Testosterone reduces the threat premium in competitive resource division
Bibliographic record
Abstract
Like other animals, humans are sensitive to facial cues of threat. Recent evidence suggests that we use this information to dynamically calibrate competitive decision-making over resources, ceding more to high-threat individuals (who appear more willing/able to retaliate) and keeping more from low-threat individuals. Little is known, however, about the biological factors that support such threat assessment and decision-making systems. In a pre-registered, double-blind, placebo-controlled, cross-over testosterone administration study ( n = 118 men), we show for the first time that testosterone reduces the effects of threat on decision-making: participants ceded more resources to high-threat (versus low-threat) individuals (replicating the 'threat premium'), but this effect was blunted by testosterone, which selectively reduced the amount of resources ceded to those highest in threat. Thus, our findings suggest that testosterone influences competitive decision-making by recalibrating the integration of threat into the decision-making process.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".