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Record W2122453858 · doi:10.1121/1.2149837

Effect of age on detection of gaps in speech and nonspeech markers varying in duration and spectral symmetry

2006· article· en· W2122453858 on OpenAlexafffund
M. Kathleen Pichora‐Fuller, Bruce A. Schneider, Nancy Benson, Stanley J. Hamstra, Edward Storzer

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2006
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStimulus (psychology)AudiologyAcousticsAge groupsDifferential effectsPsychologyPhysicsMathematicsBiologyMedicineCognitive psychology

Abstract

fetched live from OpenAlex

Gap detection thresholds for speech and analogous nonspeech stimuli were determined in younger and older adults with clinically normal hearing in the speech range. Gap detection thresholds were larger for older than for younger listeners in all conditions, with the size of the age difference increasing with stimulus complexity. For both ages, gap detection thresholds were far smaller when the markers before and after the gap were the same (spectrally symmetrical) compared to when they were different (spectrally asymmetrical) for both speech and nonspeech stimuli. Moreover, gap detection thresholds were smaller for nonspeech than for speech stimuli when the markers were spectrally symmetrical but the opposite was observed when the markers were spectrally asymmetrical. This pattern of results may reflect the benefit of activating well-learned gap-dependent phonemic contrasts. The stimulus-dependent age effects were interpreted as reflecting the differential effects of age-dependent losses in temporal processing ability on within- and between-channel gap detection.

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)0.001
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
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 head0.007
GPT teacher head0.253
Teacher spread0.246 · 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".

Quick stats

Citations107
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicHearing Loss and RehabilitationFrench-language works237,207