Maternal stress and development of infant attention to threat‐related facial expressions
Bibliographic record
Abstract
Attentional biases to threat-related stimuli, such as fearful and angry facial expressions, are important to survival and emerge early in development. Infants demonstrate an attentional bias to fearful facial expressions by 5-7 months of age and an attentional bias toward anger by 3 years of age that are modulated by experiential factors. In a longitudinal study of 87 mother-infant dyads from families predominantly experiencing low income, we examined whether maternal stress and depressive symptoms were associated with trajectories of attentional biases to threat, assessed during an attention disengagement eye-tracking task when infants were 6-, 9-, and 12-month old. By 9 months, infants demonstrated a generalized bias toward threat (both fearful and angry facial expressions). Maternal perceived stress was associated with the trajectory of the bias toward angry facial expressions between 6 and 12 months. Specifically, infants of mothers with higher perceived stress exhibited a greater bias toward angry facial expressions at 6 months that decreased across the next 6 months, compared to infants of mothers with lower perceived stress who displayed an increased bias to angry facial expressions over this age range. Maternal depressive symptoms and stressful life events were not associated with trajectories of infant attentional bias to anger or fear. These findings highlight the role of maternal perceptions of stress in shaping developmental trajectories of threat-alerting systems.
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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) | 0.001 | 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 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".