Hey Baby, what's “up”? One- and 3-Month-Olds Experience Faces Primarily Upright but Non-Upright Faces Offer the Best Views
Bibliographic record
Abstract
Experience has been theorized to shape how we process faces. Frequent face types are better discriminated and processed using expert-level holistic strategies while less frequent types are less well discriminated and processed using less mature featural strategies. Although experience is probably influencing the development of face processing, it is unclear what aspects of experience are most influential. The current study utilized infant-perspective head-mounted cameras to capture infants' daily lives at 1 and 3 months of age to measure the perceptual qualities of frequent and infrequent face types. We examined experience with upright (i.e., frequently experienced) and inverted (i.e., infrequently experienced) faces. A large majority (88%) of all face exposure was to upright faces. Most faces, regardless of orientation, were viewed near to the infant, alone in the field of view, and in a frontal viewpoint (i.e., an "ideal view"). Although they were less frequent than upright faces, proportionally more non-upright faces were viewed in an "ideal view". At this young age, nearly all faces, even non-upright faces, are seen in ways that facilitate processing.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".