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Record W2010734731 · doi:10.1121/1.4784689

The influence of task on gaze during audiovisual speech perception

2004· article· en· W2010734731 on OpenAlexaff
Julie N. Buchan, Martin Paré, Micheal Yurick, Kevin G. Munhall

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2004
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsQueen's University
Fundersnot available
KeywordsGazePerceptionConversationComprehensionIdentity (music)PsychologyNatural (archaeology)Task (project management)Cognitive psychologyEye trackingComputer scienceSpeech recognitionCommunicationArtificial intelligenceAcoustics

Abstract

fetched live from OpenAlex

In natural conversation, visual and auditory information about speech not only provide linguistic information but also provide information about the identity and the emotional state of the speaker. Thus, listeners must process a wide range of information in parallel to understand the full meaning in a message. In this series of studies, we examined how different types of visual information conveyed by a speaker’s face are processed by measuring the gaze patterns exhibited by subjects watching audiovisual recordings of spoken sentences. In three experiments, subjects were asked to judge the emotion and the identity of the speaker, and to report the words that they heard under different auditory conditions. As in previous studies, eye and mouth regions dominated the distribution of the gaze fixations. It was hypothesized that the eyes would attract more fixations for more social judgment tasks, rather than tasks which rely more on verbal comprehension. Our results support this hypothesis. In addition, the location of gaze on the face did not influence the accuracy of the perception of speech in noise.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.943
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.321
Teacher spread0.303 · 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 teacher head, 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

Citations3
Published2004
Admission routes1
Has abstractyes

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