In clinical research, populations are often selected on the sum-score of diagnostic criteria such as symptoms. Estimating statistical models where a subset of the data is selected based on a function of the analyzed variables introduces Berkson’s bias, which presents a potential threat to the validity of findings in the clinical literature. The aim of the present paper is to investigate the effect of Berkson’s bias on the performance of the two most commonly used psychological network models: the Gaussian Graphical Model (GGM) for continuous and ordinal data, and the Ising Model for binary data. In two simulation studies, we test how well the two models recover a true network structure when estimation is based on a subset of the data typically seen in clinical studies. The network is based on a dataset of 2,807 patients diagnosed with major depression, and nodes in the network are items from the Hamilton Rating Scale for Depression (HRSD). The simulation studies test different scenarios by varying (1) sample size and (2) the cut-off value of the sum-score which governs the selection of participants. The results of both studies indicate that higher cut-off values are associated with worse recovery of the network structure. As expected from the Berkson’s bias literature, selection reduced recovery rates by inducing negative connections between the items. Our findings provide evidence that Berkson’s bias is a considerable and underappreciated problem in the clinical network literature. Furthermore, we discuss potential solutions to circumvent Berkson’s bias and their pitfalls.

Addiction is a complex biopsychosocial phenomenon, impacted by biological predispositions, psychological processes, and the social environment. Using mathematical and computational models that are capable of surrogative reasoning may be a promising avenue for gaining a deeper understanding of this complex behavior. This paper reviews formal models of addiction developed in two very different but relevant fields of study: (neuro)psychological modeling of intra-individual dynamics, and social modeling of inter-individual dynamics. We find that these modeling approaches to addiction are quite disjoint and argue that in order to unravel the complexities of biopsychosocial processes of addiction, models should integrate intra- and interpersonal factors.

This paper describes the design and evaluation of a visualization that provides feedback for meeting participants on their social behavior (Social Mirror). Our Social Mirror provides feedback on participation level, interactivity level, and level of agreement. For the evaluation we conducted an experiment where two groups of four participants each took part in a meeting with and in a meeting without the Social Mirror. The results showed that the participants could easily extract information from the Social Mirror without being distracted from the topic of discussion during the meeting. Our results further suggest that the Social Mirror leads to changes in the social behavior of the participant; in particularly due to the agreement visualization. Moreover most participants prefer meetings with the presence of the Social Mirror.