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Performance of children with developmental dyslexia on high and low topological entropy artificial grammar learning task

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Abstract

Graph complexity as measured by topological entropy has been previously shown to affect performance on artificial grammar learning tasks among typically developing children. The aim of this study was to examine the effect of graph complexity on implicit sequential learning among children with developmental dyslexia. Our goal was to determine whether children’s performance depends on the complexity level of the grammar system learned. We conducted two artificial grammar learning experiments that compared performance of children with developmental dyslexia with that of age- and reading level-matched controls. Experiment 1 was a high topological entropy artificial grammar learning task that aimed to establish implicit learning phenomena in children with developmental dyslexia using previously published experimental conditions. Experiment 2 is a lower topological entropy variant of that task. Results indicated that given a high topological entropy grammar system, children with developmental dyslexia who were similar to the reading age-matched control group had substantial difficulty in performing the task as compared to typically developing children, who exhibited intact implicit learning of the grammar. On the other hand, when tested on a lower topological entropy grammar system, all groups performed above chance level, indicating that children with developmental dyslexia were able to identify rules from a given grammar system. The results reinforced the significance of graph complexity when experimenting with artificial grammar learning tasks, particularly with dyslexic participants.

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Authors and Affiliations

  1. Learning Disabilities Studies, School of Education, Bar-Ilan University, 52900, Ramat-Gan, Israel

    Pesia Katan, Shani Kahta & Rachel Schiff

  2. Haddad Center for Dyslexia and Learning Disabilities, Bar Ilan University, 52900, Ramat-Gan, Israel

    Ayelet Sasson & Rachel Schiff

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  1. Pesia Katan
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  2. Shani Kahta
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Correspondence to Rachel Schiff.

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Pesia Katan is part of the research conducted at Bar Ilan University, Ramat Gan, Israel, as partial fulfillment of her requirement for a Doctor of Philosophy degree

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Katan, P., Kahta, S., Sasson, A. et al. Performance of children with developmental dyslexia on high and low topological entropy artificial grammar learning task. Ann. of Dyslexia 67, 163–179 (2017). https://doi.org/10.1007/s11881-016-0135-1

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