Figure 10. Post-feedback increases in beta power represent attenuated precision-weighted prediction errors about reward estimates.

(A–C) Mean (and SEM) values of the $\beta$ coefficients that explain the post-feedback beta power as a linear function of a constant value (beta power) (A), the precision-weighted prediction errors driving updates in the expectation of reward (pwPE, ${ϵ}_1$) (B), and pwPE driving updates in the expectation of volatility (${ϵ}_2$) (C). The measure of beta power used here was the average within 400–1600 ms following feedback presentation and across sensorimotor and prefrontal electrodes ,as shown in Figure 9. The $\beta$ values are plotted separately for each control and experimental group. The ${\beta }_0$ and ${\beta }_1$ regression coefficients were significantly different from 0 in all groups ($P_{FDR}<0.05$). In addition, ${\beta }_0$ was larger in the anx1 group relative to the control group ($P_{FDR}<0.05$, denoted by the horizontal black line and the asterisk). In anx1 relative to control participants, we found that ${\beta }_1$ was negative and significantly smaller in anx1 participants ($P_{FDR}<0.05$). Thus, a reduction in ${ϵ}_1$ contributed to an increase in post-feedback beta power. The multiple regression analysis did not support a significant contribution of the second regressor, pwPE relating to volatility, to explaining the changes in beta power (see main text, also ${\beta }_2$ on average did not differ from 0 in any group of participants, $P>0.05$). (D) Illustration of the trajectories of pwPE ${ϵ}_1$ in one representative anx1 subject. (E) The linear regression between the trial-wise beta power and pwPE ${ϵ}_1$ for the same representative subject.

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 ${\beta }_0$ and ${\beta }_1$ regression coefficients were significantly different than 0 for each group ($P_{FDR}<0.05$). Further to this result, ${\beta }_0$ was positive and larger in anx1 than in control participants ($P_{FDR}<0.05$). By contrast, the regression coefficient ${\beta }_1$ was negative and significantly smaller in the anx1 group than in the control group ($P_{FDR}<0.05$). This outcome resembles the results in Figure 10A–C, suggesting that smaller pwPE on reward contributed to a larger rate of long beta bursts. The second regressor coefficient ${\beta }_2$ did not differ significantly from zero and changes in ${ϵ}_2$ did not contribute to better explaining the beta activity (see main text). Anx2 participants did not have significantly different regressor coefficients than the control group. In summary, the multiple regression results linked a higher post-feedback rate of long-lived oscillation bursts (as observed in anx1) with reduced updates about reward.

Figure 10—figure supplement 2. Topographic map illustrating the EEG channels used for the feedback-locked oscillatory analysis.