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Psychobiology of Competition: A Review of Men’s Endogenous Testosterone Dynamics

2022· review· en· W4283578694 on OpenAlexaff
Brian M. Bird, Lindsay Bochon, Yin Wu, Samuele Zilioli

Bibliographic record

VenueOxford University Press eBooks · 2022
Typereview
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTestosterone (patch)Competition (biology)Behavioral neurosciencePsychologyDynamics (music)Social psychologyBiologyEcologyEndocrinologyNeuroscience

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.113
GPT teacher head0.327
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueOxford University Press eBooksSame topicEvolutionary Psychology and Human BehaviorFrench-language works237,207