Page 45 - Demo

Attentional Biases to Facial Emotions432interaction between Congruency, Social anxiety traits and Autistic traits, including all two-way interactions and the control predictor terms.Exploratory Data AnalysisAs we did not observe the expected links between attentional biases and clinical trait dimension, and could not exclude that this might be due to a lack of power, we ran additional explorative data analyses, using Bayesian mixed models. Bayesian models were created in the Stan computational framework and accessed using the brms package (B%u00fcrkner, 2017, 2018), version 2.17.0. We sum coded all factorial predictors, and scaled and centered all continuous predictors. All models were run with 4 chains and 5000 iterations, of which 1000 were warmup iterations. We checked model convergence by inspecting the trace plots, histograms of the posteriors, Gelman-Rubin diagnostics, and autocorrelation plots (Depaoli & Van de Schoot, 2017). We found no divergences or excessive autocorrelation.For the exploratory analyses, we used the same dataset as for the pre-registered analyses in which extremely fast and slow reaction times were excluded by subject (see first paragraph of Data analysis section). However, for the exploratory analyses we rescaled our dependent variable in order to filter out the effect of handedness*probe location (Probe distance) and to ease setting a prior for the intercept. Thus, we centered the reaction times within Subject within Probe distancelevel (close vs. far). Thereby, we removed the distance effect and removed overall differences in reaction times between participants.First, we explored attentional biases within each emotion category by creating a model with centered reaction time as dependent variable and Congruency and Emotion Category and their interaction as predictors. Furthermore, we allowed the effects of all predictors to vary by Subject. Second, we explored whether attentional biases within each emotion category were linked to Autistic traits and Social anxiety traits by including the interactions Congruency*Emotion Category*Autistic traits and Congruency*Emotion Category*Social anxiety traits. We used regularizing Gaussian priors with M = 0 and SD = 5 for all fixed effects, a Gaussian prior with M= 0 and SD = 1 for the intercept, and default half Student t priors with 3 degrees of freedom for the random effects and residual standard deviation. We used multiple measures to summarize the posterior distribution resulting from our models: (I) the median estimate and the median absolute deviation of this estimate, (II)