Generalization patterns for reach adaptation and proprioceptive recalibration differ after visuomotor learning
Bibliographic record
Abstract
Visuomotor learning results in changes in both motor and sensory systems (Cressman EK, Henriques DY. J Neurophysiol 102: 3505-3518, 2009), such that reaches are adapted and sense of felt hand position recalibrated after reaching with altered visual feedback of the hand. Moreover, visuomotor learning has been shown to generalize such that reach adaptation achieved at a trained target location can influence reaches to novel target directions (Krakauer JW, Pine ZM, Ghilardi MF, Ghez C. J Neurosci 20: 8916-8924, 2000). We looked to determine whether proprioceptive recalibration also generalizes to novel locations. Moreover, we looked to establish the relationship between reach adaptation and changes in sense of felt hand position by determining whether proprioceptive recalibration generalizes to novel targets in a similar manner as reach adaptation. On training trials, subjects reached to a single target with aligned or misaligned cursor-hand feedback, in which the cursor was either rotated or scaled in extent relative to hand movement. After reach training, subjects reached to the training target and novel targets (including targets from a second start position) without visual feedback to assess generalization of reach adaptation. Subjects then performed a proprioceptive estimation task, in which they indicated the position of their hand relative to visual reference markers placed at similar locations as the trained and novel reach targets. Results indicated that shifts in hand position generalized across novel locations, independent of reach adaptation. Thus these distinct sensory and motor generalization patterns suggest that reach adaptation and proprioceptive recalibration arise from independent error signals and that changes in one system cannot guide adjustments in the other.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".