Recent advances in large language models using deep learning techniques have renewed interest on how languages can be learned from data. However, it is unclear whether or how these models represent grammatical information from the learned languages. In addition, the models must be pre-trained on large corpora before they can be used. In this work, we propose an alternative, more transparent and cognitively plausible architecture for learning language. Instead of using deep learning, our approach uses a minimal cognitive architecture based on sequence memory and chunking. The learning mechanism is based on the principles of reinforcement learning. We test our architecture on a number of natural-like toy languages. Results show that the model can learn these artificial languages from scratch and extract grammatical information that supports learning. Our study demonstrates the power of this simple architecture and stresses the importance of sequence memory as a key component of the language learning process. Since other animals do not seem to have a faithful sequence memory, this may explain why only humans have developed complex languages.

翻译：近年来，基于深度学习技术的大语言模型取得了重大进展，重新引发了人们对语言如何从数据中习得的兴趣。然而，这些模型是否能从所学语言中表征语法信息，以及具体如何实现这一过程，目前尚不明确。此外，这类模型在使用前必须在大规模语料库上进行预训练。在本研究中，我们提出了一种替代性、更具透明度和认知合理性的语言学习架构。与深度学习方法不同，我们的方法采用基于序列记忆与组块化处理的最小认知架构，其学习机制遵循强化学习原理。我们在多个类自然的人工语言上测试了该架构。结果表明，该模型能够从零开始学习这些人工语言，并提取出支持学习的语法信息。本研究揭示了这一简洁架构的强大能力，并强调了序列记忆作为语言学习过程中关键要素的重要性。鉴于其他动物似乎不具备精准的序列记忆能力，这或许可以解释为何只有人类发展出了复杂的语言。