Recognizing Faces Despite Variability in Appearance: Learning Mechanisms are Largely Intact in Older Adults
Bibliographic record
Abstract
Recognition of unfamiliar faces is highly error-prone, especially across changes in appearance (e.g., hairstyle, expression, lighting). Despite a lifetime of experience perceiving faces, older adults demonstrate poorer performance than young adults on unfamiliar matching and face recognition tasks. However, past studies have used tightly controlled images and so examined image recognition, rather than face recognition per se. No study to date has examined older adults' ability to recognize unfamiliar faces despite natural variation in appearance or the process by which older adults become familiar with newly encountered identities. We tested older adults (n=57) on a battery of tasks. First, we verified that older adults were highly accurate at recognizing multiple images of a familiar face (95% on a familiar card sorting task). To investigate the efficiency with which older adults learn new faces, participants performed a recognition task after learning three new identities—one from a single image, one from a low variability video captured on a single day, and one from a high variability video filmed over three days. Unlike young adults (Baker et al., 2017), older adults only showed evidence of learning in the high-variability condition (p=.002). This is consistent with evidence that children need exposure to more variability than adults to new a face (Baker et al.). To investigate whether older adults' inefficient learning is attributable to deficits in underlying mechanisms, we examined their ability to use ensemble coding (to rapidly extract an average representation of an identity) and to benefit from viewing multiple images in a perceptual identity-matching task. The results from both of these tasks suggest that these mechanisms are intact; like young adults, older adults show evidence on ensemble coding (ps< .001) and benefitted from viewing multiple images of a new identity (p< .001). Taken together these results have implications for models of perceptual expertise. 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".