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Record W2110041101 · doi:10.3109/14992027.2011.599870

Older adults expend more listening effort than young adults recognizing audiovisual speech in noise

2011· article· en· W2110041101 on OpenAlexafffund
Penny Gosselin, Jean‐Pierre Gagné

Bibliographic record

VenueInternational Journal of Audiology · 2011
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
FundersCanadian Institutes of Health Research
KeywordsActive listeningPsychologyTask (project management)Set (abstract data type)Speech recognitionNoise (video)AudiologySpeech perceptionCognitionCognitive resource theoryCognitive psychologyPerceptionCommunicationComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Objective: Using a dual task paradigm, two experiments were conducted to: (1) quantify the listening effort that young and older adults expend to recognize speech in noise when presented under audio-only (Experiment 1) and audiovisual conditions (Experiment 2) and, (2) determine the influence visual cues have on listening effort. Listening effort refers to the attentional and cognitive resources required to understand speech. Design: All participants performed a closed-set word recognition task and tactile pattern recognition task separately and concurrently. Accuracy and reaction time data were collected. The criterion for single task word recognition performance was set to 80% correct across experiments and across age groups. Study sample: For each experiment, 25 young and 25 older adults with normal hearing and normal (or corrected normal) vision participated. Results: Under equated performance conditions, older adults expended more listening effort than young adults with both audio-only and audiovisually presented speech. Furthermore, the processing demands of audiovisual speech recognition were greater than audio-only speech recognition for all participants. Conclusions: These results suggest that while visual cues can improve audiovisual speech recognition, they can also place an extra demand on processing resources with performance consequences for the word and tactile tasks under dual task conditions.SumarioObjetivo: Utilizando un paradigma de doble tarea, se realizaron dos experimentos: 1) cuantificar el esfuerzo que hace los adultos jóvenes y mayores para reconocer el lenguaje en ruido ciando se presenta solo en audio (experimento 1) y en condición audio-visual (experimento 2) y 2) determinar la influencia de las claves visuales en el esfuerzo por comprender. El esfuerzo comprensivo se refiere a los recursos cognitivos y de atención requeridos para comprender el lenguaje. Diseño: Todos los participantes realizaron una tarea de reconocimiento de palabras en contexto cerrado y una tarea de reconocimiento táctil separada y concomitantemente. Se colectaron los datos sobre la precisión y el tiempo de reacción. El criterio para el desempeñ en la prueba de tarea única de reconocimiento de palabras se fijó en 80% de aciertos a través de experimentos y a través de grupos etáreos. Muestra: Para cada experimento, participaron 25 adultos jóvenes y 25 adultos viejos con audición normal y visión normal (o normal corregida). Resultados: En condiciones equitativas de desempeño, los adultos mayores efectuaron mayor esfuerzo que los adultos jóvenes en ambas condiciones de presentación del discurso: sólo audio y audiovisual. Incluso la demanda audiovisual de procesamiento del discurso fue mayor que la auditiva en todos los participantes. Conclusiones: Estos resultados sugieren que mientras las claves visuales pueden mejorar el reconocimiento audiovisual del lenguaje, también demandan un esfuerzo extra en el procesamiento de recursos, con las consecuencias en el desempeño de las tareas táctiles y de palabras en la condición dual.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

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 head0.043
GPT teacher head0.352
Teacher spread0.309 · 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".

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Citations124
Published2011
Admission routes2
Has abstractyes

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