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Record W2002116027 · doi:10.1121/1.4778242

The effect of spatial frequency information in central and peripheral vision on natural gaze patterns and audiovisual speech perception

2005· article· en· W2002116027 on OpenAlexaff
Julie N. Buchan, Amanda Wilson, Martin Paré, Kevin G. Munhall

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2005
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsQueen's University
Fundersnot available
KeywordsGazePeripheral visionPerceptionComputer scienceNatural (archaeology)Visual fieldSet (abstract data type)Spatial frequencyVisual perceptionSpatial analysisPeripheralComputer visionArtificial intelligencePsychologyNeuroscienceGeographyPhysics

Abstract

fetched live from OpenAlex

It has been known for some time that visual information plays an important role in how we perceive speech. The present research examines how this visual information is processed in central and peripheral vision. The perception of visual images is carried out in the nervous system by a set of spatial frequency-tuned channels and the sensitivity to spatial resolution varies across the retina. The experiment was conducted using a gaze-contingent display system that records a person’s eye position and then displays different information to their peripheral and central vision. The amount of high spatial frequency information presented in the central and peripheral visual fields was manipulated and the effect on both audiovisual speech perception and natural gaze patterns was measured. Preliminary results show that when peripheral information is no longer adequate, gaze patterns become altered to gather important visual information. This suggests that information across the whole visual field influences audiovisual speech 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)0.004
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.301
Teacher spread0.293 · 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

Citations1
Published2005
Admission routes1
Has abstractyes

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Same venueThe Journal of the Acoustical Society of AmericaSame topicMultisensory perception and integrationFrench-language works237,207