Benefit perceptions of risk, dark triad personality traits, and gambling behavior
Bibliographic record
Abstract
Individual differences in dark triad traits – Machiavellianism, narcissism, and psychopathy – have been robustly associated with increased risk-taking, including gambling. Drawing on reinforcement sensitivity theory, we propose that dark triad traits facilitate perceptions of benefits from risk-taking, which in turn motivate elevated gambling behaviors. Among 293 community members recruited from a crowdsourcing platform, we demonstrate that zero-sum associations between individual differences in dark triad traits and benefit perceptions of risk are large (rs = .34 to .48), and both dark triad traits and benefit perceptions of risk are associated with behavioral gambling decisions in a blackjack task (rs = .27 to .47). Further, we show that the association of dark triad traits and gambling behavior is mediated by benefit perceptions of risk-taking. Gender analyses showed stronger associations of dark triad traits and benefit perceptions of risk among men than women, and that benefit perceptions of risk mediate associations of dark triad traits and gambling among men, but not women. Taken together, results suggest that dark triad traits appear to be a risk factor for gambling behaviors, particularly among men, and attitudes regarding benefit perceptions of risk may be a potentially fruitful target of clinical intervention.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".