Bibliographic record
Abstract
Motor learning is a very basic, essential form of learning that appears to share common mechanisms across different motor systems. We evaluate and compare a few conceptual models for learning in a relatively simple neural system, the vestibulo-ocular reflex (VOR) of vertebrates. We also compare the different animal models that have been used to study the VOR. In the VOR, a sensory signal from the semicircular canals is transformed into a motor signal that moves the eyes. The VOR can modify the transformation under the guidance of vision. The changes are persistent and share some characteristics with other types of associative learning. The cerebellar cortex is directly linked to the VOR reflex circuitry in a partnership that is present in all vertebrates, and which is necessary for motor learning. Early theories of Marr, Albus, and Ito, in which motor memories are stored solely in the cerebellar cortex, have not explained the bulk of the experimental data. Many studies appear to indicate a site of learning in the vestibular nuclei, and the most successful models have incorporated long-term memory storage in both the cerebellar cortex and the brainstem. Plausible cellular mechanisms for learning have been identified in both structures. We propose that short-term motor memory is initially stored in the cerebellar cortex, and that during consolidation of the motor memory the locus of storage shifts to include a brainstem site. We present experimental results that support our hypothesis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".