Short-Term Responsive Mating Intentions Increase with Estradiol and Testosterone across the Menstrual Cycle: Scale Development and an Observational Study
Bibliographic record
Abstract
The extent to which estradiol, progesterone, and testosterone influence mating behavior across the menstrual cycle is unclear. The Proactive and Responsive Mating Strategies Scales (PARMSS) were developed to separately examine two specific components of sexuality and were used here to test divergent hormonal associations. Preliminary psychometric data (N = 364 females) suggest that both scales consist of one factor and demonstrate strong psychometric properties (internal consistency, test–retest reliability, and construct and convergent validity). The PARMSS were used in a repeated-measures observational study to examine the relationships between changes in endogenous hormone levels and both proactive and responsive mating intentions with potential new short-term or long-term partners in healthy pre-menopausal participants (N = 38). At two points in their cycle, participants provided salivary hormone samples in the laboratory and reported the likelihood of engaging in proactive and responsive behaviors with men in photos and vignettes. Participants reported greater responsive than proactive intentions. Increases in estradiol and testosterone across the cycle were associated with increases in short-term mating intentions, particularly responsivity to potential short-term relationship partners. No associations were found for intentions that were proactive or that involved potential long-term partners or for progesterone. Changes in the three hormones explained changes in short-term responsive mating intentions (22% of the variance). The results suggest (a) cyclical changes in estradiol and testosterone are differentially associated with changes in responsive vs. proactive mating intentions and (b) context-dependent changes (i.e., short-term vs. long-term mating intentions and possibly relationship status). The findings require replication with larger and diverse samples.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".