This theoretical article examines how to construct human-like working memory
and thought processes within a computer. There should be two working memory
stores, one analogous to sustained firing in association cortex, and one
analogous to synaptic potentiation in the cerebral cortex. These stores must be
constantly updated with new representations that arise from either
environmental stimulation or internal processing. They should be updated
continuously, and in an iterative fashion, meaning that, in the next state,
some items in the set of 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 is 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
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 intelligence