The influence of kinesthetic motor imagery and effector specificity on the long-latency stretch response
Bibliographic record
Abstract
Mechanical perturbations to the arms produce short (SLFR; 25-50ms) and long-latency (LLFR; 50-100ms) feedback responses in stretched muscles. The SLFR is generated by spinal circuitry and remains immutable to intention. By contrast, the LLFR engages supra-spinal regions shared with voluntary control and possesses the capacity to modulate in a manner similar to volition (Pruszynski et al., 2008). The present study investigated whether the overt execution of a voluntary response in stretched muscle is required for facilitation of the LLFR. Participants engaged in kinesthetic motor imagery of a compensate task (Experiment 1) or were instructed to execute voluntary responses in a non-stretched contralateral muscle (Experiment 2). The LLFR in stretched right wrist flexors were compared on Imagery and Contralateral trials to standard "Not-Intervene" and "Compensate" conditions. Our findings revealed that on ~40% of Imagery and ~50% of Contralateral trials, a partial voluntary response "leaked-out" into the stretched right wrist flexor muscle. On these "leaked" trials, the early portion of the LLFR (R2: 50-75ms) was increased compared to the Not-Intervene condition and appeared indistinguishable to the facilitation observed on Compensate trials. The latter portion of the LLFR (R3: 75-100ms) showed further modulation that mirrored the patterns of voluntary activity. By contrast, the LLFR on "non-leaked" Imagery and Contralateral trials appeared similar to the Not-Intervene condition. These findings suggest that even though a hastened voluntary response cannot account for all LLFR facilitation, the overt execution of a voluntary response in stretched muscle is required for instruction-dependent modulation of this rapid feedback response.Acknowledgments: NSERC
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".