Inducing Visuomotor Adaptation Using Virtual Reality Gaming with a Virtual Shift as a Treatment for Unilateral Spatial Neglect
Why this work is in the frame
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Bibliographic record
Abstract
Unilateral spatial neglect after stroke is characterized by reduced responses to stimuli on the contralesional side, causing significant impairments in self-care and safety. Conventional visuomotor adaptation (VMA) with prisms that cause a lateral shift of the visual scene can decrease neglect symptoms but is not engaging according to patients. Performing VMA within a virtual reality (VR) environment may be more engaging but has never been tested. To determine if VMA can be elicited in a VR environment, healthy subjects (n=7) underwent VMA that was elicited by either wearing prisms that caused an optical shift, or by application of a virtual shift of the hand cursor within the VR environment. A low cost VR system was developed by coupling the Kinect v2 gaming sensor to online games via the Flexible Action and Articulated Skeleton Toolkit (FAAST) software. The adaptation phase of training consisted of a reaching task in online games or in a custom target pointing program. Following the adaptation phase the optical or virtual shift was removed and participants were assessed during the initial portion of the de-adaptation phase for the presence of an after-effect on their reaching movements, with lateral reaching errors indicating the successful induction of VMA. Results show that practicing reaching in a VR environment with a virtual shift lead to a horizontal after-effect similar to conventional prism adaptation. The results demonstrate that VMA can be elicited in a VR environment and suggest that VR gaming therapy could be used to improve recovery from unilateral spatial neglect.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it