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Record W1622529596 · doi:10.3233/ves-2009-0357

Neural substrates underlying vestibular compensation: Contribution of peripheral versus central processing

2010· review· en· W1622529596 on OpenAlexafffund
Kathleen E. Cullen, Lloyd B. Minor, Mathieu Beraneck, Soroush G. Sadeghi

Bibliographic record

VenueJournal of Vestibular Research · 2010
Typereview
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsMcGill University
FundersNational Institute on Deafness and Other Communication DisordersCanadian Institutes of Health ResearchNational Institutes of HealthMcGill University
KeywordsVestibular systemNeuroscienceCompensation (psychology)PeripheralPsychologyMotor learningNeuroplasticityAdaptation (eye)ReflexAudiologyPhysical medicine and rehabilitationCognitive psychologyComputer scienceMedicine

Abstract

fetched live from OpenAlex

The vestibulo-ocular reflex (VOR), which functions to stabilize gaze and ensure clear vision during everyday activities, shows impressive adaptation in response to environmental requirements. In particular, the VOR exhibits remarkable recovery following the loss of unilateral labyrinthine input as a result of injury or disease. The relative simplicity of the pathways that mediate the VOR, make it an excellent model system for understanding the changes (learning) that occur in the brain following peripheral vestibular loss to yield adaptive changes. This mini review considers the findings of behavioral, single unit recording and lesion studies of VOR compensation. Recent experiments have provided evidence that the brain makes use of multiple plasticity mechanisms (i.e., changes in peripheral as well as central processing) during the course of vestibular compensation to accomplish the sensory-motor transformations required to accurately guide behavior.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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.

Opus teacher head0.279
GPT teacher head0.464
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations87
Published2010
Admission routes2
Has abstractyes

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