Aging Faces and Aging Perceivers: Young and Older Adults are Less Sensitive to Deviations from Normality in Older Than in Young Adult Faces
Bibliographic record
Abstract
Past studies examining the other-age effect, the phenomenon in which own-age faces are recognized more accurately than other-age faces, are limited in number and report inconsistent results. Here we examine whether the perceptual system is preferentially tuned to differences among young adult faces. In experiment 1 young (18-25 years) and older adult (63-87 years) participants were shown young and older face pairs in which one member of each pair was undistorted and the other had compressed or expanded features. Participants indicated which member of each pair was more normal and which was more expanded. Both age groups were more accurate when tested with young compared with older faces-but only when judging normality. In experiment 2 we tested a separate group of young adults on the same two tasks but with upright and inverted face pairs to examine the differential pattern of results between the normality and discrimination tasks. Inversion impaired performance on the normality task but not the discrimination task and eliminated the young adult advantage in the normality task. Collectively, these results suggest that the face processing system is optimized for young adult faces and that abundant experience with older faces later in life does not reverse this perceptual tuning.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".