Background: Sacrificing rewarding aspects of one’s life due to potential aversive outcomes is an important characteristic of multiple psychiatric disorders. Such decisions occur during approach-avoidance conflict (AAC), which has become the topic of a growing number of behavioral and neuroimaging studies. Here we describe a novel computational modeling approach to studying AAC.Methods: A previously-validated AAC task was completed by 479 participants including healthy controls (HCs), and individuals with depression, anxiety, and/or substance use disorders (SUDs), as part of the Tulsa 1000 study. An active inference model was utilized to identify parameters corresponding to the subjective aversiveness of affective stimuli (VNegative), the subjective value of points that could be won (VPoints), and decision uncertainty (β). We used correlational analyses to examine relationships to self-reported experiences during the task, analyses of variance to examine diagnostic group differences (depression/anxiety, substance use, HCs), and exploratory machine learning analyses to examine the contribution of dimensional clinical and neuropsychological measures.Results: Model parameters correlated with self-reported experience and reaction times during the task in expected directions. Relatve to HCs, both clinical groups showed higher VNegative values, and the SUD group exhibited less decision uncertainty (lower β values). Machine learning analyses highlighted several clinical domains (i.e., alcohol use, personality, working memory) potentially contributing to task parameters.Conclusions: Our results suggest that avoidance behavior in individuals with depression, anxiety, and SUDs may be driven by increased sensitivity to predicted negative outcomes and that insufficient decision uncertainty (overconfidence) may also further contribute to avoidance in substance use disorder.
Predicting Posttraumatic Osteoarthritis Related-symptomology Using Serum Biomarkers: A Novel Explainable Machine Learning Modeling Approach
