Influence of the behavioral goal and environmental obstacles on rapid feedback responses
Bibliographic record
Abstract
The motor system must consider a variety of environmental factors when executing voluntary motor actions, such as the shape of the goal or the possible presence of intervening obstacles. It remains unknown whether rapid feedback responses to mechanical perturbations also consider these factors. Our first experiment quantified how feedback corrections were altered by target shape, which was either a circular dot or a bar. Unperturbed movements to each target were qualitatively similar on average but with greater dispersion of end point positions when reaching to the bar. On random trials, multijoint torque perturbations deviated the hand left or right. When reaching to a circular target, perturbations elicited corrective movements that were directed straight to the location of the target. In contrast, corrective movements when reaching to a bar were redirected to other locations along the bar axis. Our second experiment quantified whether the presence of obstacles could interfere with feedback corrections. We found that hand trajectories after the perturbations were altered to avoid obstacles in the environment. Importantly, changes in muscle activity reflecting the different target shapes (bar vs. dot) or the presence of obstacles were observed in as little as 70 ms. Such changes in motor responses were qualitatively consistent with simulations based on optimal feedback control. Taken together, these results highlight that long-latency motor responses consider spatial properties of the goal and environment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".