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Record W2157469922 · doi:10.1111/cogs.12128

Foundational Tuning: How Infants' Attention to Speech Predicts Language Development

2014· article· en· W2157469922 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.

Bibliographic record

VenueCognitive Science · 2014
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsActive listeningSpeech perceptionVocabularyFoundation (evidence)Language developmentPsychologyPerceptionCognitive psychologyDevelopmental psychologyLinguisticsCommunication

Abstract

fetched live from OpenAlex

Orienting biases for speech may provide a foundation for language development. Although human infants show a bias for listening to speech from birth, the relation of a speech bias to later language development has not been established. Here, we examine whether infants' attention to speech directly predicts expressive vocabulary. Infants listened to speech or non-speech in a preferential listening procedure. Results show that infants' attention to speech at 12 months significantly predicted expressive vocabulary at 18 months, while indices of general development did not. No predictive relationships were found for infants' attention to non-speech, or overall attention to sounds, suggesting that the relationship between speech and expressive vocabulary was not a function of infants' general attentiveness. Potentially ancient evolutionary perceptual capacities such as biases for conspecific vocalizations may provide a foundation for proficiency in formal systems such language, much like the approximate number sense may provide a foundation for formal mathematics.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.875
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.020
GPT teacher head0.317
Teacher spread0.297 · 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