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Neural processes underlying perceptual enhancement by visual speech gestures

2003· article· en· W1988771058 on OpenAlexaff
Daniel E. Callan, Jeffery A. Jones, Kevin G. Munhall, Akiko Callan, Christian Kroos, Eric Vatikiotis‐Bateson

Bibliographic record

VenueNeuroreport · 2003
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversity of British ColumbiaQueen's UniversityWilfrid Laurier University
Fundersnot available
KeywordsGestureMultisensory integrationSuperior temporal sulcusNeurocomputational speech processingSpeech perceptionPerceptionPsychologySpeech recognitionMiddle temporal gyrusVisual perceptionNoise (video)Motor theory of speech perceptionSuperior temporal gyrusCommunicationCognitive psychologyComputer scienceNeuroscienceFunctional magnetic resonance imagingArtificial intelligence

Abstract

fetched live from OpenAlex

This fMRI study explores brain regions involved with perceptual enhancement afforded by observation of visual speech gesture information. Subjects passively identified words presented in the following conditions: audio-only, audiovisual, audio-only with noise, audiovisual with noise, and visual only. The brain may use concordant audio and visual information to enhance perception by integrating the information in a converging multisensory site. Consistent with response properties of multisensory integration sites, enhanced activity in middle and superior temporal gyrus/sulcus was greatest when concordant audiovisual stimuli were presented with acoustic noise. Activity found in brain regions involved with planning and execution of speech production in response to visual speech presented with degraded or absent auditory stimulation, is consistent with the use of an additional pathway through which speech perception is facilitated by a process of internally simulating the intended speech act of the observed speaker.

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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.090
GPT teacher head0.398
Teacher spread0.308 · 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

Citations196
Published2003
Admission routes1
Has abstractyes

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