Tilt in Online Poker: Loss of Control and Gambling Disorder
Bibliographic record
Abstract
Online poker is a form of gambling where an element of skill may influence the outcome of the game. ‘Tilt’ in poker describes an episode during which the player can no longer control their game by rational decisions. It leads to a loss of control over the game, a loss of emotional regulation, higher cognitive distortion, and a loss of money. This phenomenon, experienced by most players, could be the gateway to excessive gambling. The aim of this study was to assess the links between the frequency of tilt episodes, cognitive distortion, anxiety, depression, sensation seeking and excessive online poker gambling. Our sample is composed of 291 online poker players, with a mean age of 33.8 years (SD = 10.6). Participants completed an online self-assessment questionnaire, measuring the frequency of tilt episodes, cognitive distortion, anxiety, depression and impulsivity. The findings indicated that the frequency of tilt episodes and cognitive distortion were the only significant predictors of excessive online gambling (respectively, r = 0.49 and r = 0.20). Tilt frequency and cognitive distortion were strongly correlated (GRCS, r = 0.60), moderate to low correlations were found for tilt and anxiety (HADS, r = 0.40), and positive and negative urgency (UPPS, r = 0.27). To date, tilt has seldom been studied, and could improve our understanding of online poker gamblers. It could be a new means of identifying at risk gamblers, and thus facilitating preventive measures specifically adapted to this population.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".