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Record W2047571333 · doi:10.1121/1.3587911

The influence of concurrent working memory tasks on the visual contributions to speech.

2011· article· en· W2047571333 on OpenAlexaff
Julie N. Buchan, Kevin G. Munhall

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2011
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsQueen's University
Fundersnot available
KeywordsPerceptComputer scienceTask (project management)Speech recognitionAsynchronous communicationPerceptionWorking memorySpeech perceptionMultisensory integrationCognitionCognitive psychologyPsychology

Abstract

fetched live from OpenAlex

Visual speech information can influence the perception of acoustic speech. In acoustically noisy environments, people can often understand more words correctly if they can see a talker as well as hear them. Additionally, conflicting visual speech information can influence the perception of acoustic speech (namely, the McGurk effect), causing a percept of a sound that was not present in actual acoustic speech. This auditory and visual speech information does not need to be perfectly synchronous in order to be integrated. Rather, there is a “synchrony window” over which this information can be integrated. The extent to which a distracting cognitive load task affects the influence of the visual speech is still not well understood. It is also unknown whether a distracting cognitive task has an influence on the integration of temporally asynchronous speech. A series of experiments using both speech-in-noise and McGurk tasks with concurrent working memory tasks was used to address this question. The temporal offset in some of the McGurk tasks was also manipulated. Overall, results suggest that while some interference of the cognitive task can be observed, this influence is quite small and does not have a substantial influence on the integration of asynchronous speech.

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)0.001
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 score0.529
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.050
GPT teacher head0.361
Teacher spread0.311 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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