Using Tools as Cues for Motor Adaptation in Virtual Reality
Bibliographic record
Abstract
Abstract Humans are adept at using multiple tools, often switching between them even when each involves distinct and potentially conflicting motor demands. This study investigated how different features of a tool influence the formation of distinct motor memories during dual adaptation to opposing visuomotor perturbations. Using an immersive virtual reality (VR) setup, participants performed an aiming task in which they launched a ball toward a target using virtual tools, each consistently associated with a different visuomotor rotation. We manipulated tool features across three groups: one in which tools differed only in colour (Colour Control), one in which they differed in shape but involved similar movement patterns (Motor Congruent), and one in which they differed both in shape and in how they were operated—for example, a paddle-like forward swing to propel the ball versus a draw-and-release motion similar to a slingshot (Motor Incongruent). A fourth control group that adapted to a single perturbation with a single tool at a time was also included. Only the Motor Incongruent group demonstrated robust dual adaptation and clear aftereffects, comparable to those observed during single-tool learning. These results suggest that distinct modes of tool operation play a critical role in supporting the formation and retention of separate internal models during sensorimotor adaptation. New & Noteworthy Humans often switch between tools with conflicting motor demands. Using immersive virtual reality, we tested whether visual features or operational differences support dual adaptation to opposing visuomotor perturbations. Only when tools differed in both shape and mode of operation did participants show robust adaptation and aftereffects, comparable to single-tool learning. These findings highlight the critical role of movement-relevant cues in forming distinct motor memories for flexible tool use.
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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 | 0.002 |
| 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) | 0.001 | 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".