Consciousness: converging insights from connectionist modeling and neuroscience.

Selective attention, working memory, and cognitive control arise from competition between widely distributed neural representations, biased by top-down signals from prefrontal cortex. Connectionist models implementing this competition share key features with models of global workspace theory, all relying on global constraint satisfaction. This review argues that these models are relevant for understanding consciousness.

Study at a glance

Abstract

Over the past decade, many findings in cognitive neuroscience have resulted in the view that selective attention, working memory and cognitive control involve competition between widely distributed representations. This competition is biased by top-down projections (notably from prefrontal cortex), which can selectively enhance some representations over others. This view has now been implemented in several connectionist models. In this review, we emphasize the relevance of these models to understanding consciousness. Interestingly, the models we review have striking similarities to others directly aimed at implementing 'global workspace theory'. All of these models embody a fundamental principle that has been used in many connectionist models over the past twenty years: global constraint satisfaction.