Preschoolers’ use of emotional prosody in an unfamiliar language
Bibliographic record
Abstract
• Recognition of another’s emotions is critical to successful communication. • 4-year-olds did not match unfamiliar language emotional utterances with objects. • 4-year-olds did match the same emotional utterances with emotional faces. • Adults matched emotional utterances with objects and faces. • There are developmental differences in the use of emotional prosody. Rapid and accurate recognition of another’s emotions is critical to successful communication. Previous research indicates that preschoolers can use emotional prosody in familiar languages to identify an object being referred to by a speaker (i.e., associate a happy tone with an intact object and a sad tone with a broken object). Here, we examined whether listeners’ use of emotional prosody is tied to language comprehension by testing English-speaking preschoolers’ and adults’ use of emotional prosody when presented with utterances spoken in Arabic. In Experiment 1, 4-year-olds listened to a series of happy- or sad-sounding Arabic utterances while simultaneously viewing a visual array of objects that varied in their association with happiness or sadness (e.g., broken vs. intact toy). In contrast to previous studies in which listeners heard English utterances, results suggested that children were unsuccessful at implicitly (demonstrated via eye gaze) or explicitly (demonstrated via pointing) linking emotional intonation with corresponding objects. A second experiment demonstrated that 4-year-olds could, however, successfully match the same emotional utterances with happy or sad faces. Experiment 3 examined these abilities in English-speaking adults using the same materials. Adults correctly linked emotional prosody to both objects and faces. Taken together, the findings demonstrate that both 4-year-olds and adults can correctly categorize emotional intonation in an unfamiliar language. Only adults, however, were successful at using emotional prosody to help identify an object a speaker is referring to. These findings demonstrate developmental differences in the use of emotional prosody.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".