No Compelling Evidence that Self-Reported Personality Traits Explain Basal Testosterone and Cortisol’s Associations with Status-Relevant Behavior
Bibliographic record
Abstract
Abstract Objective A goal of behavioral neuroendocrinology is to understand how basal hormone levels relate to behavior. Studies of human participants sometimes measure self-reported personality traits, in addition to or instead of direct behavioral observation. Although personality traits often predict their respective behaviors, whether personality explains hormone-behavior relationships remains unclear. Methods We obtained data from eight previous studies (total N = 985) that examined baseline testosterone and cortisol as predictors of status-relevant behavior (competitiveness, dominance, risk-taking, aggression, affiliation, and social status). We tested whether the previously reported hormone-behavior relationships are mediated by self-reported personality traits (e.g., trait dominance, prestige, extraversion). As a secondary research question, we also tested whether trait dominance moderated the testosterone-behavior relationships. Results As expected, self-reported personality traits often predicted status-relevant behaviors, but there was little evidence that traits also correlated with basal testosterone or the testosterone × cortisol interaction. Across all eight studies, personality traits did not significantly mediate hormone-behavior relationships. Indeed, the effect sizes of the hormone-behavior relationships were robust to the inclusion of personality traits as covariates. Further, we did not find strong or consistent evidence that trait dominance moderates the testosterone-behavior association. Conclusion Results suggest that basal testosterone and cortisol predict status-related behavior independent of self-reported personality. We discuss how these results may have broader implications for the physiological mechanisms by which testosterone and cortisol influence behavior, a process that could be unconscious and automatic. We also discuss alternative explanations, limitations, and future directions.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".