Why Is Anything Conscious?
Not on the map. Why?
Abstract (arXiv)
We tackle the problem of consciousness by taking the naturally selected, embodied organism as our starting point. We provide a formalism describing how biological systems such as human bodies self-organize to hierarchically interpret unlabelled sensory information according to valence. The system is attracted and repelled at different spatial and temporal scales. This is a qualitative interpretation of an unlabelled physical state. We show how such interpretations imply behavioural policies which are differentiated from each other only by this qualitative aspect of information processing. Natural selection favours systems that actively intervene in the world to achieve homeostatic and reproductive goals. Put provocatively, death grounds meaning. This means that in living systems information processing is necessarily subjective, that is, it has quality embedded into its very core. Qualitative information processing involves interoceptive and exteroceptive classifiers, and determines priorities for self-survival. We formulate The Psychophysical Principle of Causality as a theorem, and prove generalisation optimal learning forces this valence first ontology. Qualitative good or bad processing necessarily comes textitbefore quality neutral representations of properties (i.e. ``red'' is constructed from valence). Under selection pressures like sophisticated predation this produces a hierarchy of selves, of which reafference and reflective self awareness are a consequence. We discuss this in light of the seminal distinction between phenomenal and access consciousness. We claim that phenomenal consciousness without access is likely common, but the reverse is implausible. Our proposal lays the foundation of a formal science of consciousness, closer to human fact than zombie fiction.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 149 references that are in the map. First posted on arXiv 22 Sep 2024.
Builds on
19 of 149 references.
References · 19
- 2024Robust agents learn causal world modelscited by 81
- 2016The AGI Containment Problemcited by 36
- 2022Path integrals, particular kinds, and strange thingscited by 20
- 2015Bad Universal Priors and Notions of Optimalitycited by 16
- 2022There's Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-scale Machinescited by 12
- 2023The Optimal Choice of Hypothesis Is the Weakest, Not the Shortestcited by 12
- 2023Emergent Causality and the Foundation of Consciousnesscited by 9
- 2020A Theoretical Computer Science Perspective on Consciousnesscited by 8
- 2021Philosophical Specification of Empathetic Ethical Artificial Intelligencecited by 8
- 2023Computational Dualism and Objective Superintelligencecited by 8
- 2019Awareness as inference in a higher-order state spacecited by 7
- 2021Compression, The Fermi Paradox and Artificial Super-Intelligencecited by 7
7 more
- 2024Does Thought Require Sensory Grounding? From Pure Thinkers to Large Language Modelscited by 7
- 2022Computable Artificial General Intelligencecited by 6
- 2023On the Computation of Meaning, Language Models and Incomprehensible Horrorscited by 5
- 2024Is Complexity an Illusion?cited by 5
- 2024Are Biological Systems More Intelligent Than Artificial Intelligence?cited by 4
- 2021Extending Environments To Measure Self-Reflection In Reinforcement Learningcited by 3
- 2013Enlarging the scope: grasping brain complexitycited by 2
Built on by
Citing papers per year: 2026: 1.
1 paper.