The Role of Online Visual Feedback for the Control of Target-Directed and Allocentric Hand Movements
Bibliographic record
Abstract
Studies that have investigated how sensory feedback about the moving hand is used to control hand movements have relied on paradigms such as pointing or reaching that require subjects to acquire target locations. In the context of these target-directed tasks, it has been found repeatedly that the human sensory-motor system relies heavily on visual feedback to control the ongoing movement. This finding has been formalized within the framework of statistical optimality according to which different sources of sensory feedback are combined such as to minimize variance in sensory information during movement control. Importantly, however, many hand movements that people perform every day are not target-directed, but based on allocentric (object-centered) visual information. Examples of allocentric movements are gesture imitation, drawing, or copying. Here we tested if visual feedback about the moving hand is used in the same way to control target-directed and allocentric hand movements. The results show that visual feedback is used significantly more to reduce movement scatter in the target-directed as compared with the allocentric movement task. Furthermore, we found that differences in the use of visual feedback between target-directed and allocentric hand movements cannot be explained based on differences in uncertainty about the movement goal. We conclude that the role played by visual feedback for movement control is fundamentally different for target-directed and allocentric movements. The results suggest that current computational and neural models of sensorimotor control that are based entirely on data derived from target-directed paradigms have to be modified to accommodate performance in the allocentric tasks used in our experiments. As a consequence, the results cast doubt on the idea that models of sensorimotor control developed exclusively from data obtained in target-directed paradigms are also valid in the context of allocentric tasks, such as drawing, copying, or imitative gesturing, that characterize much of human behavior.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".