Slower Learning Rates from Negative Outcomes in Substance Use Disorder over a 1-Year Period and Their Potential Predictive Utility
| Autor: | Ryan Smith, Samuel Taylor, Jennifer L. Stewart, Salvador M. Guinjoan, Maria Ironside, Namik Kirlic, Hamed Ekhtiari, Evan J. White, Haixia Zheng, Rayus Kuplicki, Tulsa 1000 Investigators, Martin P. Paulus |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Computational Psychiatry, Vol 6, Iss 1 (2022) |
| Druh dokumentu: | article |
| ISSN: | 2379-6227 15561941 |
| DOI: | 10.5334/cpsy.85 |
| Popis: | 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 ≤ 'r's ≤ .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. |
| Databáze: | Directory of Open Access Journals |
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