Contribution of Retinal Motion to the Impulse Control of Target-Directed Aiming
Bibliographic record
Abstract
Abstract Contemporary models of sensorimotor control contend that visually regulated movement adjustments may unfold early during a target-directed limb movement through an impulse control process that makes use of anticipatory forward models. To date, evidence surrounding impulse control has involved adjustments to a purported misperception in limb velocity following the unexpected onset of a moving background. That is, the limb is perceived to move faster and undershoots more when there is an incongruent moving background and vice versa. However, this particular behavior may manifest from an independent oculo-manual-following response. The present study aimed to deconstruct these proposals and, with them, the processes that underlie impulse control. Participants had to rapidly reach upward to land their index finger accurately on a target. On 33% of trials, the background, over which the movement was made, moved either up, down, right, or left. Displacements in the primary and perpendicular directions of movement showed spatial trajectories that were consistent with the directions of the moving backgrounds. This behavior was most prevalent in measurements taken at the movements’ peak negative acceleration and endpoints. Moreover, analysis of standardized displacements in the moving background conditions indicated no significant differences in the extent of the movements toward each of the moving backgrounds. These findings indicate that movement adjustments can manifest from an oculo-manual-following response rather than a misperception of limb velocity. We suggest that the anticipatory forward model that comprises impulse control may incorporate features of the environment that surround the vicinity of the limb.
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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 | 0.001 | 0.000 |
| 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".