The effect of smiling on the perceived age of male and female faces across the lifespan
Bibliographic record
Abstract
Previous research has shown an unintuitive effect of facial expression on perceived age: smiling faces are perceived as older compared to neutral faces of the same people. The aging effect of smiling (AES), which is thought to result from the presence of smile-related wrinkles around the eyes, contradicts the common belief that smiling faces should be perceived as younger, not older. Previous research, however, has focused on faces of young adults, where the absence of inherent, age-related wrinkles and other age signs is offset by the weight of the smile-related wrinkles. In a series of experiments, we tested whether the AES extends to male and female faces in older age groups. We replicated the AES in young adults (20-39) and showed that it disappeared in older adults (60-79) of both genders. For photos of middle-aged adults (40-59), however, AES was found only for male, but not for female faces, who showed fewer and less prominent smile-related wrinkles. The results suggest that a person's apparent age is perceived in a holistic manner in which age-related cues in the region of the eyes are weighted against age cues in other regions of the face.
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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 | 0.003 |
| 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) | 0.002 | 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".