Page 112 - Demo

Chapter 5110at the left picture. In looking time 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 calculating the proportion of time looking at the attractive face for all trials. However, in our case, we have no categorical variables but continuous ones, namely attractiveness ratings. Thus, we cannot calculate an informative bias like in the example above. Therefore, we have used the location of the photos as a reference point to calculate the looking time bias, by calculating the bias toward the left picture. Hereafter, we have tested whether this bias is affected by (1) the attractiveness ratings of the left and right picture, and (2) date outcome.To study the association between attractiveness and voluntary attention, we modelled Looking time bias score (proportion of time looking at the left picture) as a function of Attractiveness rating of the left picture and Attractiveness rating of the right picture, and their interactions with Gender. We allowed the intercept to vary by Subject. Importantly, we weighed each trial by the looking time in that trial relative to the subject’s average (see Data Processing). Thus, trials in which the participant paid more attention to the screen had a larger weight in the analysis. In this manner, we avoided that trials where participants were distracted or disinterested would have a large influence on the outcome of our analysis. Furthermore, we specified the same formulas for the precision parameter (phi; shape of the beta distribution), the zero–one inflation parameter (zoi; probability of observing a zero or a one), and the conditional one-inflation parameter (coi; probability of observing a one if a zero or one is observed). To study the association between date outcome (i.e., willingness to go on another date with dating partner) and voluntary attention, we followed the same procedure as described above. However, the predictors Attractiveness rating of the right pictureand Attractiveness rating of the left picture were replaced with Date again right picture (binary: yes/no) and Date again left picture (binary: yes/no).We used a Gaussian prior with M= 0 and SD= 0.25 for the Intercept of the beta component of the model. For the independent variables, we specified regularizing Gaussian priors with M= 0 and SD= 0.5. This also applied to the independent variables in the formulas for phi, coi, and zoi. For all variance parameters, we kept the default Student’s t priors with 3 degrees of freedom. Furthermore, we kept the default logistic priors for the Intercepts of zoi and coi, and default Student’s t prior with 3 degrees of freedom for the Intercept of phi.Tom Roth.indd 110 08-01-2024 10:41