Background: Disruptions in the decision-making processes that guide action selection are a core feature of many psychiatric disorders, including addiction. Decision making is influenced by the goal-directed and habitual systems that can be computationally characterized using model-based and model-free reinforcement learning algorithms, respectively. Recent evidence suggests an imbalance in the influence of these reinforcement learning systems on behavior in individuals with substance dependence, but it is unknown whether these disruptions are a manifestation of chronic drug use and/or are a preexisting risk factor for addiction. Methods: We trained adult male rats on a multistage decision-making task to quantify model-free and model-based processes before and after self-administration of methamphetamine or saline. Results: Individual differences in model-free, but not model-based, learning prior to any drug use predicted subsequent methamphetamine self-administration; rats with lower model-free behavior took more methamphetamine than rats with higher model-free behavior. This relationship was selective to model-free updating following a rewarded, but not unrewarded, choice. Both model-free and model-based learning were reduced in rats following methamphetamine self-administration, which was due to a decrement in the ability of rats to use unrewarded outcomes appropriately. Moreover, the magnitude of drug-induced disruptions in model-free learning was not correlated with disruptions in model-based behavior, indicating that drug self-administration independently altered both reinforcement learning strategies. Conclusions: These findings provide direct evidence that model-free and model-based learning mechanisms are involved in select aspects of addiction vulnerability and pathology, and they provide a unique behavioral platform for conducting systems-level analyses of decision making in preclinical models of mental illness.
Bibliographical noteFunding Information:
This research was supported by Public Health Service grants from the National Institute on Drug Abuse (Grant Nos. DA041480 and DA043443 [to JRT]), a NARSAD Young Investigator Award from the Brain and Behavior Research Foundation (to SMG), and funding provided by the State of Connecticut .
© 2019 Society of Biological Psychiatry
- Computational psychiatry
- Drug addiction
- Model-based reinforcement learning
- Model-free reinforcement learning