Culture Shapes Efficiency of Facial Age Judgments
Bibliographic record
Abstract
BACKGROUND: Cultural differences in socialization can lead to characteristic differences in how we perceive the world. Consistent with this influence of differential experience, our perception of faces (e.g., preference, recognition ability) is shaped by our previous experience with different groups of individuals. METHODOLOGY/PRINCIPAL FINDINGS: Here, we examined whether cultural differences in social practices influence our perception of faces. Japanese, Chinese, and Asian-Canadian young adults made relative age judgments (i.e., which of these two faces is older?) for East Asian faces. Cross-cultural differences in the emphasis on respect for older individuals was reflected in participants' latency in facial age judgments for middle-age adult faces--with the Japanese young adults performing the fastest, followed by the Chinese, then the Asian-Canadians. In addition, consistent with the differential behavioural and linguistic markers used in the Japanese culture when interacting with individuals younger than oneself, only the Japanese young adults showed an advantage in judging the relative age of children's faces. CONCLUSIONS/SIGNIFICANCE: Our results show that different sociocultural practices shape our efficiency in processing facial age information. The impact of culture may potentially calibrate other aspects of face processing.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".