Effort matching between arms depends on relative limb geometry and personal control
Bibliographic record
Abstract
Proprioception encompasses our sense of position and movement of our limbs, as well as the effort with which we engage in voluntary actions. Historically, sense of effort has been linked to centrally generated signals that elicit voluntary movements. We were interested in determining the effect of differences in limb geometry and personal control on sense of effort. In experiment 1, subjects exerted either extension or flexion torques to resist a torque applied by a robot exoskeleton to their reference elbow. They attempted to match this torque by exerting an equal effort torque (in a congruent direction with the reference arm) with their opposite (matching) arm in different limb positions (±15°). Subjects produced greater matching torque when their matching arm exerted effort toward the mirrored position of the reference (e.g., reference/matching arms at 90°/105° elbow flexion) vs. away (e.g., 90°/75° flexion). In experiment 2, a larger angular difference between arms (30°) resulted in a larger discrepancy in matched torques. Furthermore, in both experiments 1 and 2, subjects tended to overestimate the reference arm torque. This motivated a third experiment to determine whether providing more personal control might influence perceived effort and reduce the overestimation of the reference torques that we observed ( experiments 3a and 3b). Overestimation of the matched torques decreased significantly when subjects self-selected the reference torque that they were matching. Collectively, our data suggest that perceived effort between arms can be influenced by signals relating to the relative geometry of the limbs and the personal control of motor output during action. NEW & NOTEWORTHY This work highlights how limb geometry influences our sense of effort during voluntary motor actions. It also suggests that loss of personal control during motor actions leads to an increase in perceived effort.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".