Neural imaging and computational modeling rest on the evidence used to clarify the development. The first lesson that is learnt from this exercise is that development is fundamentally constructivist in nature. ‘Neuroconstructivism’ is elaborated in this chapter. The chapter also extends beyond the level of the single neuron and explores how the recruitment of new cortical areas and the development of inter-area processes can guide cognitive development in a constructivist way. It also examines the view that the mechanisms underlying changes in representations are uniform across the neural and cognitive levels of explanation of cognitive development. In other words, it aims to develop cognitive models. It argues that these neurally consistent ‘cognitive models’ are better at explaining developmental phenomena such as emergent modularization, regionalization, specialization, learning occurring between systems rather than within systems, representation re-description, cross-model generalization, and learning of control functions. In addition, it addresses the identification of neurocomputational principles from the study of lower-level and simpler phenomena, and then implementation of these principles in higher-level models of more complex cognitive phenomena.
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