Infants Recognized Other‐Race Faces When Learning Them With Incidental Emotional Sounds
Bibliographic record
Abstract
Infant face recognition shows plasticity, with recent evidence indicating enhancement by the presence of emotional facial expressions. The mechanisms and domain-generality of this effect remain largely unknown. This study tested whether auditory emotional cues (vocalizations) facilitated infants' recognition of other-race faces, a perceptual challenge during the first year of life. Infants (N = 89) were presented with emotionally neutral faces paired with happy, sad, or neutral vocal sounds in a within-subjects design. Experiment 1 assessed recognition using identical face images between the familiarization and test phases, while Experiment 2 examined face recognition across viewpoint changes. Across both experiments, infants exhibited successful face recognition only when they were learned with emotional sounds (happy and sad). This facilitative effect remained stable across the tested age range and did not differ between happy and sad vocalizations. Infants' eye movement data revealed comparable face-looking patterns across conditions, suggesting that the facilitation was not driven by changes in visual attention. Thus, incidental, cross-modal emotional signals significantly enhance infant face recognition. This underscores the early integrative nature of emotion processing and its catalytic role in cognitive development.
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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 | 0.001 |
| 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".