Preregistered analysis
LME, nonconvergence
SECONDARY
Statistical Analysis was performed in R (Acquisition data for SCR and PSR were analyzed with a preregistered 2 (group) × 2 (condition, i.e., CS+/CS−) × 40 (trial) linear mixed effects (LME) model using the R package “lmerTest” (version 3.1.2) function lmer() with trial number as a linear predictor across conditions. The trial numbers are represented as across trials to reflect that CS presentation is randomized and SCR estimates habituate over time (rather than just within conditions). This leads to an unbalanced model which is amenable to the LME approach. For consistency with previous work, we also averaged response estimates from placebo participants across all CS+ and CS− trials separately and computed a paired Our preregistered primary outcome was SEBR data from the recall session, averaged across CS+ and CS− trials separately, and compared in a two-sample Our preregistered secondary outcome was SCR from the recall session. To account for time effects, these were analyzed in a 2 (group) × 2 (condition) × 30 (trial) LME model. Again, using the function lmer() of the R package “lmerTest” (version 3.1.2) with trial number as a function of time across conditions. Significant results were Holm–Bonferroni corrected for three comparisons (i.e., SCR CS time point, SCR trace interval and SCR US time point).For all LMEs, we tested different random effect structures and retained the model with lowest Akaike’s information criterion (AIC) using the “stats” package (version 4.0.2) function AIC(). In case of nonconvergence with the default optimizer, we tested convergence with all available optimizers using the allFit() function of the “lme4” package (version 1.1.23). If models did not converge with alternate optimizers, the respective random effect structure was not considered further. Following this procedure, for all data from the acquisition session and for SCR to the time point of expected US presentation in the recall session, we retained a model with a random intercept per subject. For the remaining analyses, models with random effects accounting for subject and trial were retained. For effect size estimation we used the function eta_squared() of the “effect size” package 0.6.0.1.