Probabilistic Graphical Models of Dyslexia

Reading is a complex cognitive process, errors in which may assume diverse forms. In this study, introducing a novel approach, we use two families of probabilistic graphical models to analyze patterns of reading errors made by dyslexic people: an LDA-based model and two Naive Bayes models which differ by their assumptions about the generation process of reading errors. The models are trained on a large corpus of reading errors. Results show that a Naive Bayes model achieves highest accuracy compared to labels given by clinicians (AUC = 0.801 +/- 0.05), thus providing the first automated and objective diagnosis tool for dyslexia which is solely based on reading errors data. Results also show that the LDA-based model best captures patterns of reading errors and could therefore contribute to the understanding of dyslexia and to future improvement of the diagnostic procedure. Finally, we draw on our results to shed light on a theoretical debate about the definition and heterogeneity of dyslexia. Our results support a model assuming multiple dyslexia subtypes, that of a heterogeneous view of dyslexia.