Proceedings of the Annual Meeting of the Cognitive Science Society

Global Workspace Theory (GWT) is a prominent account of cognitive access in humans. In the decades since its proposal, there have been a number of computational models developed to study the hypothetical dynamics of the global workspace, most of which are hand-designed to reflect the expectations of the theory. Here we examine a recently successful general deep learning architecture, the Perceiver, as a potential theoretical candidate for the global workspace. We find that despite being developed in an unrelated context, the Perceiver meets a number of theoretical requirements of the global workspace. More importantly, it demonstrates empirical behavior consistent with that expected by GWT in both attentional control and working memory tasks drawn from the cognitive science literature. Taken together, this evidence suggests that the Perceiver and related models may be a useful tool for studying the global workspace and its potential realization in both artificial and biological agents.