用大模型增强认知架构，让心理合理性与计算能力兼得
Enhancing Computational Cognitive Architectures with LLMs: A Case Study
- 以克拉里昂架构的显性-隐性双系统为基础，融合大模型
- 实现心理可解释性与强大计算能力的协同提升
- 适合研究认知建模与AI融合的学者参考
计算认知架构是整合多种心理学功能并以心理上合理且经过验证的方式构建的广义人类心智模型。然而，这类模型至今计算能力有限，主要受限于所采用的计算工具。近期研究表明，大语言模型在计算能力上已超越以往所有工具。为同时应对现实世界的复杂性与心理真实性，将大语言模型融入认知架构成为关键任务。本文以克拉里昂（Clarion）认知架构与大模型的协同结合为例进行探讨。利用克拉里昂固有的显性-隐性二分机制，实现与大模型的无缝集成，从而融合大模型的强大计算能力与克拉里昂的心理合理性。
原文摘要 · Abstract (English)
Computational cognitive architectures are broadly scoped models of the human mind that combine different psychological functionalities (as well as often different computational methods for these different functionalities) into one unified framework. They structure them in a psychologically plausible and validated way. However, such models thus far have only limited computational capabilities, mostly limited by the computational tools and techniques that were adopted. More recently, LLMs have proved to be more capable computationally than any other tools. Thus, in order to deal with both real-world complexity and psychological realism at the same time, incorporating LLMs into cognitive architectures naturally becomes an important task. In the present article, a synergistic combination of the Clarion cognitive architecture and LLMs is discussed as a case study. The implicit-explicit dichotomy that is fundamental to Clarion is leveraged for a seamless integration of Clarion and LLMs. As a result, computational power of LLMs is combined with psychological nicety of Clarion.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。