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 over 40 original figures to systematically demonstrate how the iterative updating of these working memory stores provides functional structure to behavior, cognition, and consciousness. In an AI implementation, these two memory 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。该框架描述了注意力与记忆应如何被构建、更新及利用，以搜索思维流中的联想性新增内容。研究重点在于复现哺乳动物工作记忆系统的动力学特征，该系统包含两种持续活动形式：持续性放电（以秒级保存信息）与突触增强（以分钟至小时级保存信息）。本文通过超过40幅原创示意图，系统性地展示了工作记忆存储的迭代更新如何为行为、认知与意识提供功能结构。在人工智能实现中，这两种记忆存储应持续并以迭代方式进行更新，即每个状态应保留前一个状态中部分共激活表征。由此，工作记忆中的概念集合将随时间渐进式演变，使得每个状态成为前一个状态的修正版本，并导致相邻状态在所含信息上产生重叠与融合。当持续活动通过层级网络传播激活能量，从长期记忆中搜索最合适的表征加入全局工作空间时，状态间的转换便得以发生。其结果是形成一条由联想性中间状态构成的链条，能够逐步趋近解决方案或目标。本文中的“迭代更新”既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，也被视作设计及编程通用人工智能的一种算法。