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

T. V. Lim, R. N. Cardinal, G. Savulich, P. S. Jones, A. A. Moustafa, T. W. Robbins, K. D. Ersche*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

20 Citations (Scopus)
39 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)2359-2371
Number of pages13
Issue number8
Publication statusPublished - 1 Aug 2019
Externally publishedYes


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