Ethnicity and gender effects in the perception of age in faces
Bibliographic record
Abstract
Observers recognize faces of their own race more easily than other-race faces. This is attributed, in part, to differential experience with faces of unfamiliar ethnicities. Furthermore, differential experience is known to also have an impact on other aspects of face perception such as gender categorization (O'Toole & Peterson, 1996). One study found evidence for other-race effects in age perception in a group of African observers, though this effect was not present in the Caucasian group (Dehon & Brédart, 2001). Here, we investigated age perception in East Asian and Caucasian observers who viewed 288 faces that ranged in age from 18 to 89 years old (1:1 race and gender ratio). Observers' average age estimates increased monotonically with the true age of the face stimuli. Accuracy for age estimation was maximal for the middle age range, while age was overestimated for younger faces, and underestimated for older faces. Overall, Caucasian faces and male faces were perceived to be older than East Asian and female faces, respectively. Female observers overestimated age of male faces by one year, though these faces were perceived veridically by male observers. There was no such difference in the perception of female faces, which were slightly underestimated by both female and male observers. Importantly, perception of age in other-race faces showed a bi-phasic pattern that switched between over- and under-estimation of age around 42-47 years of age for both groups of observers. East Asian observers rated Caucasian faces older and East Asian faces younger than did Caucasian observers before this age boundary. The pattern reversed after this age range. These results reflect physiognomic features in Caucasian and East Asian, as well as female and male, faces that influence perception of age. In addition, they represent evidence of other-gender and other-race effects in age perception. Meeting abstract presented at VSS 2018
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".