Reaching While Learning to Sit: Capturing the Kinematics of Co‐Developing Skills at Home
Bibliographic record
Abstract
This study examined the co-development of infant reaching and postural control across the transition to arms-free sitting at home. We observed infants with typical likelihood (TL; n = 24) and elevated likelihood (EL; n = 20) for autism at four biweekly sessions spanning the transition to arms-free sitting (infant age = 4.5-8 months at first session). At each session, infants sat on a pressure-sensitive mat with external support or independently, wore magneto-inertial sensors on both wrists, and reached for toys presented at midline. Analyses focused on characterizing and comparing control of sitting during reaching actions and standard kinematic metrics of reaching during Supported versus Independent Sitting. Although EL infants achieved arms-free sitting later than TL peers, there were no group differences on any measures. Across sessions, infants' control of the sitting posture during concurrent reaching movements improved in both contexts, though they were less stable as they reached when sitting independently compared to when sitting with support. A similar effect was apparent in the kinematics of reaches, with overall improvement over time, but evidence of poorer control in Independent relative to Supported Sitting. Taken together, these findings underscore the mutually influential and dynamic relations between emerging skills and well-established behaviors.
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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 | 0.001 | 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".