Generalization patterns for sensory and reach adaptation following exposure to visual-proprioceptive discrepancies
Bibliographic record
Abstract
The CNS evolved sensory and reach adaptation as types of plasticity to deal with body-growth changes and variability in the surrounding world. Reach adaptation generalize to untrained contexts and transfer between limbs. We examined the extent by which proprioceptive recalibration generalize to the untrained hand, across novel locations in the workspace. In experiment 1, subjects trained to reach with an aligned and translated cursor, we assessed the resulting changes in hand movements (without cursor) and felt hand position for both trained and untrained hand. Reach adaptation transferred between hands, proprioceptive recalibration did not transfer. In experiment 2, we measured reach adaptation and proprioceptive recalibration at novel locations following training with a rotated cursor. Reach and sensory adaptation generalized to novel locations at different distances, however, sensory changes generalized with smaller extent at far-locations. In experiment 3, we removed the motor component during training so that subjects exposed to a proprioceptive-visual discrepancy in which they see the cursor heading to the training target while the robot gradually rotates their unseen hand-path. Subjects reached to one target from a starting-position(S1) then we measured reach and sensory changes at novel locations from S1 and from a novel starting-position (S2). We found proprioceptive recalibration at the trained and novel locations from S1 and S2. Additionally, we found reach adaptation at the same locations but with smaller extent. Our findings suggest that reach and sensory adaptation may be independent, mere exposure to proprioceptive-visual discrepancy results in proprioceptive recalibration which drive partial reach adaptation that follow similar generalization pattern.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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".