Angry, Sad, or Scared? Within-valence Mapping of Emotion Words to Facial and Body Cues in 2 to 4-Year-Old Children
Bibliographic record
Abstract
The acquisition of emotion words is critical to children’s socio-emotional development. Previous studies report that children acquire emotion words gradually during ages 3–5 and beyond. The majority of this work, however, has used demanding tasks for young children (e.g., asking children to label emotion-related facial configurations) and has predominantly relied on facial configurations. Here we designed a child-friendly, word-comprehension task incorporating both facial configurations and body language. In two preregistered online experiments, we asked two to four-year-olds (N = 96) to connect emotion words—happy, sad, angry, and scared—to either facial configurations (Experiment 1) or combined facial and body cues (Experiment 2). We found relatively early competence in understanding emotion words, especially those of the same-valence. All age groups, including 2-year-olds, successfully linked emotion words to corresponding facial configurations (Experiment 1). Experiment 2 replicated this pattern and further showed that children performed equally well (though not substantially better) when given additional body cues. Parental reports of children’s exposure to and use of masks during the COVID-19 pandemic did not correlate with children’s performance in either experiment. Even before children can produce emotion words in an adult-like manner, they possess at least a partial understanding of those words and can map them to emotion cues within valence domains.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".