Virtual worlds and games for rehabilitation and research
Bibliographic record
Abstract
Expensive and bulky setups, and center-out whole-arm reaching paradigms have been used extensively in motor control research to explore the neural control of movement in humans and primates. They have led to a number of robust findings about the effect of modified task dynamics onto movement kinematics and motor skill acquisition. A number of controversial findings are related to the role of peripheral reflexes and the effect of practice on motor learning, adaptation, and consolidation A related issue is whether the brain actually uses strategies similar to engineering optimal control. Finally, aspects that typically do not get much attention are the roles of subject motivation and the functional relevance of the task to daily activity. Here we introduce a suite of virtual environments for motor skill acquisition, running on ubiquitous highly affordable and portable PC hardware. We explore case studies in both a research and a rehabilitation context, and suggest that our observations are compatible with theory by N.A.Bernstein according to which the brain may perform feasibility search. Unlike a robotic system, the brain tries to achieve solutions to the behavioral tasks that satisfy imposed constraints, even if not yet optimal. When more than one solution is identified, the brain retains the better one for future use. In comparison, a robotic system would search (depth- or width-first) throughout the whole parameter space. This difference is not unlike the one between a grand-master and a chess playing program. Based on a rich variety of acquired behavioral data, we demonstrate clearly that the virtual environments we introduced here have a very large potential to: 1) Reproduce individually targeted elements of motor behavior, which are functionally relevant in everyday life; 2) Drive incremental re-acquisition of increasingly complex and adapted motor programs; 3) Achieve the latter goal by selectively augmenting or suppressing particular strategies depending on their compatibility with residual and readapted patient capacity at a particular point of the rehabilitation process.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".