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 models across three sessions during 30 days of nicotine abstinence. Contrary to our preregistered hypothesis, punishment sensitivity emerged as the only parameter that changed during smoking cessation. While it is plausible that some changes in task performance could be attributed to task repetition effects, we observed a clear impact of the Nicotine Withdrawal Syndrome (NWS) on RL, and a dynamic relationship between craving and reward and punishment sensitivity over time, suggesting a significant recalibration of cognitive processes during abstinence. In this context, the heightened sensitivity to negative outcomes observed at the last session (30 days after quitting) compared to the previous sessions, may be interpreted as a cognitive adaptation aimed at fostering long-term abstinence. While further studies are needed to clarify the mechanisms underlying punishment sensitivity during nicotine abstinence, these results highlight the need for personalized treatment approaches tailored to individual needs.