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 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 over 40 original figures to systematically demonstrate how the iterative updating of these working memory stores provides functional structure to thought and consciousness. In an AI implementation, these two stores should be updated continuously and in an iterative fashion, meaning each state should preserve a proportion of the coactive representations from the state before it. Thus, the set of concepts in working memory will evolve gradually and incrementally over time. This makes each state a revised iteration of the preceding state and causes successive states to overlap and blend with respect to the information they contain. 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 general intelligence.

翻译：本文提出了一种在计算机中模拟类人思维过程的分析框架。它描述了如何构建、更新和利用注意力与记忆，以搜索思维流中的联想性补充。研究聚焦于复制哺乳动物的工作记忆系统，该系统具有两种持续活动形式：持续放电（在秒级时间内保持信息）和突触增强（在数分钟至数小时内保持信息）。本文通过40余幅原创图示系统性地展示了这些工作记忆存储的迭代更新如何为思维与意识提供功能性结构。在人工智能实现中，这两个存储应持续且迭代地更新，即每个状态应保留前一状态中共同激活表征的一定比例。因此，工作记忆中的概念集合将随时间逐渐且增量地演化。这使得每个状态成为前一状态的修正迭代，并导致连续状态在所含信息上发生重叠与融合。状态间的转换通过持续活动将激活能量扩散至层级化网络，在长期记忆中搜索最适合添加至全局工作区的表征。由此形成一条由联想链接中间状态构成的链条，能够逐步推进至解决方案或目标。本文将迭代更新概念化为一种信息处理策略、工作记忆模型、意识理论，以及设计编程通用人工智能的算法。