Testing the utility of Mouseview.js for measuring associations between alcohol related attentional bias and problematic alcohol use
Bibliographic record
Abstract
This study examined the utility of Mouseview.js, an alternative to webcam-based eye-tracking, to assess associations between alcohol-related attentional bias and problematic alcohol use. Canadians recruited through Academic Prolific ( N = 526) completed an online free-viewing task using Mouseview.js to measure biased attention to alcohol-related images. Eighteen alcohol-related images were paired with neutral images matched on visual characteristics. Attentional bias was estimated as the differences in trial-level dwell times for the image pairs. The sample consisted of 132 non-drinkers, 241 recreational drinkers, and 153 problematic drinkers. Participants’ mean age was 33.5 years, 50.8% were men, and 40.4% identified as a racialized minority. Multi-level modelling was used for statistical analyses. The difference in dwell times between alcohol-related and neutral images was larger for problematic drinkers compared to non-drinkers, B = 3.75, z = 2.61, p = .009. There were no significant differences between recreational drinkers and non-drinkers, or between recreational and problematic drinkers. Reliability for dwell times and dwell time differences ranged from acceptable (≥ .70) to good (≥ 0.80). There was an attentional bias for alcohol-related images among problematic drinkers relative to non-drinkers and the Mouseview.js indices of bias demonstrated adequate reliability. These findings provide preliminary support for the utility of Mouseview.js as a measure of attentional bias in problematic drinkers. With further validation studies, Mouseview.js may prove to be a powerful tool for conducting high-powered online studies of attentional biases associated with addictive behaviors. • Mouseview.js can identify an attentional bias for alcohol-related images. • Problematic drinkers displayed an attentional bias for alcohol-related images. • Results using Mouseview.js ranged from acceptable to good reliability.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".