A Cognitive Architecture for Machine Consciousness and Artificial Superintelligence: Updating Working Memory Iteratively

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.