Little ears, literal emotions: the developmental pattern of emotional speech processing in elementary school-age children and the mediating role of expressive lexicon
Bibliographic record
Abstract
Processing spoken emotions, a critical skill for social interactions, develops from birth to adulthood. It relies on processing information in two auditory channels: semantics and prosody, and their integration. The current study examined the developmental pattern of emotional speech processing, comparing 8- and 12-year-old elementary school children (ES-Juniors and ES-Seniors, respectively). This age-range reflects developmental stages in emotional processing, social understanding, lexical development, and executive functions. Three basic abilities were tested: (1) Identifying semantic/prosodic emotions, (2) Selectively attending to a single channel, and (3) Integrating the two channels. Sixty participants rated how much they agreed that a spoken sentence expressed a specific emotion (happiness, sadness, or anger), in one or both channels. The ES-Senior group outperformed the ES-Junior group in semantic identification and selective attention. No significant differences were found for prosody. ES-Seniors showed better channel integration: While ES-Juniors performed with semantic dominance, ES-Seniors showed no significant dominance, approaching adult-like performance. Finally, expressive lexicon moderated group differences in semantic identification and prosody-semantics integration. The ES-Seniors' advantage over ES-Junior in these measures disappears for individuals with higher language scores. Findings may inform interventions for ES children experiencing emotional processing challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".