Page 111 - Demo

Individual attractiveness preferences predict attention1095Dot-probeTo analyze the dot-probe data we used Bayesian mixed models with a Gaussian distribution. First, to study the association between attractiveness and immediate attention, we modeled Reaction time (mean-centered by subject) as a function of Attractiveness rating of probe picture and Attractiveness rating of distractor picture, and their interactions with Gender. We allowed the intercept and the effects of Attractiveness rating of probe picture and Attractiveness rating of distractor pictureto vary by Subject. Second, to study the association between date outcome (i.e., willingness to go on another date with dating partner) and immediate attention, we followed the same procedure as described above. However, the predictors Attractiveness rating of probe picture and Attractiveness rating of distractor picturewere replaced with Date again probe picture (binary: yes/no) and Date again distractor 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 of the model. For the independent variables, we specified regularizing Gaussian priors with M=0 and SD=5. For all variance parameters, we kept the default Student’s t priors with 3 degrees of freedom. After running the models, we used the emmeans-package (Lenth, 2022) to obtain estimates and pairwise contrasts based on the posterior predictive distribution. Using these values, we calculated multiple quantitative measures to describe the 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 of the conventional 95% to reduce the likelihood that the credible intervals are interpreted as strict hypothesis tests (McElreath, 2018). Instead, the main goal of the credible intervals is to communicate the shape of the posterior distributions. Third, we report the probability of direction (pd), i.e., the probability of a parameter being strictly positive or negative, which varies between 0.5 and 1 (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 the occurrences of zeros and ones (Ospina & Ferrari, 2012). For each trial we calculated a Looking time bias score by dividing the time fixating on the left picture by the total time fixating on the pictures. Thus, this score reflects the proportion of fixation time spent looking Tom Roth.indd 109 08-01-2024 10:41