Looking for Ms. Right: Allocating Attention to Facilitate Mate Choice Decisions
Bibliographic record
Abstract
Through various signals, the human body provides information that may be used by receivers to make decisions about mate value. Here, we investigate whether there exists a complementary psychological system designed to selectively attend to these signals in order to choose, and direct effort toward the acquisition of, a potential mate. We presented young men with three images of the same woman (six women in total) simultaneously, varying the waist-to-hip ratio (WHR) of each image while holding other traits constant. While participants chose their preferred image, we monitored visual attention using an infrared eye-tracker. We found that participants focused their attention selectively on body regions known to provide reproductive information in a manner consistent with the research hypothesis: Reproductively relevant body regions, especially the head and breasts, received the most visual attention. Likewise, images with lower WHRs and reproductively relevant regions in images with lower WHRs received the most visual attention and were chosen as most attractive. Finally, irrespective of WHR size, participants fixated more often and for longer durations on the images that they selected as most attractive.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".