Bibliographic record
Abstract
Skilled motor ability depends on efficiently integrating sensory afference into the appropriate motor commands. Afferent inhibition provides a valuable tool to probe the procedural and declarative influence over sensorimotor integration during skilled motor actions. This manuscript describes the methodology and contributions of short-latency afferent inhibition (SAI) for understanding sensorimotor integration. SAI quantifies the effect of a convergent afferent volley on the corticospinal motor output evoked by transcranial magnetic stimulation (TMS). The afferent volley is triggered by the electrical stimulation of a peripheral nerve. The TMS stimulus is delivered to a location over the primary motor cortex that elicits a reliable motor-evoked response in a muscle served by that afferent nerve. The extent of inhibition in the motor-evoked response reflects the magnitude of the afferent volley converging on the motor cortex and involves central GABAergic and cholinergic contributions. The cholinergic involvement in SAI makes SAI a possible marker of declarative-procedural interactions in sensorimotor performance and learning. More recently, studies have begun manipulating the TMS current direction in SAI to tease apart the functional significance of distinct sensorimotor circuits in the primary motor cortex for skilled motor actions. The ability to control additional pulse parameters (e.g., the pulse width) with state-of-the-art controllable pulse parameter TMS (cTMS) has enhanced the selectivity of the sensorimotor circuits probed by the TMS stimulus and provided an opportunity to create more refined models of sensorimotor control and learning. Therefore, the current manuscript focuses on SAI assessment using cTMS. However, the principles outlined here also apply to SAI assessed using conventional fixed pulse width TMS stimulators and other forms of afferent inhibition, such as long-latency afferent inhibition (LAI).
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.387 | 0.286 |
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".