Perceiving female physical attractiveness and expressive traits from body features and body motion
Bibliographic record
Abstract
BACKGROUND: The perception of female physical attractiveness is known to be predicted by body features(e.g. BMI). However, the role of body motion (e.g. postures) and the relative contribution of each type of cues are unclear. Little research reported how body cues modulate the perception of female expressive traits (e.g. warmth). METHODS: We photographed and filmed 15 female posers and recorded their anthropometric data. In picture stimuli, each poser adopted neutral, instructed attractive, or spontaneous attractive and unattractive postures. In video stimuli, posers introduced a place in neutral or passionate manner. Fifty-four perceivers watched these pictures and silent videos and rated their physical attractiveness and feminine expressive traits on 7-point scales. RESULTS: Lasso regression and proportion of variance explained analyses revealed that Body features demonstrated stronger predictive power for physical attractiveness than body motions across both picture and video stimuli. However, for feminine traits, body motions showed greater predictive validity in videos, whereas neither body features nor body motions effectively predicted feminine traits in static images. CONCLUSION: Different roles of body features and body motion play for perceiving different levels of personal characteristics in social perception. Perception of expressive traits appears to rely more substantially on body motions, whereas the judgment of physical attractiveness depends more fundamentally on body features.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".