The role of valence in children’s and adults’ cross-modal integration of emotional prosody and emotional faces
Bibliographic record
Abstract
Previous research indicates that preschoolers match emotional prosody with a corresponding emotional face when choosing between a positively versus negatively-valenced face. However, it is unclear whether these decisions are guided by discrete emotion categories or by coarse-grained valence distinctions. Here, we examined adults' and children's cross-modal matching of emotional prosody with one of two faces whose depicted emotions either contrasted in valence (e.g., happy vs. sad) or fell within the same valence category (e.g., sad vs. angry). First, to provide a basis for comparison for children's performance, adults were presented with auditory stimuli and asked to choose a matching emotional face when the non-matching alternative either contrasted in valence (Exp. 1) or had the same valence (Exp. 2). Adults correctly matched emotional prosody with corresponding faces in both cases. In contrast, although 5-year-olds correctly matched emotional prosody when the alternative faces contrasted in valence (Exp. 3), they succeeded with within-valence distinctions only when differentiating negatively-valenced discrete emotions and not positively-valenced ones (Exp. 4). By 8 years of age, children accurately differentiated same-valence discrete emotions regardless of whether they were negatively- or positively-valenced (Exp. 5). Implications for the developmental trajectory of children's recognition of discrete emotional prosody categories are discussed.
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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 | 0.004 |
| 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 | 0.001 | 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".