Modelling developmental disorders

Abstract

Recent theoretical approaches to studying the origin of developmental deficits have stressed the importance of considering the contribution of the developmental process itself. This translates to the study of experience-dependent and maturational mechanisms of change, and the details of their interactions with a structured social and physical environment. One valuable tool for investigating mechanisms of change is via building computational models of development. Such models allow researchers to address what mechanisms are sufficient to explain normal development of cognitive abilities, and the conditions under which development becomes atypical. Modelling serves to clarify theories and generate testable predictions. In this chapter, we review how computational models have generated insights into developmental disorders. Example models include those addressed to specific language impairment, developmental dyslexia, autism and Williams syndrome. We consider theoretical issues such as the relation of developmental deficits to acquired deficits in adulthood, when the developmental process will exaggerate or attenuate early developmental deficits, and the role of risk and protective factors in modulating the relationship between the underlying causes of disorders and their manifestation in behavioural profiles. Computational methods discussed include associative artificial neural networks, reinforcement learning, dynamical systems modelling, and population modelling.