Computational Approximations to Intellectual Disability in Down Syndrome

Intellectual disability (ID) is generally characterized by a limited development in adaptive behavior, and poor cognitive and social skills, which contribute to the overall level of intelligence (Vissers, Gilissen, & Veltman, 2015). ID has attracted considerable attention due to its contribution to our understanding of the nature of cognition and behavior. A common genetic cause of ID is Down syndrome (DS), with an incidence around 1 in 800 births (de Graaf, Buckley, & Skotko, 2015). Therefore, DS provides a rich framework for analyzing ID. Here we argue that our understanding of ID in general, and DS in particular, could benefit from computational modeling, which has been widely used for the study of typical and atypical cognitive development (JohnsonGlenberg, 2008; Thomas & Karmiloff-Smith, 2003). Despite the wealth of empirical research and the high incidence of DS, it has been relatively disregarded in the field of computational modeling. We will first review the theoretical framework of ID; particularly, we will focus on the debate of modular vs. neuroconstructivist approaches. Second, we will describe the genetic cause, and prevalent psychological and neurological features observed in DS; the studies summarized here emphasize the high variability of behavioral outputs and brain alterations. Then, we review and discuss how some computational models—most of which were developed to analyze non-DS populations—partially cover a number of behaviors observed in DS, thus offering a first computational framework of this syndrome. Finally, we emphasize the need for further computational models to account for the currently uncovered features of DS.