Infants’ ability to recognize and respond to negative emotional expressions
Bibliographic record
Abstract
Perceiving others’ emotional facial and vocal expressions is nearly effortless for adults, and most discrete emotions are universally recognizable. Although the ability to accurately detect and distinguish emotions is present in adulthood, it is still unclear how this ability develops early in life. Both behavioral and neurocognitive studies suggest that in the first year of life, infants can differentiate discrete emotions; however, this is only evidenced by differential processing of emotions that belong to contrasting valence categories (positive vs. negative); it remains unclear whether infants demonstrate differential processing of emotions that belong to the same valence category (i.e., negative emotions). I considered the limitations of classic paradigms used to investigate emotion processing in infancy and the gaps left in the literature as a consequence, and conducted two experiments that explore new avenues to measure infants’ ability to differentiate negative emotions. These studies investigated how infants integrate sensory information and differentially respond to facial expressions to understand how infants distinguish between negative emotions.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".