Background: 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. Methods: 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, and/or 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: Results indicate that: (a) SUDs show poorer task performance than HCs (p=.03, Cohen’s d = .33), with model estimates revealing less precise action selection mechanisms (p=.004, d = .43), a lower learning rate from losses (p=.02, d = .36), and a greater learning rate from gains (p=.04, d = .31); and (b) groups do not differ significantly in goal-directed information seeking. Conclusions: 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.
Imprecise Action Selection in Substance Use Disorder: Evidence for Active Learning Impairments When Solving the Explore-Exploit Dilemma
