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Record W2405506510

Older and younger adults use fewer neural resources during audiovisual than during auditory speech perception.

2009· article· en· W2405506510 on OpenAlexaff
Axel H. Winneke, Natalie A. Phillips

Bibliographic record

VenueAVSP · 2009
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsConcordia University
Fundersnot available
KeywordsAudiologySpeech perceptionPsychologyPerceptionCognitionSpeechreadingCognitive resource theoryMedicineNeuroscience
DOInot available

Abstract

fetched live from OpenAlex

This study looks at age-related differences in the brain processes involved in audiovisual (AV) speech perception in multi-talker background babble. The behavioural findings clearly show that both younger adults (YA) and older adults (OA) benefited equally well from AV speech relative to auditory-only (A) speech. Results pertaining to a condition that presented only a photograph alongside spoken words (AVphoto) supports the notion that an AV speech benefit cannot be achieved without the availability of dynamic visual speech cues provided by the lips. Interestingly, OA performed more poorly than YA in speechreading but, in line with the inverse effectiveness hypothesis, OA showed larger auditory enhancement effects suggesting that OA benefit more from AV speech. Analyses of the auditory N1 event-related potential (ERP) showed that AV speech trials lead to an amplitude reduction relative to A-only trials. This reduction was similar in YA and OA. In addition to the amplitude reduction, in both age groups the N1 related to AV speech trials peaked earlier, but this latency shift was larger for OA indicating that OA benefit more from AV speech than YA. These findings suggest that AV speech processing is more efficient because fewer neural resources are required to achieve superior performance. This idea of efficiency is further discussed with implications to higher-level cognition and successful aging.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0050.001

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 head0.020
GPT teacher head0.298
Teacher spread0.278 · 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.

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
Published2009
Admission routes1
Has abstractyes

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