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Record W2022642890 · doi:10.1097/wnr.0b013e32832a0a7c

Joint attention helps infants learn new words: event-related potential evidence

2009· article· en· W2022642890 on OpenAlexaff
Masako Hirotani, Manuela Stets, Tricia Striano, Angela D. Friederici

Bibliographic record

VenueNeuroreport · 2009
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsCarleton University
FundersMax-Planck-Institut für Kognitions- und NeurowissenschaftenAlexander von Humboldt-Stiftung
KeywordsPsychologyJoint attentionLexiconCognitive psychologyPriming (agriculture)Negativity effectMental lexiconTone (literature)Event-related potentialDevelopmental psychologyCognitionLinguisticsNeuroscienceAutism

Abstract

fetched live from OpenAlex

This study investigated the role of joint attention in infants' word learning. Infants aged 18-21 months were taught new words in two social contexts, joint attention (eye contact, positive tone of voice) or non-joint attention (no eye contact, neutral tone of voice). Event-related potentials were measured as the infants saw objects either congruent or incongruent with the taught words. For both social contexts, an early negativity was observed for the congruent condition, reflecting a phonological-lexical priming effect between objects and the taught words. In addition, for the joint attention, the incongruent condition elicited a late, widely distributed negativity, attributed to semantic integration difficulties. Thus, social cues have an impact on how words are learned and represented in a child's mental lexicon.

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 score0.003

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 head0.030
GPT teacher head0.313
Teacher spread0.283 · 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

Citations78
Published2009
Admission routes1
Has abstractyes

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