the time interval following the STOP signal and cvIKI across trials. There was a significant …
Figure 9 with 2 supplements
Time course of the beta power and burst rate throughout trial performance and following reward feedback.
(A) Time course of the feedback-locked beta power during sequence performance in the learning blocks, shown separately for anx1, anx2 and control groups. Average across sensorimotor and prefrontal …
Figure 9—figure supplement 1
Beta power spectral density and burst rate during reward-based learning.
(A–C) During learning, the general level of normalized PSD did not differ between groups (). The learning-related PSD was normalized into decibels (dB) with the PSD of the initial resting state …
Figure 9—figure supplement 2
Higher gamma band activity analysis rules out an explanation in which muscle artifacts influence feedback-related changes in power.
Broadband high-frequency gamma band activity, above 50 Hz, has been linked to muscle artifacts (Muthukumaraswamy, 2013). To rule out the possibility that muscle artifacts could explain the …
Figure 10 with 2 supplements
Post-feedback increases in beta power represent attenuated precision-weighted prediction errors about reward estimates.
(A–C) Mean (and SEM) values of the coefficients that explain the post-feedback beta power as a linear function of a constant value (beta power) (A), the precision-weighted prediction errors …
Figure 10—figure supplement 1
The rate of long beta bursts following feedback is modulated by the magnitude of precision-weighted prediction errors relating to reward.
(A–C) Same as Figure 10A–C but for the grand-averaged rate of post-feedback long beta bursts. The and regression coefficients were significantly different than 0 for each group (). Further …
Figure 10—figure supplement 2
Topographic map illustrating the EEG channels used for the feedback-locked oscillatory analysis.
Author response image 1
Tables
Table 1
Means and variances of the priors on perceptual parameters and initial values.
Priors on the parameters and initial values of the HGF perceptual model for continuous inputs. The continuous inputs here were the trial-by-trial scores that the participants received, normalized to …
| Prior mean | Prior variance | |
|---|---|---|
| log() | log(1) | 0 |
| log-variance of 1:20 input scores: −3.04 | 16 | |
| –4 | 16 | |
| log() | negative log-variance of 1:20 input scores: 3.04 | 4 |
| value of the first input score: 0.21 | variance of 1:20 input scores: 0.05 | |
| log() | log-variance of 1:20 input scores: −3.04 | 1 |
| 1 | 0 | |
| log() | log(0.01) | 1 |
| individual mean of behavioral parameter | 4 | |
| 0 | 4 | |
| 0 | 4 |
