Determinants of Offline Processing of Visual Information for the Control of Reaching Movements
Bibliographic record
Abstract
The authors investigated the use of visual feedback as a form of knowledge of results (KR) for the control of rapid (200-250 ms) reaching movements in 40 participants. They compared endpoint accuracy and intraindividual variability of a full-vision group (FV) with those of no-vision groups provided with KR regarding (a) the endpoint in numerical form, (b) the endpoint in visual form, or (c) the endpoint and the trajectory in visual form (DEL). The FV group was more accurate and less variable than were the no-vision groups, and the analysis of limb trajectory variability indicated that their superior performance resulted primarily from better movement planning rather than from online visual processes. The FV group outperformed the DEL group even though both groups were obtaining the same amount of spatial visual information from every movement. That finding suggests that the effectiveness with which visual feedback is processed offline is not a simple function of the amount of visual information available, but depends on how that information is presented.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".