Interlimb Coordination in Rhythmic Leg Movements: Spontaneous and Training-Induced Manifestations in Human Infants
Bibliographic record
Abstract
Different rhythmic leg movements in vertebrates can share coordinating neural circuitry. These movements are often similar kinematically, and smooth transitions between the different movements are common. We focused on interlimb coordination of the legs in young infants to determine whether weight bearing and non-weight bearing movements might share coordinating circuitry. If interlimb coordination is controlled by the same circuitry, the same coordination (i.e., either synchronous or alternate) should be seen in different rhythmic movements. Moreover, if we altered the interlimb coordination in one movement through exercise, it should translate to a change in coordination in another rhythmic movement that received no exercise. Video and electrogoniometry were recorded while 46 infants (age, 6.2+/-1.4 mo) performed non-weight bearing and weight bearing movements. Interlimb coordination was quantified by the phase lag between the movement cycles of each leg. Most infants (83%) showed the same coordination in weight bearing and non-weight bearing movements. Ten infants practiced the form of coordination they did not exhibit in the first visit, in weight bearing for 4 wk. Following practice, 8 of 10 infants changed their interlimb coordination in weight bearing to that practiced. Some who practiced synchronous coordination also changed their coordination in non-weight bearing activity. More infants showed both forms of coordination after practice and smooth transitions between the two forms. The results suggest that interlimb coordination is malleable in infants, and there is a partial sharing of the neural substrates for interlimb coordination between different rhythmic leg movements in infants.
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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".