The effects of control signal noise on simultaneous submovements
Bibliographic record
Abstract
Understanding the stereotypical characteristics of human movement can better inform rehabilitation practices by providing a template of healthy and expected human motor control. Multiplicative noise is inherent in goal-directed movement, such as reaching to grasp an object. Multiplicative noise plays an important role in computational motor control models to help support phenomena such as stereotypical kinematic profiles in time-constrained and unconstrained tasks. Most tasks are not carried out along an isolated degree-of-freedom (DOF), and modelling the contribution of noise can be difficult. Here we add a noise term proportional to the degree of simultaneity for multi-DOF tasks to approximate the contribution of system noise. With this approach, we are able to explain previously observed motor phenomena including the presence of submovements in multi-DOF tasks, and the transition from simultaneous to sequential control of joints without the presence of feedback. Inclusion of a simultaneous multiplicative noise term presents a simple theory that expands on previous research in order to describe characteristics of multiple-DOF movements. This model can be used as a guide to compare healthy human motor control to the movements of patients receiving rehabilitation in an effort to improve their motor planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".