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Record W2126106997 · doi:10.1097/wnr.0b013e328353764d

Experience-dependent plasticity in pitch encoding

2012· review· en· W2126106997 on OpenAlexaff
Ananthanarayan Krishnan, Jackson T. Gandour, Gavin M. Bidelman

Bibliographic record

VenueNeuroreport · 2012
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsBaycrest Hospital
FundersNational Institute on Deafness and Other Communication DisordersNational Institutes of Health
KeywordsSalience (neuroscience)Sensory systemSensory processingFeed forwardPerceptionNeuroscienceEncoding (memory)Computer scienceAuditory cortexCognitionPitch (Music)Speech recognitionPsychology

Abstract

fetched live from OpenAlex

Linguistic and musical pitch provide an analytic window to evaluate how neural representations of important pitch attributes of a sound undergo transformation from early sensory to later cognitive stages of processing in the human brain, and how pitch-relevant experience shapes these representations. These pitch attributes are shaped differentially depending on their functional relevance to a listener. Neural encoding of pitch-relevant information is shaped by the perceptual salience of domain-specific features at subcortical (auditory brainstem) and cortical stages of processing. The emergence of a functional ear asymmetry in the neural encoding of pitch-relevant information at a lower sensory processing level supports the view that local and feedforward and feedback mechanisms are involved in pitch-relevant processing. A theoretical framework for a neural network is proposed involving coordination between local, feedforward, and feedback components that can account for experience-induced enhancement of pitch representations at multiple levels of the auditory pathway.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.240
GPT teacher head0.394
Teacher spread0.154 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations51
Published2012
Admission routes1
Has abstractyes

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