Evidence map›Paper›PMID 38656669›Full record

ArticleJournal of psycholinguistic research2024

ENIGMA: A Web Application for Running Online Artificial Grammar Learning Experiments.

Tsung-Ying Chen

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In one paragraph

Article in Journal of psycholinguistic research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Tsung-Ying ChenDepartment of Foreign Languages and Literature, National Tsing Hua University, 101, Section 2, Guangfu Road, Hsinchu, 300044, Taiwan. chen.ty@mx.nthu.edu.tw.ORCID http://orcid.org/0000-0002-3876-8491

Funding

Ministry of Science and Technology, Taiwan 108-2410-H-007-030-MY3
6 · The paper itself

Abstract

Artificial grammar learning (AGL) is an experimental paradigm frequently adopted to investigate the unconscious and conscious learning and application of linguistic knowledge. This paper will introduce ENIGMA ( https://enigma-lang.org ) as a free, flexible, and lightweight Web-based tool for running online AGL experiments. The application is optimized for desktop and mobile devices with a user-friendly interface, which can present visual and aural stimuli and elicit judgment responses with RT measures. Without limits in time and space, ENIGMA could help collect more data from participants with diverse personal and language backgrounds and variable cognitive skills. Such data are essential to explain complex factors influencing learners' performance in AGL experiments and answer various research questions regarding L1/L2 acquisition. The introduction of the core features in ENIGMA is followed by an example study that partially replicated Chen (Lang Acquis 27(3):331-361, 2020) to illustrate possible experimental designs and examine the quality of the collected data.

Indexed as

LearningHumansInternetLanguageLinguisticsMultilingualismPsycholinguisticsArtificial grammar learningImplicit and explicit learningOnline experimentWeb-based experiment tool

Identifiers

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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.