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Record W1978081563 · doi:10.1121/1.1798354

Human temporal auditory acuity as assessed by envelope following responses

2004· article· en· W1978081563 on OpenAlexafffund
David W. Purcell, Sasha M. John, Bruce A. Schneider, Terence W. Picton

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2004
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of TorontoBaycrest Hospital
FundersCanadian Institutes of Health Research
KeywordsAudiologyModulation (music)Envelope (radar)Amplitude modulationScalpAuditory systemNoise (video)Frequency modulationAcousticsPsychologyComputer scienceMedicinePhysicsArtificial intelligenceRadio frequencyTelecommunications

Abstract

fetched live from OpenAlex

Temporal auditory acuity, the ability to discriminate rapid changes in the envelope of a sound, is essential for speech comprehension. Human envelope following responses (EFRs) recorded from scalp electrodes were evaluated as an objective measurement of temporal processing in the auditory nervous system. The temporal auditory acuity of older and younger participants was measured behaviorally using both gap and modulation detection tasks. These findings were then related to EFRs evoked by white noise that was amplitude modulated (25% modulation depth) with a sweep of modulation frequencies from 20 to 600 Hz. The frequency at which the EFR was no longer detectable was significantly correlated with behavioral measurements of gap detection (r = -0.43), and with the maximum perceptible modulation frequency (r = 0.72). The EFR techniques investigated here might be developed into a clinically useful objective estimate of temporal auditory acuity for subjects who cannot provide reliable behavioral responses.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
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 head0.026
GPT teacher head0.316
Teacher spread0.290 · 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 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

Citations299
Published2004
Admission routes2
Has abstractyes

Explore more

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