Modeling the time course of change following visuomotor adaptation in movement, proprioception and prediction
Bibliographic record
Abstract
The ability to make goal-directed movements relies on estimates of limb position, based on vision, proprioception, as well as efference-based predictions. We measure the plasticity of proprioceptive and efferent-based estimates of the hand using a series of visuomotor adaptation experiments, which involve reaching with a rotated cursor. This rotated cursor training leads to changes in movements, proprioception and prediction. We measure the speed of these changes as well as fit them to the multi-rate model (Smith et al., 2006) which consists of a fast and slow process. We used a multiphase experiment which alternated between one rotated training trial and then one of 3 intervening tasks. To measure changes in hand localization, participants estimated the location of the unseen hand when it is moved by the robot (passive localization) or when they generated their own movement (active localization). By comparing the differences between these hand estimates after passive (only proprioception) or active (both proprioception and efferent-based prediction) movements, we are able to measure predicted sensory consequences of movement. The 3rd intervening task type was a no-cursor reach where no visual feedback of hand position or trial success was given. The trial-by-trial data suggest that proprioception recalibrates extremely fast, and is simply proportional to the visual-proprioceptive discrepancy, and does not reflect either model process. Prediction and and no-cursor reaches do not reflect either process fitted to training, but appear to be best explained by their own set of dual processes. Thus, these changes seem to reflect separate adaptation mechanisms.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".