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Record W2020922525 · doi:10.1121/1.3249186

Development of auditory phase-locked activity for music sounds.

2009· article· en· W2020922525 on OpenAlexaboutno aff
Antoine J. Shahin

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2009
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPhase lockingAudiologyAuditory cortexAcousticsPsychologyPhase (matter)Duration (music)PhysicsMedicine

Abstract

fetched live from OpenAlex

The auditory cortex undergoes anatomical and functional development that reflects specialization for learned sounds. Auditory maturation is evident in the transient auditory evoked potentials (AEPs) and auditory phase-locked oscillatory activity. Development of AEPs and the phase-locking strength of oscillatory auditory responses to piano, violin, and pure tones were examined. The hypothesis was that if oscillatory activity in different frequency bands reflects different aspects of sound processing, then the development of phase-locking at these frequencies will have different maturational trajectories. Phase-locking for theta (4–8 Hz), alpha (8–14 Hz), lower-to-mid-beta (14–25 Hz), and upper beta and gamma (25–70 Hz) bands strengthened with age and was stronger for musical than pure tones. The phase-locking increase for gamma and upper beta bands mainly reflected the maturation of the spectral representations for sounds. In contrast, increase in phase-locking for theta, alpha, and lower-to-mid-beta was mainly attributed to sensitivity to sound temporal onset rise time. Frequency-specific phase-locking provides a tool to assess auditory development for spectral and temporal aspects of naturalistic complex sounds. [This research was supported by grants from the Canadian Institutes of Health Research and the National Institutes of Health, National Institute on Deafness and other Communication Disorders.]

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)0.002
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.550
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.046
GPT teacher head0.385
Teacher spread0.338 · 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 designNot applicable
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

Citations4
Published2009
Admission routes1
Has abstractyes

Explore more

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