Reference Frame Conversions for Repeated Arm Movements
Bibliographic record
Abstract
The aim of this study was to further understand how the brain represents spatial information for shaping aiming movements to targets. Both behavioral and neurophysiological studies have shown that the brain represents spatial memory for reaching targets in an eye-fixed frame. To date, these studies have only shown how the brain stores and updates target locations for generating a single arm movement. But once a target's location has been computed relative to the hand to program a pointing movement, is that information reused for subsequent movements to the same location? Or is the remembered target location reconverted from eye to motor coordinates each time a pointing movement is made? To test between these two possibilities, we had subjects point twice to the remembered location of a previously foveated target after shifting their gaze to the opposite side of the target site before each pointing movement. When we compared the direction of pointing errors for the second movement to those of the first, we found that errors for each movement varied as a function of current gaze so that pointing endpoints fell on opposite sides of the remembered target site in the same trial. Our results suggest that when shaping multiple pointing movements to the same location the brain does not use information from the previous arm movement such as an arm-fixed representation of the target but instead mainly uses the updated eye-fixed representation of the target to recalculate its location into the appropriate motor frame.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".