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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).

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