Guiding the eye with the hand: Role of proprioception in spatial updating for saccades
Bibliographic record
Abstract
The saccade generator accounts for previous saccades in order to ‘update’ internal representations for subsequent saccades. Here we tested the role of limb proprioception on updating the locations of hand-held objects for the saccade generator. We measured radial saccades (8 directions, approximately 7 and 13 degrees eccentricity) from a central target in 6 human subjects under the following conditions: 1. Visual controls (saccade to the peripheral flashing LED, with or without memory delay); 2. Proprioception alone (after hand-held LED target was extinguished, hand was moved passively or actively from the center to the periphery, followed by saccade to the perceived target location); 3. Combination of vision and proprioception (like #2, but target flashed again after the hand movement). In all trials, the eye held the central fixation point until an auditory command told them to saccade to the peripherally shifted target (in complete darkness). We recorded eye and arm movements with search coil and optotrak systems respectively. Subjects were able to use the spatial updating from proprioception to generate approximately accurate saccades, but tended to overshoot the targets. Proprioception alone trials were least accurate, followed by combination of vision and proprioception, and then vision alone (which was best). In addition, proprioception-related errors were different for different movement directions (being greater for horizontal and centripetal movements). Surprisingly, Compared to visual controls, the combination of proprioception and vision gave reduced saccade accuracy, perhaps due to the proportionate weighting of each of these two different inputs during the integration.
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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) | 0.002 | 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".