Cerebellar and Subcortical Contributions to Working Memory Manipulation
Bibliographic record
Abstract
Working memory enables us to temporarily store and manipulate information, a crucial function for problem-solving. However, most working memory models emphasize cortical interactions ignoring contributions from subcortical and cerebellar regions. Given the dense connectivity between the cerebellum, subcortex, and cortex, we hypothesize that these regions provide unique contributions during working memory manipulation. We tested this hypothesis using functional Magnetic Resonance Imaging (fMRI) to measure blood oxygen-level dependent (BOLD) activity during a mental rotation task, where participants judged whether rotated pairs of three-dimensional stimuli were identical. Our results revealed a distributed network spanning the cortex, subcortex, and cerebellum that differentiates rotated from non-rotated stimuli and correct from incorrect responses. BOLD recruitment in these regions increased with larger angles of rotation. We observed delayed responses in premotor, subcortical, and cerebellar regions during incorrect trials. These findings suggest that cerebellar and subcortical regions support working memory manipulation, highlighting a broader mechanism by which distributed brain regions interact to coordinate higher cognitive functions.
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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) | 0.001 | 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 | 0.001 |
| 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".