The role of orgasm in the development and shaping of partner preferences
Bibliographic record
Abstract
BACKGROUND: The effect of orgasm on the development and shaping of partner preferences may involve a catalysis of the neurochemical mechanisms of bonding. Therefore, understanding such process is relevant for neuroscience and psychology. METHODS: A systematic review was carried out using the terms Orgasm, Sexual Reward, Partner Preference, Pair Bonding, Brain, Learning, Sex, Copulation. RESULTS: In humans, concentrations of arousing neurotransmitters and potential bonding neurotransmitters increase during orgasm in the cerebrospinal fluid and the bloodstream. Similarly, studies in animals indicate that those neurotransmitters (noradrenaline, oxytocin, prolactin) and others (e.g. dopamine, opioids, serotonin) modulate the appetitive and consummatory phases of sexual behavior and reward. This suggests a link between the experience of orgasm/sexual reward and the neurochemical mechanisms of pair bonding. Orgasm/reward functions as an unconditioned stimulus (UCS). Some areas in the nervous system function as UCS-detection centers, which become activated during orgasm. Partner-related cues function as conditioned stimuli (CS) and are processed in CS-detector centers. CONCLUSIONS: Throughout the article, we discuss how UCS- and CS-detection centers must interact to facilitate memory consolidation and produce recognition and motivation during future social encounters.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".