Luck, come here! Automatic approach tendencies toward gambling cues in moderate‐ to high‐risk gamblers
Bibliographic record
Abstract
BACKGROUND AND AIMS: Similar to substance addictions, reward-related cognitive motivational processes, such as selective attention and positive memory biases, have been found in disordered gambling. Despite findings that individuals with substance use problems are biased to approach substance-related cues automatically, no study has yet focused on automatic approach tendencies for motivationally salient gambling cues in problem gamblers. We tested if moderate- to high-risk gamblers show a gambling approach bias and whether this bias was related prospectively to gambling behaviour and problems. DESIGN: Cross-sectional assessment study evaluating the concurrent and longitudinal correlates of gambling approach bias in moderate- to high-risk gamblers compared with non-problem gamblers. SETTING: Online study throughout the Netherlands. PARTICIPANTS: Twenty-six non-treatment-seeking moderate- to high-risk gamblers and 26 non-problem gamblers community-recruited via the internet. MEASUREMENTS: Two online assessment sessions 6 months apart, including self-report measures of gambling problems and behaviour (frequency, duration and expenditure) and the gambling approach avoidance task, with stimuli tailored to individual gambling habits. FINDINGS: Relative to non-problem gamblers, moderate- to high-risk gamblers revealed a stronger approach bias towards gambling-related stimuli than neutral stimuli (P = 0.03). Gambling approach bias was correlated positively with past-month gambling expenditure at baseline (P = 0.03) and with monthly frequency of gambling at follow-up (P = 0.02). In multiple hierarchical regressions, baseline gambling approach bias predicted monthly frequency positively (P = 0.03) and total duration of gambling episodes (P = 0.01) 6 months later, but not gambling problems or expenditure. CONCLUSIONS: In the Netherlands, relative to non-problem gamblers, moderate- to high-risk gamblers appear to have a stronger tendency to approach rather than to avoid gambling-related pictures compared with neutral ones. This gambling approach bias is associated concurrently with past-month gambling expenditure and duration of gambling and has been found to predict persistence in gambling behaviour over time.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".