Disruption of efference copy signals in fronto-parietal networks with rTMS.
Bibliographic record
Abstract
It has been hypothesized that the brain generates efference copy signals for the purpose of predicting the sensory consequences of movement. While research has provided theoretical framework for this process, how motor planning signals are used to determine the spatial configuration of the limbs is unclear. In particular, previous studies have not differentiated between potential contributions of efference copy vs. state estimation signals. In the current experiment, we examined this issue by applying repetitive transcranial magnetic stimulation (rTMS) to candidate sites in the fronto-parietal network to determine how they would influence decisions in a temporal order judgement (TOJ) task under conditions in which bimanual arm crossing movements were performed (moving) or not (stationary). Previous work has shown that under stationary conditions, error rates increase when participants have their arms crossed or are about to cross their arms. In the current study, when the hands were stationary and uncrossed, we observed an increase in TOJ error compared to baseline when rTMS was applied to the posterior parietal cortex (PPC), but not when applied to the dorsal premotor cortex (dPMC). However under moving conditions, error rates were decreased compared to baseline when rTMS was applied to either the dPMC or the PPC. Additionally, targeting a control site (area V4), resulted in no change in TOJ performance. Taken together, this suggests that predictions about the sensory consequences of the spatial configuration of the limbs uses efference copy signals generated in the dPMC and state estimation about the position of the limbs from PPC. Acknowledgments: This research was supported by the Natural Sciences and Engineering Research Council of Canada.
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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 | 0.001 |
| 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) | 0.001 | 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".