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Record W2096648477 · doi:10.1093/cercor/bhq242

Mother and Stranger: An Electrophysiological Study of Voice Processing in Newborns

2010· article· en· W2096648477 on OpenAlexaff
M. Beauchemin, B. Gonzalez-Frankenberger, Julie Tremblay, Phetsamone Vannasing, Eduardo Martínez‐Montes, Pascal Belin, Renée Béland, Denise Francoeur, Ana Carceller, Fabrice Wallois, Maryse Lassonde

Bibliographic record

VenueCerebral Cortex · 2010
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsMcGill UniversityInternational Laboratory for Brain, Music and Sound ResearchUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsPsychologyCognitionElectrophysiologyNeuroscienceAudiologyDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

In the mature adult brain, there are voice selective regions that are especially tuned to familiar voices. Yet, little is known about how the infant's brain treats such information. Here, we investigated, using electrophysiology and source analyses, how newborns process their mother's voice compared with that of a stranger. Results suggest that, shortly after birth, newborns distinctly process their mother's voice at an early preattentional level and at a later presumably cognitive level. Activation sources revealed that exposure to the maternal voice elicited early language-relevant processing, whereas the stranger's voice elicited more voice-specific responses. A central probably motor response was also observed at a later time, which may reflect an innate auditory-articulatory loop. The singularity of left-dominant brain activation pattern together with its ensuing sustained greater central activation in response to the mother's voice may provide the first neurophysiologic index of the preferential mother's role in language acquisition.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.015
GPT teacher head0.300
Teacher spread0.285 · 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 designObservational
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

Citations131
Published2010
Admission routes1
Has abstractyes

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Same venueCerebral CortexSame topicLanguage Development and DisordersFrench-language works237,207