Evidence map›Paper›PMID 36574655›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2023

Rapid infant learning of syntactic-semantic links.

Monica Barbir, Mireille J Babineau, Anne-Caroline Fiévet, Anne Christophe

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Monica BarbirLaboratoire de Sciences Cognitives et Psycholinguistique, Ecole Normale Supérieure, Paris Sciences et Lettres, 75005 Paris, France.ORCID 0000-0001-9940-669X
Mireille J BabineauLaboratoire de Sciences Cognitives et Psycholinguistique, Ecole Normale Supérieure, Paris Sciences et Lettres, 75005 Paris, France.ORCID 0000-0002-8946-7082
Anne-Caroline FiévetLaboratoire de Sciences Cognitives et Psycholinguistique, Ecole Normale Supérieure, Paris Sciences et Lettres, 75005 Paris, France.ORCID 0000-0002-4612-9753
Anne ChristopheLaboratoire de Sciences Cognitives et Psycholinguistique, Ecole Normale Supérieure, Paris Sciences et Lettres, 75005 Paris, France.ORCID 0000-0001-7695-7528

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the second year of life, infants begin to rapidly acquire the lexicon of their native language. A key learning mechanism underlying this acceleration is syntactic bootstrapping: the use of hidden cues in grammar to facilitate vocabulary learning. How infants forge the syntactic-semantic links that underlie this mechanism, however, remains speculative. A hurdle for theories is identifying computationally light strategies that have high precision within the complexity of the linguistic signal. Here, we presented 20-mo-old infants with novel grammatical elements in a complex natural language environment and measured their resultant vocabulary expansion. We found that infants can learn and exploit a natural language syntactic-semantic link in less than 30 min. The rapid speed of acquisition of a new syntactic bootstrap indicates that even emergent syntactic-semantic links can accelerate language learning. The results suggest that infants employ a cognitive network of efficient learning strategies to self-supervise language development.

Indexed as

LearningSemanticsHumansInfantLanguageLanguage DevelopmentLinguisticsVocabularycognitive developmentgrammar learninginfant cognitionlanguage acquisitionword learning

Identifiers

PMID36574655
PMCPMC9910616

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.