The cerebellar components of the human language network
Bibliographic record
Abstract
The cerebellum's capacity for neural computation is arguably unmatched. Yet despite now ample evidence of cerebellar contributions to cognition, including language, its precise role in language processing remains debated. Here, we systematically characterize cerebellar language-responsive regions using precision fMRI. We identify four cerebellar regions that respond to language across modalities (Experiments 1a-b, n=754). One region-spanning Crus I/II/lobule VIIb-is selective for language relative to diverse non-linguistic perceptual, cognitive, and motor tasks (Experiments 2a-f, n=732), and the rest exhibit mixed-selective profiles, responding strongly to language but also to one or more of the non-linguistic conditions. Similar to the neocortical language system, the language-selective region is engaged by sentence-level meanings during comprehension and production (Experiments 3a-b, n=100) and shows fine-grained sensitivity to linguistic processing difficulty (Experiment 3c, n=5). Further, this region's response to language is not due to the frequent presence of social content in language, as it is strongly engaged by both social and nonsocial sentences (Experiment 3d, n=10). Finally, all four regions, but especially Crus I/II/VIIb, are functionally connected to the neocortical language system (Experiment 4, n=85). We propose that these cerebellar regions constitute components of the extended language network, with one region supporting linguistic semantic processing and closely mirroring the selectivity of the neocortical language network, and the other three plausibly integrating information from diverse neocortical regions.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".