Errors make you better: Behavioral, theoretical and neurophysiological determinants of error processing in motor learning
Bibliographic record
Abstract
According to Schmidt and Lee's (2011) textbook definition, motor learning involves a set of internal processes associated with practice or experience leading to relatively permanent changes in the capability to perform a motor skill. In this symposium our speakers will examine the role of error processing in motor learning, using behavioral, theoretical and neurophysiological techniques. Motor learning related to the acquisition of a novel motor task/new motor pattern, as well as the adaptation of a well-learned movement to meet changing environmental and/or body demands will be examined. Michael Carter will begin the session by discussing the benefit of providing learners with choice over when they receive error feedback within a practice session. Findings will be extended to establish the window of time when feedback is most optimal for improvements in performance. Denise Henriques will then consider error signals that arise when processing conflicting sensory information (visual and proprioceptive) related to hand position. The processing of sensory feedback received and predicted based on an efference copy will be examined in light of their contribution to motor adaptation. Pierre-Michel Bernier will end the session by addressing the contribution of neocortical regions to the encoding of sensory prediction errors in the context of motor adaptation. The neural bases of target error processing will also be discussed, including how they are modulated by performance-contingent monetary incentives. Together, these 3 speakers will provide insight into the strategic (conscious) and implicit (unconscious) error processing mechanisms underlying 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 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".