Variations in infants’ physical and social environments shape spontaneous locomotion.
Bibliographic record
Abstract
Independent locomotion is associated with a range of positive developmental outcomes, but unlike cognitive, linguistic, and social skills, acquiring motor skills requires infants to generate their own input for learning. We tested factors that shape infants' spontaneous locomotion by observing forty 12- to 22-month-olds (19 girls, 21 boys) during free play. Infants were recruited from the New York City area, and caregivers reported that 25 infants were White, six were Asian, four were Black, and five had multiple races; four were Hispanic or Latino. All infants played in four conditions: two environmental conditions (gross-motor toys, fine-motor toys) crossed with two social conditions (alone, together with a caregiver). Infants moved more in the gross-motor toy conditions than in the fine-motor toy conditions. However, the effect of playing with a caregiver differed by toy condition. In the gross-motor toy conditions, playing with a caregiver did not affect how much infants moved, but in the fine-motor toy conditions, playing with a caregiver further depressed infant locomotion. Infants with more walking experience moved more with gross-motor toys but not with fine-motor toys. Differences in the amount of locomotion between conditions were related to how infants used toys and the interactions between infants and caregivers. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.003 |
| 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".