Force and time control in bimanual finger force production
Bibliographic record
Abstract
Central predictive mechanisms cause self-produced forces to be perceived as weaker leading to a compensatory, over production of the force magnitudes required when there is no reference. Earlier work has focused on the serial production of unimanual forces, but the influence of visual feedback in the bimanual production of forces remains less clear. Evidence has been found for synergistic activation of the desired musculature in both rhythmic coordination and multi-effector force production tasks. These synergies served to minimize variability and stabilize performance variables of interest. In this study, we examined the effect of timing constraints on repetitive unimanual and bimanual force production sequences. Participants produced series of pinch grip forces in time to a metronome and to visually specified force magnitudes. Periodically, the metronome, visual feedback of force output or both were removed 10 s in to the trail, with participants performing continued responses for the remaining 20 s. In continuation trials, a negative lag-1 autocorrelation in the inter-response intervals (IRIs) was observed as is commonly seen in motor timing research. Removal of visual feedback however, resulted in an increase in the force magnitudes produced as well as an increase in variability in the bimanual condition. We suggest that attenuation of sensory signals occurs for the two limbs equally and the resulting perceptual errors are compensated independently resulting in an increase in force magnitude from the collective effort of both effectors.Acknowledgments: NSERC, CRC, all members of SNL Lab at McMaster University
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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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".