Reach adaptation precedes proprioceptive recalibration
Bibliographic record
Abstract
When subjects are presented with altered visual feedback of their hand during a goal-directed movement (i.e. subjects view a cursor representing their hand that is shifted from their hand’s actual position while reaching in a virtual reality environment), they typically adapt their movements so that the cursor is brought to the target, thus reducing reaching errors (i.e. reach adaptation). In addition to reach adaptation, it has recently been shown that reaching with altered visual feedback of the hand results in changes in felt hand position, such that proprioceptive estimates of hand position are shifted in the direction of the visual feedback provided (Cressman and Henriques, 2009). In order to examine the relationship between reach adaptation and proprioceptive recalibration, the current experiment investigated the time-course of reach adaptation and proprioceptive recalibration in response to reaching with altered visual feedback of the hand. Specifically, subjects trained to reach to a single visual target while seeing a cursor that was aligned with the actual position of their hand (50 trials: aligned reach training), or while seeing a cursor that was rotated 30° clockwise (CW) relative to their hand (150 trials: rotated reach training). Reach errors and proprioceptive sense of felt hand position were assessed following the aligned reach training trials and at 7 different times during the rotated reach training trials, by having subjects reach to the target without visual feedback and provide estimates of their hand relative to a visual reference marker respectively. Results revealed an increase in reach adaptation and proprioceptive recalibration over the course of reach training with a rotated cursor relative to the aligned reach training. Moreover, results showed a much earlier and steeper increase in reach adaptation, as compared to proprioceptive recalibration. These findings suggest that reach adaptation and proprioceptive recalibration arise due to separate 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".