Longitudinal changes in reinforcement learning during smoking cessation: a computational analysis using a probabilistic reward task.

ABSTRACT: Despite progress in smoking reduction in the past several decades, cigarette smoking remains a significant public health concern world-wide, with many smokers attempting but ultimately failing to maintain abstinence. However, little is known about how decision-making evolves in quitting smokers. Based on preregistered hypotheses and analysis plan ( https://osf.io/yq5th ), we examined the evolution of reinforcement learning (RL), a key component of decision-making, in smokers during acute and extended nicotine abstinence. In a longitudinal, within-subject design, we used a probabilistic reward task (PRT) to assess RL in twenty smokers who successfully refrained from smoking for at least 30 days. We evaluated changes in reward-based decision-making using signal-detection analysis and five RL

SUBMITTER: Montemitro C 

PROVIDER: S-EPMC11688494 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature