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58Chapter 3rating of distractor picture, and their interactions with Gender. We allowedthe intercept and the effects of Attractiveness rating of probe picture andAttractiveness rating of distractor picture to vary by Subject. Second, tostudy the association between date outcome (i.e., willingness to go on another date with dating partner) and immediate attention, we followed thesame procedure as described above. However, the predictors Attractivenessrating of probe picture and Attractiveness rating of distractor picture werereplaced with Date again probe picture (binary: yes/no) and Date againdistractor picture (binary: yes/no), also in the random effect formula.We used a Gaussian prior with M = 0 and SD = 2.5 for the Intercept ofthe model. For the independent variables, we specified regularizing Gaussian priors with M = 0 and SD = 5. For all variance parameters, we keptthe default Student’s t priors with 3 degrees of freedom. After running themodels, we used the emmeans-package (Lenth et al., 2023) to obtain estimates and pairwise contrasts based on the posterior predictive distribution.Using these values, we calculated multiple quantitative measures to describethe effects. First, we report the median estimate b, and median absolute deviation of the estimate between square brackets. Second, we report an 89%credible interval of the estimate (89% CrI). We have chosen 89% instead ofthe conventional 95% to reduce the likelihood that the credible intervals areinterpreted as strict hypothesis tests (McElreath, 2018). Instead, the maingoal of the credible intervals is to communicate the shape of the posteriordistributions. Third, we report the probability of direction (pd), i.e., theprobability of a parameter being strictly positive or negative, which variesbetween 50% and 100% (Makowski et al., 2019).Eye-trackingTo analyze the eye-tracking data, we used a zero-one inflated beta model,which is suitable for continuous proportions containing zeros and ones.These models consist of two components, namely a beta component to describe the values between 0 and 1, and a binary component to predict theoccurrences of zeros and ones (Ospina & Ferrari, 2012). For each trial wecalculated a Looking time bias score by dividing the time fixating on the leftpicture by the total time fixating on the pictures. Thus, this score reflectsthe proportion of fixation time spent looking at the left picture. In lookingtime studies, it is common practice to calculate a looking time bias (proportion of total looking time). In the case of clear categories, this is no problem.For example, imagine a study where one examines attention to attractive vs.unattractive faces. One could calculate a looking time bias by calculatingthe proportion of time looking at the attractive face for all trials. However,in our case, we have no categorical variables but continuous ones, namelyattractiveness ratings. Thus, we cannot calculate an informative bias likein the example above. Therefore, we have used the location of the photosIliana Samara 17x24.indd 58 08-04-2024 16:35