Figure 2. Effect of agency and learning block on %Correct.a. %Correct mean and standard error across all participants (N=23) in the agent condition (solid line ) and observer condition (dotted line ) across learning blocks. Asterisks mark learning blocks in which significant differences between conditions were found in post-hoc tests. b. Difference between the agent and observer conditions for each participant across learning blocks (black dots ), as well as the median and interquartile range across participants (boxplots ). The difference between the agent and observer conditions is inversely correlated with learning block. An exponential curve was fitted to the data (blue solid line ) to illustrate the decreasing effect of agency with the progress of learning. The effect of agency decreases rapidly during the first three learning blocks and then remains constantly low in the remaining four learning blocks.

Electrophysiological results

For each effect and interaction studied, we present here two complementary approaches: a data-driven analysis using cluster-based permutation tests, and an ERP component driven analysis using ANOVAs to assess effects on targeted ERP components.

Acquisition sounds

Learning progress was reflected in ERPs as an attenuation of the P3a component. The cluster-based analysis comparing early and late acquisition sounds (late – early learning stage) revealed a negative cluster with a fronto-central distribution (T = 4659.8, p < .01; 220 ms to 400 ms), encompassing the P2 and P3a components (figure 3a). The targeted-component ANOVA yielded a significant main effect of learning stage on the amplitudes of the P3a component at Fz [F(1,22) = 14.436, p < .001, ηp2 = 0.40], reflecting more negative values with increased learning stage.
Regarding the effect of agency, we found more positive ERPs in parietal electrodes in the agent compared to the observer condition (figure 3b). The cluster-based permutation test comparing the agent and observer conditions detected a significant positive cluster with an occipito-parietal distribution (T = 10900, p < .01; 60 ms to 400 ms). The cluster temporally encompasses the P2 and P3 components, revealing overall more positive amplitudes in the agent condition. The targeted-component ANOVA detected a significant main effect of agency on the P3b component at Pz [F = 25.706, p < .0001, ηp2 = 0.54]. The occipito-parietal distribution of the effect led us to suspect that the observed differences are due to motor differences related to the control of the sound stimuli. To explore this possibility, we conducted further analyses, which can be found in the supplementary materials.
In order to study possible interactions between agency and learning stage using cluster-based permutation tests, we subtracted the early learning stage from the late learning stage in both the agent and observer condition and then ran a cluster-based analysis (late minus early learning stage in agent condition versus late minus early learning stage in observer condition). Studying this interaction, possible confounding factors due to eye movement differences (see discussion in supplementary materials) were eliminated from the data. However, this analysis yielded no significant results. We also tested for interaction effects in the targeted-component ANOVAs, but this also did not produce any significant interaction effects. All in all, no interactions between the factors agency and learning stage were detected.