No Evidence for Accurate Visuomotor Memory: Systematic and Variable Error in Memory-Guided Reaching
Bibliographic record
Abstract
The authors explored whether the motor system has access to highly accurate information about the aiming environment after visual occlusion. Participants (N = 14) reached to 1 of 3 midsagittal targets in 4 visual conditions (open-loop, brief-delay, 500-ms delay, and 2,000-ms delay). In all conditions, the aiming environment was first viewed for 2,000 ms. Movements were cued immediately after the initial viewing period in the open-loop and brief-delay conditions. Vision was not occluded until movement onset in the open-loop condition, whereas vision was occluded coincidentally with the movement cue in the brief-delay condition. In the 2 longer delay conditions, the movement was cued following a 500- or a 2,000-ms no-vision delay period. Participants overshot the target in the open-loop condition, but that tendency was significantly reduced in the 3 delay conditions. Moreover, endpoint variability was greater in the 3 delay conditions than in the open-loop condition. A speed-accuracy tradeoff account could not explain the differences between open-loop and delayed reaching. Those findings suggest that the motor system does not have access to highly accurate information about the aiming environment for any appreciable period of time following visual occlusion, consistent with the view that the visuomotor system operates in real time.
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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 | 0.002 | 0.026 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".