I spy with my little eye: Typical, daily exposure to faces documented from a first‐person infant perspective
Bibliographic record
Abstract
Exposure to faces is known to shape and change the face processing system; however, no study has yet documented infants' natural daily first-hand exposure to faces. One- and three-month-old infants' visual experience was recorded through head-mounted cameras. The video recordings were coded for faces to determine: (1) How often are infants exposed to faces? (2) To what type of faces are they exposed? and (3) Do frequently encountered face types reflect infants' typical pattern of perceptual narrowing? As hypothesized, infants spent a large proportion of their time (25%) exposed to faces; these faces were primarily female (70%), own-race (96%), and adult-age (81%). Infants were exposed to more individual exemplars of female, own-race, and adult-age faces than to male, other-race, and child- or older-adult-age faces. Each exposure to own-race faces was longer than to other-race faces. There were no differences in exposure duration related to the gender or age of the face. Previous research has found that the face types frequently experienced by our participants are preferred over and more successfully recognized than other face types. The patterns of face exposure revealed in the current study coincide with the known trajectory of perceptual narrowing seen later 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".