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Recently, a number of articles have argued that deep learning models such asGPT could also capture key aspects of language processing in the human mind andbrain. However, I will argue that these models are not suitable as neuralmodels of human language. Firstly, because they fail on fundamental boundaryconditions, such as the amount of learning they require. This would in factimply that the mechanisms of GPT and brain language processing arefundamentally different. Secondly, because they do not possess the logistics ofaccess needed for compositional and productive human language processing.Neural architectures could possess logistics of access based on small-worldlike network structures, in which processing does not consist of symbolmanipulation but of controlling the flow of activation. In this view, twocomplementary approaches would be needed to investigate the relation betweenbrain and cognition. Investigating learning methods could reveal how 'learnedcognition' as found in deep learning could develop in the brain. However,neural architectures with logistics of access should also be developed toaccount for 'productive cognition' as required for natural or artificial humanlanguage processing. Later on, these approaches could perhaps be combined tosee how such architectures could develop by learning and development from asimpler basis.

Towards a neural architecture of language: Deep learning versus logistics of access in neural architectures for compositional processing