Visual-based Sensory Motor Learning During Dynamic Balance Tasks Viewed in a Virtual Environment
Bibliographic record
Abstract
In this research, we applied a transformation to the normal trajectory used to move and track a visual target in a virtual environment, in order to evaluate adaptation to a visual-based sensory motor transformation. The ability to recalibrate internal to external spatial reference frames is important when changing the relationship between the self and the environment. The virtual task was controlled by the subject's center of foot pressure (COP); the physical COP location is mapped (slaved) to an on-screen cursor (avatar). Target balloons appeared randomly on the screen and the subject was instructed to move the cursor (COP) to intersect the balloon and burst it. When the experimental transformation was applied, the trajectory of the avatar underwent a counter-clockwise rotation of 60 degrees; this required the subjects to update their spatial reference coordinates between the physical COP position and the game avatar. Two parameters were calculated in order to investigate if learning occurred: 1) the displacement angle between the COP trajectory and the direct line path between the starting COP position and target position; and 2) the maximum perpendicular displacement between the COP trajectory and the direct line path to the balloon target. The results showed a decrease in movement error with learning.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".