The specificity of attentional biases by type of gambling: An eye-tracking study
Bibliographic record
Abstract
A growing body of research indicates that gamblers develop an attentional bias for gambling-related stimuli. Compared to research on substance use, however, few studies have examined attentional biases in gamblers using eye-gaze tracking, which has many advantages over other measures of attention. In addition, previous studies of attentional biases in gamblers have not directly matched type of gambler with personally-relevant gambling cues. The present study investigated the specificity of attentional biases for individual types of gambling using an eye-gaze tracking paradigm. Three groups of participants (poker players, video lottery terminal/slot machine players, and non-gambling controls) took part in one test session in which they viewed 25 sets of four images (poker, VLTs/slot machines, bingo, and board games). Participants' eye fixations were recorded throughout each 8-second presentation of the four images. The results indicated that, as predicted, the two gambling groups preferentially attended to their primary form of gambling, whereas control participants attended to board games more than gambling images. The findings have clinical implications for the treatment of individuals with gambling disorder. Understanding the importance of personally-salient gambling cues will inform the development of effective attentional bias modification treatments for problem gamblers.
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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 | 0.001 | 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".