  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 characterises 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
one-dimensional 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.
