This article provides an analytical framework for how to simulate human-like thought processes within a computer. It describes how attention and memory should be structured, updated, and utilized to search for associative additions to the stream of thought. The focus is on replicating the dynamics of the mammalian working memory system, which features two forms of persistent activity: sustained firing (preserving information on the order of seconds) and synaptic potentiation (preserving information from minutes to hours). The article uses a series of figures to systematically demonstrate how the iterative updating of these working memory stores provides functional organization to behavior, cognition, and awareness. In a machine learning implementation, these two memory stores should be updated continuously and in an iterative fashion. This means each state should preserve a proportion of the coactive representations from the state before it (where each representation is an ensemble of neural network nodes). This makes each state a revised iteration of the preceding state and causes successive configurations to overlap and blend with respect to the information they contain. Thus, the set of concepts in working memory will evolve gradually and incrementally over time. Transitions between states happen as persistent activity spreads activation energy throughout the hierarchical network, searching long-term memory for the most appropriate representation to be added to the global workspace. The result is a chain of associatively linked intermediate states capable of advancing toward a solution or goal. Iterative updating is conceptualized here as an information processing strategy, a model of working memory, a theory of consciousness, and an algorithm for designing and programming artificial intelligence (AI, AGI, and ASI).

翻译：本文提出了一个分析框架，用以阐述如何在计算机中模拟类人思维过程。它描述了应如何构建、更新和利用注意力与记忆，以在思维流中搜索联想性补充内容。重点在于复现哺乳动物工作记忆系统的动态特性，该系统具有两种形式的持续活动：持续放电（在秒量级保存信息）和突触增强（在数分钟至数小时内保存信息）。本文通过一系列图示，系统性地论证了这些工作记忆存储的迭代更新如何为行为、认知和意识提供功能性组织。在机器学习实现中，这两种记忆存储应以连续且迭代的方式更新。这意味着每个状态都应保留其前一状态中部分共激活的表征（每个表征是一个神经网络节点集合）。这使得每个状态都成为前一状态的修订迭代，并导致连续配置在所包含信息方面存在重叠与融合。因此，工作记忆中的概念集合将随时间逐渐且增量式地演化。状态间的转换发生在持续活动将激活能量扩散至整个分层网络的过程中，该过程在长期记忆中搜索最合适的表征以添加到全局工作空间。其结果是一条由联想链接的中间状态组成的链条，能够朝着解决方案或目标推进。迭代更新在此被概念化为一种信息处理策略、一种工作记忆模型、一种意识理论，以及一种用于设计和编程人工智能（AI、AGI 和 ASI）的算法。