Page 151 - Demo

Interoception and Facial Emotion Perception1495Data AnalysisAll analyses were preregistered on OSF (https://osf.io/97a6e). As explained in the Data Analysis section of Experiment 1, we focused on Autistic traits as main predictor in our analyses. Comparable to Experiment 1, significant medium positive correlations between Autistic traits, Social anxiety traits and Alexithymiawere observed (LSAS-AQ: rs = 0.32, p = .001; LSAS-TAS: rs = 0.25, p = .01; AQ-TAS: rs= 0.35, p < .001). Prior to model fitting, all continuous variables were standardized (i.e., centered and scaled) to obtain standardized beta coefficients. As a first step, we replicated the mediation analysis as outlined in the Data analysis section of Experiment 1 by fitting three models, using the lmerTest package (Kuznetsova et al., 2017), and quantifying the indirect effect of Trait interoceptive accuracy in the association between Autisitc traits and Emotion recognition accuracy for specific Emotion category levels using the RMediation package (Tofighi, 2023). We also explored once again whether Autistic traits, Trait interoceptive accuracy or Interoceptive sensibility would be systematically linked to variations in (1) Perceived emotional intensity and (2) Confidence in emotion recognition in two separate models (see Data analysis section of Experiment 1).As a second step, we investigated how Cardiac interoceptive accuracy would relate to subjective measures of interoception (Trait interoceptive accuracy and Trait interoceptive attention) and Autistic traits by running two zero-order correlation analyses. According to the 2x2 factor model by Murphy and colleagues (2019), we should observe a significant positive relationship between Trait interoceptive accuracy and Cardiac interoceptive accuracy, whereas there should be no such relationship between Trait interoceptive attention and Cardiac interoceptive accuracy . Furthermore, a partial correlation between Autistic traits and Cardiac interoceptive accuracy, while controlling for Alexithymia, was performed. Lastly, a potentially stronger Interoceptive trait prediction error with higher Autistic traitswas examined (Garfinkel et al., 2016), using a zero-order correlation. P-values of the four correlations were adjusted with the Holm-method. To test the expected positive relation between Cardiac interoceptive accuracy and Emotion recognition accuracy, we fitted a binomial GLMM on Emotion recognition accuracy (1= correct, 0 = incorrect) with Emotion category (angry, happy, fearful, sad and neutral), Cardiac interoceptive accuracy and their interaction as fixed effects, and random intercepts for each participant and each stimulus.