Impairments in reinforcement learning do not explain enhanced habit formation in cocaine use disorder.

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Lim, TV 
Cardinal, RN 
Savulich, G 
Jones, PS 
Moustafa, AA 

RATIONALE: Drug addiction has been suggested to develop through drug-induced changes in learning and memory processes. Whilst the initiation of drug use is typically goal-directed and hedonically motivated, over time, drug-taking may develop into a stimulus-driven habit, characterised by persistent use of the drug irrespective of the consequences. Converging lines of evidence suggest that stimulant drugs facilitate the transition of goal-directed into habitual drug-taking, but their contribution to goal-directed learning is less clear. Computational modelling may provide an elegant means for elucidating changes during instrumental learning that may explain enhanced habit formation. OBJECTIVES: We used formal reinforcement learning algorithms to deconstruct the process of appetitive instrumental learning and to explore potential associations between goal-directed and habitual actions in patients with cocaine use disorder (CUD). METHODS: We re-analysed appetitive instrumental learning data in 55 healthy control volunteers and 70 CUD patients by applying a reinforcement learning model within a hierarchical Bayesian framework. We used a regression model to determine the influence of learning parameters and variations in brain structure on subsequent habit formation. RESULTS: Poor instrumental learning performance in CUD patients was largely determined by difficulties with learning from feedback, as reflected by a significantly reduced learning rate. Subsequent formation of habitual response patterns was partly explained by group status and individual variation in reinforcement sensitivity. White matter integrity within goal-directed networks was only associated with performance parameters in controls but not in CUD patients. CONCLUSIONS: Our data indicate that impairments in reinforcement learning are insufficient to account for enhanced habitual responding in CUD.

Appetitive discrimination learning, Computational modelling, Extinction, Goal-directed learning/behaviour, Habit, Hierarchical Bayesian, Perseveration, Positive feedback, Reinforcement sensitivity, Bayes Theorem, Brain, Cocaine-Related Disorders, Discrimination Learning, Female, Habits, Humans, Magnetic Resonance Imaging, Male, Motivation, Photic Stimulation, Reinforcement, Psychology
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Psychopharmacology (Berl)
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Springer Science and Business Media LLC
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Medical Research Council (MR/J012084/1)
Medical Research Council (MC_PC_17213)
Wellcome Trust (104631/Z/14/Z)
This research was funded by the Medical Research Council (MR/J012084/1) and the NIHR Cambridge Biomedical Research Centre and was conducted at the NIHR Cambridge Clinical Research Facility. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. This research was also supported in part by a Medical Research Council (MRC) Clinical Research Infrastructure award (MR/M009041/1). R.N.C. consults for Campden Instruments and receives royalties from Cambridge Enterprise, Routledge, and Cambridge University Press. RNC’s research is supported by the UK Medical Research Council (MC_PC_17213). T.W.R. discloses consultancy with Cambridge Cognition, Lundbeck, Mundipharma and Unilever; he receives royalties for CANTAB from Cambridge Cognition and editorial honoraria from Springer Verlag and Elsevier. T.V.L., G.S. P.S.J., A.A.M. and K.D.E. declare to have no potential conflict of interest.