Are online corrections to visual targets really a distinct class of movement?
Bibliographic record
Abstract
Summary Humans have a remarkable capacity to adjust reaching movements rapidly and accurately when visual targets jump to a new location. The short latency of such online corrections has led to the hypothesis that they constitute a distinct class of movement and arise from an ‘automatic pilot’ that is selectively engaged only during ongoing movements. Here, we test this idea by measuring muscle recruitment, force, and kinematics in a jumping target reaching task. In separate blocks of trials, participants were instructed to respond to target jumps by (1) following the jumped target, (2) stopping the on-going movement, or (3) ignoring the jumped target. This allowed us to establish the automaticity and timing of responses to target jumps and to compare such measures to the original reaching movement initiated from rest. We find that the earliest phase of muscle recruitment elicited by the jumped target corresponds to a subcortical reflex, beginning at ∼80ms and ending by ∼120ms, preceding the onset of voluntary recruitment at ∼130ms. This reflex inexorably drives a reaching adjustment towards the new target in all three blocks; it is only somewhat reduced in the ‘stop’ and ‘ignore’ blocks. Critically, this earliest phase of muscle recruitment was also present at the exact same latency (80ms) for the original reaching movement initiated from rest. Thus, rather than supporting the model of online corrections as distinct class of movement that is mediated by an ‘automatic pilot,’ our results suggest that all reaches, whether adjusted in mid-flight or initiated from rest, arise from a common nested control system featuring subcortical and cortical components whose influence can be strategically preset by task demands. Our results also reinforce the importance of considering movement biomechanics when interpreting kinematic latency differences across movements made in different situations.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".