Imprecise action selection in substance use disorder: Evidence for active learning impairments when solving the explore-exploit dilemma

• Published in: Drug and alcohol dependence Vol. 215; p. 108208
• Main Authors: Smith, Ryan, Schwartenbeck, Philipp, Stewart, Jennifer L., Kuplicki, Rayus, Ekhtiari, Hamed, Paulus, Martin P.
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01-10-2020; Elsevier Science Ltd
• Subjects: Active inference; Adult; Bayes Theorem; Bayesian analysis; Behavior; Cannabis; Computational modeling; Computer applications; Decision making; Directed exploration; Drug addiction; Drug use; Explore-exploit dilemma; Female; Gambling; Hallucinogens; Health risks; Health services utilization; Help seeking behavior; Humans; Information seeking behavior; Intelligence tests; Learning; Learning rate; Male; Marijuana; Narcotics; Opioids; Problem-Based Learning; Public health; Regression analysis; Sedative drugs; Sedatives; Stimulants; Substance use; Substance use disorder; Substance use disorders; Substance-Related Disorders - psychology; Task performance; Task Performance and Analysis; Uncertainty; Young Adult; Substance use disorders; Directed exploration; Computational modeling; Explore-exploit dilemma; Active inference; Learning rate
• ISSN: 0376-8716; 1879-0046
• DOI: 10.1016/j.drugalcdep.2020.108208

•Decision-making mechanisms in substance use disorders (SUDs) remain poorly understood.•We used computational modeling to better understand these mechanisms.•SUD patients showed less precise action selection mechanisms than healthy subjects.•SUD patients also learned slower from negative outcomes than healthy subjects.•This could help explain continued patterns of maladaptive choices in SUDs.
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, 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 indicate that: (a) SUDs show poorer task performance than HCs (p = 0.03, Cohen’s 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.