A Biologically Based Computational Model of Working Memory

Abstract

FIVE CENTRAL FEATURES OF THE MODEL We define working memory as controlled processing involving active maintenance and/or rapid learning, where controlled processing is an emergent property of the dynamic interactions of multiple brain systems, but the prefrontal cortex (PFC) and hippocampus (HCMP) are especially influential owing to their specialized processing abilities and their privileged locations within the processing hierarchy (both the PFC and HCMP are well connected with a wide range of brain areas, allowing them to influence behavior at a global level). The specific features of our model include: (1) A PFC specialized for active maintenance of internal contextual information that is dynamically updated and self-regulated, allowing it to bias (control) ongoing processing according to maintained information (e.g., goals, instructions, partial products). (2) An HCMP specialized for rapid learning of arbitrary information, which can be recalled in the service of controlled processing, whereas the posterior perceptual and motor cortex (PMC) exhibits slow, long-term learning that can efficiently represent accumulated knowledge and skills. (3) Control that emerges from interacting systems (PFC, HCMP, and PMC). (4) Dimensions that define continua of specialization in different brain systems: for example, robust active maintenance, fast versus slow learning. (5) Integration of biological and computational principles. Working memory is an intuitively appealing theoretical construct – perhaps deceptively so.