The effects of visual cues in an immersive virtual reality environment on adaptation to internal and external errors
Bibliographic record
Abstract
When performing motor tasks, we improve performance by modifying future movements to correct for observed errors. The assigned source of the errors can affect many aspects of adaptation including its generalizability and the updating of internal models. Adaptation to errors assigned to internal sources (e.g., our arm) is often specific to the arm and are poorly generalized when acting with a different effector. Adaptation to errors assigned to external sources (e.g., the environment) on the other hand, is agnostic to the effector being used but may be specific to the environment. Since the cause of an error is often ambiguous, sensory cues can be used to estimate the likely source of the error. We developed a task in which motor errors could be assigned to internal or external sources. Participants made arm movements to roll a ball toward targets in a head-mounted virtual reality environment. We induced errors by either modifying the mapping between the arm movement and the initial movement of the ball, or by applying a change in the ball path only after the release of the ball. Additionally, we used informative visual cues to signal changes in the environment to increase external error attribution. Pilot data show visual cues can facilitate the assignment of errors to external sources. The visual cues can be dynamics of the ball movement or global environmental changes. External error attribution can in turn allow for rapid switching between motor memories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.011 |
| 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 | 0.001 |
| Open science | 0.000 | 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".