对比大模型与人类认知，揭示智能本质的共性与差异。
Large Language Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challenges
- 从认知科学视角分析大模型的思维机制与人类类比
- 指出大模型存在认知偏见并提出优化路径
- 适合对人工智能与心智研究感兴趣的学者参考
本文系统综述了大语言模型（LLMs）与认知科学的交叉领域，探讨其与人类认知过程的相似性与差异。分析了评估大模型认知能力的方法，讨论其作为认知模型的潜力。涵盖大模型在多个认知领域的应用，揭示对认知科学研究的启发。评估大模型的认知偏见与局限性，并提出改进方法。探讨大模型与认知架构的融合，展现提升人工智能能力的前景。识别关键挑战与未来研究方向，强调需持续优化大模型以更贴近人类认知。本综述为当前状态与未来发展提供平衡视角，助力深化对人工与人类智能的理解。
原文摘要 · Abstract (English)
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.
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