Cerebellar contributions to motor control and language comprehension: searching for common computational principles
Bibliographic record
Abstract
The past 25 years have seen the functional domain of the cerebellum extend beyond the realm of motor control, with considerable discussion of how this subcortical structure contributes to cognitive domains including attention, memory, and language. Drawing on evidence from neuroanatomy, physiology, neuropsychology, and computational work, sophisticated models have been developed to describe cerebellar function in sensorimotor control and learning. In contrast, mechanistic accounts of how the cerebellum contributes to cognition have remained elusive. Inspired by the homogeneous cerebellar microanatomy and a desire for parsimony, many researchers have sought to extend mechanistic ideas from motor control to cognition. One influential hypothesis centers on the idea that the cerebellum implements internal models, representations of the context-specific dynamics of an agent's interactions with the environment, enabling predictive control. We briefly review cerebellar anatomy and physiology, to review the internal model hypothesis as applied in the motor domain, before turning to extensions of these ideas in the linguistic domain, focusing on speech perception and semantic processing. While recent findings are consistent with this computational generalization, they also raise challenging questions regarding the nature of cerebellar learning, and may thus inspire revisions of our views on the role of the cerebellum in sensorimotor control.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".