Investigating emotion recognition in the first year of life using eye-tracking Methodology
Bibliographic record
Abstract
The ability to recognize facial expressions of emotion is a critical part of human social interaction. Infants improve in this ability across the first year of life, but the mechanisms driving these changes and the origins of individual differences in this ability are largely unknown. This thesis used eye tracking to characterize infant scanning patterns of expressions. In study 1 (n = 40), I replicated the preference for fearful faces, and found that infants either allocated more attention to the eyes or the mouth across both happy and fearful expressions. In study 2 (n = 40), I found that infants differentially scanned the critical facial features of dynamic expressions. In study 3 (n = 38), I found that maternal depressive symptoms and positive and negative affect were related to individual differences in infants’ scanning of emotional expressions. Implications for our understanding of the development of emotion recognition are discussed. Key Words: emotion recognition, infancy eye tracking, socioemotional development
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.002 |
| 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 teacher head, 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".