Computational Explanation of Consciousness: A Predictive Processing-based Understanding of Consciousness
Zhichao Gong
Journal of Human Cognition 2024 DOI: 10.47297/wspjhcwsp2515-469905.20240802 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Citations | 1 |
| Key points | Argues that the neural-correlates approach embodies a reductionist physicalism that inevitably fails to explain consciousness, and proposes that predictive processing, supported by free energy theory, offers a fresh paradigm and neural foundation for computationally explaining consciousness. |
Abstract
In the domain of cognitive science, understanding consciousness through the investigation of neural correlates has been the primary research approach. The exploration of neural correlates of consciousness is focused on identifying these correlates and reducing consciousness to a physical phenomenon, embodying a form of reductionist physicalism. This inevitably leads to challenges in explaining consciousness itself. The computational interpretation of consciousness takes a functionalist view, grounded in physicalism, and models conscious experience as a cognitive function, elucidated through computational means. This paper posits that predictive processing offers a fresh paradigm for comprehending human cognition and serves as a neural foundation for the computational elucidation of consciousness. Additionally, free energy theory, as a mathematical construct explaining the predictive processing model, furnishes a theoretical scaffold for understanding consciousness computationally.