Factors Impacting the Time Course of Visuomotor Reach Adaptation
Bibliographic record
Abstract
Reaching with altered visual feedback leads to adaptation of internal motor plans, which result in aftereffects, deviated reaching without visual feedback, and proprioceptive recalibration, a shift in perceived hand location (Cressman & Henriques, 2010). However, the rate or speed by which these implicit motor and sensory changes emerge and how this timecourse may be affected by the quality of the feedback during training has yet to be investigated. In a series of experiments, I looked at the speed and size of implicit changes, specifically reach aftereffects and shifts in felt hand position, how fast they emerge and how they vary as a function of the quality of error signals and certainty of the rotation during training. In the first experiment, participants had full access to error signals during training with altered visual feedback of their hand, and during this training, reach aftereffects, and active and passive hand localizations were measured after every single reach-training trial. This gave us a baseline of how fast these implicit components shifted during ‘classic’ training. Shifts in felt hand position reached saturation within one trial and reach aftereffects also reached saturation within three trials of visuomotor rotation training which is much faster than previously believed. In the second experiment we reduced error signal information during training by removing the hand cursor until the reach movement was complete or by constraining hand movements along a channel, so the cursor always went straight to the target. The goal was to investigate if and to what extent these error signals affected the timecourse of proprioceptive recalibration. Despite this reduction, we could not detect a decrease in the rate or size of shifts in felt hand position, indicating the robustness and invariance of these visually-induced changes in proprioceptive estimates. In the third and final experiment, we reduced certainty in the rotation by changing it every 12 trials and still measured estimates of felt hand position on a trial-by-trial basis. We once again found shifts in felt hand position in the expected size and direction that peaked just as fast as the previous experiments, indicating that proprioceptive recalibration is a consistent aspect of reach adaptation to altered visual feedback. The rapid speed by which saturation is attained may also suggest that shifts in proprioceptive recalibration may be a driving factor in reach adaptation, as it saturates far earlier than adaptation does.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".