Statistical modeling is generally meant to describe patterns in data in service of the broader scientific goal of developing theories to explain those patterns. Statistical models support meaningful inferences when models are built so as to align parameters of the model with potential causal mechanisms and how they manifest in data. When statistical models are instead based on assumptions chosen by default, attempts to draw inferences can be uninformative or even paradoxical—in essence, the tail is trying to wag the dog. These issues are illustrated by van Doorn et al. (this issue) in the context of using Bayes Factors to identify effects and interactions in linear mixed models. We show that the problems identified in their applications (along with other problems identified here) can be circumvented by using priors over inherently meaningful units instead of default priors on standardized scales. This case study illustrates how researchers must directly engage with a number of substantive issues in order to support meaningful inferences, of which we highlight two: The first is the problem of coordination, which requires a researcher to specify how the theoretical constructs postulated by a model are functionally related to observable variables. The second is the problem of generalization, which requires a researcher to consider how a model may represent theoretical constructs shared across similar but non-identical situations, along with the fact that model comparison metrics like Bayes Factors do not directly address this form of generalization. For statistical modeling to serve the goals of science, models cannot be based on default assumptions, but should instead be based on an understanding of their coordination function and on how they represent causal mechanisms that may be expected to generalize to other related scenarios.

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The study investigated whether lifetime stress exposure is associated with greater impulsivity and addictive behavior. We also examined whether stress and impulsivity interactively predicted food addiction and alcohol-related behavior. Greater lifetime stress exposure was related to more impulsivity and food addictive behaviors, but not alcohol-related consequences. There were no interactions between lifetime stress exposure and impulsivity in predicting addictive behaviors. Exploratory analyses revealed that early and adulthood stress exposure predicted food addiction, whereas only adulthood stress predicted alcohol-related consequences. Therefore, lifetime stress exposure is related to impulsivity and addiction, but these effects differ by addiction outcome and specific timing of stress exposure.

The aim of this study was to assess the influence of diurnal cortisol profile on decision making under risk in individuals with problem gambling and a healthy control group. We examined the relationship between diurnal cortisol, assessed over the course of two days, and a battery of tasks that assessed decision making under risk, including the Columbia Card Task and the Cups Task. Thirty individuals with problem gambling and 29 healthy individuals took part in the study. Those with problem gambling showed blunted diurnal cortisol and more risk taking behavior compared to those in the healthy control group. Blunted cortisol profile was associated with more risky behavior and less sensitivity to losing money in problem gambling. These findings suggest that blunted stress physiology plays a role in specific parameters of risky decision making in problem gambling.

Abstractvan Doorn et al. (2021) outlined various questions that arise when conducting Bayesian model comparison for mixed effects models. Seven response articles offered their own perspective on the preferred setup for mixed model comparison, on the most appropriate specification of prior distributions, and on the desirability of default recommendations. This article presents a round-table discussion that aims to clarify outstanding issues, explore common ground, and outline practical considerations for any researcher wishing to conduct a Bayesian mixed effects model comparison.