Visual Targets Aren’t Irreversibly Converted to Motor Coordinates: Eye-Centered Updating of Visuospatial Memory in Online Reach Control
Bibliographic record
Abstract
Counter to current and widely accepted hypotheses that sensorimotor transformations involve converting target locations in spatial memory from an eye-fixed reference frame into a more stable motor-based reference frame, we show that this is not strictly the case. Eye-centered representations continue to dominate reach control even during movement execution; the eye-centered target representation persists after conversion to a motor-based frame and is continuously updated as the eyes move during reach, and is used to modify the reach plan accordingly during online control. While reaches are known to be adjusted online when targets physically shift, our results are the first to show that similar adjustments occur in response to changes in representations of remembered target locations. Specifically, we find that shifts in gaze direction, which produce predictable changes in the internal (specifically eye-centered) representation of remembered target locations also produce mid-transport changes in reach kinematics. This indicates that representations of remembered reach targets (and visuospatial memory in general) continue to be updated relative to gaze even after reach onset. Thus, online motor control is influenced dynamically by both the external and internal updating mechanisms.
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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 | 0.002 |
| 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 | 0.001 |
| 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".