Not feeling it? The influence of proprioception on impulse regulation processes.
Bibliographic record
Abstract
Accurate reaching movements can theoretically benefit from multisensory feedback. Both vision and proprioception should be useful when performing early online limb trajectory amendments (i.e., impulse regulation; see Elliott et al., 2010). However, the notion of impulse regulation was developed with limb trajectory perturbations (i.e., altered movement demands but unaltered proprioception: Grierson & Elliott, 2008, 2009). The current study aimed to test the roles of movement demands versus proprioception on impulse regulation processes. Participants displaced one of two visually-identical aluminum cubes (i.e., 505 g and 480 g) towards a visual target (30 cm amplitude). The heavier cube was covertly switched to the lighter cube on one third of the trials, altering movement demands, and eliciting impulse regulation processes. Furthermore, to perturb proprioception, between-trial muscle tendon vibration was employed (Goodman & Tremblay, 2017). Finally, visual feedback was available on 50% of trials in a randomized fashion, to alter reliance on proprioception. Results confirmed that online vision led to more accurate and precise endpoint distributions. However, no variables analyzed yielded an interaction involving vision, suggesting that the effects of tendon vibration and cube weight were stable across vision conditions. In contrast, the lighter cube weight yielded higher peak velocities but no significant differences in endpoint distributions (i.e., effective impulse regulation). Finally, although vibration led to shifts in the average endpoint position, no measures associated with online control yielded any effects of tendon vibration. Even if methodological differences from previous work should be investigated, impulse regulation appears to be predominantly based on visual feedback.Acknowledgments: Natural Sciences and Engineering Research Council of Canada (NSERC), Canada Foundation for Innovation (CFI), Ontario Research Fund (ORF), University of Toronto (UofT).
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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 | 0.013 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 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".