Computations for geometrically accurate visually guided reaching in 3-D space
Bibliographic record
Abstract
A fundamental question in neuroscience is how the brain transforms visual signals into accurate three-dimensional (3-D) reach commands, but surprisingly this has never been formally modeled. Here, we developed such a model and tested its predictions experimentally in humans. Our visuomotor transformation model used visual information about current hand and desired target positions to compute the visual (gaze-centered) desired movement vector. It then transformed these eye-centered plans into shoulder-centered motor plans using extraretinal eye and head position signals accounting for the complete 3-D eye-in-head and head-on-shoulder geometry (i.e., translation and rotation). We compared actual memory-guided reaching performance to the predictions of the model. By removing extraretinal signals (i.e., eye-head rotations and the offset between the centers of rotation of the eye and head) from the model, we developed a compensation index describing how accurately the brain performs the 3-D visuomotor transformation for different head-restrained and head-unrestrained gaze positions as well as for eye and head roll. Overall, subjects did not show errors predicted when extraretinal signals were ignored. Their reaching performance was accurate and the compensation index revealed that subjects accounted for the 3-D visuomotor transformation geometry. This was also the case for the initial portion of the movement (before proprioceptive feedback) indicating that the desired reach plan is computed in a feed-forward fashion. These findings show that the visuomotor transformation for reaching implements an internal model of the complete eye-to-shoulder linkage geometry and does not only rely on feedback control mechanisms. We discuss the relevance of this model in predicting reaching behavior in several patient groups.
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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 | 0.001 |
| 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 | 0.001 | 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".