Do infants discriminate non-linguistic vocal expressions of positive emotions?
Bibliographic record
Abstract
Adults are highly proficient in understanding emotional signals from both facial and vocal cues, including when communicating across cultural boundaries. However, the developmental origin of this ability is poorly understood, and in particular, little is known about the ontogeny of differentiation of signals with the same valence. The studies reported here employed a habituation paradigm to test whether preverbal infants discriminate between non-linguistic vocal expressions of relief and triumph. Infants as young as 6 months who had habituated to relief or triumph showed significant discrimination of relief and triumph tokens at test (i.e. greater recovery to the unhabituated stimulus type), when exposed to tokens from a single individual (Study 1). Infants habituated to expressions from multiple individuals showed less consistent discrimination in that consistent discrimination was only found when infants were habituated to relief tokens (Study 2). Further, infants tested with tokens from individuals from different cultures showed dishabituation only when habituated to relief tokens and only at 10-12 months (Study 3). These findings suggest that discrimination between positive emotional expressions develops early and is modulated by learning. Further, infants' categorical representations of emotional expressions, like those of speech sounds, are influenced by speaker-specific information.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".