Visuomotor feedback gains are modulated by gaze position
Bibliographic record
Abstract
During goal-directed reaching, people typically direct their gaze to the target before the start of the hand movement and maintain fixation until the hand arrives. This gaze strategy improves reach accuracy in two ways. It enables the use of central vision at the end of movement, and it allows the use of extraretinal information in guiding the hand to the target. Here we tested whether fixating the reach target further facilitates reach accuracy by optimizing the use of peripheral vision in detecting, and rapidly responding to, reach errors during the ongoing movement. We examined automatic visuomotor corrections in response to displacements of the cursor representing the hand position as a function of gaze fixation location during unimanual goal-directed reaching. Eight fixation targets were positioned either in line with, or at different angles relative to, the straight-ahead movement direction (manipulation of fixation angle), and at different distances from the location of the visual perturbation (manipulation of fixation distance). We found that corrections were fastest and strongest when gaze was directed at the reach target compared with when gaze was directed to a different location in the workspace. We found that the gain of the visuomotor response was strongly affected by fixation angle, and to a smaller extent by fixation distance, with lower gains as the angle or distance increased. We submit that fixating the reach target improves reach accuracy by facilitating rapid visuomotor responses to reach errors viewed in peripheral vision. NEW & NOTEWORTHY It is well known that directing gaze to the reach target allows the use of foveal visual feedback and extraretinal information to improve the accuracy of reaching movements. Here we demonstrate that target fixation also optimizes rapid visuomotor corrections to reach errors viewed in peripheral vision, with the angle of gaze relative to the hand movement being a critical determinant in the gain of the visuomotor response.
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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 | 0.000 |
| 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 | 0.000 |
| 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".