Cigarette Cravings, Impulsivity, and the Brain
Bibliographic record
Abstract
Craving is a core feature of tobacco use disorder as well as a significant predictor of smoking relapse. Studies have shown that appetitive smoking-related stimuli (e.g., someone smoking) trigger significant cravings in smokers impede their self-control capacities and promote drug seeking behavior. In this review, we begin by an overview of functional magnetic resonance imaging (fMRI) studies investigating the neural correlates of smokers to appetitive smoking cues. The literature reveals a complex and vastly distributed neuronal network underlying smokers' craving response that recruits regions involved in self-referential processing, planning/regulatory processes, emotional responding, attentional biases, and automatic conducts. We then selectively review important factors contributing to the heterogeneity of results that significantly limit the implications of these findings, namely between- (abstinence, smoking expectancies, and self-regulation) and within-studies factors (severity of smoking dependence, sex-differences, motivation to quit, and genetic factors). Remarkably, we found that little to no attention has been devoted to examine the influence of personality traits on the neural correlates of cigarette cravings in fMRI studies. Impulsivity has been linked with craving and relapse in substance and tobacco use, which prompted our research team to examine the influence of impulsivity on cigarette cravings in an fMRI study. We found that the influence of impulsivity on cigarette cravings was mediated by fronto-cingulate mechanisms. Given the high prevalence of cigarette smoking in several psychiatric disorders that are characterized by significant levels of impulsivity, we conclude by identifying psychiatric patients as a target population whose tobacco-smoking habits deserve further behavioral and neuro-imaging investigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".