The effects of verbal and spatial working memory on short- and long-latency sensorimotor circuits in the motor cortex
Bibliographic record
Abstract
Multiple sensorimotor loops converge in the motor cortex to create an adaptable system capable of context-specific sensorimotor control. Afferent inhibition provides a non-invasive tool to investigate the substrates by which procedural and cognitive control processes interact to shape motor corticospinal projections. Varying the transcranial magnetic stimulation properties during afferent inhibition can probe specific sensorimotor circuits that contribute to short- and long-latency periods of inhibition in response to the peripheral stimulation. The current study used short- (SAI) and long-latency (LAI) afferent inhibition to probe the influence of verbal and spatial working memory load on the specific sensorimotor circuits recruited by posterior-anterior (PA) and anterior-posterior (AP) TMS-induced current. Participants completed two sessions where SAI and LAI were assessed during the short-term maintenance of two- or six-item sets of letters (verbal) or stimulus locations (spatial). The only difference between the sessions was the direction of the induced current. PA SAI decreased as the verbal working memory load increased. In contrast, AP SAI was not modulated by verbal working memory load. Visuospatial working memory load did not affect PA or AP SAI. Neither PA LAI nor AP LAI were sensitive to verbal or spatial working memory load. The dissociation of short-latency PA and AP sensorimotor circuits and short- and long-latency PA sensorimotor circuits with increasing verbal working memory load support multiple convergent sensorimotor loops that provide distinct functional information to facilitate context-specific supraspinal control.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".