Page 141 - Demo

Interoception and Facial Emotion Perception1395Data AnalysisWe preregistered the data analyses to test our hypotheses on the Open Science Framework (https://osf.io/wugq7). The data of the two experiments was collected at different stages of the Covid-19 pandemic. Since this might have resulted in biased replies on the social anxiety trait measure, which included, for example, questions about avoidance of social situations, we decided to focus on Autistic traits as the main predictor in our analyses, which were all conducted in R 4.2.2 (R Core Team, 2022) . As preregistered, interactions between Social anxiety traitsand Emotion category were still included in all clinical-trait-score-related analyses as control predictors, similar to Alexithymia. The two clinical trait score measures showed to have significant medium positive correlations with one another, as well as with Alexithymia (LSAS-AQ: rs = 0.47, p < .001; LSAS-TAS: rs = 0.34, p < .001; AQ-TAS: rs = 0.30, p = .003), supporting our approach to control for Social anxiety traits and Alexithymia in all models. Before fitting our models, all continuous variables were standardized (i.e., centered and scaled) to obtain standardized beta coefficients. In order to test whether trait interoceptive accuracy would (partially) mediate the link between autistic trait levels and emotion recognition accuracy, we fitted three models using the lmerTest package (Kuznetsova et al., 2017): First, we tested whether emotion recognition accuracy was decreased with higher autistic trait levels (path c) while controlling for social anxiety traits and alexithymia. Previous literature has reported emotion-specific alterations in recognition performance with regard to autistic traits, but also with regard to social anxiety traits. Therefore, the binary outcome Emotion recognition accuracy (1= correct, 0 = incorrect) was predicted by a two-way interaction between Emotion category (angry, happy, fearful, sad and neutral) and Autistic traits as well as by a two-way interaction between Emotion category and Social anxiety traits, and Alexithymia as control predictors. Random intercepts for each stimulus (50 stimuli) as well as for each participant (99 participants) were added. After model fitting, slopes for the relation between Autistic traits and Emotion recognition accuracy were estimated for each level of Emotion category, using the emtrends function of the emmeans packages(Lenth, 2023; Holm method for p-value adjustment). Second, we examined whether Trait interoceptive accuracy was reduced with higher Autistic traits levels (path a), while controlling for Social anxiety traits and Alexithymia. A linear regression analysis was performed with Trait interoceptive accuracy as outcome variable and Autistic traitsas predictor of interest, as well as Alexithymia and Social anxiety traits as control predictors. In the third and final model fit, we added an interaction between