Anticipation and long-latency reflex modulation
Bibliographic record
Abstract
Perturbations applied to the upper limbs elicit short (M1: 25-50 ms) and long-latency (M2: 50-100 ms) reflexes in the stretched muscle. M1 is produced by a spinal reflex loop, while M2 receives contribution from a longer trans-cortical pathway and is susceptible to intention. Thus when the participant is asked to counteract the perturbation, M1 is usually unaffected while M2 increases in size. This reflexive activity is followed shortly thereafter by a voluntary response. While many studies have examined modulation of M2 between passive and active conditions, through the use of constant foreperiods, it has also been shown that M2 size in a passive condition can change based on factors such as habituation and anticipation of perturbation delivery (Rothwell et al., 1986). The purpose of the present study was to further examine the influence of temporal anticipation on M2 modulation. Fifteen participants performed active and passive responses to a perturbation which stretched wrist flexors. Each block of trials had either a short (2.5-3.5 seconds; high predictability) or long (2.5-10 seconds; low predictability) variable foreperiod. As expected, no differences were found between conditions for M1 (all p values >.10), and M2 was larger (p=.005) in the active rather than passive conditions. Interestingly, within the two passive conditions, the long variable foreperiods resulted in a larger M2 (p=.045) than the trials with short foreperiods. These results suggest that perturbation predictability, even when using a variable foreperiod, can influence excitability of the pathway(s) contributing to the long-latency reflex. Acknowledgments: This research was supported by 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 | 0.003 |
| 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".