Eye tracking of smoking-related stimuli in tobacco use disorder: A proof-of-concept study combining attention bias modification with alpha-transcranial alternating current stimulation
Bibliographic record
Abstract
BACKGROUND: Tobacco use disorder (TUD) is characterized by the presence of an attentional bias (AB) towards smoking-related stimuli. We investigated whether combining an AB modification paradigm (ABM) with transcranial alternating current stimulation (tACS) applied over the dorsolateral prefrontal cortex (DLPFC) reduces the AB towards smoking-related stimuli, as well as craving level and impulsive choices. METHODS: In a sham-controlled, crossover preliminary study, 19 subjects with TUD received two stimulation arms: 1) active tACS (10 Hz, 2 mA, 30 min) combined with ABM and 2) sham tACS combined with ABM, in a randomized order, separated by one week. AB towards smoking cues during passive observation of smoking and neutral cues was assessed with an eye-tracking device and reactions times at a visual-probe task. Craving level was measured with the Questionnaire of Smoking Urges. Impulsive choices were assessed with the delay discounting task. RESULTS: Active tACS combined with ABM reduced the amount of time spent looking at smoking-related pictures (p = 0.03), prevented the increase of self-reported desire to smoke (p = 0.026), and reduced the proportion of impulsive choices (p = 0.049), compared to sham tACS combined with ABM. No significant effects were reported on other craving dimensions and on AB based on reaction times. CONCLUSIONS: These preliminary findings suggest that combining tACS with ABM may help smokers who wish to quit by reducing the desire to smoke, attention to smoking-cues, and impulsive decision-making.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".