Using tools as cues for dual adaptation to opposing visuomotor rotations in virtual reality
Bibliographic record
Abstract
Humans are experts at designing and utilizing unique tools to accomplish various tasks, like wielding an axe to chop wood. Humans are also capable of using different tools to accomplish opposing motor tasks simultaneously (using a fork and knife to cut meat). While a lot is known about motor adaptation with changed visual feedback of the hand, we rarely consider how we adapt tool use in novel situations requiring different movement patterns. Here we test whether having two tools that require different movements to accomplish a similar goal would serve as sufficient cues that allow dual tool-use adaptation, akin to lead-in movements in dual motor adaptations. We ran an immersive Virtual Reality experiment where 40 participants used 2 different tools to launch a ball towards a target; a paddle (forward motion) or slingshot (backward motion). Participants swapped between tools every 8 trials. After a familiarization phase, we added visually opposite perturbations to the ball after it was launched from each tool (30 degrees clockwise or counterclockwise rotation). Participants in a control group learned to use each tool with perturbed ball movements separately. We found that participants could form distinct motor memories for both tools, adapting their movements to the opposing perturbations. However, errors following exposure to the perturbation remained above baseline, suggesting that complete dual motor adaptation learning did not occur. These findings suggest separate motor memories form more slowly in dual-tool adaptation.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".