The Role of Sexual and Romantic Attraction in Human Mate Preferences
Bibliographic record
Abstract
Sex differences in mate preferences are ubiquitous, having been evidenced across generations and cultures. Their prevalence and persistence have compellingly placed them in the evolutionarily adaptive context of sexual selection. However, the psycho-biological mechanisms contributing to their generation and maintenance remain poorly understood. As such a mechanism, sexual attraction is assumed to guide interest, desire, and the affinity toward specific partner features. However, whether sexual attraction can indeed explain sex differences in partner preferences has not been explicitly tested. To better understand how sex and sexual attraction shape mate preferences in humans we assessed how partner preferences differed across the spectrum of sexual attraction in a sample of 479 individuals that identified as asexual, gray-sexual, demisexual or allosexual. We further tested whether romantic attraction predicted preference profiles better than sexual attraction. Our results show that sexual attraction accounts for highly replicable sex differences in mate preferences for high social status and financial prospects, conscientiousness, and intelligence; however, it does not account for the enhanced preference for physical attractiveness expressed by men, which persists even in individuals with low sexual attraction. Instead, sex differences in physical attractiveness preference are better explained by the degree of romantic attraction. Furthermore, effects of sexual attraction on sex differences in partner preferences were grounded in current rather than previous experiences of sexual attraction. Taken together, the results support the idea that contemporary sex differences in partner preferences are maintained by several psycho-biological mechanisms that evolved in conjunction, including not only sexual but also romantic attraction.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".