Shifts in the eye-centered frame of reference may underlie saccades, visual perception, and eye-hand coordination
Bibliographic record
Abstract
Conventional, computational theories limit the understanding of how action and perception are controlled. In an alternative scheme, the nervous system controls the values of physical and neurophysiological parameters that predetermine the choice of the spatial frames of reference (FRs) for action and perception. For example, all possible eye positions, Q, can be considered as comprising a spatial FR in which extraocular muscles (EOMs) stabilize gaze directions. The origin or referent point of this FR is a specific, threshold eye position, R, at which EOMs can be quiescent but activated depending on the difference between Q and R. Starting before eye motion, shifts in R cause displacement of the FR and resetting of the stable equilibrium position to which the eyes are forced to move. Rather than corollary discharge, the depiction of visual images integrated across the entire retina in the shifted spatial FR is responsible for remapping visual receptive fields and visual constancy. These suggestions are illustrated in computer models of saccades in the referent control framework in humans and monkeys. The existence of three types of visual RF remapping during saccades is suggested. Properly scaled, shifts in the R underlying a saccade are transmitted to motoneurons of arm muscles to guide reach-to-grasp motion in the same, eye-centered FR. Some predictions of the proposed control scheme have been verified and new tests are suggested. The scheme is applicable to several eye-hand coordination deficits including micrography in Parkinson's disease and explains why vision helps deafferented subjects diminish movement deficits.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".