Dynamic Advances in Emotion Processing: Differential Attention towards the Critical Features of Dynamic Emotional Expressions in 7-Month-Old Infants
Bibliographic record
Abstract
<p>Infants’ visual processing of emotion undergoes significant development across the first year of life, yet our knowledge regarding the mechanisms underlying these advances is limited. Additionally, infant emotion processing is commonly examined using static faces, which do not accurately depict real-world emotional displays. The goal of this study was to characterize 7-month-olds’ visual scanning strategies when passively viewing dynamic emotional expressions to examine whether infants modify their scanning patterns depending on the emotion. Eye-tracking measures revealed differential attention towards the critical features (eyes, mouth) of expressions. The eyes captured the greatest attention for angry and neutral faces, and the mouth captured the greatest attention for happy faces. A time-course analysis further elucidated at what point during the trial differential scanning patterns emerged. The current results suggest that 7-month-olds are sensitive to the critical features of emotional expressions and scan them differently depending on the emotion. The scanning patterns presented in this study may serve as a link to understanding how infants begin to differentiate between expressions in the context of emotion recognition.</p> <p><br></p>
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".