No Compelling Evidence that Self-Reported Personality Traits Explain Basal Testosterone and Cortisol’s Associations with Status-Relevant Behavior
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it