Differences in Own- and Other-race Face Scanning in Infants
Bibliographic record
Abstract
The other-race effect has been found to exist in both adults (Meissner & Brigham, 2001) and infants (Kelly et al., 2007). It is most often described in terms of discrimination abilities and manifests itself as an own-race recognition advantage. While recognition advantages for own-race faces have been found as early as 3 months (Sangrigoli & de Schonen, 2004), what remains unclear is whether different attentional patterns can be detected during the scanning of own- versus other-race faces in infancy. The present study investigated whether infants viewing own- and other-race faces displayed differential scanning and fixation patterns that may contribute to the previously reported own-race recognition advantage. Participants were Caucasian infants (n =22) aged 6 to 10 months (M = 8.5 months). Infants were presented with two videos on a Tobii Eye-Tracking screen while their fixations and scanning patterns were recorded. Each video contained the face of an adult female talking directly into the camera against a neutral background for a duration of 30 seconds. The identity of the face and the order of the presentations were counterbalanced and randomized across participants. Data was analyzed by comparing the proportion of infant' fixations to the different facial features across conditions (races). An analysis of variance (with results to date) revealed a significant interaction between race and feature, in that infants looked significantly longer at the eyes of the own-race faces as compared to the other-race faces, p <0.5. A significant 3- way interaction of race by feature by age was also found, in that older infants looked significantly more at other-race mouths compared to own-race mouths, p <0.001. The present results contribute to the understanding of the underlying perceptual processes that may influence recognition differences for the processing 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 | 0.000 | 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) | 0.002 | 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".