Emotional expressions reinstate recognition of other-race faces in infants following perceptual narrowing.
Bibliographic record
Abstract
Perceptual narrowing occurs in human infants for other-race faces. A paired-comparison task measuring infant looking time was used to investigate the hypothesis that adding emotional expressiveness to other-race faces would help infants break through narrowing and reinstate other-race face recognition. Experiment 1 demonstrated narrowing for White infants viewing neutral Asian faces: whereas 3-month-olds differentiated Asian faces, 6-month-olds did not. Experiment 2 showed that White 6-month-olds differentiated the same Asian faces depicted with angry or happy expressions. Experiments 3 and 4 yielded comparable results for 6- and 9-month-olds tested with Black faces (i.e., narrowing with neutral faces, reinstatement of sensitivity when the faces were presented with emotion). Experiment 5 showed that White 6-month-olds did not differentiate inverted angry or happy Asian faces, and that White 9-month-olds did not differentiate inverted angry or happy Black faces. Looking time during familiarization did not differ for upright neutral and emotional faces, indicating that the expressions did not yield more salient stimuli. Also, consistent with the inversion findings, analyses of the low-level image properties as well as equivalent pairwise similarity ratings obtained from White adults for the neutral and emotional faces indicated that the expressions did not simply create more discriminable stimuli. Without support for the lower-level accounts, we discuss the possibility that the infants processed the communicative intent of the expressions. Because angry faces pose threat and happy faces invite affiliation, expression may create motivation to individuate. Overall, the data suggest that early perceptual-social linkage in face representation can arise via a social-to-perceptual pathway. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.004 | 0.002 |
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; both teacher heads agree on what is shown here.
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".