Understanding Dyslexia Through Personalized Large-Scale Computational Models
Open accessAbstract
Learning to read is foundational for literacy development, yet many children in primary school fail to become efficient readers despite normal intelligence and schooling. This condition, referred to as developmental dyslexia, has been hypothesized to occur because of deficits in vision, attention, auditory and temporal processes, and phonology and language. Here, we used a developmentally plausible computational model of reading acquisition to investigate how the core deficits of dyslexia determined individual learning outcomes for 622 children (388 with dyslexia). We found that individual learning trajectories could be simulated on the basis of three component skills related to orthography, phonology, and vocabulary. In contrast, single-deficit models captured the means but not the distribution of reading scores, and a model with noise added to all representations could not even capture the means. These results show that heterogeneity and individual differences in dyslexia profiles can be simulated only with a personalized computational model that allows for multiple deficits.
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Some children struggle to enter the unsupervised self-teaching loop because they may lack sufficient basic decoding skills, vocabulary, or reading opportunities.
“Yet, some children struggle to enter this unsupervised self-teaching loop because they might lack sufficient basic decoding skills, vocabulary or reading opportunities (Perry et al., 2019).”
From Testing the Effects of GraphoGame Against a Computer-Assisted Math Intervention in Primary School (2022) · cites this paper as Perry et al., 2019
The Model Description and Method section (page unavailable) describes self-teaching as requiring successful decoding and a matching word in the phonological lexicon, supporting the decoding-skills and vocabulary parts of the claim. The Results (page unavailable) report that simulated poor readers learned fewer words through decoding. Our copy does not identify insufficient reading opportunities as a reason children struggle to enter the loop. (LLM, conf=0.86)
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References 22 · Cited by 109
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- Authors
- Perry, C., Zorzi, M., & Ziegler, J. C.
- Affiliations
- Swinburne University of Technology (AU)
- University of Padua (IT)
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Perry, Conrad
- Venue
- Psychological Science
- Publisher
- SAGE Publications
- Published
- 2019-02-07
- Volume
- 30
- Issue
- 3
- Pages
- 386-395
- ISSN
- 0956-7976 (ISSN-L)
- 1467-9280
- PMC
- PMC6419236
- OpenAlex
- W2912475619
- Cited by
- 109
- References
- 22
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 11 · 2023: 20 · 2022: 22 · 2021: 18 · 2020: 14 · 2019: 6
- FWCI
- 27.32
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Psychological Science (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by-nc)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion, cc-by-nc)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by-nc)
- Funders
- Agence Nationale de la Recherche (ANR-11-IDEX-0001, ANR-16-CE28-0005, ANR-11-LABX-0036, ANR-16-CONV-0002, ANR-13-APPR-0003)
- Swinburne University of Technology
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (P50HD027802)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Reading and Literacy Development
- Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
- Social Sciences › Psychology › Neuropsychology and Physiological Psychology › Developmental and Educational Neuropsychology
- Keywords
- developmental dyslexia, reading acquisition, computational modeling of reading, individual differences, orthography, phonology, vocabulary, personalized models, learning trajectories, literacy development
- MeSH terms
- Literacy, Computer Simulation, Dyslexia, Humans, Language, Learning, Longitudinal Studies, Noise, Phonetics, Reading, Vocabulary
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