The Unnoticed Contributions of the Cerebellum to Language
Bibliographic record
Abstract
BACKGROUND: In addition to its well-known role in motor processing, the cerebellum has been shown to contribute to a number of nonmotor cognitive abilities. However, despite (1) the acknowledged demonstration of the motor, perceptual and cognitive contributions of the cerebellum and (2) the growing number of neuroimaging studies allowing for the exploration of the neurobiological bases of language abilities, only a small number of neuroimaging studies focus on the cerebellar contribution to language. AIMS: To look for unreported cerebellar activations in the neuroimaging literature for language, in order to systematically describe the unreported or otherwise unnoticed cerebellar activations associated with language tasks. METHODS: A recent review paper by Démonet et al. [Physiol Rev 2005;85:49-95] was used as a base in order to investigate the literature on the neuroimaging of language abilities. RESULTS: Of the 450 papers cited in this review, 100 articles were directly related to single-word processing, of which only 34 reported cerebellum activations. CONCLUSION: The full integration of the cerebellum in the network allowing for language and communication is still to come, as very few neuroimaging studies do report cerebellar activations underlying the processing of words.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".