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 appear inconsistent with the predictions of the model. Further, the macroanatomical 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 the active inference framework, that captures central architectural elements of the GNW and that can 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 described 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.
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