Smiling makes you look older, even when you wear a mask: the effect of face masks on age perception
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The widespread use of face masks in the era of the Covid-19 pandemic has promoted research on their effect on the perception and recognition of faces. There is growing evidence that masks hinder the recognition of identity and expression, as well as the interpretation of speech from facial cues. It is less clear whether and in what manner masks affect the perception of age from facial cues. Recent research has emphasized the role of the upper region of the face, a part not covered by a mask, in the evaluation of age. For example, smile-related wrinkles in the region of the eyes make smiling faces appear older than neutral faces of the same individuals (the aging effect of smiling, AES). In two experiments, we tested the effect of face masks on age evaluations of neutral and smiling faces in a range of different age groups from 20 to 80 years. The results showed that smiling faces were perceived as older than neutral faces even when individuals were wearing a face mask-and there was no effect of masks on bias in age evaluations. Additional analyses showed reduced accuracy in age evaluations for smiling compared to neutral faces and for masked compared to unmasked faces. The results converge on previous studies emphasizing the importance of the upper region of the face in evaluations of age.
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.
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 | 0.002 | 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 | 0.002 | 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 it