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 used to search for associative additions to the thought process. The working memory of mammals is made possible by two forms of persistent activity: sustained firing (preserving information on the order of seconds) and synaptic potentiation (preserving information on the order of 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 dynamic, functional structure to thought and consciousness. In an AI implementation, these two stores should be updated continuously and in an iterative fashion, meaning that, in the next state, some proportion of the coactive representations should always be retained. Thus, the set of concepts coactive 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 set of representations they contain. It is argued that without this overlap, AI systems cannot achieve mental continuity or machine consciousness. Persistent activity spreads activation energy throughout the hierarchical network to search for the next associative update. This search of long-term memory locates 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 computational and neurophysiological determinant of the stream of thought, and an algorithm for designing and programming artificial general intelligence.

翻译：本文提供了一个分析框架，用于描述如何在计算机中模拟类似人类的思维过程。它阐述了注意与记忆应如何组织、更新，并被用于搜索思维过程中联想性的新增内容。哺乳动物的工作记忆通过两种形式的持续性活动得以实现：持续放电（在秒级时间内维持信息）和突触长时程增强（在数分钟至数小时内维持信息）。本文通过超过40幅原创图表系统性地展示了：这些工作记忆存储的迭代更新如何为思维和意识提供动态、功能性的结构。在人工智能实现中，这两种存储应当以迭代方式持续更新，这意味着在下一状态中，部分共激活的表征应当始终被保留。由此，工作记忆中共同激活的概念集合将随时间逐步且渐进地演化。这使得每个状态都是前一个状态的修正迭代，并导致连续出现的状态在其所含表征集上存在重叠与交融。本文论证指出，缺乏这种重叠，人工智能系统将无法实现心智连续性或机器意识。持续性活动将激活能量扩散至整个层级化网络，以搜索下一个联想性更新内容。这种对长时记忆的搜索定位出最合适的表征，并将其添加至全局工作空间。最终形成一系列由联想链接的中间状态链，这些状态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、思维流在计算与神经生理层面的决定性因素，以及设计编程人工通用智能的算法。