Learning from experience: exposure to, attention to, discrimination of, and brain response to faces at 3, 6, and 9 months
Bibliographic record
Abstract
Infants learn and develop immensely in the first year of life. They show substantial learning in their ability to use the information provided by faces. Faces are important stimuli in infants‘ world and infants reliably prefer faces over other visual stimuli (Fantz, 1963). While experience likely plays a role in infants‘ early face processing, little is known about how infants‘ natural exposure to faces shapes attention and learning. We use head-mounted infant-perspective cameras to capture infants‘ natural experience with faces. We also measured infants‘ attentional preference for, ability to discriminate between, and electrical brain response to familiar (i.e., female, own-race) and unfamiliar (i.e., male, other-race) face types. Infants‘ face experience was highly homogenous: their primary caregiver‘s face represents the 57% of infants‘ experience and was present in all locations and nearly all contexts. Infants‘ other caregiver represented only 11% of their face experience, but was also highly consistent across location and context. Infants showed greater visual attention to female faces of familiar race at 3 months, but not later. They showed no race preference at any age. At 3 months, infants discriminated all face types except for male own-race faces. At 6 months, infants discriminated all face types. At 9 months infants discriminated all face types except for male other-race faces. Electrical brain response only differentiated male from female faces at 6 months, not at 3 or 9 months; there was no effect of race at any age. This may be due to the immaturity of the early face processing system or differential processing being indexed at later attentional components. Infants‘ overall face exposure, mom face exposure, and attentional preference for female faces predicted female own-race face discrimination at 3 months, accounting for 62% of the variance. Exposure to male faces correlated with attention to male faces and attention to male faces predicted discrimination of male faces at 3 months, accounting for 11% of the variance. At 6 months dad face exposure predicted discrimination of male faces, accounting for 17% of the variance. Infants‘ early experience, particularly to caregivers‘ faces, tunes infants‘ attention to faces, which in turn predicts discrimination.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".