What Can Animal Models Tell Us about Human Sexual Response?
Bibliographic record
Abstract
In all species, sexual behavior is directed by a complex interplay between steroid hormone actions in the brain that give rise to sexual arousability and experience with sexual reward that gives rise to expectations of competent sexual activity, including sexual arousal, desire, and performance. Sexual experience allows animals to form instrumental and Pavlovian associations that predict sexual outcome and thereby directs the strength of sexual responding. Although the study of animal sexual behavior by neuroendocrinologists has traditionally been concerned with mechanisms of copulatory responding, more recent use of conditioning and preference paradigms, and a focus on environmental circumstances and experience, has revealed behaviors and processes that resemble human sexual responses. In this paper, we review behavioral paradigms used with rodents and other species that are analogous or homologous to human sexual arousal, desire, reward, and inhibition. The extent to which these behavioral paradigms offer predictive validity and practicality as preclinical tools and models is discussed. Identification of common neurochemical and neuroanatomical substrates of sexual responding between animals and humans suggests that the evolution of sexual behavior has been highly conserved and indicates that animal models of human sexual response can be used successfully as preclinical tools.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".