Statistical Analysis of Behavioral Responses

Statistical analyses of behavior were performed in R (R Foundation for Statistical Computing). We used a linear mixed model implemented in the nlme package (see https://CRAN.R-project.org/package=nlme) with regressors motor context (act to continue, act to stop), rTMS condition (real-rTMS, sham-rTMS), and session number (session 1, session 2) for the analysis of mean stopping amounts.
Response times (RTs) were log-transformed to meet the assumption of normal distribution. The mean ± SD is reported. For correlational analyses, Pearson’s r is reported. To test whether participants might have been aware of differences between real and sham stimulation, we used Bayesian paired t tests to test for differences between their ratings of stimulation effectiveness and discomfort (JASP software version 0.14.3). We used Bayesian tests to be able to test for the absence of an effect. Bayes factors are classified according to the scheme of Jeffreys [1998 ; Bayes Factor (BF) = 1–3, anecdotal evidence; BF = 3–10, moderate evidence; BF = 10–30, strong evidence; BF = 30–100, very strong evidence; BF > 100, extreme evidence]. BF₁₀ denotes evidence in favor of a given model against the null model, while BF₀₁ denotes evidence in favor of the null model (Keysers et al., 2020 (link)).