On the Role of Visual Afferent Information for the Control of Aiming Movements Toward Targets of Different Sizes
Bibliographic record
Abstract
The authors investigated (a). whether the specificity of practice hypothesis is mediated by the importance of visual afferent information for the control of manual aiming movements and (b). how movement planning and online correction processes to the movement initial impulse are affected by the withdrawal of visual information in transfer. In acquisition, participants (N = 40) aimed at targets of different sizes in a full-vision or in a target-only condition before being transferred to a target-only condition without knowledge of results. The results supported the hypothesis that learning is specific to the source or sources of afferent information that are more likely to ensure optimal performance. The results also suggested that individuals will not always use visual afferent information more extensively when aiming at a small rather than at a large target. Instead, in a temporally constrained task, the relative efficiency of visually based corrections appears to mediate how exclusively an individual will rely on online visual afferent information for movement control. Finally, the detailed kinematic analysis performed in the present study clearly indicated that online modifications to the movement primary impulse are possible, arguing for a continuous or pseudo-continuous control of relatively slow aiming movements on the basis of visual afferent input.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".