Young Children of Mothers with a History of Depression Show Attention Bias to Sad Faces: An Eye-tracking Study
Bibliographic record
Abstract
Maternal depression is a predictor of the emergence of depression in the offspring. Attention bias (AB) to negative emotional stimuli in children may serve as a risk factor for children of depressed parents. The present study aimed to examine the effect of maternal major depressive disorder (MDD) history on AB to emotional faces in children at age four, before the age of onset for full-blown psychiatric symptoms. The study also compared AB patterns between mothers and their offspring. Fifty-eight mothers and their four-year-old children participated in this study, of which 27 high-risk (HR) children had mothers with MDD during their children's lifetime. Attention to emotional faces was measured in both children and their mothers using an eye-tracking visual search task. HR children exhibited faster detection and longer dwell time toward the sad than happy target faces. The low-risk (LR) children also displayed a sad bias but to a lesser degree. Children across both groups showed AB towards angry target faces, likely reflecting a normative AB pattern. Our findings indicate that AB to sad faces may serve as an early marker of depression risk. However, we provided limited support for the mother-child association of AB. Future research is needed to examine the longitudinal intergenerational transmission of AB related to depression and possible mechanisms underlying the emergence of AB in offspring of depressed parents.
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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".