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Record W2580413956

The Role of Internal Noise in Self-Motion Perception

2016· dissertation· en· W2580413956 on OpenAlexaboutno aff
Jacek Filip Khan

Bibliographic record

VenueTSpace (University of Toronto) · 2016
Typedissertation
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsNoise (video)Vestibular systemPerceptionAcousticsMotion (physics)Internal modelFilter (signal processing)AmplitudeWork (physics)Computer sciencePhysicsArtificial intelligenceComputer visionPsychologyNeuroscienceOptics
DOInot available

Abstract

fetched live from OpenAlex

In this study, vestibular-based motion perception was investigated. The vestibular system, located in the inner ear, is responsible for sensing translational and rotational motion. A previous study carried out at the University of Toronto found that motion discriminability degraded with increasing motion intensity. Namely, difference thresholds were observed to grow when the amplitude of stimuli increased, suggesting internal noise effects. The current work served as a follow-up to this study and developed a simulation framework to reproduce the results. Analysis of the experiment confirmed that trends in thresholds did not arise due to external motion base noise. Signal detection theory was used as a framework to model the discrimination task and a particle filter perception model was modified to permit the estimation of noise in the otolith, the organ responsible for detecting specific force. Given the limited dataset, simulations successfully reproduced the experimental findings while quantifying the contributions of the internal noise source.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.277
Teacher spread0.261 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2016
Admission routes1
Has abstractyes

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