This comprehensive review explores the intersection of Large Language Models (LLMs) and cognitive science, examining similarities and differences between LLMs and human cognitive processes. We analyze methods for evaluating LLMs cognitive abilities and discuss their potential as cognitive models. The review covers applications of LLMs in various cognitive fields, highlighting insights gained for cognitive science research. We assess cognitive biases and limitations of LLMs, along with proposed methods for improving their performance. The integration of LLMs with cognitive architectures is examined, revealing promising avenues for enhancing artificial intelligence (AI) capabilities. Key challenges and future research directions are identified, emphasizing the need for continued refinement of LLMs to better align with human cognition. This review provides a balanced perspective on the current state and future potential of LLMs in advancing our understanding of both artificial and human intelligence.

本研究探讨了大型语言模型（LLMs）与认知科学的交集，分析了LLMs与人类认知过程之间的相似性和差异性。研究的主要发现是，尽管LLMs表现出某些认知能力，但仍存在认知偏见与局限性，需要进一步研究和改进，以推动理解人工智能与人类智能的进步。

大型语言模型与认知科学：相似性、差异性及挑战的综合评审