Visual processing is a language-agnostic window into heterogeneity in early reading development
Mahalakshmi Ramamurthy1, Klint Kanopka2, Julian M Siebert3
1Division of Developmental-Behavioral Pediatrics, Department of Pediatrics, Stanford University School of Medicine, 453 Quarry Road, Stanford, CA 94305, USA; Graduate School of Education, Stanford University, 507 Lasuen Mall, Stanford, CA 94305, USA.
Current Biology : CB
|May 21, 2026
Summary
Early identification of reading disabilities can be improved by assessing visual processing skills alongside language abilities. This approach helps identify at-risk children and tailor interventions for better reading outcomes.
Area of Science:
- Cognitive Psychology
- Developmental Psychology
- Educational Neuroscience
Background:
- Reading disabilities significantly impact global educational and socioeconomic outcomes.
- Identifying early predictors for reading difficulties has been challenging, with historical debate on the role of visual processing.
- Previous research often used small, homogeneous samples, limiting generalizability.
Purpose of the Study:
- To investigate the predictive value of visual processing measures for reading development in a diverse population.
- To identify distinct subgroups of children based on visual processing and language skills.
- To explore the equity of visual processing measures across different socioeconomic and linguistic backgrounds.
Main Methods:
- Administered validated visual processing measures (multi-element letter/pseudo-letter processing, global motion coherence) to a large, diverse cohort of kindergarten and first-grade children.
- Utilized latent profile analysis to identify subgroups.
- Included language-based assessments and reading outcomes for a subset of participants (n ≈ 400).
- Employed machine learning models to assess predictive importance.
Main Results:
- Identified subgroups with specific profiles: strong language/poor visual processing (persistent reading difficulties) and strong visual processing/weaker language (reading success).
- Visual processing measures were equitable across socioeconomic status and home language.
- These measures independently accounted for 12%-16% of variance in reading outcomes.
- Visual processing emerged as a consistent predictor of reading risk in machine learning models.
Conclusions:
- Integrating rapid visual processing, particularly multi-element processing, into early identification is crucial.
- This approach can reveal subgroups missed by conventional screening.
- It facilitates mechanistically informed, personalized interventions that capitalize on individual strengths for improved reading development.
Keywords:
dyslexia screeningearly identificationglobal motion coherencemulti-element processingreading developmentreading heterogeneityvisual processingMore Related Videos
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