“That’s my teacher!”: Children’s recognition of familiar and unfamiliar faces in images containing natural variability
Bibliographic record
Abstract
Adults’ ability to recognize unfamiliar faces across images that capture within-person variability is poor, whereas their familiar face recognition is extremely good (Jenkins, White, Van Montford & Burton, 2011). Very little is known about children’s ability to recognize personally familiar faces and most of what we know about unfamiliar face recognition comes from studies measuring recognition of only one or two highly controlled images of an identity. Therefore the purpose of the present study was to examine the effect of within-person variability on identity perception across childhood. Children aged between 6 – 11 years were presented with a teacher's house (either their teacher [n = 27] or an unfamiliar teacher [n = 21]) and a pile of pictures. Half of the pictures were of the teacher and the other half were of a physically similar unfamiliar identity, and all the pictures captured natural within-person variability in appearance. Children were asked to put all of the pictures of the teacher, but not the other woman, into the house. Children familiar with the teacher were highly accurate (M d’ = 3.10) with no improvement with age (r(25) = -.001, p = .995). However, children unfamiliar with the teacher were less accurate (M d’ = 1.15), their performance (d’) improved with age (r(19) = .62, p = .002), with most errors comprising misses (failing to put a teachers’ photo into the house). In an ongoing study, data-to-date (n = 19) show a familiar face recognition advantage for younger children (4-5 years), although the younger children made more errors (M d’ = 2.51) than older children. These findings suggest that children’s familiar face recognition is adult-like at age 6, whereas unfamiliar face recognition continues to improve across childhood. Understanding within-person variability is essential for understanding the development of expertise in face recognition. Meeting abstract presented at VSS 2015
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".