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Vestibular System: The Many Facets of a Multimodal Sense

2008· review· en· W2103125163 on OpenAlexafffund
Dora E. Angelaki, Kathleen E. Cullen

Bibliographic record

VenueAnnual Review of Neuroscience · 2008
Typereview
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsMcGill University
FundersNational Institute on Deafness and Other Communication DisordersNational Eye InstituteCanadian Institutes of Health ResearchCanadian Space AgencyNational Institutes of HealthMcGill UniversityNational Aeronautics and Space Administration
KeywordsVestibular systemNeuroscienceSensory systemOtolithVestibular nucleiSemicircular canalProprioceptionOrientation (vector space)PerceptionPsychologyCommunicationBiology

Abstract

fetched live from OpenAlex

Elegant sensory structures in the inner ear have evolved to measure head motion. These vestibular receptors consist of highly conserved semicircular canals and otolith organs. Unlike other senses, vestibular information in the central nervous system becomes immediately multisensory and multimodal. There is no overt, readily recognizable conscious sensation from these organs, yet vestibular signals contribute to a surprising range of brain functions, from the most automatic reflexes to spatial perception and motor coordination. Critical to these diverse, multimodal functions are multiple computationally intriguing levels of processing. For example, the need for multisensory integration necessitates vestibular representations in multiple reference frames. Proprioceptive-vestibular interactions, coupled with corollary discharge of a motor plan, allow the brain to distinguish actively generated from passive head movements. Finally, nonlinear interactions between otolith and canal signals allow the vestibular system to function as an inertial sensor and contribute critically to both navigation and spatial orientation.

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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.047
GPT teacher head0.337
Teacher spread0.290 · 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

Citations927
Published2008
Admission routes2
Has abstractyes

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