Integration of Predictive Feedforward and Sensory Feedback Signals for Online Control of Visually Guided Movement
Bibliographic record
Abstract
Online control of movement requires complex integration of predictive central feedforward and peripheral sensory feedback signals. We studied the hand trajectories of human subjects pointing to visual targets that abruptly changed locations by different amounts and modeled the mechanism of rapid online correction using a dynamic model of a two-joint limb. Small unperceived and large detected target displacements could be attributed to different origins (motor execution errors vs. environmental changes, respectively) and compensated differently. However, the behavioral findings indicate that the rapid feedback pathway is recruited regardless of the amplitude or subjective awareness of target displacement and that the size of the earliest correction is always proportional to the amplitude of the target displacement over the tested range of perturbations. The modeling findings suggest that the rapid online corrections can be accomplished by superimposing a dynamically appropriate error correction signal onto the outgoing feedforward motor command to the original target. Furthermore, the modeling shows that the online correction mechanism must include compensation for the dynamic mechanical properties of the limb and for sensory delays in its error-correction pathway.
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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 | 0.001 |
| 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 | 0.001 |
| 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".