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 smo...
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84091-y |
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| author | Chiara Montemitro Paolo Ossola Thomas J. Ross Quentin J. M. Huys John R. Fedota Betty Jo Salmeron Massimo di Giannantonio Elliot A. Stein |
| author_facet | Chiara Montemitro Paolo Ossola Thomas J. Ross Quentin J. M. Huys John R. Fedota Betty Jo Salmeron Massimo di Giannantonio Elliot A. Stein |
| author_sort | Chiara Montemitro |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c6acf5394dbf4593b4d2e58d21f9d21d |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c6acf5394dbf4593b4d2e58d21f9d21d2025-08-20T02:16:59ZengNature PortfolioScientific Reports2045-23222024-12-0114111410.1038/s41598-024-84091-yLongitudinal changes in reinforcement learning during smoking cessation: a computational analysis using a probabilistic reward taskChiara Montemitro0Paolo Ossola1Thomas J. Ross2Quentin J. M. Huys3John R. Fedota4Betty Jo Salmeron5Massimo di Giannantonio6Elliot A. Stein7Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of HealthDepartment of Medicine and Surgery, University of ParmaNeuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of HealthApplied Computational Psychiatry Laboratory, Mental Health Neuroscience Department, Division of Psychiatry and Max Planck Centre for Computational Psychiatry and Ageing Research, Queen Square Institute of Neurology, University College LondonNeuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of HealthNeuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of HealthDepartment of Neuroscience, Imaging and Clinical Sciences, “G. d’Annunzio” UniversityNeuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of HealthAbstract 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.https://doi.org/10.1038/s41598-024-84091-ySmoking cessationReinforcement learningDecision-makingWithdrawalNicotine abstinencePunishment sensitivity |
| spellingShingle | Chiara Montemitro Paolo Ossola Thomas J. Ross Quentin J. M. Huys John R. Fedota Betty Jo Salmeron Massimo di Giannantonio Elliot A. Stein Longitudinal changes in reinforcement learning during smoking cessation: a computational analysis using a probabilistic reward task Scientific Reports Smoking cessation Reinforcement learning Decision-making Withdrawal Nicotine abstinence Punishment sensitivity |
| title | Longitudinal changes in reinforcement learning during smoking cessation: a computational analysis using a probabilistic reward task |
| title_full | Longitudinal changes in reinforcement learning during smoking cessation: a computational analysis using a probabilistic reward task |
| title_fullStr | Longitudinal changes in reinforcement learning during smoking cessation: a computational analysis using a probabilistic reward task |
| title_full_unstemmed | Longitudinal changes in reinforcement learning during smoking cessation: a computational analysis using a probabilistic reward task |
| title_short | Longitudinal changes in reinforcement learning during smoking cessation: a computational analysis using a probabilistic reward task |
| title_sort | longitudinal changes in reinforcement learning during smoking cessation a computational analysis using a probabilistic reward task |
| topic | Smoking cessation Reinforcement learning Decision-making Withdrawal Nicotine abstinence Punishment sensitivity |
| url | https://doi.org/10.1038/s41598-024-84091-y |
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