Kinematic profiles suggest differential control processes involved in bilateral in-phase and anti-phase movements
Bibliographic record
Abstract
In-phase and anti-phase movements represent two basic coordination modes with different characteristics: during in-phase movements, bilateral homologous muscle groups contract synchronously, whereas during anti-phase movements, they contract in an alternating fashion. Previous studies suggested that in-phase movements represent a more stable and preferential bilateral movement template in humans. The current experiment aims at confirming and extending this notion by introducing new empirical measures of spatiotemporal dynamics during performance of a bilateral circle drawing task in an augmented-reality environment. First, we found that anti-phase compared to in-phase movements were performed with higher radial variability, a result that was mainly driven by the non-dominant hand. Second, the coupling of both limbs was higher during in-phase movements, corroborated by a lower inter-limb phase difference and higher inter-limb synchronization. Importantly, the movement acceleration profile between bilateral hands followed an in-phase relationship during in-phase movements, while no specific relationship was found in anti-phase condition. These spatiotemporal relationships between hands support the hypothesis that differential neural processes govern both bilateral coordination modes and suggest that both limbs are controlled more independently during anti-phase movements, while bilateral in-phase movements are elicited by a common neural generator.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.003 | 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".