Visuomotor Map Determines How Visually Guided Reaching Movements are Corrected Within and Across Trials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When a visually guided reaching movement is unexpectedly perturbed, it is implicitly corrected in two ways: immediately after the perturbation by feedback control (online correction) and in the next movement by adjusting feedforward motor commands (offline correction or motor adaptation). Although recent studies have revealed a close relationship between feedback and feedforward controls, the nature of this relationship is not yet fully understood. Here, we show that both implicit online and offline movement corrections utilize the same visuomotor map for feedforward movement control that transforms the spatial location of visual objects into appropriate motor commands. First, we artificially distorted the visuomotor map by applying opposite visual rotations to the cursor representing the hand position while human participants reached for two different targets. This procedure implicitly altered the visuomotor map so that changes in the movement direction to the target location were more insensitive or more sensitive. Then, we examined how such visuomotor map distortion influenced online movement correction by suddenly changing the target location. The magnitude of online movement correction was altered according to the shape of the visuomotor map. We also examined offline movement correction; the aftereffect induced by visual rotation in the previous trial was modulated according to the shape of the visuomotor map. These results highlighted the importance of the visuomotor map as a foundation for implicit motor control mechanisms and the intimate relationship between feedforward control, feedback control, and motor adaptation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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 it