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Record W2045945708 · doi:10.1162/jocn.2007.19.5.878

Mismatch Responses to Pitch Changes in Early Infancy

2007· article· en· W2045945708 on OpenAlexafffund
Chao He, Lisa Hotson, Laurel J. Trainor

Bibliographic record

VenueJournal of Cognitive Neuroscience · 2007
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsMismatch negativityPsychologyAudiologyLateralization of brain functionDevelopmental psychologyStimulus (psychology)ElectroencephalographyNeuroscienceCognitive psychologyMedicine

Abstract

fetched live from OpenAlex

We investigated the emergence of discriminative responses to pitch by recording 2-, 3-, and 4-month-old infants' electro-encephalogram responses to infrequent pitch changes in piano tones. In all age groups, infants' responses to deviant tones were significantly different from responses to standard tones. However, two types of mismatch responses were observed simultaneously in the difference waves. An increase in the left-lateralized positive slow wave was prominent in 2-month-olds, present in 3-month-olds, but insignificant in 4-month-olds. A faster adultlike mismatch negativity (MMN), lateralized to the right hemisphere, emerged at 2 months of age and became earlier and stronger as age increased. The coexistence and dissociation of two types of mismatch responses suggests different underlying neuromechanisms for the two responses. Furthermore, the earlier emergence of the MMN-like component to changes in pitch compared to other sound features implies that neural circuits involved in generating MMN-like responses have different maturational timetables for different sound features.

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)0.002
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
Metaresearch0.0000.002
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 head0.063
GPT teacher head0.352
Teacher spread0.289 · 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

Citations169
Published2007
Admission routes2
Has abstractyes

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