The predictive global neuronal workspace: A formal active inference model of visual consciousness

• Published in: Progress in neurobiology 2021-04, Vol.199, p.101918-101918, Article 101918
• Main Authors: Whyte, Christopher J., Smith, Ryan
• Format: Article
• Language: English
• Subjects: Active inference; Attention; Bayes Theorem; Bayesian brain hypothesis; Cognition; Computational neuroscience; Consciousness; Global neuronal workspace; Humans; Neurons; Predictive global neuronal workspace; Predictive processing
• ISSN: 0301-0082; 1873-5118
• DOI: 10.1016/j.pneurobio.2020.101918

•We propose an Active Inference model that captures key elements of the global neuronal workspace.•Conscious access is cast as a process of (approximately) Bayes optimal hierarchical inference.•Simulations of previously reported findings unify a host of otherwise conflicting results.•Simulations of previous results are extended to generate novel predictions. The global neuronal workspace (GNW) model has inspired over two decades of hypothesis-driven research on the neural basis of 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 behavioural 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.