Effects Of Tool Use And Perturbation During Motor Adaptation On Hand Localization In Immersive Virtual Reality
Bibliographic record
Abstract
Our brain has a remarkable capacity for learning movements and adapting them to accomplish a motor goal. In many adaptation studies, participants move in a 2D plane while their hand is represented by a cursor. When visual feedback of hand position is misaligned, people can quickly compensate for this perturbation, show persistent reach aftereffects, and even misestimate the location of the unseen hand in the direction of previous visual training. However, it is unknown how well this generalizes to real-world settings or to the tools we use every day. Immersive virtual reality was used to test if end-effector shifts are also observed in more naturalistic virtual reality environments and if they extend to tools as end effectors. In the Hand Experiment, previous work from our lab was replicated where we found shifts in end-effector localization after adapting reach movements to a 30° and 60° visuomotor rotation of the hand, showing a similar magnitude of both shifts in where people indicate their perceived/felt hand and reach aftereffects following training to the perturbation in the VR environment. In the Pen Experiment, this paradigm was extended to investigate how well people can adapt when aiming with a common tool, like a pen, and whether the tool location is also recalibrated. The extent that the unseen location of hand-held tool, as well as the hand (in separate trials) recalibrates with adaptation was measured. Our results provide insight into the adaptative processes involved when learning to wield tools in more complicated, realistic environments.
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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 | 0.000 | 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 | 0.000 |
| 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".