Newborn left amygdala volume associates with attention disengagement from fearful faces at eight months
Bibliographic record
Abstract
After 5 months of age, infants begin to prioritize attention to fearful over other facial expressions. One key proposition is that amygdala and related early-maturing subcortical network, is important for emergence of this attentional bias - however, empirical data to support these assertions are lacking. In this prospective longitudinal study, we measured amygdala volumes from MR images in 65 healthy neonates at 2-5 weeks of gestation corrected age and attention disengagement from fearful vs. non-fearful facial expressions at 8 months with eye tracking. Overall, infants were less likely to disengage from fearful than happy/neutral faces, demonstrating an age-typical bias for fear. Left, but not right, amygdala volume (corrected for intracranial volume) was positively associated with the likelihood of disengaging attention from fearful faces to a salient lateral distractor (r = .302, p = .014). No association was observed with the disengagement from neutral or happy faces in equivalent conditions (r = .166 and .125, p = .186 and .320, respectively). These results are the first to link the amygdala volume with the emerging perceptual vigilance for fearful faces during infancy. They suggest a link from the prenatally defined variability in the amygdala size to early postnatal emotional and social traits.
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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".