Evidence map›Paper›PMID 37036081›Full record

ArticleChild development

Preschoolers rely on rich speech representations to process variable speech.

Margaret Cychosz, Tristan Mahr, Benjamin Munson, Rochelle Newman, Jan R Edwards

Abstract read
In one paragraph

Article in Child development. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

5 authors.

Margaret CychoszDepartment of Linguistics, University of California, Los Angeles, Los Angeles, California, USA.ORCID 0000-0001-7523-0503
Tristan MahrWaisman Center, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Benjamin MunsonDepartment of Speech-Language-Hearing Sciences, University of Minnesota, Twin Cities, Minneapolis, Minnesota, USA.
Rochelle NewmanDepartment of Hearing and Speech Sciences and Maryland Language Science Center, University of Maryland, College Park, College Park, Maryland, USA.
Jan R EdwardsDepartment of Hearing and Speech Sciences and Maryland Language Science Center, University of Maryland, College Park, College Park, Maryland, USA.

Funding

Longitudinal Study of Vocabulary Growth and Phonological DevelopmentR01DC002932 · NIDCD · UNIV OF MARYLAND, COLLEGE PARK · PI BECKMAN, MARY E., EDWARDS, JAN · 1998 to 2015
$4.9M
How children with cochlear implants learn speech from their environmentsF32DC019539 · NIDCD · UNIV OF MARYLAND, COLLEGE PARK · PI CYCHOSZ, MARGARET EMILYN · 2021 to 2022
$137k
NIDCD NIH HHS F32 DC019539NIDCD NIH HHS F32DC019539NIDCD NIH HHS R01 DC002932NIDCD NIH HHS R01DC02932
6 · The paper itself

Abstract

To learn language, children must map variable input to categories such as phones and words. How do children process variation and distinguish between variable pronunciations ("shoup" for soup) versus new words? The unique sensory experience of children with cochlear implants, who learn speech through their device's degraded signal, lends new insight into this question. In a mispronunciation sensitivity eyetracking task, children with implants (N = 33), and typical hearing (N = 24; 36-66 months; 36F, 19M; all non-Hispanic white), with larger vocabularies processed known words faster. But children with implants were less sensitive to mispronunciations than typical hearing controls. Thus, children of all hearing experiences use lexical knowledge to process familiar words but require detailed speech representations to process variable speech in real time.

Indexed as

Cochlear ImplantationCochlear ImplantsSpeech PerceptionChildHumansLanguageSpeech

Identifiers

PMID37036081
PMCPMC10330571

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.