Own- and other-race face identity recognition in children: The effects of pose and feature composition.
Bibliographic record
Abstract
We used a matching-to-sample task and manipulated facial pose and feature composition to examine the other-race effect (ORE) in face identity recognition between 5 and 10 years of age. Overall, the present findings provide a genuine measure of own- and other-race face identity recognition in children that is independent of photographic and image processing. The current study also confirms the presence of an ORE in children as young as 5 years of age using a recognition paradigm that is sensitive to their developing cognitive abilities. In addition, the present findings show that with age, increasing experience with familiar classes of own-race faces and further lack of experience with unfamiliar classes of other-race faces serves to maintain the ORE between 5 and 10 years of age rather than exacerbate the effect. All age groups also showed a differential effect of stimulus facial pose in their recognition of the internal regions of own- and other-race faces. Own-race inner faces were remembered best when three-quarter poses were used during familiarization and frontal poses were used during the recognition test. In contrast, other-race inner faces were remembered best when frontal poses were used during familiarization and three-quarter poses were used during the recognition test. Thus, children encode and/or retrieve own- and other-race faces from memory in qualitatively different ways.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".