Human Cerebellum Plays an Important Role in Memory-Timed Finger Movement: An fMRI Study
Bibliographic record
Abstract
The purpose of this study was to determine, by using functional magnetic resonance imaging, the areas of the brain activated during a memory-timed finger movement task and compare these with those activated during a visually cued movement task. Because it is likely that subjects engage in subvocalization associated with chronometric counting to achieve accurate timing during memory-timed movements, the authors sought to determine the areas of the brain activated during a silent articulation task in which the subjects were instructed to reproduce the same timing as for the memory-timed movement task without any lip movements or vocalization. The memory-timed finger movement task induced activation of the anterior lobe of the cerebellum (lobules IV and V) bilaterally, the contralateral primary motor area, the supplementary motor area (SMA), the premotor area (PMA), the prefrontal cortex, and the posterior parietal cortex bilaterally, compared with the resting condition. The same areas in the SMA and left prefrontal cortex were activated during the silent articulation task compared with the resting condition. The anterior lobe of the cerebellum on both sides was also activated during the silent articulation task compared with the resting condition, but these activations did not reach statistical significance (P < 0.05 corrected). In addition, the anterior cerebellum on both sides showed significant activation during the memory-timed movement task when compared with the visually cued finger movement task. The visually cued finger movement task specifically activated the ipsilateral PMA and the intraparietal cortex bilaterally. The results indicate that the anterior lobe of the cerebellum of both sides, the SMA, and the left prefrontal cortex were probably involved in the generation of accurate timing, functioning as a clock within the CNS, and that the dorsal visual pathway may be involved in the generation of visually cued movements.
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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 | 0.001 | 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".