Visual feedback is used to guide the hand towards endpoints not along trajectories
Bibliographic record
Abstract
Many studies have shown that visual feedback of the hand is used to monitor or adjust ongoing movements. It is still unknown, however, whether vision is used to steer the hand along a desired trajectory or to guide the hand towards a desired endpoint. Even though these two functions of visual feedback appear similar on the surface, they are different from both a conceptual and a computational point of view. Here we tested if visual feedback is used to steer the hand along a desired trajectory or towards a desired endpoint. We manipulated how visual information relevant for moving was presented to subjects (Endpoint vs. Trajectory task) and the availability of visual feedback of the moving hand (no feedback vs. feedback). We tested both direct and tool mediated movements (i.e. computer-mouse mediated cursor movements). In addition, we compared performance between free viewing and fixation of a peripheral target. Finally, we investigated whether or not performance changes when the visual information that specifies the desired Endpoint or Trajectory is extinguished at the moment of movement onset. We found that subjects use visual feedback to correct movement errors online in both direct and tool mediated movements. Most importantly, we found that the availability of visual feedback reduces errors significantly more in the Endpoint than in the Trajectory task. The general pattern of results holds even when subjects fixate a peripheral target and when the visual information that specifies the desired Endpoint or Trajectory is extinguished at movement onset. We conclude that visual information about the moving hand is used primarily to guide the hand towards a specific endpoint rather than to steer it along a trajectory. Moreover, this is true whether or not participants move their eyes, see the target during the movement, or use a mouse cursor rather than their hand.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".