Cognitive distortions and gambling near-misses in Internet Gaming Disorder: A preliminary study
Bibliographic record
Abstract
Increased cognitive distortions (i.e. biased processing of chance, probability and skill) are a key psychopathological process in disordered gambling. The present study investigated state and trait aspects of cognitive distortions in 22 individuals with Internet Gaming Disorder (IGD) and 22 healthy controls. Participants completed the Gambling Related Cognitions Scale as a trait measure of cognitive distortions, and played a slot machine task delivering wins, near-misses and full-misses. Ratings of pleasure ("liking") and motivation to play ("wanting") were taken following the different outcomes, and gambling persistence was measured after a mandatory phase. IGD was associated with elevated trait cognitive distortions, in particular skill-oriented cognitions. On the slot machine task, the IGD group showed increased "wanting" ratings compared with control participants, while the two groups did not differ regarding their "liking" of the game. The IGD group displayed increased persistence on the slot machine task. Near-miss outcomes did not elicit stronger motivation to play compared to full-miss outcomes overall, and there was no group difference on this measure. However, a near-miss position effect was observed, such that near-misses stopping before the payline were rated as more motivating than near-misses that stopped after the payline, and this differentiation was attenuated in the IGD group, suggesting possible counterfactual thinking deficits in this group. These data provide preliminary evidence for increased incentive motivation and cognitive distortions in IGD, at least in the context of a chance-based gambling environment.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".