Affective impulsivity moderates the relationship between disordered gambling severity and attentional bias in electronic gaming machine (EGM) players
Bibliographic record
Abstract
Background and aims: Attentional bias to gambling-related stimuli is associated with increased severity of gambling disorder. However, the addiction-related moderators of attentional bias among those who gamble are largely unknown. Impulsivity is associated with attentional bias among those who abuse substances, and we hypothesized that impulsivity would moderate the relationship between disordered electronic gaming machine (EGM) gambling and attentional bias. Methods: We tested whether facets of impulsivity, as measured by the UPPS-P (positive urgency, negative urgency, sensation seeking, lack of perseverance, lack of premeditation) and the Barratt Impulsiveness Scale-11 (cognitive, motor, non-planning) moderated the relationship between increased severity of gambling disorder, as measured by the Problem Gambling Severity Index (PGSI), and attentional bias. Seventy-five EGM players participated in a free-viewing eye-tracking paradigm to measure attentional bias to EGM images. Results: Attentional bias was significantly correlated with Barratt Impulsiveness Scale-11 (BIS-11) motor, positive urgency, and negative urgency. Only positive and negative urgency moderated the relationship between PGSI scores and attentional bias. For participants with high PGSI scores, higher positive and negative urgency were associated with larger attentional biases to EGM stimuli. Discussion: The results indicate that affective impulsivity is an important contributor to the association between gambling disorder and attentional bias.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".