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Listening to the Brainstem: Musicianship Enhances Intelligibility of Subcortical Representations for Speech

2015· article· en· W2024006773 on OpenAlexaff
Michael W. Weiss, Gavin M. Bidelman

Bibliographic record

VenueJournal of Neuroscience · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsCollege of Family Physicians of CanadaUniversity of Toronto
FundersGRAMMY Foundation
KeywordsPsychologyActive listeningBrainstemNeural correlates of consciousnessIntelligibility (philosophy)Speech perceptionPerceptionCognitive psychologyAudiologyNeuroscienceCognitionCommunication

Abstract

fetched live from OpenAlex

Auditory experiences including musicianship and bilingualism have been shown to enhance subcortical speech encoding operating below conscious awareness. Yet, the behavioral consequence of such enhanced subcortical auditory processing remains undetermined. Exploiting their remarkable fidelity, we examined the intelligibility of auditory playbacks (i.e., "sonifications") of brainstem potentials recorded in human listeners. We found naive listeners' behavioral classification of sonifications was faster and more categorical when evaluating brain responses recorded in individuals with extensive musical training versus those recorded in nonmusicians. These results reveal stronger behaviorally relevant speech cues in musicians' neural representations and demonstrate causal evidence that superior subcortical processing creates a more comprehensible speech signal (i.e., to naive listeners). We infer that neural sonifications of speech-evoked brainstem responses could be used in the early detection of speech-language impairments due to neurodegenerative disorders, or in objectively measuring individual differences in speech reception solely by listening to individuals' brain activity.

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)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.042
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.198
GPT teacher head0.402
Teacher spread0.204 · 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.

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

Citations73
Published2015
Admission routes1
Has abstractyes

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