The Effect of Starting School on Preschoolers' Ability to Recognize Child and Adult Faces
Bibliographic record
Abstract
Although rudimentary skills emerge during infancy (e.g., Morton & Johnson, 1991; Pascalis & de Schonen, 1995; Pascalis, de Haan, Nelson, & de Schonen, 1998), the recognition of facial identity improves into adolescence (Mondloch, Le Grand, & Maurer, 2002). Here we examined the influence of entering school on face recognition. We hypothesize that the increase in exposure to the faces of unfamiliar children that is associated with entering school may lead to improved face recognition for children’s faces. To test this hypothesis, we measured the face recognition abilities of preschoolers who began attending school for the first time in September 2010 (school group; n=18) and of an age-matched control group (n=18) not yet in school. Both groups completed a 2AFC task with adult and child faces, presented both in an upright and inverted orientation, at Time 1 (within the first month of entering school for the school group) and at Time 2 (5 months later). A repeated measures ANCOVA revealed a significant main effect of the covariate, age at Time 1, (p<.0001), reflecting better overall performance by older participants than younger participants. In addition, there was a significant interaction between time, age of face and group (p=.047) that resulted from a significant improvement between Time 1 and Time 2 for child faces in the school group (p<.0001), but not the control group (p=.118) and no significant improvement for adult faces in either group (ps<.4). The results suggest that sudden exposure to a large number of child faces during childhood improves recognition of novel exemplars of this face category. This exposure may shape a child’s face-space (see Valentine, 1991) by producing a more stable and accurate norm, as well as refining its dimensions so that they code better for the physical differences that covary reliably with identity. 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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".