Psychobiology of Competition: A Review of Men’s Endogenous Testosterone Dynamics
Bibliographic record
Abstract
Abstract Competition is a defining feature of most living organisms. Among humans, the engagement in, and the associated outcomes of, competition (i.e., win or loss) can hold important consequences for survival, individual and group status, and mating-related opportunities. As such, considerable research efforts have been devoted to identifying and delineating the factors that influence human competitive decision-making. The steroid hormone testosterone, in particular, has been identified as one such factor, not only influencing competitive decision-making and behavior, but also responding flexibly to competitive cues and outcomes to then feedback to ongoing pursuits. Growing evidence suggests that the extent to which testosterone exerts its effects may depend on variables across a number of domains, such as individual preferences or dispositions, social cues, and other hormones. This chapter provides an overview of such competitive biopsychology with a focus on men’s testosterone dynamics. The authors provide a brief introduction to testosterone, and a summary of some of the key theoretical approaches to understanding testosterone dynamics in humans, followed by an overview of correlational and experimental studies that examine the independent and interactive effects of testosterone on competition and competition-related variables.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".