Iterative Updating Model of Working Memory and Consciousness with Jared Reser скачать в хорошем качестве

Iterative Updating Model of Working Memory and Consciousness with Jared Reser

Contemporary models of working memory generally do not explicitly address gradual change in information persistence. In many discussions, updating of the information held in working memory is considered to be complete rather than partial, meaning that after being updated, the contents from the previous state are entirely replaced. In other discussions, information can be updated without complete replacement, such as when working memory holds three words and then accommodates a fourth word in addition to the first three (e.g., Pina et. al., 2018, Niklaus et al., 2019, Miller et. al., 2018, Manohar et. al., 2019). In contrast, the present account explores the hypothesis that partial updating occurs continuously. As some representations are added, some are subtracted, and others from the previous state remain, due to persistent neural activity. This cascading persistence allows successive states to share a large proportion of their content in common, creating complex causal relationships between them (Reser, 2011, 2012). This perspective may be useful because it illuminates how the gradually transforming collection of representations in working memory allows iterative progress as updated states elaborate intelligently on the states that came before them (Reser, 2013, 2016). The working memory store maintains and coactivates representations, using them to search long-term memory for appropriate associative updates to the global workspace. The working memory stores are updated continuously, and in an iterative fashion, meaning that, in the next state, some proportion of the coactive items are always retained. Thus, the set of concepts coactive in working memory evolves 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 is used to spread activation energy throughout the hierarchical network to search for the next associative update. 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.