Awareness as inference in a higher-order state space

Humans have the ability to report the contents of their subjective experience-we can say to each other, 'I am aware of X'. The decision processes that support these reports about mental contents remain poorly understood. In this article, I propose a computational framework that characterizes awareness reports as metacognitive decisions (inference) about a generative model of perceptual content. This account is motivated from the perspective of how flexible hierarchical state spaces are built during learning and decision-making. Internal states supporting awareness reports, unlike those covarying with perceptual contents, are simple and abstract, varying along a 1D continuum from absent to present. A critical feature of this architecture is that it is both higher-order and asymmetric: a vast number of perceptual states is nested under 'present', but a much smaller number of possible states nested under 'absent'. Via simulations, I show that this asymmetry provides a natural account of observations of 'global ignition' in brain imaging studies of awareness reports.