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Record W2082469506 · doi:10.1142/s0219635213500283

Peripheral auditory tuning for vowels

2013· article· en· W2082469506 on OpenAlexafffund
Aravind Kumar Namasivayam, Duc James Le, Jennifer Hard, Samantha Lewis-Fung, Chris Neufeld, Pascal van Lieshout

Bibliographic record

VenueJournal of Integrative Neuroscience · 2013
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsToronto Rehabilitation InstituteUniversity of Toronto
FundersToronto Rehabilitation InstituteCanada Research Chairs
KeywordsLibrary sciencePascal (unit)English languageMedia studiesSociologyPsychologyComputer science

Abstract

fetched live from OpenAlex

In this study, 35 young, healthy adults were tested on whether speech-like stimuli evoke a unique response in the auditory efferent system. To this end, descending cortical influences on medial olivocochlear (MOC) activity were indirectly evaluated by studying the effects of contralateral suppression on distortion product otoacoustic emissions (DPOAEs) under four conditions: (a) in the absence of any contralateral noise (Baseline), (b) presence of contralateral broadband noise (Noise Baseline), (c) vowel discrimination-in-noise task (VDN) and (d) tone discrimination-in-noise (TDN) task. A statistically significant release from suppression was evident across all tested DPOAE frequencies (1, 1.5 and 2 kHz) only for the VDN task (p < 0.05), which yielded greater release from suppression than the TDN task. These findings indicate that during active listening in the presence of noise, the MOC activity may be differentially modulated depending on the type of stimulus (vowel vs. tone). Specifically, in the presence of background noise, vowels may show a greater release from suppression in the cochlea than frequency, intensity and duration matched tones.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.852
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.051
GPT teacher head0.383
Teacher spread0.332 · 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

Citations2
Published2013
Admission routes2
Has abstractyes

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