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

•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 th...

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Veröffentlicht in:Drug and alcohol dependence 2020-10, Vol.215, p.108208-108208, Article 108208
Hauptverfasser: Smith, Ryan, Schwartenbeck, Philipp, Stewart, Jennifer L., Kuplicki, Rayus, Ekhtiari, Hamed, Paulus, Martin P.
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container_end_page 108208
container_issue
container_start_page 108208
container_title Drug and alcohol dependence
container_volume 215
creator Smith, Ryan
Schwartenbeck, Philipp
Stewart, Jennifer L.
Kuplicki, Rayus
Ekhtiari, Hamed
Paulus, Martin P.
description •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.
doi_str_mv 10.1016/j.drugalcdep.2020.108208
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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. 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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. 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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. 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source Applied Social Sciences Index & Abstracts (ASSIA); MEDLINE; Elsevier ScienceDirect Journals
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
title Imprecise action selection in substance use disorder: Evidence for active learning impairments when solving the explore-exploit dilemma
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