Taking risks to feel excitement: Detailed personality profile and genetic associations
Bibliographic record
Abstract
This study mapped the personality and genetics of risky excitement-seekers focusing on skydiving behavior. We compared 298 skydivers to 298 demographically matched controls across the NEO Personality Inventory-3 domains, facets, and 240 items. The most significant item-level effects were aggregated into a poly-item score of skydiving-associated personality markers (Study 1), where higher scores describe individuals who enjoy risky situations but have no self-control issues. The skydiving-associated personality marker score was associated with greater physical activity, higher rate of traumatic injuries, and better mental health in a sample of 3558 adults (Study 2). From genetic perspective, we associated skydiving behavior with 19 candidate variants that have previously been linked to excitement-seeking (Study 1). Polymorphisms in the SERT gene were the strongest predictors of skydiving, but the false discovery rate-adjusted (FDR-adjusted) p-values were non-significant. In Study 2, we predicted the skydiving-associated personality marker score and E5: Excitement-seeking from multiple risk-taking polygenic scores, using publicly available summary data from genome-wide association studies. While E5: Excitement-seeking was most strongly predicted by general risk tolerance and risky behaviors’ polygenic scores, the skydiving-associated personality marker score was most strongly associated with the adventurousness polygenic scores. Phenotypic and polygenic scores associations suggest that skydiving is a specific—perhaps more functional—form of excitement-seeking, which may nevertheless lead to physical injuries.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".