Evidence map›Paper›PMID 41066701›Full record

ArticleJournal of cognitive neuroscience2026

Time-efficient Methodology for Robustly Assessing Speech-related Mismatch Responses in Adults and Infants.

Tzu-Han Zoe Cheng, Victoria Hennessy, Tian Christina Zhao

Abstract read
In one paragraph

Article in Journal of cognitive neuroscience, 2026. 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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0citing papers in PubMed
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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

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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

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

3 authors.

Tzu-Han Zoe ChengUniversity of Washington.ORCID 0000-0003-1687-8304
Victoria HennessyUniversity of California, San Diego.
Tian Christina ZhaoUniversity of Washington.

Funding

Neural mechanisms underlying the sensitive period for phonetic learning in infants at-risk for Developmental Language DisorderR01DC020419 · NIDCD · UNIVERSITY OF WASHINGTON · PI Tian Christina Zhao · 2022 to 2026
$2.5M
Institute of Translational Health Sciences REDCapNational Center for Advancing Translational Sciences of the National Institutes of Health UL1TR002319NIDCD NIH HHS R01 DC020419NIDCD NIH HHS R01DC020419
6 · The paper itself

Abstract

The mismatch response (MMR) is a critical neural indicator of the discrimination of speech contrasts. Previous research has demonstrated that language experience can affect MMRs: speech contrasts in one's native language differ from nonnative speech contrasts. This effect is observed as early as 11-12 months, but not at 6-7 months, reflective of early learning of speech sounds. Yet, many challenges remain when using MMRs to study speech learning in infants, including prolonged recording time, inefficient use of data, and a lack of reconciliation of MMR recorded using different technologies (i.e., EEG vs. magnetoencephalography [MEG]). Using an improved recording paradigm and analysis approach, the current study addressed these challenges by examining (1) whether MEG-MMR is linked to the well-established EEG-MMR in the same adults and (2) whether our methods capture the difference in MEG-MMR between native and nonnative speech contrasts in adults and (3) in older infants. Results from 18 adults with simultaneous M/EEG demonstrated a high correlation between the MEG-MMR and the EEG-MMR. Additionally, MEG-MMRs to native speech contrasts were different from ones to nonnative speech contrasts, replicating spatiotemporal patterns documented in the existing literature. Finally, we replicated this effect in the MEG-MMR in 14 infants aged between 9 and 14 months using the same methods. These findings validate our new methodologies with less than 15 mins of recording time for acquiring and analyzing speech-related MMRs across ages, paving the way for studying early language development, and improving early detection of language-related disorders.

Indexed as

ElectroencephalographyLanguage DevelopmentMagnetoencephalographySpeech PerceptionAdultFemaleHumansInfantMaleYoung Adult

Identifiers

PMID41066701
PMCPMC13276657

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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.