The Predictive Global Neuronal Workspace: A Formal Active Inference Model of Visual Consciousness

Summary

A new computational model called the 'predictive global workspace' combines ideas from the global neuronal workspace (GNW) theory of consciousness with Active Inference, a framework that treats brain activity as Bayesian inference. The model reproduces electrophysiological and behavioral results from studies of inattentional blindness and a four-way taxonomy linking consciousness, attention, and sensory signal strength. It also reconciles conflicting findings, extends the taxonomy to include prior expectations, and suggests new experimental paradigms. The model addresses limitations of current GNW research by enabling precise, testable predictions at both behavioral and neural levels.

Study at a glance

Abstract

The global neuronal workspace (GNW) model has inspired over two decades of hypothesis driven research on the neural basis consciousness. However, recent studies have reported findings that are at odds with empirical predictions of the model. Further, the macro-anatomical focus of current GNW research has limited the specificity of predictions afforded by the model. In this paper we present a neurocomputational model – based on Active Inference – that captures central architectural elements of the GNW and is able to address these limitations. The resulting ‘predictive global workspace’ casts neuronal dynamics as approximating Bayesian inference, allowing precise, testable predictions at both the behavioural and neural levels of description. We report simulations demonstrating the model’s ability to reproduce: 1) the electrophysiological and behaviour results observed in previous studies of inattentional blindness; and 2) the previously introduced four-way taxonomy predicted by the GNW, which describes the relationship between consciousness, attention, and sensory signal strength. We then illustrate how our model can reconcile/explain (apparently) conflicting findings, extend the GNW taxonomy to include the influence of prior expectations, and inspire novel paradigms to test associated behavioural and neural predictions.