  Recently, a number of articles have argued that deep learning models such as
GPT could also capture key aspects of language processing in the human mind and
brain. However, I will argue that these models are not suitable as neural
models of human language. Firstly, because they fail on fundamental boundary
conditions, such as the amount of learning they require. This would in fact
imply that the mechanisms of GPT and brain language processing are
fundamentally different. Secondly, because they do not possess the logistics of
access needed for compositional and productive human language processing.
Neural architectures could possess logistics of access based on small-world
like network structures, in which processing does not consist of symbol
manipulation but of controlling the flow of activation. In this view, two
complementary approaches would be needed to investigate the relation between
brain and cognition. Investigating learning methods could reveal how 'learned
cognition' as found in deep learning could develop in the brain. However,
neural architectures with logistics of access should also be developed to
account for 'productive cognition' as required for natural or artificial human
language processing. Later on, these approaches could perhaps be combined to
see how such architectures could develop by learning and development from a
simpler basis.
