This article presents a theoretical framework for adapting the Common Model
of Cognition to large generative network models within the field of artificial
intelligence. This can be accomplished by restructuring modules within the
Common Model into shadow production systems that are peripheral to a central
production system, which handles higher-level reasoning based on the shadow
productions' output. Implementing this novel structure within the Common Model
allows for a seamless connection between cognitive architectures and generative
neural networks.

该文章提出了将认知通用模型调整为人工智能领域中的大型生成网络模型的理论框架。通过将通用模型中的模块重组为辅助于中心生产系统的阴影生成系统，可以实现这一目标，该中心生产系统基于阴影生成系统的输出进行高级推理。在通用模型中实现这种创新结构，可以实现认知体系结构和生成神经网络之间无缝的连接。

将生成网络与认知的共同模型相结合

Bridging Generative Networks with the Common Model of Cognition

There have been increasing challenges to dual-system descriptions of System-1
and System-2, critiquing them as imprecise and fostering misconceptions. We
address these issues here by way of Dennett's appeal to use computational
thinking as an analytical tool, specifically we employ the Common Model of
Cognition. Results show that the characteristics thought to be distinctive of
System-1 and System-2 instead form a spectrum of cognitive properties. By
grounding System-1 and System-2 in the Common Model we aim to clarify their
underlying mechanisms, persisting misconceptions, and implications for
metacognition.

该研究采用计算思维作为分析工具并运用认知共同模型来解决对 System-1 和 System-2 的双系统描述难以精确和滋生误解的问题，结果表明 System-1 和 System-2 的特点形成了认知属性的一种谱系，并阐明了其潜在机制、存在的误解及对元认知的影响。

透过认知通用模型阐明系统 1 和 2

Clarifying System 1 & 2 through the Common Model of Cognition

Attempts to import dual-system descriptions of System-1 and System-2 into AI
have been hindered by a lack of clarity over their distinction. We address this
and other issues by situating System-1 and System-2 within the Common Model of
Cognition. Results show that what are thought to be distinctive characteristics
of System-1 and 2 instead form a spectrum of cognitive properties. The Common
Model provides a comprehensive vision of the computational units involved in
System-1 and System-2, their underlying mechanisms, and the implications for
learning, metacognition, and emotion.

本文旨在将关于 System-1 和 System-2 的双重系统描述引入人工智能领域，并通过将其置于认知共同模型中解决这一和其他问题。结果表明，被认为是 System-1 和 System-2 特征的不同性质实际上形成了一个认知属性光谱。共同模型提供了有关 System-1 和 System-2 所涉及的计算单元、其基本机制以及对学习、元认知和情感的影响的全面视野。