Attentional Bias in Non–Smoking Electronic Cigarette Users: An Eye-Tracking Study
Bibliographic record
Abstract
INTRODUCTION: This study examined attentional bias (AB) to e-cigarette cues among a sample of non-smoking daily e-cigarette users (n = 27), non-smoking occasional e-cigarette users (n = 32), and control participants (n = 61) who did not smoke or use e-cigarettes. The possibility that e-cigarette users develop a transference of cues to traditional cigarettes was also examined. METHODS: AB was assessed using a free-viewing eye-gaze tracking methodology, in which participants viewed 180 pairs of images for 4 seconds (e-cigarette and neutral image, e-cigarette and smoking image, smoking and neutral image). RESULTS: Daily and occasional e-cigarette users attended to pairs of e-cigarette and neutral images equally, whereas non-users attended to neutral images significantly more than e-cigarette images. All three groups attended to e-cigarette images significantly more than smoking images, with significantly larger biases for e-cigarette users. There were no between-group differences in attention to pairs of smoking and neutral images. A moderation analysis indicated that for occasional users but not daily users, years of vaping reduced the bias toward neutral images over smoking images. CONCLUSIONS: Taken together, the results indicate that the e-cigarette users exhibit heightened attention to e-cigarettes relative to non-users, which may have implications as to how they react to e-cigarette cues in real-world settings. AB for e-cigarettes did not transfer to traditional cigarette cues, which indicates that further research is required to identify the mechanisms involved in the migration of e-cigarettes to traditional cigarettes. IMPLICATIONS: This study is the first attempt to examine attentional biases for e-cigarette cues among non-smoking current e-cigarette users using eye-gaze tracking. The results contribute to the growing literature on the correlates of problematic e-cigarette use and indicate that daily and occasional e-cigarette use is associated with attentional biases for e-cigarettes. The existence of attentional biases in e-cigarette users may help to explain the high rate of failure to quit e-cigarettes and provides support for the utility of attentional bias modification in the treatment of problematic e-cigarette use.
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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) | 0.002 | 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".