The human somatosensory cortex contributes to the encoding of newly learned movements
Bibliographic record
Abstract
Recent studies have indicated somatosensory cortex involvement in motor learning and retention. However, the nature of its contribution is unknown. One possibility is that the somatosensory cortex is transiently engaged during movement. Alternatively, there may be durable learning-related changes which would indicate sensory participation in the encoding of learned movements. These possibilities are dissociated by disrupting the somatosensory cortex following learning, thus targeting learning-related changes which may have occurred. If changes to the somatosensory cortex contribute to retention, which, in effect, means aspects of newly learned movements are encoded there, disruption of this area once learning is complete should lead to an impairment. Participants were trained to make movements while receiving rotated visual feedback. The primary motor cortex (M1) and the primary somatosensory cortex (S1) were targeted for continuous theta-burst stimulation, while stimulation over the occipital cortex served as a control. Retention was assessed using active movement reproduction, or recognition testing, which involved passive movements produced by a robot. Disruption of the somatosensory cortex resulted in impaired motor memory in both tests. Suppression of the motor cortex had no impact on retention as indicated by comparable retention levels in control and motor cortex conditions. The effects were learning specific. When stimulation was applied to S1 following training with unrotated feedback, movement direction, the main dependent variable, was unaltered. Thus, the somatosensory cortex is part of a circuit that contributes to retention, consistent with the idea that aspects of newly learned movements, possibly learning-updated sensory states (new sensory targets) which serve to guide movement, may be encoded there.
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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) | 0.003 | 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".