Time course of attentional bias for gambling information in problem gambling.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is a wealth of evidence showing enhanced attention toward drug-related information (i.e., attentional bias) in substance abusers. However, little is known about attentional bias in deregulated behaviors without substance use such as abnormal gambling. This study examined whether problem gamblers (PrG, as assessed through self-reported gambling-related craving and gambling dependence severity) exhibit attentional bias for gambling-related cues. Forty PrG and 35 control participants performed a change detection task using the flicker paradigm, in which two images differing in only one aspect are repeatedly flashed on the screen until the participant is able to report the changing item. In our study, the changing item was either neutral or related to gambling. Eye movements were recorded, which made it possible to measure both initial orienting of attention as well as its maintenance on gambling information. Direct (eye-movements) and indirect (change in detection latency) measures of attention in individuals with problematic gambling behaviors suggested the occurrence of both engagement and of maintenance attentional biases toward gambling-related visual cues. Compared to nonproblematic gamblers, PrG exhibited (a) faster reaction times to gambling-cues as compared to neutral cues, (b) higher percentage of initial saccades directed toward gambling pictures, and (c) an increased fixation duration and fixation count on gambling pictures. In the PrG group, measures of gambling-related attentional bias were not associated with craving for gambling and gambling dependence severity. Theoretical and clinical implications of these results are discussed.
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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 | 0.001 | 0.000 |
| 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 it