The Inertial Anisotropy of the Arm Is Accurately Predicted during Movement Planning
Bibliographic record
Abstract
An important theoretical concept in motor control is the idea that the CNS uses an internal model of the motor system and environment to predict the sensory consequences of motor commands. In arm movement control, a critical factor affecting the transformation from motor commands to sensory consequences is limb dynamics, including the inertial anisotropy of the arm, which refers to the fact that the inertial resistance of the arm depends on hand movement direction. Here we show that the CNS maintains an accurate internal model of the inertial anisotropy of the arm by demonstrating that the motor system can precisely predict direction-dependent variations in hand acceleration. Subjects slid an object, held beneath the index finger, across a frictionless horizontal surface to radially located targets. We recorded the normal (vertical) force exerted by the fingertip, as well as the tangential (horizontal) force proportional to hand acceleration. We found that normal force was precisely scaled in anticipation of tangential force, which, as expected, varied with direction. The peak rates of change of the normal and tangential forces, observed early in the movement, were highly correlated. Similar results were obtained regardless of whether the start position of the hand was located directly in front of the subject or rotated 45 degrees to the right. Finally, we observed reduced force correlations under reaction time conditions. This suggests that the process of prediction, based on an internal model of the limb, is not fully completed within the reaction time interval.
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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 | 0.004 |
| 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) | 0.000 | 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 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".