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Record W4413113423 · doi:10.1016/j.infbeh.2025.102125

Listening to development: How electroencephalography informs infant language and music research

2025· article· en· W4413113423 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInfant Behavior and Development · 2025
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectroencephalographyPsychologyCognitive psychologyPerceptionActive listeningCognitive scienceCommunicationNeuroscience

Abstract

fetched live from OpenAlex

In this review, we discuss how advances in infant electroencephalography (EEG) in the last quarter century have allowed developmental scientists to revisit old questions and ask new ones about early auditory perception. We specifically focus on integrating research on language and music perception given both methodological and theoretical overlaps. We discuss how EEG’s high temporal resolution has provided insights into how infants process subtle changes in language and music, detecting phonemic contrasts, rhythmic patterns, and melodic cues sometimes even before these abilities are observable behaviorally. More recently, advanced methods have uncovered how neural coherence and neural tracking reflect auditory processing and predict future developmental outcomes. Coupling EEG with behavioral measures has enriched our insights into developmental milestones in cognition and perception that traditional methods may miss. Looking forward, we consider how advances in technology such as mobile EEG and hyperscanning can open doors for exploring auditory processing in naturalistic environments, such as during live caregiver interactions. We also discuss pressing challenges in the field, such as the focus on WEIRD populations and a lack of standardized data processing and analysis pipelines. Ultimately, the insights gained from infant language and music EEG research provide a strong foundation for informing parental guidance, and supporting early cognitive and linguistic growth. The continued integration of innovative technologies with rigorous, inclusive methodologies will be crucial in deepening our understanding of how infants perceive and learn language and music, two domains that connect infants to their social and cultural world. • Music and language infant researchers have much to gain from knowledge sharing. • We review how EEG advancements have shaped infant music and language research. • Foundational research focused on time-locked responses unveil early perception. • New analyses allow researchers to capture continuous sound pattern processing. • These fields can inform applied research on early developmental outcomes.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.032
GPT teacher head0.347
Teacher spread0.315 · 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