Mid-lateral Cerebellar Purkinje Cells Provide a Cognitive Error Signal When Monkeys Learn a New Visuomotor Association
Bibliographic record
Abstract
Abstract How do we learn to establish associations between arbitrary visual cues (like a red light) and movements (like braking the car)? We investigated the neural correlates of visuomotor association learning in the monkey mid-lateral cerebellum. Here we show that, during learning but not when the associations were overlearned, individual Purkinje cells reported the outcome of the monkey’s most recent decision, an error signal, which was independent of changes in hand movement or reaction time. At the population level, Purkinje cells collectively maintained a memory of the most recent decision throughout the entire trial period, updating it after every decision. This error signal decreased as the performance improved. Our results suggest a role of mid-lateral cerebellum in visuomotor associative learning and provide evidence that cerebellum could be a generalized learning system, essential in non-motor learning as well as motor learning.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".