Multiple errors, multiple systems: The Hierarchical Error Processing Hypothesis
Bibliographic record
Abstract
The simple act of reaching for a cup of coffee is closely monitored by multiple error evaluation and learning systems within the brain. The reason we need multiple error systems to evaluate our movements is simple – different neural systems are tasked with evaluating different types of errors. High-level errors, errors indicating the failure to achieve system goals, appear to be evaluated within the medial-frontal cortex. Low-level errors, errors brought about by environmental changes and/or errors in a motor command, appear to be resolved within an ongoing movement by error processing systems within the posterior parietal cortex and the cerebellum. Here, we conducted a series of studies using event-related brain potentials (ERP) to examine error evaluation during the execution of goal-directed aiming movements. Our results demonstrate that high-level errors elicit the error-related negativity – an ERP component associated with error evaluation by a reinforcement learning system within the medial-frontal cortex. Low-level errors elicited larger N100 responses – an ERP component typically associated with early visual processing and the focusing of visuospatial attention. Additionally, low-level errors also modulated the amplitude of the P300, an ERP component that has historically been associated with the updating of an internal model of the task environment. Together, our results provide further support for the hierarchical error-processing hypothesis – a hypothesis that posits that the multiple error evaluation systems within the brain are hierarchically organized to provide an overall system that is responsible for both motor control and 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".