Context-dependent concurrent adaptation to static and moving targets
Bibliographic record
Abstract
Is the neural control of movements towards moving targets independent to that of static targets? In the following experiments, we used a visuomotor rotation adaptation paradigm to examine the extent to which adapting arm movements to static targets generalize to that of moving targets (i.e. pursuit or tracking). In the first and second experiments, we showed that adaptation to perturbed tracking movements generalizes to reaching movements; reach aftereffects following perturbed tracking were about half the size (≈9°) of those produced following reach training (≈ 19°). Given these findings, in the final experiment we associated opposing perturbations (-30° and +30°) with either reaching or tracking movements and presented them within the same experimental block to determine whether these contexts allow for dual adaptation. We found that the group that experienced opposing perturbations was able to reduce both reaching and tracking errors, as well as produce reach aftereffects following dual training of ≈7°, which were substantially smaller than those produced when reach training was not concurrent with tracking training. This reduction in reach aftereffects is consistent with the extent of the interference from tracking training as measured by the reach aftereffects produced when only that condition was performed. These results suggest partial, but not complete, overlap in the learning processes involved in the acquisition of tracking and reaching movements.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".