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Enregistrement W2330702035 · doi:10.1097/01.hj.0000434657.23951.54

In Noise, a Spouseʼs Voice is Better Tracked, and Ignored

2013· article· en· W2330702035 sur OpenAlexaboutno aff
Paul Bufano

Notice bibliographique

RevueThe Hearing Journal · 2013
Typearticle
Langueen
DomaineComputer Science
ThématiqueSpeech and dialogue systems
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSpouseActive listeningPsychologyCognitionCognitive resource theoryTask (project management)Social psychologySociologyCommunication

Résumé

récupéré en direct d'OpenAlex

Figure: © iStockphoto.com/monkeybusinessimagesAmid the tumble of voices at a noisy cocktail party, a familiar voice, like that of a spouse, stands out from the crowd. And this phenomenon cuts both ways—a spouse's voice is easier to follow, and easier to ignore, new research has shown. In a study of 23 married couples age 44 to 79 who had been living together for at least 18 years, performance on a listening task was better when a spouse's voice was one of two competing voices heard. This relationship was seen not only when the spouse's voice was the one to track, but also when it was the one to tune out in favor of a stranger's voice. The results, which were published in Psychological Science (doi: 10.1177/0956797613482467), go a step further: While the ability to understand a stranger's voice declined with age, the ability to understand a spouse's voice did not. “We wanted to research this topic because of our frustration with the aging process and with the degenerative nature of hearing,” said lead author Ingrid Johnsrude, PhD, professor of psychology and Canada Research Chair in Cognitive Neuroscience at Queen's University in Kingston, Ontario. “Older people have experience, particularly with a voice. As people get older, their resources are diminished, so we wanted to see if familiarity, specifically in a noisy environment, could improve hearing and cognition.” Participants were recorded speaking 128 scripted sentences from the coordinate-response-measure database. They returned a week to a month later to listen to the recordings. Each listener heard his or her spouse's voice and two novel voices that belonged to other participants' spouses, who were age- and sex-matched to the listener's spouse. In each trial of the listening session, participants simultaneously heard two different sentences spoken by two different voices. The spouse's voice was the target voice to track in one-third of the trials, and the masker to ignore in another third. In the remaining third, both voices were novel.Figure. David: B. Pisoni, PhDThe finding that familiarity with a voice not only helps a person recognize the target signal, but also inhibits competing voices, is important, as it reflects the auditory–cognitive connection, said David B. Pisoni, PhD, director of the Speech Research Laboratory at Indiana University. “A comparable example would be if we went to a bar in Manhattan and all of the competing voices were English, we would have a harder time carrying on a conversation than if they were Chinese,” Dr. Pisoni said. “We now know that the ear is connected to the brain and the brain is connected to the ear, and that these reciprocal connections are working together as one system for a single goal. It's very exciting that audiologists and cognitive scientists are beginning to work together in this new field of cognitive hearing science.””Figure: Rochelle Newman, PhDRochelle Newman, PhD, director of graduate studies for the Department of Hearing and Speech Sciences and for the Program in Neuroscience and Cognitive Science at the University of Maryland, said she hopes that this study will help researchers learn more about the skills used to understand a conversation. “I think one area that needs to be pursued more is how these results apply to children,” Dr. Newman said. “Because kids are often spoken to in noisy and distracting situations, like in school, understanding how well they can listen to one person and ignore another is important for identifying learning issues that involve difficulty paying attention, such as ADD and ADHD.” The finding that people benefit from listening to a voice they know is noteworthy in and of itself, but there are many places this research can go from here, Dr. Johnsrude said. “We wonder if this cognitive effect also holds true with other types of maskers, and whether or not it will work with other levels of familiarity, such as one year of marriage instead of 20.” “We also want to see if there's any evidence for whether or not a mother's voice is processed differently compared with the voice of another person's mother. In the end, we're really interested in understanding listening effort and anything that could potentially make listening easier.” HJ Return to thehearingjournal.com

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,114
Score d'incertitude au seuil0,383

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,1140,021

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,019
Tête enseignante GPT0,231
Écart entre enseignants0,212 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2013
Routes d'admission1
Résumé présentoui

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