Page 142 - Demo

Chapter 5140Emotion category and Trait interoceptive accuracy as a mediator of the association between Autistic Traits and Emotion Recognition, next to the predictors in the first model, to be able to identify whether the effect of Autistic traits on Emotion recognition accuracy for certain levels of Emotion Category was mediated by Trait interoceptive accuracy (path ab). The causal mediation model was tested using the RMediation package (Tofighi, 2023). From the previously defined models, path a was defined as the effect of Autistic traits on Trait interoceptive accuracy and path b as the effect of Trait interoceptive accuracy on Emotion recognition accuracy of expression(s) that were less well recognized with higher autistic trait levels. The indirect effect (path ab) of Trait interoceptive accuracy on the association between Autistic traits and Emotion recognition accuracy of (certain) emotional expressions was also tested for significance.We further explored the role of both autistic traits as well as self-reported interoception measures in determining the two other emotion recognition task outcomes, namely confidence in emotion recognition and perceived emotional intensity of seen expressions. As we did not expect the variables to influence each other in predicting the outcomes and aimed to avoid inflation of type I error, all predictor variables were included in one mixed model for each outcome. Perceived emotional intensity was thus predicted by two-way interactions between Emotion category and Autistic traits, Emotion category and Trait interoceptive accuracy,and Emotion category and Interoceptive sensibility as well as by the two-way interaction between Emotion category and Social anxiety traits, and Alexithymia as control predictors. In line with the Emotion recognition accuracy models, random intercepts for each stimulus and each participant were added. The distribution of the confidence data was not normal and the highest value (100) was selected in many trials (20%), indicating full confidence. Therefore, we fitted a Bayesian GLMM, using the brms package (B%u00fcrkner, 2017), with a zero-one-inflated family instead of a LMM to predict Confidence in emotion recognition with the same random and fixed effect structure as the intensity model. Thus, we estimated separate parameters for a beta regression excluding zeros and ones (phi), for the proportion of zeros and ones only (zoi) as well as for the proportion of ones versus zeros (coi). Integrated posterior estimates for the slopes at three different values of the predictors of interest (-1, 0, 1) were obtained using the emtrends function of the emmeans package. All visualizations of effects and all model fit tables are based on the sjPlot package (L%u00fcdecke, 2021).