Broadly tuned face representation in older adults assessed by categorical perception.
Bibliographic record
Abstract
Studies of face recognition in older adults (60 years of age and older) report increases in false alarms over younger adults (usually 18-30 years of age), but no age differences in hits. To examine this phenomenon, we compared older and younger adults in categorical perception of faces. We hypothesized that face representations in older adults would be broadly tuned, resulting in overlapping representations, manifested by a shallower slope in identity categorization than in younger adults, and age-related reductions in the advantage for between-categories, as compared with within-category, face discrimination. We morphed faces to change linearly from one identity to another. We used familiar or unfamiliar faces in separate conditions to examine the role of familiarity. Categorical perception was assessed in an identity-classification task and a discrimination task. Older adults showed a shallower slope and poorer discrimination compared with younger adults, and both groups exhibited better performance with familiar than unfamiliar faces. Enhanced discriminability for between-categories as compared with within-category faces was seen for both familiar and unfamiliar faces in younger adults, but only for familiar faces in older adults. The more broadly tuned representations of unfamiliar faces in older adults may lead to misidentification and greater false alarms for unfamiliar faces, but not for familiar faces.
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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 | 0.000 | 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 | 0.000 |
| 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".