Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1783403904556335104 |
---|---|
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. |
format | Online Article Text |
id | pubmed-6419236 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT perryconrad understandingdyslexiathroughpersonalizedlargescalecomputationalmodels AT zorzimarco understandingdyslexiathroughpersonalizedlargescalecomputationalmodels AT zieglerjohannesc understandingdyslexiathroughpersonalizedlargescalecomputationalmodels |