Amending Ongoing Upper-Limb Reaches: Visual and Proprioceptive Contributions?
Bibliographic record
Abstract
In order to maximize the precise completion of voluntary actions, humans can theoretically utilize both visual and proprioceptive information to plan and amend ongoing limb trajectories. Although vision has been thought to be a more dominant sensory modality, research has shown that sensory feedback may be processed as a function of its relevance and reliability. As well, theoretical models of voluntary action have suggested that both vision and proprioception can be used to prepare online trajectory amendments. However, empirical evidence regarding the use of proprioception for online control has come from indirect manipulations from the sensory feedback (i.e., without directly perturbing the afferent information; e.g., visual-proprioceptive mismatch). In order to directly assess the relative contributions of visual and proprioceptive feedback to the online control of voluntary actions, direct perturbations to both vision (i.e., liquid crystal goggles) and proprioception (i.e., tendon vibration) were implemented in two experiments. The first experiment employed the manipulations while participants simply performed a rapid goal-directed movement (30 cm amplitude). Results from this first experiment yielded no significant evidence that proprioceptive feedback contributed to online control processes. The second experiment employed an imperceptible target jump to elicit online trajectory amendments. Without or with tendon vibration, participants still corrected for the target jumps. The current study provided more evidence of the importance of vision for online control but little support for the importance of proprioception for online limb-target regulation mechanisms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".