How do humans learn language, and can the first language be learned at all?
These fundamental questions are still hotly debated. In contemporary
linguistics, there are two major schools of thought that give completely
opposite answers. According to Chomsky's theory of universal grammar, language
cannot be learned because children are not exposed to sufficient data in their
linguistic environment. In contrast, usage-based models of language assume a
profound relationship between language structure and language use. In
particular, contextual mental processing and mental representations are assumed
to have the cognitive capacity to capture the complexity of actual language use
at all levels. The prime example is syntax, i.e., the rules by which words are
assembled into larger units such as sentences. Typically, syntactic rules are
expressed as sequences of word classes. However, it remains unclear whether
word classes are innate, as implied by universal grammar, or whether they
emerge during language acquisition, as suggested by usage-based approaches.
Here, we address this issue from a machine learning and natural language
processing perspective. In particular, we trained an artificial deep neural
network on predicting the next word, provided sequences of consecutive words as
input. Subsequently, we analyzed the emerging activation patterns in the hidden
layers of the neural network. Strikingly, we find that the internal
representations of nine-word input sequences cluster according to the word
class of the tenth word to be predicted as output, even though the neural
network did not receive any explicit information about syntactic rules or word
classes during training. This surprising result suggests, that also in the
human brain, abstract representational categories such as word classes may
naturally emerge as a consequence of predictive coding and processing during
language acquisition.

探讨了人类语言习得中词汇分类的起源是基于先天内在结构还是出于自然学习的能力，利用人工神经网络预测语句中下一个词，揭示了也许由于预测编码和过程，大脑也可能形成抽象的词汇分类，即使其在训练时没有接收到有关词汇分类或语法规则的任何明确信息。