Abstractband zum 16. Kongress des Bundesverbandes Legasthenie

Abstractband 16. Kongress des Bundesverbandes Legasthenie
Understanding Dyslexia through Computational Modeling
Johannes Ziegler
Université de Provence and CNRS, Marseille, France
The strength of computational modeling lies in the fact that all processes and representations
that are relevant for reading have to be implemented in a computer program, which
allows the modeler to make precise simulations of reading performance. These models
can be used to explore dyslexia by “lesioning” or “impairing” the processes that are
thought to underlie dyslexia. This is precisely what we did in the context of the dual route
cascaded model of reading (DRC, Coltheart, Rastle, Perry, Langdon & Ziegler, 2001).
In the first step, we designed tasks that would allow us to estimate the deficits for each
dyslexic in the different levels of the model: attention, letter processing, access to the
orthographic and phonological lexicons and phoneme processing. In a second step, we
simulated reading deficits for each child by impairing those levels that were found to be
deficient in the reading component tasks. The model not only accounted fairly well for
individual reading patterns but also captured the different dyslexia profiles discussed
in the literature (i.e., surface, phonological, mixed, and mild dyslexia). Thus, taking into
account the multiplicity of underlying deficits on an individual basis provides a parsimonious
and accurate description of developmental dyslexia. This underscores the necessity
of investigating dyslexia at the level of individuals rather than as a unitary disorder.
References
Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. C. (2001). DRC: A dual route
cascaded model of visual word recognition and reading aloud. Psychological Review,
108, 204-256.
Ziegler, J. C., Castel, C., Pech-Georgel, C., George, F., Alario, F. X., & Perry, C. (2008).
Developmental Dyslexia And The Dual Route Model Of Reading: Simulating Individual
Differences and Subtypes. Cognition, 107, 151–178.
Korrespondenzautor:
Johannes Ziegler
johannes.ziegler@univ-provence.fr
136