The role of reinforcement learning in shaping the decision policy in methamphetamine use disorders

The prevalence of methamphetamine use disorder (MUD) as a major public health problem has increased dramatically over the last two decades, reaching epidemic levels, which pose high costs to healthcare systems worldwide and is commonly associated with experience-based decision-making (EDM) aberrant. However, precise mechanisms underlying such non-optimally in choice patterns still remain poorly understood. In this talk, to uncover the latent neurobiological and psychological meaningful processes of such impairment, we apply a reinforcement learning diffusion decision model (RL-DDM) while methamphetamine abuser participants (n = 18, all men; mean (±SD) age: 27.3±5) and age/sex-matched healthy controls (n = 25, all men; mean (±SD) age: 26.8.0±3.63) perform choices to resolve uncertainty within a simple probabilistic learning task with rewards and punishments. Preliminary behavior results indicated that addicts made maladaptive patterns of learning that mirrored in both choices and response times (RTs). Furthermore, modeling results revealed that such EDM impairment (maladaptive pattern in optimal selection) in addicts was more imputable to both increased learning rates (more sensitive to outcome fluctuations) and decreased drift rate (less reward sensitivity) compared to healthy. In addition, addicts also showed substantially longer non-decision times (attributed to slower RTs), as well as lower decision boundary criteria (reflection of impulsive choice). Taken together, our findings reveal precise mechanisms associated with EDM impairments in methamphetamine use disorder and confirm the debility of the options values assignment system as the main hub in learning-based decision-making.