Imprecise action selection in substance use disorder Evidence for active learning impairments when solving the explore-exploit dilemma.
Substance use disorders (SUDs) are a major public health risk. However, mechanisms accounting for continued patterns of poor choices in the face of negative life consequences remain poorly understood. We use a computational (active inference) modeling approach, combined with multiple regression and hierarchical Bayesian group analyses, to examine how treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, andor opioids N 147) and healthy controls (HCs N 54) make choices to resolve uncertainty within a gambling task. A subset of SUDs (N 49) and HCs (N 51) propensity-matched on age, sex, and verbal IQ were also compared to replicate larger group findings. Results indicate that (a) SUDs show poorer task performance than HCs (p 0.03, Cohens d 0.33), with model estimates revealing less precise action selection mechanisms (p 0.004, d 0.43), a lower learning rate from losses (p 0.02, d 0.36), and a greater learning rate from gains (p 0.04, d 0.31) and (b) groups do not differ significantly in goal-directed information seeking. Findings suggest a pattern of inconsistent behavior in response to positive outcomes in SUDs combined with a tendency to attribute negative outcomes to chance. Specifically, individuals with SUDs fail to settle on a behavior strategy despite sufficient evidence of its success. These learning impairments could help account for difficulties in adjusting behavior and maintaining optimal decision-making during and after treatment.