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Record W2037120922 · doi:10.1167/8.14.5

The influence of retinal and extra-retinal motion cues on perceived object motion during self-motion

2008· article· en· W2037120922 on OpenAlexaff
R. T. Dyde, Laurence R. Harris

Bibliographic record

VenueJournal of Vision · 2008
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsYork University
Fundersnot available
KeywordsEfference copySaccadic maskingEye movementObserver (physics)Computer visionMotion (physics)Smooth pursuitGazeMotion perceptionVestibular systemCommunicationPsychologyArtificial intelligenceVisual spaceComputer sciencePhysicsNeurosciencePerception

Abstract

fetched live from OpenAlex

Eye, head, and body movement are intimately linked. During self-motion, the eyes track objects by a combination of vestibular reflexes and smooth pursuit eye movements but although the world appears stable during saccadic gaze changes, it does not appear stable during physical self-motion. We determined the amount by which a fixated object needed to be moved in space in order to appear earth stationary to a linearly moving observer. Observers were oscillated sinusoidally either passively or under their own control, under lit and fully darkened conditions. The visual targets always needed to move (in space) in the same direction as the observer to be judged as earth stationary. Targets needed to be moved more in order to be judged as earth stationary when movement was in the dark, rather than in the light, and also when movement was passive rather than when it was active. Efference copy motor signals, visual movement, and non-visual cues all contribute significantly and approximately additively to the estimate of self-motion. Errors in perceived self-motion can produce subsequent illusory visual motion.

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)0.002
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.311
Teacher spread0.281 · 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

Citations32
Published2008
Admission routes1
Has abstractyes

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