The impact of precommitment on risk-taking while gambling: A preliminary study
Bibliographic record
Abstract
Background and aims Precommitment refers to the ability to prospectively restrict the access to temptations. This study examined whether risk-taking during gambling is decreased when an individual has the opportunity to precommit to his forthcoming bet. Methods Sixty individuals participated in a gambling task that consisted of direct choice (simply chose one monetary option among four available ones, ranging from low-risk to high-risk options) or precommitment trials (before choosing an amount, participants had the opportunity to make a binding choice that made high-risk options unavailable). Results We found that participants utilized the precommitment option, such that risk-taking was decreased on precommitment trials compared to direct choices. Within the precommitment trials, there was no significant difference in risk-taking following decisions to restrict versus non-restrict. Discussion These findings suggest that the opportunity to precommit may be sufficient to reduce the attractiveness of risk. Conclusions Present results might be exploited to create interventions aiming at enhancing one's ability to anticipate self-control failures while gambling.
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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 | 0.004 |
| 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 | 0.001 | 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".