Structural or dispositional? An experimental investigation of the experience of winning in social casino games (and impulsivity) on subsequent gambling behaviors
Bibliographic record
Abstract
Background and aims In the present research, we experimentally investigated whether the experience of winning (i.e., inflated payout rates) in a social casino game influenced social casino gamers’ subsequent decision to gamble for money. Furthermore, we assessed whether facets of dispositional impulsivity – negative and positive urgency in particular – also influenced participants’ subsequent gambling. Methods Social casino gamers who were also current gamblers (N = 318) were asked to play a social casino game to assess their perceptions of the game in exchange for $3. Unbeknownst to them, players were randomly assigned to one of three experimental conditions: winning (n = 110), break-even (n = 103), or losing (n = 105). After playing, participants were offered a chance to gamble their $3 renumeration in an online roulette game. Results A total of 280 participants (88.1%) elected to gamble, but no between-condition variation in the decision to gamble emerged. Furthermore, there were no differences in gambling on the online roulette between condition. However, higher levels of both negative and positive urgency increased the likelihood of gambling. Finally, impulsivity did not moderate the relationship between experience of winning and decision to gamble. Conclusion The results suggest that dispositional factors, including impulsive urgency, are implicated in the choice to gamble for social casino gamers following play.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".