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.

翻译：本全面综述探讨了大语言模型（LLMs）与认知科学的交叉领域，审视了LLMs与人类认知过程之间的相似性和差异性。我们分析了评估LLMs认知能力的方法，并讨论了其作为认知模型的潜力。综述涵盖了LLMs在多个认知领域中的应用，重点阐述了其为认知科学研究带来的启示。我们评估了LLMs的认知偏差与局限性，以及提出的改进其性能的方法。本文考察了LLMs与认知架构的整合，揭示了增强人工智能（AI）能力的有前景的途径。文中指出了关键挑战与未来研究方向，强调需要持续改进LLMs以更好地与人类认知对齐。本综述为LLMs在增进我们对人工智能和人类智能理解方面的现状与未来潜力提供了一个平衡的视角。