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Record W2096821089 · doi:10.1093/cercor/bhs050

Development of Simultaneous Pitch Encoding: Infants Show a High Voice Superiority Effect

2012· article· en· W2096821089 on OpenAlexafffund
Céline Marie, Laurel J. Trainor

Bibliographic record

VenueCerebral Cortex · 2012
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health Research
KeywordsMismatch negativitySemitonePsychologyPerceptionMelodyActive listeningAudiologyEchoic memoryElectroencephalographyContext (archaeology)Speech recognitionAuditory perceptionCognitive psychologyCognitionCommunicationComputer scienceMusicalNeuroscience

Abstract

fetched live from OpenAlex

Infants must learn to make sense of real-world auditory environments containing simultaneous and overlapping sounds. In adults, event-related potential studies have demonstrated the existence of separate preattentive memory traces for concurrent note sequences and revealed perceptual dominance for encoding of the voice with higher fundamental frequency of 2 simultaneous tones or melodies. Here, we presented 2 simultaneous streams of notes (15 semitones apart) to 7-month-old infants. On 50% of trials, either the higher or the lower note was modified by one semitone, up or down, leaving 50% standard trials. Infants showed mismatch negativity (MMN) to changes in both voices, indicating separate memory traces for each voice. Furthermore, MMN was earlier and larger for the higher voice as in adults. When in the context of a second voice, representation of the lower voice was decreased and that of the higher voice increased compared with when each voice was presented alone. Additionally, correlations between MMN amplitude and amount of weekly music listening suggest that experience affects the development of auditory memory. In sum, the ability to process simultaneous pitches and the dominance of the highest voice emerge early during infancy and are likely important for the perceptual organization of sound in realistic environments.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.128
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.283
Teacher spread0.251 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations86
Published2012
Admission routes2
Has abstractyes

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