Afferent Input, Efference Copy, Signal Noise, and Biases in Perception of Joint Angle During Active Versus Passive Elbow Movements
Bibliographic record
Abstract
Psychophysical studies have reported an overestimation of limb position in the direction of movement during the early part of active movements. The main hypothesis tested in this study is that the overestimation results from a process of forward prediction of limb state driven by an efference copy of the outgoing motor command. This hypothesis predicts that position overestimation should decrease or disappear during passive movements, for which there should be no efference copy. Seven subjects were asked to remember and to report the perceived angle of their elbow joint at different times during active and passive movements. They showed a highly velocity-dependent overestimation of the elbow joint angle near the beginning of the movement in both active and passive trials. Toward the end of the movement, subjects showed a relatively velocity-independent underestimation of their elbow angle in all trials. Contrary to the prediction of the efference copy hypothesis, the amplitude and the velocity-dependent slope of the elbow angle overestimation were both greater during the early part of passive movements than active movements. This indicates that psychophysical evidence of early overestimation of arm position on its own is not a sufficient proof of forward prediction based on an efference copy, at least under the conditions of this study. Decreased errors during active movements suggest that an efference copy can improve the accuracy of state estimation during active movements. Error patterns seem to parallel the likely level of sensorimotor noise, suggesting a probabilistic mechanism for position estimation.
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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 | 0.012 |
| 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 | 0.000 |
| 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".