| Summary: | In this thesis I investigate the modelling of cognitive development with Constructivist neural networks. I argue that the constructivist nature of development, that is, the building of a cognitive system through active interactions with its environment, is an essential property of human development and should be considered in models of cognitive development. I evaluate this claim on the basis of evidence from cortical development, cognitive development, and learning theory. In an empirical evaluation of this claim, I then present a constructivist neural network model of the acquisition of the English past tense and of impaired inflectional processing in German agrammatic aphasics. The model displays a realistic course of acquisition, closely modelling the U-shaped learning curve and more detailed effects such as frequency and family effects. Further, the model develops double dissociations between regular and irregular verbs. I argue that the ability of the model to account for the human data is based on its constructivist nature, and this claim is backed by an analogous, but non-constructivist model that does not display many aspects of the human behaviour. Based on these results I develop a taxonomy for cognitive models that incorporates architectural and developmental aspects besides the traditional distinction between symbolic and subsymbolic processing. When the model is trained on the German participle and is then lesioned by removing connections, the breakdown in performance reflects the profiles of German aggrammatic aphasics. Irregular inflections are selectively impaired and are often overregularised. Further, frequency effects and the regularity-continuum effect that are observed in aphasic subjects can also be modelled. The model predicts that an aphasic profile with selectively impaired regular inflections would be evidence for a locally distinct processing of regular and irregular infections.
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