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.

该文就深度学习模型 GPT 作为人类语言处理神经模型的适用性进行探讨，认为由于 GPT 在学习量、逻辑访问等方面与人类语言处理存在根本差异，因此必须采取补充方法探索大脑和认知之间的关系，并在学习和发展的基础上进行进一步研究。