Proprioceptive Guidance of Saccades in Eye–Hand Coordination
Bibliographic record
Abstract
The saccade generator updates memorized target representations for saccades during eye and head movements. Here, we tested if proprioceptive feedback from the arm can also update handheld object locations for saccades, and what intrinsic coordinate system(s) is used in this transformation. We measured radial saccades beginning from a central light-emitting diode to 16 target locations arranged peripherally in eight directions and two eccentricities on a horizontal plane in front of subjects. Target locations were either indicated 1) by a visual flash, 2) by the subject actively moving the handheld central target to a peripheral location, 3) by the experimenter passively moving the subject's hand, or 4) through a combination of the above proprioceptive and visual stimuli. Saccade direction was relatively accurate, but subjects showed task-dependent systematic overshoots and variable errors in radial amplitude. Visually guided saccades showed the smallest overshoot, followed by saccades guided by both vision and proprioception, whereas proprioceptively guided saccades showed the largest overshoot. In most tasks, the overall distribution of saccade endpoints was shifted and expanded in a gaze- or head-centered cardinal coordinate system. However, the active proprioception task produced a tilted pattern of errors, apparently weighted toward a limb-centered coordinate system. This suggests the saccade generator receives an efference copy of the arm movement command but fails to compensate for the arm's inertia-related directional anisotropy. Thus the saccade system is able to transform hand-centered somatosensory signals into oculomotor coordinates and combine somatosensory signals with visual inputs, but it seems to have a poorly calibrated internal model of limb properties.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".