Motor adaptation and internal model formation in a robot-mediated forcefield
Bibliographic record
Abstract
Background: Motor adaptation relies on error-based learning for accurate movements in changing environments. However, the neurophysiological mechanisms driving individual differences in performance are unclear. Transcranial magnetic stimulation (TMS)-evoked potential can provide a direct measure of cortical excitability. Objective: To investigate cortical excitability as a predictor of motor learning and motor adaptation in a robot-mediated forcefield. Methods: A group of 15 right-handed healthy participants (mean age 23 years) performed a robot-mediated forcefield perturbation task. There were two conditions: unperturbed non-adaptation and perturbed adaptation. TMS was applied in the resting state at baseline and following motor adaptation over the contralateral primary motor cortex (left M1). Electroencephalographic (EEG) activity was continuously recorded, and cortical excitability was measured by TMS-evoked potential (TEP). Motor learning was quantified by the motor learning index. Results: = 0.0018) following motor adaptation. Conclusions: ERN reflected the formation of a predictive internal model adapted to the forcefield perturbation. Attenuation in TEP N100 amplitude reflected an increase in cortical excitability with motor adaptation reflecting neuroplastic changes in the sensorimotor cortex. TEP N100 is a potential biomarker for predicting the outcome in robot-mediated therapy and a mechanism to investigate psychomotor abnormalities in depression.
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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".