Evidence for high-level processing of postural errors
Bibliographic record
Abstract
The ability to detect and correct postural errors is critical for maintaining an equilibrium position while standing. There is growing evidence that error detection in humans is hierarchically organized into separate systems for high-level errors (failure to meet goals) and low-level errors (perturbations in the action or environment). However, the role of high-level error processing in postural control is currently unclear. In the present study, we provided high-level feedback to participants while they maintained one of four different postural equilibrium positions. Electroencephalographic data recordings revealed that error feedback in this task elicited an error-related negativity (ERN): a component of the event-related brain potential (ERP) associated with high-level error evaluation within medial-frontal cortex. This result provides the first strong evidence that although postural control is subserved by midbrain structures (i.e., the basal ganglia), high-level error-evaluation systems within the brain play a key role in learning to control our body posture—a conclusion that lends further support to Krigolson and Holroyd's (2007) hierarchical error-processing hypothesis.Acknowledgments: This research was supported by the Natural Sciences and Engineering Research Council of Canada.
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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 | 0.004 |
| 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) | 0.003 | 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".