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Record W2982822984 · doi:10.1038/s41467-019-13003-w

Evolving perspectives on the sources of the frequency-following response

2019· review· en· W2982822984 on OpenAlexaff
Emily B. J. Coffey, Trent Nicol, Travis White‐Schwoch, Bharath Chandrasekaran, Jennifer Krizman, Erika Skoe, Robert J. Zatorre, Nina Kraus

Bibliographic record

VenueNature Communications · 2019
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMontreal Neurological Institute and HospitalConcordia UniversityMcGill UniversityInternational Laboratory for Brain, Music and Sound ResearchCentre for Research on Brain Language and Music
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsFidelityNeuroscienceAuditory cortexPerspective (graphical)Relevance (law)Encoding (memory)Computer scienceNeuroplasticityCognitive sciencePsychologyCognitive psychologyArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

The auditory frequency-following response (FFR) is a non-invasive index of the fidelity of sound encoding in the brain, and is used to study the integrity, plasticity, and behavioral relevance of the neural encoding of sound. In this Perspective, we review recent evidence suggesting that, in humans, the FFR arises from multiple cortical and subcortical sources, not just subcortically as previously believed, and we illustrate how the FFR to complex sounds can enhance the wider field of auditory neuroscience. Far from being of use only to study basic auditory processes, the FFR is an uncommonly multifaceted response yielding a wealth of information, with much yet to be tapped.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0000.002
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.003

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 head0.119
GPT teacher head0.389
Teacher spread0.270 · 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

Citations217
Published2019
Admission routes1
Has abstractyes

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