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.