# Awareness as inference in a higher-order state space

arXiv: https://arxiv.org/abs/1906.00728 · HTML: https://mlsanity.com/paper/1906.00728/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2019.

## Built on by (most cited first, 7 of 7)

- [Consciousness in Artificial Intelligence: Insights from the Science of Consciousness](https://mlsanity.com/paper/2308.08708/) (2023)
- [Dissociating Artificial Intelligence from Artificial Consciousness](https://mlsanity.com/paper/2412.04571/) (2024)
- [Designing explainable artificial intelligence with active inference: A framework for transparent introspection and decision-making](https://mlsanity.com/paper/2306.04025/) (2023)
- [From internal models toward metacognitive AI](https://mlsanity.com/paper/2109.12798/) (2021)
- [Why Is Anything Conscious?](https://mlsanity.com/paper/2409.14545/) (2024)
- [Characterizing higher-order representations through generative diffusion models explains human decoded neurofeedback performance](https://mlsanity.com/paper/2503.14333/) (2025)
- [When Should We Protect AI? A Precautionary Framework for Consciousness Uncertainty](https://mlsanity.com/paper/2606.05528/) (2026)

## Abstract (arXiv)

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.