Effects of immersive visual environment-change cues on motor learning during a virtual-reality target hitting task
Bibliographic record
Abstract
When performing motor tasks, we improve performance by modifying future movements to correct for observed errors. We do so by updating existing internal models of the movement interaction, or by creating, switching to, and switching from new internal models. Assignment of the error’s source, termed error attribution, can impact whether we update existing or create new internal models. Since the cause of an error is often ambiguous, sensory cues can be used to estimate the likely source of the error. In a target-hitting task, participants made arm movements to roll a ball to targets in a virtual reality environment. To facilitate motor adaptation, we induced errors by either modifying the mapping between the arm movement and the initial movement of the ball, or by applying a constant acceleration to the ball only after release. When adapting to either type of error, we explored if informative visual-slant cues successfully facilitate model creation and switching, rather than model updating. We find that the error induction method alone, and not the visual cues, determined whether errors led to model creation and switching, or model updating. In follow-up experiments, we find updates to internal models during this task account for errors assigned to the hand used in the movement, as well as the physical properties of the environment on which the interaction occurs. That is, the internal models being updated are not purely models for the control of limb movement, but interaction models.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".