Evidence map›Paper›PMID 31929721›Full record

ArticleBilingualism (Cambridge, England)2020

From Klingon to Colbertian: Using Artificial Languages to Study Word Learning.

Sayuri Hayakawa, Siqi Ning, Viorica Marian

Abstract read
In one paragraph

Article in Bilingualism (Cambridge, England), 2020. 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

3 authors.

Sayuri HayakawaNorthwestern University.
Siqi NingNorthwestern University.
Viorica MarianNorthwestern University.

Funding

Cognitive Architecture of Bilingual Language ProcessingR01HD059858 · NICHD · NORTHWESTERN UNIVERSITY · PI Viorica Marian · 2010 to 2026
$6.2M
NICHD NIH HHS R01 HD059858
6 · The paper itself

Abstract

Vocabulary acquisition is a critical part of learning a new language. Yet, due to structural, historical, and individual variability associated with natural languages, isolating the impact of specific factors on word learning can be challenging. Artificial languages are versatile tools for addressing this problem, allowing researchers to systematically manipulate properties of the language and control for learners' past experiences. Here, we review how artificial languages have been used to study bilingual word learning, with a particular focus on the influences of language input (e.g., word properties) and language experience (e.g., bilingualism). We additionally discuss the advantages and limitations of artificial languages for bilingual research and suggest resources for researchers considering the use of artificial languages. Used and interpreted properly, artificial language studies can inform our understanding of a wide range of factors relevant to word learning.

Indexed as

artificial languagesbilingualismvocabulary acquisitionword learning

Identifiers

PMID31929721
PMCPMC6953750

What OpenQuestion holds

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Registered trials

None linked

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.