Statistical analysis
Kolmogorov-Smirnov test was used to study the variables distribution. Then, 1) self-reports were analyzed through separate univariate ANOVAs between highs and lows; 2) RR, SD, RMSSD and LF/HF were analyzed by repeated measures ANOVAs with a 2 groups (highs, lows) x 6 Conditions (B, R, IND1, IND2, NH, P) design. Bonferroni correction was applied for multiple comparisons; 3) the HEP amplitude was analyzed through repeated measures ANOVA with hypnotizability (highs, lows) as between subject factor and Condition (B, R, IND1, IND2, NH, Post) as within-subject factor, using the Factorial Mass Univariate Toolbox (Fields & Kuperberg, 2020). Particularly, we used a permutation-bootstrap approach with 2000 permutations to test for significant condition, group and interaction effects. The analysis was performed for each time-point in (200, 600) ms time interval. The time interval from 0 to 200 ms was excluded from analysis because of the potential presence of residual cardiac artefacts on EEG signals. Multiple hypothesis testing was controlled with the cluster correction method (Oostevenld et al., 2011). Post hoc analyses were carried out by means of paired t-tests for condition main effect, unpaired t-test for group main effect, and by means of paired t-tests (for within factor) and unpaired t-tests (for between factor) for interaction main effect. All tests were carried out with significance level set at α = .05.