Trained movement direction influences reach adaptation independent of proprioceptive recalibration
Bibliographic record
Abstract
Reaching with rotated visual feedback of the hand leads to reach adaptation and shifts in felt hand position (i.e. proprioceptive recalibration). We have previously shown that proprioceptive recalibration generalizes across a greater area of the workspace than reach adaptation (Lombardo et al. 2014). In the current study we looked to determine if these different generalization patterns are dependent on the movement direction (i.e. vector) experienced during reach training. Subjects trained to reach to a single target with distorted hand-cursor feedback from one of two start positions (S1 = aligned with body midline and S2 = 21 cm to the right of S1). Cursor feedback was rotated 30° clockwise relative to subjects' actual hand position. Subsequently, subjects reached without visual feedback to (1) the same target; (2) the same target from the other start position; and (3) a novel target. Subjects also estimated their felt hand position after moving out from both start positions in order to determine the position at which they perceived their hand was aligned with the reach targets. Results indicated that proprioceptive recalibration generalized to a greater extent than reach adaptation regardless of the movement vector experienced during reach training trials. Interestingly, generalization patterns of reach adaptation differed depending on the trained start position such that subjects tended to move to similar goal locations experienced during training from S1 but not from S2. Together, these findings suggest that the movement vector experienced during training does not influence proprioceptive recalibration but changes the processes involved in reach adaptation. Acknowledgments: This study was supported by NSERC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".