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Citation |
Smith R, Taylor S, Stewart JL, Guinjoan SM, Ironside M, Kirlic N, Ekhtiari H, White EJ, Zheng H, Kuplicki R, Paulus MP. Comput. Psychiatr. 2022; 6(1): 117-141. |
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Copyright |
(Copyright © 2022, Ubiquity Press) |
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DOI |
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PMID |
38774781 |
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PMCID |
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Abstract |
Computational modelling is a promising approach to parse dysfunctional cognitive processes in substance use disorders (SUDs), but it is unclear how much these processes change during the recovery period. We assessed 1-year follow-up data on a sample of treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, and/or opioids; N = 83) that were previously assessed at baseline within a prior computational modelling study. Relative to healthy controls (HCs; N = 48), these participants were found at baseline to show altered learning rates and less precise action selection while completing an explore-exploit decision-making task. Here we replicated these analyses when these individuals returned and re-performed the task 1 year later to assess the stability of baseline differences. We also examined whether baseline modelling measures could predict symptoms at follow-up. Bayesian and frequentist analyses indicated that: (a) group differences in learning rates were stable over time (posterior probability = 1); and (b) intra-class correlations (ICCs) between model parameters at baseline and follow-up were significant and ranged from small to moderate (.25 ≤ ICCs ≤.54). Exploratory analyses also suggested that learning rates and/or information-seeking values at baseline were associated with substance use severity at 1-year follow-up in stimulant and opioid users (.36 ≤ rs ≤.43). These findings suggest that learning dysfunctions are moderately stable during recovery and could correspond to trait-like vulnerability factors. In addition, computational measures at baseline had some predictive value for changes in substance use severity over time and could be clinically informative. Language: en |
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Keywords |
Active Inference; Computational Modeling; Directed Exploration; Explore-Exploit Dilemma; Learning Rate; Substance Use Disorders |