Visuomotor Adaptation and Proprioceptive Recalibration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Motor learning, in particular motor adaptation, is driven by information from multiple senses. For example, when arm control is faulty, vision, touch, and proprioception can all report on the arm's movements and help guide the adjustments necessary for correcting motor error. In recent years we have learned a lot about how the brain integrates information from multiple senses for the purpose of perception. However, less is known about how multisensory data guide motor learning. Most models of, and studies on, motor learning focus almost exclusively on the ensuing changes in motor performance without exploring the implications on sensory plasticity. Nor do they consider how discrepancies in sensory information (e.g., vision and proprioception) related to hand position may affect motor learning. Here, we discuss research from our lab and others that shows how motor learning paradigms affect proprioceptive estimates of hand position, and how even the mere discrepancy between visual and proprioceptive feedback can affect learning and plasticity. Our results suggest that sensorimotor learning mechanisms do not exclusively rely on motor plasticity and motor memory, and that sensory plasticity, in particular proprioceptive recalibration, plays a unique and important role in motor learning.
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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) | 0.001 | 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 it