Evidence map›Paper›PMID 30120448›Full record

ReviewLanguage, speech, and hearing services in schools2018

Learning Without Trying: The Clinical Relevance of Statistical Learning.

Elena Plante, Rebecca L Gómez

Abstract readReview
In one paragraph

Review in Language, speech, and hearing services in schools, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

  1. Trial
  2. Exploring Input Parameters in an Expressive Vocabulary Treatment With Late Talkers.Journal of speech, language, and hearing research : JSLHR · 2020
    Trial
  3. Trial
  4. Article
  5. Article
  6. Review
  7. Review
  8. Article
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  11. Article
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  14. Translating Enhanced Conversational Recast to a Telepractice Setting.Language, speech, and hearing services in schools · 2022
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. 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

2 authors.

Elena PlanteThe University of Arizona, Tucson.
Rebecca L GómezThe University of Arizona, Tucson.

Funding

Identification of treatment parameters that maximize language treatment efficacy for children. TALK Initiative supplement.R01DC015642 · NIDCD · UNIVERSITY OF ARIZONA · PI MARY ALT, Elena M Plante · 2016 to 2026
$6.8M
NIDCD NIH HHS R01 DC015642
6 · The paper itself

Abstract

Purpose: Statistical learning research seeks to identify the means by which learners, with little perceived effort, acquire the complexities of language. In the past 50 years, numerous studies have uncovered powerful learning mechanisms that allow for learning within minutes of exposure to novel language input. Method: We consider the value of information from statistical learning studies that show potential for making treatment of language disorders faster and more effective. Results: Available studies include experimental research that demonstrates the conditions under which rapid learning is possible, research showing that these findings apply to individuals with disorders, and translational work that has applied learning principles in treatment and educational contexts. In addition, recent research on memory formation has implications for treatment of language deficits. Conclusion: The statistical learning literature offers principles for learning that can improve clinical outcomes for children with language impairment. There is potential for further applications of this basic research that is yet unexplored.

Indexed as

LanguageLanguage DevelopmentLearningBrain MappingChildHumansLanguage DisordersMaleModels, NeurologicalModels, Statistical

Identifiers

PMID30120448
PMCPMC6198914

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.