Abstract

A considerable number of people successfully give up tobacco smoking. In nicotine-dependent individuals, tobacco choice is determined by greater expected drug value; however, less is known about the underlying mechanisms through which people quit smoking. This study aimed to explore whether computational parameters of value-based decision-making (VBDM) characterize recovery from nicotine addiction. Using a preregistered, between-subject design, current daily smokers (n = 51) and ex-smokers who used to smoke daily (n = 51) were recruited from the local community. Participants completed a two-alternative forced choice task in which they chose between either two tobacco-related images (in one block) or tobacco-unrelated images (in a different block). During each trial, participants pressed a computer key to select the image they rated most positively during a previous task block. To estimate evidence accumulation (EA) processes and response thresholds during the different blocks, a drift-diffusion model was fitted to the reaction time and error data. Ex-smokers had significantly higher response thresholds when making tobacco-related decisions (p = .01, d = 0.45) compared to current smokers, although there were no significant group differences during tobacco-unrelated decisions. Furthermore, there were no significant group differences in EA rates when making tobacco or tobacco-unrelated decisions. Greater cautiousness when making value-based decisions about tobacco-related cues characterized recovery from nicotine addiction. The number of people dependent on nicotine has decreased steadily during the past decade; however, the mechanisms that underlie recovery are currently less well understood. The present study applied advances in the measurement of value-based choice. The aim was to explore whether the internal processes that underpin VBDM discriminate current daily tobacco smokers from ex-tobacco smokers who used to smoke daily. Findings revealed that recovery from nicotine addiction was characterized by higher response thresholds when making value-based decisions about tobacco-related cues; this may serve as a novel target for treatment interventions that focus on helping people to stop smoking.

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