The Roles and Interplay of Reinforcement-Based and Error-Based Processes on Exploratory Behaviour in Parkinson’s Disease and Neurologically Intact Populations
Bibliographic record
Abstract
Exploration is important when attempting to relearn motor skills following neurological disorders. Converging neuroanatomical evidence shows bidirectional connections between reinforcement-based (basal ganglia) and error-based (cerebellum) neural circuitries. However, it is unknown whether these bidirectional neural connections would influence exploratory behaviour. Here we designed two experiments and a computational model to investigate the unique and interacting roles of reinforcement and error feedback on motor exploration. Participants grasped the handle of a robotic manipulandum. They made reaching movements to a large target that promoted exploratory behaviour, without vision of their hand. Participants received either reinforcement feedback (pleasant sound, monetary gain for a success) and/or error feedback (small cursor showing hand position) at the end of their reach. We computed trial-by-trial statistical random walks (lag-1 autocorrelations) to quantify exploration. Aligned with model predictions, in Experiment 1 we found that neurologically intact individuals displayed significantly greater exploration with reinforcement feedback compared to error feedback (p < 0.001). Participants displayed moderate levels of exploration when receiving both forms of feedback, which was greater than isolated error feedback (p = 0.035) and less than isolated reinforcement feedback (p < 0.001). In Experiment 2, we considered those with Parkinson’s disease, who have compromised reinforcement-based neural circuits. Individuals with Parkinson’s had less exploration with reinforcement feedback compared to healthy age-matched controls (p < 0.001). Taken together, our results and model suggest that reinforcement-based and error-based processes respectively boost and suppress exploration, while in concert these processes oppose one another to result in moderate exploratory behaviour.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".