The Late Positive Potentials Evoked by Cigarette-Related and Emotional Images Show no Gender Differences in Smokers
Bibliographic record
Abstract
When trying to quit, women are less likely than men to achieve long-term smoking abstinence. Identifying the neuropsychological mechanisms underlying women's higher relapse vulnerability will help clinicians to develop effective tailored smoking cessation interventions. Here we used event-related potentials (ERPs), a direct measure of brain activity, to evaluate the extent to which neurophysiological responses to cigarette-related and other emotional stimuli differ between female and male smokers. Both women and men showed similar patterns of brain reactivity across all picture categories; pleasant and unpleasant images prompted larger Late Positive Potentials (LPPs, a robust measure of motivational relevance) than neutral images in both groups, and cigarette-related images prompted lower LPPs than high arousing emotional images in both groups. Unlike previous studies, there were no differences between male and female smokers with regard to LPP responses to cigarette-related images. This suggests that the LPP may not be ideally suited to discriminate neurophysiological gender differences or that there are simply no gender differences in the neurophysiological responses to cigarette-related stimuli. We collected ERPs from 222 non-nicotine-deprived smokers (101 women) while they watched a slideshow that included high and low emotionally arousing pleasant and unpleasant pictures, cigarette-related, and neutral pictures. We used the mean amplitude of the LPP to assess the affective significance that participants attributed to these pictures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".