Learning and recognizing facial identity in variable images: New insights from older adults
Bibliographic record
Abstract
Recent research has emphasized the importance of using images that incorporate natural variability in appearance (i.e., ambient images) to assess face learning and recognition. Across five tasks, we provide the first examination of older adults’ face learning and recognition in ambient images. Young and older adults showed comparable performance in three tasks: when recognizing a familiar face across ambient images, extracting average representations of an identity (i.e., ensemble coding) and learning a new identity from multiple images in a perceptual task. However, compared to young adults, older adults have even more difficulty matching images of unfamiliar faces and despite showing comparable benefits in sensitivity, older adults adopted a more conservative response bias after being exposed to low variability in appearance in a face memory task, resulting in them failing to recognize novel instances of a newly learned identity. We discuss the implications of our findings for older adults and the insights our findings provide for understanding both the development of face learning and recognition in childhood and the own-race recognition advantage.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".