Statistical analyses
All statistical analyses were carried out in RStudio (Version 4.1.2, RStudio Team, 2021). In contrast to the pre-registration of this study, we analyzed the data with Linear mixed models instead of rm ANOVA which is often used in generalization research. This decision was based on several limitations of rm ANOVA including the assumption of sphericity which is often unmet for generalization data (Vanbrabant et al., 2015). Instead, Linear mixed models has fewer assumptions and offer a more reliable statistical inference (Vanbrabant et al., 2015). Furthermore, there is a rise of Linear mixed models use in generalization research the last years (Ginat-Frolich et al., 2019; Struyf et al., 2018).
Linear mixed models were conducted separately for each experimental phase with SCR, ssVEPs, valence, arousal, and US-expectancy as separate dependent variables. These models were fitted using the packages lme4 and lmerTest (Bates et al., 2015; Kuznetsova et al., 2017) and significance is reported with the Kenward-Rogers approximation for the degrees of freedom (Kenward & Roger, 1997). All analyses included the intercept of the Participants as a random effect. For habituation, Stimulus (CS+, CS-) and Group (LU, MU, HU) were fixed factors. In acquisition, the ratings of valence and arousal were analyzed in the same manner as in habituation. For ssVEPs, SCR, and US-expectancy, Stimulus, Time (Acq1 for the first half of Acquisition, Acq2 for the second half of Acquisition), and Group were fixed factors. Significant interactions were followed up with planned contrasts on the development of the differential stimulus responding from Acquisition 1 to Acquisition 2 for all group comparisons (LU-HU, LU-MU, HU-MU). For the factors Time and Stimulus, Acquisition 1 and CS- were the reference levels, respectively. In generalization, Stimulus and Group were entered as fixed factors but this time Stimulus had six levels (CS+, GS1, GS2, GS3, GS4, CS-). Significant main effects for Stimulus were followed-up with simple contrasts models with CS- as reference point (Lissek et al., 2008). In case of significant interactions with the factor Group we further described the shape of the gradients with trend analyses. Specifically, we assessed whether the gradients differed in terms of linearity or curvature across groups. To this end, two orthogonal polynomial trend repeated measures contrasts across all test stimuli served as fixed factors to examine the shape of generalization gradient. Specifically, a linear trend repeated measures contrast assessed a monotonic gradient across all test stimuli while the quadratic trend assessed curvature gradients.
Furthermore, we quantified the strength of the generalization with a linear deviation score (([GS1, GS2, GS3, GS4] ∕4) – ([CS+, CS-] ∕2); LDS). The LDS is a single number representing the steepness and strength of the generalization gradient. Positive values correspond to shallow and stronger generalization gradients while negative values correspond to steeper and weaker generalization gradients (Berg et al., 2021; Kaczkurkin et al., 2017). LDS scores of each group for each measure were compared with one-way ANOVAs with LDS as dependent variable and Group as between-subjects factor. Finally, as an exploratory analysis, we correlated the sum of scores of the IUS with the LDS in order to explore if dispositional intolerance of uncertainty played a role in participants’ generalized responses. Additionally, we investigated whether participants in the three groups differed in how well they discriminated between CS+ and the other test stimuli. Participant’s responses were transformed to 1 (accurate response) and 0 (inaccurate response). We then calculated the average discrimination response per participant for the five comparisons and calculated between-groups differences for the three groups with one-way ANOVAs. For the analysis of the discrimination task, eight participants were further excluded due to equipment failure, resulting in 80 participants included in this analysis. For all statistical analyses, alpha level was set at .05 and Bonferroni correction was used to adjust the alpha level for multiple comparisons.