Integration and de-integration of bimanual movements
Bibliographic record
Abstract
Research has suggested that bimanual movements are not the sum of two unimanual movements, but they reflect the integration of two unimanual movements into a bimanual movement. Bimanual asymmetric movements in choice RT tasks take longer to prepare than symmetric movements, and this cost is likely caused by unifying both arms into a single bimanual movement. The purpose of this study was to investigate the process of unimanual movements into a bimanual movement and de-integrating bimanual movements into unimanual movements during movement preparation. We predicted that integrating two different unimanual movements into a single bimanual asymmetric movement would take more time than when two similar movements are combined into a bimanual symmetric movement. Conversely, the de-integration of a bimanual asymmetric movement would take more time than a bimanual symmetric movement. In both studies, each block consisted of two unimanual movements and one bimanual movement. In the integration study, 80% of trials were unimanual and 20% were bimanual. The bimanual movements were symmetric or asymmetric and they were the combination of the two unimanual movements or the opposite. In the de-integration study, 80% of trials were bimanual and 20% were unimanual. The unimanual movements were one arm of the bimanual movement or one of the opposite. In both studies there were preparation costs to integrate or de-integrate bimanual asymmetric movements and the opposite movements. These results support that bimanual movements are the integration of two unimanual movements and that asymmetric movements take longer to integrate (and de-integrate) than symmetric ones.Acknowledgments: Supported by the Natural Sciences and Engineering Research Council of Canada
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".