Proprioceptive contributions to online limb-target regulation processes?
Bibliographic record
Abstract
Background: Online control processes are used during goal-directed movements to ensure that the limb reaches the target. Some of these online corrections require contrasting the position of the limb vs. the target (i.e., limb-target regulation) and have been presumed to require visual and proprioceptive inputs (Elliott et al., 2010). In the current study, we sought to investigate the importance of proprioceptive information for the implementation of online limb-target regulation processes. Methods: Thirteen participants were asked to perform rapid goal-directed reaches with tendon vibrators on the distal biceps and triceps brachii tendons and while wearing liquid-crystal goggles. Trials began with participants fixating to the start position. Then, the target appeared (30 cm amplitude) which prompted participants to initiate a saccade and a reaching movement. After that, the goggles were occluded. Then, on one third of the trials, the location of the target was shifted 3 cm closer to the participant. Before achieving peak limb velocity, participants were provided with a brief visual window (20ms), to see the original or jumped target (and their hand). On separate blocks of trials, tendon vibration was applied between trials to both the biceps and triceps, to decrease the sensitivity of the muscle spindles (Ribot-Ciscar et al., 1998). Results: Tendon vibration led to shorter movement times, which were explained by shorter limb deceleration phase durations. In contrast, seeing the jumped target location for 20 ms always led to a shift in the endpoint distributions (4.7 mm), as compared to when the original target was seen during the brief window regardless of the presence of vibration. Conclusion: While the proprioceptive perturbation did influence the motor performance, the limb-target regulation processes associated with the target jump did not significantly differ across tendon vibration conditions. Altogether, online limb-target regulation processes are predominantly visuomotor in nature. Meeting abstract presented at VSS 2016
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".