Vision at high limb velocities: The importance of visual feedback for online control at high limb velocities early in a movement
Bibliographic record
Abstract
Our previous work has shown that the use of vision during rapid upper limb reaches is optimal at high limb velocities. When providing vision only above .8 m/s, reaching endpoints are as precise as in normal vision and vision only provided below .8 m/s does not yield better endpoint control than without vision. In the current study, vision could be provided during 3 limb velocity windows above .8 m/s (Early [between .8 m/s & 1.4 m/s], Middle [between 1.4 m/s & 1.4 m/s] & Late [between 1.4 m/s & 0.8 m/s]). All possible combinations were used in a factorial design, yielding 7 vision conditions presented in a randomized order. Each vision condition was presented 20 times and a no vision condition was presented 140 times. Full vision pre- and post-tests were also performed. Our main dependent variables were tied to the variability (i.e., precision) and bias (i.e., accuracy) of movement endpoint distributions. In the primary movement axis, movement endpoint control was more precise when vision was provided in both the early and middle vision conditions than in the no vision condition. Providing vision in the late vision condition resulted in worse endpoint precision than the full vision pre- and post-tests. Our results indicate that visual information may be used most efficiently for endpoint precision when the limb is moving quickly early in a movement and in the portion of the trajectory that includes peak limb velocity. In contrast, vision above .8 m/s but below 1.4 m/s late in a movement does not appear to contribute to endpoint precision control. Thus, the use of visual information may be tied to the kinematics of a movement and be most effective early in a movement.
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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 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".