Own‐ and other‐race face scanning in infants: Implications for perceptual narrowing
Bibliographic record
Abstract
The present study investigated how 6- and 9-month-old Caucasian infants scan Caucasian and Chinese dynamic faces using eye-tracking methodology. Analyses of looking times revealed that with increased age, infants decreased their looking time to other-race noses, while maintaining their looking time for own-race noses. From 6 to 9 months, infants increased their looking time for the eyes of both races of faces. Analyses of scan paths showed that infants were no more likely to shift their fixation between the eyes of own-race faces than other-race faces. Similarity between participants' scan paths suggested that facial information was collected more efficiently for own- versus other-race faces at 9 months of age. Combined with previous eye-tracking studies of infants' face scanning (Liu et al. [2011] Journal of Experimental Child Psychology, 108, 180-189; Wheeler et al. [2011] PLoS ONE, 6, e18621. doi: 10.1371/journal.pone.0018621; Xiao et al. [2013] International Journal of Behavioral Development, 37, 100-105), the findings are interpreted in the context of perceptual narrowing and suggest differential contributions of visual experience, facial physiognomy, and culture in accounting for similarity and difference in infants scanning of own- and other-race faces.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".