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Understanding Dyslexia Through Personalized Large-Scale Computational Models

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, a...

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Detalles Bibliográficos
Autores principales: Perry, Conrad, Zorzi, Marco, Ziegler, Johannes C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6419236/
https://www.ncbi.nlm.nih.gov/pubmed/30730792
http://dx.doi.org/10.1177/0956797618823540
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author Perry, Conrad
Zorzi, Marco
Ziegler, Johannes C.
author_facet Perry, Conrad
Zorzi, Marco
Ziegler, Johannes C.
author_sort Perry, Conrad
collection PubMed
description 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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spelling pubmed-64192362019-04-01 Understanding Dyslexia Through Personalized Large-Scale Computational Models Perry, Conrad Zorzi, Marco Ziegler, Johannes C. Psychol Sci Research Articles 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. SAGE Publications 2019-02-07 2019-03 /pmc/articles/PMC6419236/ /pubmed/30730792 http://dx.doi.org/10.1177/0956797618823540 Text en © The Author(s) 2019 http://www.creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Research Articles
Perry, Conrad
Zorzi, Marco
Ziegler, Johannes C.
Understanding Dyslexia Through Personalized Large-Scale Computational Models
title Understanding Dyslexia Through Personalized Large-Scale Computational Models
title_full Understanding Dyslexia Through Personalized Large-Scale Computational Models
title_fullStr Understanding Dyslexia Through Personalized Large-Scale Computational Models
title_full_unstemmed Understanding Dyslexia Through Personalized Large-Scale Computational Models
title_short Understanding Dyslexia Through Personalized Large-Scale Computational Models
title_sort understanding dyslexia through personalized large-scale computational models
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6419236/
https://www.ncbi.nlm.nih.gov/pubmed/30730792
http://dx.doi.org/10.1177/0956797618823540
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