Evidence map›Paper›PMID 32716372›Full record

ArticleJournal of visualized experiments : JoVE2020

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques.

Julie M Schneider, Anqi Hu, Jennifer Legault, Zhenghan Qi

Registry-linked trialAbstract readVideo-Audio Media
In one paragraph

Article in Journal of visualized experiments : JoVE, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06332144 (Predicting Language and Literacy Growth in Children With ASD Using Statistical Learning), which is not on this map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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.

NCT06332144 nacompletednot on this mapstarted 2024, after this paper: background citation

Predicting Language and Literacy Growth in Children With ASD Using Statistical Learning

TypeinterventionalSponsorNortheastern UniversityRan2024 to 2025Enrolled41ConditionsAutism Spectrum Disorder, Language Development, LiteracyArmsStructural vs. Random sequences of stimuli, Intact vs. Degraded speech, Repeating 5-syllable nonwords or 2-syllable nonwords, Recall letter or syllable strings that either contain highly frequent bigram/trigram items or infrequent items according to English Corpus data.
3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

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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.

Julie M SchneiderDepartment of Linguistics and Cognitive Science, University of Delaware; juschnei@udel.edu.
Anqi HuDepartment of Linguistics and Cognitive Science, University of Delaware.
Jennifer LegaultDepartment of Linguistics and Cognitive Science, University of Delaware.
Zhenghan QiDepartment of Linguistics and Cognitive Science, University of Delaware; zqi@udel.edu.

Funding

The role of statistical learning in the atypical language development in ASDR21DC017576 · NIDCD · UNIVERSITY OF DELAWARE · PI QI, ZHENGHAN · 2019 to 2022
$547k
NIDCD NIH HHS R21 DC017576
6 · The paper itself

Abstract

Statistical learning, a fundamental skill to extract regularities in the environment, is often considered a core supporting mechanism of the first language development. While many studies of statistical learning are conducted within a single domain or modality, recent evidence suggests that this skill may differ based on the context in which the stimuli are presented. In addition, few studies investigate learning as it unfolds in real-time, rather focusing on the outcome of learning. In this protocol, we describe an approach for identifying the cognitive and neural basis of statistical learning, within an individual, across domains (linguistic vs. non-linguistic) and sensory modalities (visual and auditory). The tasks are designed to cast as little cognitive demand as possible on participants, making it ideal for young school-aged children and special populations. The web-based nature of the behavioral tasks offers a unique opportunity for us to reach more representative populations nationwide, to estimate effect sizes with greater precision, and to contribute to open and reproducible research. The neural measures provided by the functional magnetic resonance imaging (fMRI) task can inform researchers about the neural mechanisms engaged during statistical learning, and how these may differ across individuals on the basis of domain or modality. Finally, both tasks allow for the measurement of real-time learning, as changes in reaction time to a target stimulus is tracked across the exposure period. The main limitation of using this protocol relates to the hour-long duration of the experiment. Children might need to complete all four statistical learning tasks in multiple sittings. Therefore, the web-based platform is designed with this limitation in mind so that tasks may be disseminated individually. This methodology will allow users to investigate how the process of statistical learning unfolds across and within domains and modalities in children from different developmental backgrounds.

Indexed as

ChildEducation, DistanceFemaleHumansLearningMaleNeuroimaging

Identifiers

PMID32716372
PMCPMC7425813

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

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.