Awareness as inference in a higher-order state space

Humans can report being aware of something, but the decision processes behind such reports are poorly understood. This article proposes a computational framework treating awareness reports as metacognitive inferences about a generative model of perceptual content. Internal states supporting awareness reports are simple and abstract, varying along a one-dimensional continuum from absent to present. A critical feature is that this architecture is higher-order and asymmetric: many perceptual states are nested under "present," but far fewer under "absent." Simulations show this asymmetry naturally accounts for "global ignition" observations in brain-imaging studies of awareness reports.

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Abstract

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.