Testing the influence of testosterone administration on men’s honesty in a large laboratory experiment
Bibliographic record
Abstract
The impact of testosterone on decision-making is a growing literature, with several reports of economically relevant outcomes. Similar to Wibral et al. (2012), we investigate the effects of exogenous testosterone administration on deception in a double-blind placebo controlled study. Participants (N = 242) were asked to roll a die in private and were paid according to their reported roll, which creates the opportunity to lie about the outcome to increase earnings. We find evidence for self-serving lying in both treatment and control groups and a statistically insignificant negative effect (d = -0.17, 95% CI[-0.42, 0.08]) indicating more honest behavior (i.e., lower reports) following testosterone administration. Although insignificant, the direction was the same as in the Wibral et al. study, and the meta-analytic effect of the two studies demonstrates lower reporting (i.e., more honesty) following testosterone (vs. placebo) administration, significant at the 0.05 level (d = -0.27, 95% CI[-0.49, -0.06]). We discuss how our results and methodology compare with Wibral et al. and identify potential causes for differences in findings. Finally, we consider several plausible connections between testosterone and lying that may be further investigated using alternative methodologies.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".