# Awareness as inference in a higher-order state space

## TL;DR

This paper introduces a computational framework modeling awareness as a metacognitive inference within a hierarchical state space, explaining neural activation patterns observed during awareness reports.

## Contribution

It proposes a novel higher-order, asymmetric state space model for awareness, linking subjective reports to underlying generative perceptual models.

## Key findings

- Simulations replicate 'global ignition' brain imaging phenomena.
- The model explains the asymmetry in awareness states.
- Provides a new perspective on metacognitive decision processes.

## 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.