Equivalent visuomotor adaptation for variable reach practice
Bibliographic record
Abstract
Forming an internal model for adapted reaching movements to altered visual feedback requires a certain amount of practice. Numerous studies have shown the brain can quickly adapt to visual and force perturbations while performing reaching movements to both trained target and novel targets. But many of these studies have participants reach to only a small number of target locations repeatedly. Is learning comparable in the case where target locations are constantly different and participants only have a chance to reach once to each of them? We addressed this question by having subjects adapt their reaches to altered visual feedback of the hand either when repeatedly reaching to four targets (Repeated practice) or reaching only once to numerous target directions (Single practice). We also examined the extent to which this adaptation could transfer to untrained target locations. We found there is very little difference in learning rate between the two practice conditions. That is, participants were just as fast at learning a new visuomotor mapping when reaching once to each new target as they were when reaching over and over to the same targets. Likewise, we found that participants generalized to untrained targets similarly across exposure conditions. This suggests that the brain is as capable of deducing the required visuomotor adjustments following variable practice with unique targets as it is with repeated practice with the same targets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".