Limb-target regulation processes: Further evidence for a sweet spot.
Bibliographic record
Abstract
Recently, we proposed that limb-target regulation processes are primarily based on visual feedback that is available when the limb travels between 1.0 and 1.1 m/s (Tremblay et al. 2013a). We have also observed that perceptual judgments of endpoint accuracy are better when a brief visual window (20 ms) is provided when the limb travels at 1.0 m/s, compared to faster and slower limb velocity criteria (Tremblay et al. 2013b). In this study, we implemented a target jump procedure. We reasoned that if limb-target regulation processes are more likely to take place at limb velocities neighbouring 1.0 m/s, then participants should more effectively amend their trajectory to a target jump presented at 1.0 m/s, compared to other limb velocities. Thirteen participants were asked to maintain their gaze on their finger until a go signal (target and brief tone, presented for 20 ms), which prompted them to perform a reaching movement as accurately as possible while maintaining a 325-375 ms movement time bandwidth. Participants performed 20 control trials with vision throughout the movement (full vision). In addition, they performed reaching movements with 20 ms of vision combining 3 limb velocity conditions (0.6, 1.0, 1.4 m/s: before peak velocity) with 2 target conditions (no-jump: 30 cm, jump: 27 cm). Participants performed 30 trials under each velocity condition while the target jump occurred on 10 of these 30 trials. By contrasting the no-jump and jump trials separately for each velocity condition, we observed that participants exhibited longer movement times, longer times spent in the deceleration phase, and shorter reaching amplitudes with the 1.0 m/s condition only. Therefore, we provide further evidence for the predominance of limb-target regulation processes when the limb velocity reaches 1.0 m/s (or the corresponding time or position of the trajectory) compared to other limb velocities. Meeting abstract presented at VSS 2014
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".