Naturalistic Face Learning in Infants and Adults
Bibliographic record
Abstract
Everyday face recognition presents a difficult challenge because faces vary naturally in appearance as a result of changes in lighting, expression, viewing angle, and hairstyle. We know little about how humans develop the ability to learn faces despite natural facial variability. In the current study, we provide the first examination of attentional mechanisms underlying adults’ and infants’ learning of naturally varying faces. Adults ( n = 48) and 6- to 12-month-old infants ( n = 48) viewed videos of models reading a storybook; the facial appearance of these models was either high or low in variability. Participants then viewed the learned face paired with a novel face. Infants showed adultlike prioritization of face over nonface regions; both age groups fixated the face region more in the high- than low-variability condition. Overall, however, infants showed less ability to resist contextual distractions during learning, which potentially contributed to their lack of discrimination between the learned and novel faces. Mechanisms underlying face learning across natural variability are discussed.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".