Accurate planning of manual tracking requires a 3D visuomotor transformation of velocity signals
Bibliographic record
Abstract
Humans often perform visually guided arm movements in a dynamic environment. To accurately plan visually guided manual tracking movements, the brain should ideally transform the retinal velocity input into a spatially appropriate motor plan, taking the three-dimensional (3D) eye-head-shoulder geometry into account. Indeed, retinal and spatial target velocity vectors generally do not align because of different eye-head postures. Alternatively, the planning could be crude (based only on retinal information) and the movement corrected online using visual feedback. This study aims to investigate how accurate the motor plan generated by the central nervous system is. We computed predictions about the movement plan if the eye and head position are taken into account (spatial hypothesis) or not (retinal hypothesis). For the motor plan to be accurate, the brain should compensate for the head roll and resulting ocular counterroll as well as the misalignment between retinal and spatial coordinates when the eyes lie in oblique gaze positions. Predictions were tested on human subjects who manually tracked moving targets in darkness and were compared to the initial arm direction, reflecting the motor plan. Subjects spatially accurately tracked the target, although imperfectly. Therefore, the brain takes the 3D eye-head-shoulder geometry into account for the planning of visually guided manual tracking.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".