Do Young Human Infants Show Empathy for Others in Distress?
Bibliographic record
Abstract
Results from a number of studies of human empathy are interpreted as demonstrating that young infants exhibit concern towards others who are suffering. Studies of empathy in young infants, however, often confound interest in intensity and ecologically valid stimuli with concern about others' suffering. Using a perceptually controlled design with ecologically valid stimuli, we investigated whether very young human infants preferentially look at a peer in distress. We showed 78 3-6-month-old infants videos of four babies who were crying and cooing along with synthetically generated control videos of the same babies that preserved their perceptual features. Results showed that infants overwhelmingly looked longer at babies who were crying versus cooing, with the same relative difference observed for crying versus cooing controls, although infants found real babies more interesting than controls. Results suggest that infants' attention to differences in emotional valence related to empathy cannot be clearly interpreted without controlling for associated perceptual differences. SUMMARY: Three- to six-month-old infants look more at crying than cooing babies, even when they are perceptually scrambled controls. Real babies are looked at more than their perceptually scrambled controls. No clear evidence exists for young infants' empathy as measured through looking times.
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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".