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Record W2966022563 · doi:10.1038/s41598-019-47643-1

Neural indices of listening effort in noisy environments

2019· article· en· W2966022563 on OpenAlexaff
Andrew Dimitrijevic, Michael L. Smith, Darren S. Kadis, David R. Moore

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsSunnybrook HospitalHealth Sciences CentreSunnybrook Health Science Centre
FundersNational Institute on Deafness and Other Communication DisordersManchester Biomedical Research CentreNational Institutes of HealthNational Institute for Health and Care Research
KeywordsActive listeningAuditory cortexAudiologyNoise (video)Speech perceptionSpeech recognitionPsychologyCoherence (philosophical gambling strategy)PerceptionComputer scienceCognitive psychologyNeuroscienceMedicineCommunicationArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Listening in a noisy environment is challenging for individuals with normal hearing and can be a significant burden for those with hearing impairment. The extent to which this burden is alleviated by a hearing device is a major, unresolved issue for rehabilitation. Here, we found adult users of cochlear implants (CIs) self-reported listening effort during a speech-in-noise task that was positively related to alpha oscillatory activity in the left inferior frontal cortex, canonical Broca's area, and inversely related to speech envelope coherence in the 2-5 Hz range originating in the superior-temporal plane encompassing auditory cortex. Left frontal cortex coherence in the 2-5 Hz range also predicted speech-in-noise identification. These data demonstrate that neural oscillations predict both speech perception ability in noise and listening effort.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score0.226

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 communication0.0000.000
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 head0.016
GPT teacher head0.259
Teacher spread0.242 · 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 designBench or experimental
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

Citations132
Published2019
Admission routes1
Has abstractyes

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