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Related Experiment Video

Updated: Sep 22, 2025

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

From decomposition to distributed theories of morphological processing in reading.

Patience Stevens¹, David C Plaut²

¹Department of Psychology, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213-3890, USA. pstevens@andrew.cmu.edu.

Psychonomic Bulletin & Review

|May 20, 2022

Summary

Word processing relies on how complex words are structured. A distributed approach, rather than decomposition, better explains morphological processing across reading development and languages.

Keywords:

Cross-linguistic Morphological processing Reading acquisition Visual word recognition

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Related Experiment Videos

Last Updated: Sep 22, 2025

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

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Published on: September 17, 2019

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Published on: June 29, 2021

Area of Science:

Cognitive Psychology
Psycholinguistics
Computational Linguistics

Background:

Morphological structure significantly influences visual word recognition.
Processing mechanisms evolve during reading acquisition and differ across languages.

Purpose of the Study:

To review mechanisms of morphological processing.
To evaluate decomposition vs. distributed accounts of word recognition.

Main Methods:

Theoretical review of existing research.
Synthesis of findings within cognitive and linguistic theories.

Main Results:

Many findings align with morphological decomposition models.
A distributed approach offers a more comprehensive explanation for diverse results.

Conclusions:

Distributed models provide a more natural fit for the full spectrum of morphological processing data.
This perspective highlights benefits beyond explicit morpheme representation.