Integrating the global neuronal workspace into the framework of predictive processing: Towards a working hypothesis

Hohwy (2013) proposed an account of conscious access that integrates the global neuronal workspace (GNW) into the framework of predictive processing, a view that I term the predictive global neuronal workspace (PGNW). Whilst promising, the PGNW is theoretically underdeveloped and empirically underexplored. The aim of this article is to outline the empirical predictions that distinguish the PGNW from other workspace models. I do so by (i) placing the PGNW in close contact with experimental work and cashing out a set of predictions that distinguish it from the standard formulation of the GNW, (ii) exploring the evidence for the first of these predictions in the context of bistable perception and the conscious processing of auditory regularities, and (iii), contrasting the PGNW with Chanes and Barrett (2016) limbic workspace. Combined, these arguments show that the PGNW is both testable and supported by current evidence.