Assessing attentional bias for alcohol-related cues using eye tracking in a virtual reality environment
Bibliographic record
Abstract
Several experimental paradigms were developed to measure attentional biases towards alcohol-related cues. However, most of them are based on reaction times to two-dimensional stimuli displayed on a computer screen, such that their ecological validity has been questioned. To address this, we integrated an eye tracking system into a virtual reality headset (ET-VR) and measured attentional biases in a subclinical population of alcohol users. In this exploratory study, forty social drinkers were recruited and immersed in a virtual bar including alcohol-related stimuli. Attentional focus was assessed using dwell time and number of fixations for these alcohol-related stimuli as well as for neutral stimuli unrelated to alcohol consumption. The results show that the number of fixations and, to a lesser extent, the dwell time for alcohol-related cues were positively correlated with the drinking motivation of the participants. In contrast, no significant correlation was found for neutral stimuli. In conclusion, the present study shows that alcohol-induced attentional biases can be studied using an ET-VR device in a subclinical population of alcohol users.
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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 | 0.002 |
| 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) | 0.001 | 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".