What drives the attentional bias for fearful faces? An eye‐tracking investigation of 7‐month‐old infants’ visual scanning patterns
Bibliographic record
Abstract
Seven-month-old infants display a robust attentional bias for fearful faces; however, the mechanisms driving this bias remain unclear. The objective of the current study was to replicate the attentional bias for fearful faces and to investigate how infants' online scanning patterns relate to this preference. Infants' visual scanning patterns toward fearful and happy faces were captured using eye tracking in a paired-preference task, specifically exploring if the fear preference is driven by increased attention to particular facial features. Infants allocated increased attention toward the fearful face compared to the happy face overall, thus successfully replicating the attentional bias, and greater attention toward the fearful eyes was associated with a greater magnitude of the fear preference. The current findings suggest that the fearful eyes are a salient facial feature in capturing infants' attention toward the fearful face and that increased scanning of the fearful eyes may be one mechanism driving the overall fear preference. In addition, scanning patterns, and attention to critical features specifically, are highlighted as a strategy for examining the mechanisms underlying the development of emotion recognition abilities in infancy.
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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".