Development of face scanning for own- and other-race faces in infancy
Bibliographic record
Abstract
The present study investigated whether infants visually scan own- and other-race faces differently as well as how these differences in face scanning develop with age. A multi-method approach was used to analyze the eye-tracking data of 6- and 9-month-old Caucasian infants scanning dynamically displayed own- and other-race faces. We found that 6-month-olds showed differential fixation, fixating significantly more on the left eye and mouth of own-race faces, but more on the nose of other-race faces. Infants at 9 months of age fixated more on the eyes of own-race faces, but more on the mouth of other-race faces. A scan path analysis revealed that infants shifted their attention between the eyes of the own-race faces significantly more frequently than for other-race faces. Overall, younger and older infants responded differentially to own- versus other-race faces not only in the absolute amount of time spent fixating specific features, but also on their fixation shifts between features.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".