Evidence map›Paper›PMID 39059118›Full record

ArticleCognition2024

The development of real-time spoken and word recognition derives from changes in ability, not maturation.

Ethan Kutlu, Jamie Klein-Packard, Charlotte Jeppsen, J Bruce Tomblin, Bob McMurray

Abstract read
In one paragraph

Article in Cognition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

5 authors.

Ethan KutluDepartment of Linguistics, University of Iowa, Iowa City, 52242, USA; Department of Psychological & Brain Sciences, University of Iowa, Iowa City, 52242, USA. Electronic address: ethan-kutlu@uiowa.edu.
Jamie Klein-PackardDepartment of Psychological & Brain Sciences, University of Iowa, Iowa City, 52242, USA. Electronic address: jamie-klein-1@uiowa.edu.
Charlotte JeppsenDepartment of Psychological & Brain Sciences, University of Iowa, Iowa City, 52242, USA. Electronic address: charlotte-jeppsen@uiowa.edu.
J Bruce TomblinDepartment of Communication Sciences & Disorders, University of Iowa, Iowa City, 52242, USA. Electronic address: j-tomblin@uiowa.edu.
Bob McMurrayDepartment of Psychological & Brain Sciences, University of Iowa, Iowa City, 52242, USA; Department of Communication Sciences & Disorders, University of Iowa, Iowa City, 52242, USA; Department of Otolaryngology-Head and Neck Surgery, University of Iowa, Iowa City, 52242, USA. Electronic address: bob-mcmurray@uiowa.edu.

Funding

The Development of Real Time Spoken and Written Word Recognition: Cognitive Bases of Language and Educational OutcomesR01DC008089 · NIDCD · UNIVERSITY OF IOWA · PI MCMURRAY, BOB · 2007 to 2023
$7.3M
NIDCD NIH HHS R01 DC008089
6 · The paper itself

Abstract

In typical adults, recognizing both spoken and written words is thought to be served by a process of competition between candidates in the lexicon. In recent years, work has used eye-tracking in the visual world paradigm to characterize this competition process over development. It has shown that both spoken and written word recognition continue to develop through adolescence (Rigler et al., 2015). It is still unclear what drives these changes in real-time word recognition over the school years, as there are dramatic changes in language, the onset of reading instruction, and gains in domain general function during this time. This study began to address these issues by asking whether changes in real-time word recognition derive from changes in overall language and reading ability or reflect more general age-related development. This cross-sectional study examined 278 school-age children (Grades 1-3) using the Visual World Paradigm to assess both spoken and written word recognition, along with multiple measures of language, reading and phonology. A structural equation model applied to these ability measures found three factors representing language, reading, and phonology. Multiple regression analyses were used to understand how these three factors relate to real-time spoken and written word recognition as well as a non-linguistic variant of the VWP intended to capture decision speed, eye-movement factors, and other non-language/reading differences. We found that for both spoken and written word recognition, the speed of activating target words in both domains was more closely tied to the relevant ability (e.g., reading for written word recognition) than was age. We also examined competition resolution (how fully competitors were suppressed late in processing). Here, spoken word recognition showed only small, developmental effects that were only related to phonological processing, suggesting links to developmental language disorder. However, in written word recognition, competitor resolution showed large impacts of development which were strongly linked to reading. This suggests the dimensionality of real-time lexical processing may differ across domains. Importantly, neither spoken nor written word recognition is fully described by changes in non-linguistic skills assessed with non-linguistic VWP, and the non-linguistic VWP was linked to differences in language and reading. These findings suggest that spoken and written word recognition continue past the first year of life and are mostly driven by ability and not only by overall maturation.

Indexed as

ReadingRecognition, PsychologySpeech PerceptionAdolescentChildChild DevelopmentCross-Sectional StudiesFemaleHumansLanguage DevelopmentMalePattern Recognition, VisualEye-trackingReadingSchool-age language developmentSEMSpoken word recognitionWritten word recognition

Identifiers

PMID39059118
PMCPMC11470444

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.