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Record W2399040452

Communicative signals promote abstract rule learning by 7-month-old infants

2014· article· en· W2399040452 on OpenAlexfundno aff
Brock Ferguson, Casey Lew‐Williams

Bibliographic record

VenueeScholarship (California Digital Library) · 2014
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaAmerican Speech-Language-Hearing FoundationAmerican Hearing Research FoundationNational Science Foundation
KeywordsPsychologyLanguage acquisitionGrammarCognitionLanguage developmentHuman languageCognitive scienceCognitive psychologyLinguisticsDevelopmental psychology
DOInot available

Abstract

fetched live from OpenAlex

Infants' ability to detect patterns in speech input is central to their acquisition of language, and recent evidence suggests that their cognitive faculties may be specifically tailored to this task: Seven-month-olds reliably abstract rule-like structures (e.g., ABB vs. ABA) from speech, but not other stimuli.Here we ask what drives this speech advantage.Specifically, we propose that infants' learning from speech is driven by their representation of speech as a communicative signal.As evidence for this claim, we report an experiment in which 7-month-old infants (N=28) learned rules from a novel sound (sine-wave tones) introduced as a communicative signal, but failed to learn the same rules from tones presented in non-communicative contexts.These findings highlight the powerful influence of social-communicative contexts on infants' learning.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.246
Teacher spread0.234 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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Same venueeScholarship (California Digital Library)Same topicLanguage Development and DisordersFrench-language works237,207