Estimating the Associations between Big Five Personality Traits, Testosterone, and Cortisol
Bibliographic record
Abstract
**Objective**: Hormones are often conceptualized as biological markers of individual differences and have been associated with a variety of behavioral indicators and characteristics, such as mating behavior or acquiring and maintaining dominance. However, before researchers create strong theoretical models for how hormones modulate individual and social behavior, information on how hormones are associated with dominant models of personality are needed. Although there have been some studies attempting to quantify the associations between personality traits, testosterone, and cortisol, there are many inconsistencies across these studies. **Methods**: In this registered report, we examined associations between testosterone, cortisol, and Big Five personality traits. We aggregated 25 separate samples to yield a single sample of 3,964 (50.3% women; 27.7% of women were on hormonal contraceptives). Participants completed measures of personality and provided saliva samples for testosterone and cortisol assays.**Results**: The results from multi-level models and meta-analyses revealed mostly weak, non-significant associations between testosterone or cortisol and personality traits. The few significant effects were still very small in magnitude (e.g. testosterone and conscientiousness: r = -0.05). A series of moderation tests revealed that hormone-personality associations were mostly similar in men and women, those using hormonal contraceptives or not, and regardless of the interaction between testosterone and cortisol (i.e., a variant of the dual-hormone hypothesis). **Conclusions**: Altogether, we did not detect many robust associations between Big Five personality traits and testosterone or cortisol. The findings are discussed in the context of biological models of personality and the utility of examining heterogeneity in hormone-personality associations.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".