Attentional bias in tobacco use disorder using eye tracking: A systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Attentional bias, defined as the disproportionate attentional allocation towards drug-related stimuli, is well-demonstrated in substance use disorders. However, studies investigating attentional bias in tobacco use disorder have revealed inconclusive findings. In recent years, eye-tracking technology has emerged as an innovative technique for exploring attentional bias. This systematic review aims to provide a comprehensive overview of eye-tracking studies examining attentional bias in tobacco use disorder. Methods: Using PRISMA guidelines, 18 papers that assessed attentional bias using eye-tracking technology among people who smoke cigarettes were extracted from the following databases: PsychINFO, MEDLINE, and EMBASE. Search terms included "attentional bias", "tobacco use disorder", and "eye tracking" and their respective subject headings and synonyms. Selected papers were assessed for methodological quality using a standardized procedure. Selected studies reviewed were categorized into studies making comparisons between 1) people who smoke and people who do not smoke and 2) between smoking-related cues and neutral cues among people who smoke. Results: Overall, most studies showed that people who smoke had significantly greater attentional bias to smoking-related cues, as indexed by greater dwell times and fixation counts. Although findings using measures of early orienting biases were mixed, people who smoke displayed a tendency to initially shift attention to smoking-related cues more frequently than neutral cues. Conclusions: While methodological inconsistencies across studies preclude any definitive conclusions, findings suggest that maintained attention may be a more precise reflection of the specific attentional processes influenced by incentive salience. Suggestions for future research include establishing methodological standards for future eye-tracking studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it