Modeling spoken language acquisition with a generic cognitive architecture for associative learning

Human neo-cortex can be viewed as a modality invariant system for pattern discovery and associative learning. Similarly, research in the field of distributional learning suggests that much of human language acquisition can be explained by generic statistical learning mechanisms. The current paper argues that pattern processing capabilities of the human brain can be better understood if the process of early language acquisition is modeled using an entire cognitive architecture capable of unsupervised pattern discovery and associative learning. A highlevel motivation and description for generic processing principles in such architecture are given, followed by examples of our current work in the field.