Bibliographic record
Abstract
Attentional bias to smoking-related stimuli is an important behavioural feature of tobacco dependence. The overall aim for this thesis is to investigate if attentional bias can serve as a potential phenotypic biomarker to assist in understanding treatment effectiveness, as well as predicting treatment response.First, a comprehensive systematic review was conducted to examine the association between attentional bias to smoking-related cues using eye-tracking indices in smokers and to identify gaps in the literature. Following a thorough literature search, 18 unique articles were identified. It was determined that smokers have increased attentional bias to smoking-related cues in comparison to non-smokers. Second, we aimed to investigate differences in attentional bias to smoking, affective, and sensation-seeking cues in smokers and non-smokers using novel, free-viewing, eye- tracking technology. Smokers (n=50) spent over 2 times longer looking at smoking-related images than non-smokers (n=50; F=25.50, p<0.001). As well, greater impulsivity was significantly associated with increased attentional bias to sensation-seeking cues (R2=0.059, F=2.98, p=0.04) in smokers but not non-smokers. Finally, we investigated whether smoking cessation treatment combining varenicline and transcranial direct current stimulation applied on the left and right dorsolateral prefrontal cortex altered attentional bias to smoking cues, and whether eye-tracking can serve as a proxy measure for changes in neural responses. Preliminary findings show a decrease (coeff=-0.007, p=0.004) in attentional bias to smoking-related cues from baseline to end of treatment across treatment conditions. A positive correlation was found between attentional bias to smoking cues outside the scanner and BOLD signal during smoking cue presentation in the anterior cingulate cortex (rs=0.36, p=0.03) at baseline. This finding was reduced to a trend when number of cigarettes smoked per day was accounted for in the model. No significant stimulation type-by-time or quit status-by-time interaction effects were found. Findings of this thesis suggest that smoking-related attentional bias is an important characteristic of tobacco dependence and can reflect treatment-related changes. As well, findings support the use of eye-tracking as a non-invasive and affordable proxy for assessing brain reactivity to drug-related cues. Findings offer insights on underlying mechanisms influencing treatment-related changes and has the potential to inform new and effective cessation interventions.
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.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".