Aging Faces and Aging Perceivers: Are There Developmental Changes in Face Space Later in Life?
Bibliographic record
Abstract
Adults' expertise in face processing has been attributed to norm-based coding, a representation that develops during childhood and may be optimized for own-age faces (Macchi Cassia et al., 2009). Here, we examined how young and older adult faces are represented in face space and the extent to which aftereffects transfer across age and sex categories. In Experiment 1, 16 young (18-26 years) and 16 older adults (62-82 years) were adapted to compressed female older adult faces. Before and after adaptation, they indicated which member of ±10% same-identity face pairs looked more normal; participants judged male and female young and older adult faces. Older adults demonstrated smaller aftereffects than young adults, p <.05, but the magnitude of aftereffects did not differ across age and sex categories for either age group (i.e., were not largest for female older adult faces), p > .10. In Experiment 2, we examined whether sensitivity to differences in ±10% face pairs varies as a function of participant age and/or face age. Young and older adults (n = 16 per group) were shown ±10%, ±20%, and ±30% same-identity face pairs and indicated which face in each pair appeared more expanded. Accuracy was > 75% in all conditions and did not differ with face age. Young adults were more accurate than older adults, but only for ±10% pairs, p <.01. Participants also rated the normality of young and older adult faces that ranged from +60% expanded to -60% compressed in 10% increments. Overall, older adults were less sensitive than young adults, p <.001, and both groups demonstrated slightly greater sensitivity to distortions in young faces, p <.05. Collectively, these results suggest that the dimensions underlying norm-based coding are most refined for young adult faces and that sensitivity to these dimensions declines in older adults. Meeting abstract presented at VSS 2012
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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 | 0.000 | 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) | 0.002 | 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".