Interpreting ambiguous visual information in motor learning
Bibliographic record
Abstract
Previous studies have shown that learning to reach accurately with an imposed visuomotor rotation requires a remapping of the relationship between vision and motor output. In this preliminary study, we examine how the brain works out the appropriate motor adjustments, in this case for both arms, based on visual images. Specifically, we investigate how visual errors seen while adapting reaches to visual targets affect the movements of both the trained and untrained hand. In our task subjects learned to make accurate reaches to targets in four visual feedback conditions: rotated 45 degrees, rotated 105 degrees, reversed left to right and rotated 45 degrees plus reversed. In all conditions the rotation was applied to the subject's feedback of their hand and not the targets. In the reversed and rotated-reversed condition, when the subject used their right hand, the feedback looked like their left hand (and vice versa). After a training period with one hand (e.g., right) subjects were tested with the opposite hand (e.g., left) on the same task. We predicted that after reaching with the right hand with reversed visual feedback the control of the left arm would also be altered-more so than after learning an equal-sized adjustment to right-arm reaching with a rotated, but non-reversed, view of their hand movements. Our results showed that people were able to learn the visuomotor adaptation with reversed visual feedback, but more interestingly, that learning occurred for the untrained hand as well for the reversed conditions alone. Here, vision alone--when it resembles the image of the opposite hand--led to improved initial performance for this opposite, untrained arm when reaching in a similar task. The brain seems to take advantage of reversed visual feedback of the arm to adjust the motor commands to the untrained arm in a way that facilitates transfer of the adaptation from one arm to the other.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".