The cerebellum contributes to prediction error coding in reinforcement learning - complementary evidence from stroke patients and from cerebellar transcranial magnetic stimulation
Bibliographic record
Abstract
To survive and thrive in our ever-changing environment, we need to be able to predict the consequences of our actions. We update these predictions by learning through trial and error, and associated prediction errors (PEs). Recent rodent data suggest that the cerebellum – a region typically associated with processing sensory PEs in supervised error-based learning – also processes PEs in reinforcement learning (RL-PEs; i.e., learning from action outcomes). A proxy of action outcome processing in regions traditionally associated with RL-PE coding, such as striatum and anterior cingulate cortex, can be measured in a component of the feedback-locked event-related potential (ERP), i.e., the feedback-related negativity (FRN). We tested the hypothesis that cerebellar output is necessary for this RL-PE coding in the FRN in a probabilistic feedback learning task. In that case, altered cerebellar output should result in changes in the FRN. Two complementary experiments were performed. First, patients with chronic cerebellar stroke were tested. Second, single-pulse cerebellar transcranial magnetic stimulation (TMS) was applied in healthy participants, thus implementing a virtual lesion approach. Different from controls and control (vertex) TMS, no significant RL-PE processing was observed in the FRN in patients with cerebellar stroke, and in participants receiving cerebellar TMS. Only minor deficits in behavioural flexibility were found, with learning success preserved, possibly due to compensation by other brain areas within the reinforcement learning network. Findings in both experiments show that frontal RL-PE processing depends on cerebellar output. Our results provide evidence for involvement of the cerebellum in processing of RL-PEs in humans, complementing and extending previous findings in rodents.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".