Understanding Dyslexia Through Personalized Large-Scale Computational Models - Archive ouverte HAL Access content directly
Journal Articles Psychological Science Year : 2019

Understanding Dyslexia Through Personalized Large-Scale Computational Models

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Abstract

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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Dates and versions

hal-02011721 , version 1 (08-02-2019)

Licence

Attribution - NonCommercial - CC BY 4.0

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Conrad Perry, Marco Zorzi, Johannes Ziegler. Understanding Dyslexia Through Personalized Large-Scale Computational Models. Psychological Science, 2019, pp.1-10. ⟨10.1177/0956797618823540⟩. ⟨hal-02011721⟩
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