Preschoolers’ real-time coordination of vocal and facial emotional information
Bibliographic record
Abstract
An eye-tracking methodology was used to examine the time course of 3- and 5-year-olds' ability to link speech bearing different acoustic cues to emotion (i.e., happy-sounding, neutral, and sad-sounding intonation) to photographs of faces reflecting different emotional expressions. Analyses of saccadic eye movement patterns indicated that, for both 3- and 5-year-olds, sad-sounding speech triggered gaze shifts to a matching (sad-looking) face from the earliest moments of speech processing. However, it was not until approximately 800ms into a happy-sounding utterance that preschoolers began to use the emotional cues from speech to identify a matching (happy-looking) face. Complementary analyses based on conscious/controlled behaviors (children's explicit points toward the faces) indicated that 5-year-olds, but not 3-year-olds, could successfully match happy-sounding and sad-sounding vocal affect to a corresponding emotional face. Together, the findings clarify developmental patterns in preschoolers' implicit versus explicit ability to coordinate emotional cues across modalities and highlight preschoolers' greater sensitivity to sad-sounding speech as the auditory signal unfolds in time.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".