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

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

Bibliographic record

VenueThe Hearing Journal · 2013
Typearticle
Languageen
FieldComputer Science
TopicSpeech and dialogue systems
Canadian institutionsnot available
Fundersnot available
KeywordsSpouseActive listeningPsychologyCognitionCognitive resource theoryTask (project management)Social psychologySociologyCommunication

Abstract

fetched live from 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

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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 score0.639
Threshold uncertainty score0.424

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.231
Teacher spread0.212 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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