Behavioral and metabolic data
weight loss
We analyzed the behavioral and metabolic data with R (version 4.0; R Core Team, 2019).We built two statistical models. The first model aimed at testing the relationship between weight loss (measured by subtracting the participants’ BMI after the intervention from their BMI before the intervention) and the intervention. We entered (1) intervention (placebo or liraglutide) as a fixed effect and (2) age and (3) sex as control factors. We entered intercepts for participants as a random effect. We built the model as follows:The second model aimed at testing the relationship between the perceived pleasantness of the tastes and the intervention. We entered (1) the taste stimulus (milkshake or tasteless), (2) session (pre- or post-intervention), (3) intervention (placebo or liraglutide), and (4) a linear decreasing contrast over trials to account for satiation as fixed factors. For the random effects, we entered intercepts for participants as well as by-participant random slopes for the effect of the interaction between taste stimulus sessions and trials. We did so to reduce the likelihood of a false positive. From a conceptual point of view, we expected that the intervention effect on the satiation processes for the milkshake would differ from one participant to another (e.g., the effect is large for some, medium for others, etc.) We built the model as follows:We used the lme4 package [