Using altered proprioceptive sensation and online vision occlusion to assess multisensory control mechanisms
Bibliographic record
Abstract
It has been suggested that vision is particularly important to plan the amplitude of voluntary upper-limb reaches while proprioception is important for the online control (e.g., Bagesteiro et al., 2006). One possible concern with such research is the use of a visual-proprioceptive mismatch to assess proprioceptive feedback contributions. In order to assess the relative use of visual and proprioceptive feedback during the control of action, vision occlusion and tendon vibration were employed. Participants (n=15) performed a simple voluntary reaching task (30 cm). Liquid-crystal goggles were used to manipulate vision between movement onset and offset. The biceps and triceps muscles’ distal tendons were simultaneously vibrated between trials only, which yields decreased proprioceptive sensation, even when the vibration is stopped. Right index finger trajectories were recorded to obtain movement kinematics and endpoint performance. Movement times were longer with online vision while longer times after peak velocity were observed with online vision and without tendon vibration. Both manipulations significantly influenced endpoint bias, in both the amplitude and direction axes of the movement. Vision withdrawal yielded larger endpoint distributions in both axes while tendon vibration significantly influenced endpoint variability only in the amplitude axis. A 2 vision (full, none) by 2 vibration (on, off) by 4 movement time proportion (25%, 50%, 75%, 100%) ANOVA of the standard deviation of the finger position in the amplitude axis only revealed a main effect of vibration. For the same ANOVA employed with the direction axis, vision did yield a significant main effect while a proportion by vibration interaction revealed more trajectory variability with vibration at 75% of movement time. Overall, the results indicate that vision is relatively more important than proprioception to control trajectories in the amplitude movement axis while both modalities are important to control trajectories the direction of the movement.Acknowledgments: Natural Sciences and Engineering Research Council of Canada (NSERC); Canada Foundation for Innovation (CFI); Ontario Research Fund (ORF); Perceptual Motor Behaviour Laboratory
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".