This article examines how to construct human-like working memory and thought processes within a computer. The focus is on simulating the mammalian working memory system. There should be two interacting working memory stores, one analogous to sustained firing lending the system a focus of attention, and another analogous to synaptic potentiation lending the system a short-term memory. These working memory stores retain and coactivate representations, using them to search long-term memory for appropriate updates. The working memory stores should be updated continuously, and in an iterative fashion, meaning that, in the next state, some proportion of the coactive items 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. As new representations are added and old ones are subtracted, some remain active for several seconds over the course of these changes. This persistent activity, similar to that used in contemporary artificial recurrent neural networks, is used to spread activation energy throughout the global workspace to search for the next associative update. The result is a chain of associatively linked intermediate states that are 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：本文探讨如何在计算机中构建类似人类的工作记忆与思维过程，重点在于模拟哺乳动物工作记忆系统。该系统应包含两种相互交互的工作记忆存储：一种类似持续放电，赋予系统注意焦点；另一种类似突触增强，赋予系统短期记忆能力。这些工作记忆存储保留并联合激活表征，利用它们搜索长期记忆以获取适当的更新。工作记忆存储应以连续且迭代的方式进行更新，这意味着在下一状态中，应始终保留一定比例的联合激活项目。因此，工作记忆中共同激活的概念集将随时间逐渐渐进演化。这使得每个状态成为前一状态的修正迭代，并导致连续状态在表征集合上产生重叠与融合。当新表征加入而旧表征被移除时，部分表征会在数秒内持续活跃。这种持续活动类似于当代人工递归神经网络中的机制，用于在整个全局工作空间中传播激活能量，以搜索下一个联想更新。最终形成由联想连接的中介状态链，能够逐步向解决方案或目标推进。本文认为迭代更新是一种信息处理策略，既是思维流的计算与神经生理学决定因素，也是设计并编程通用人工智能的算法范式。