A Conceptual Framework for Consciousness Based on a Deep Understanding of Matter

Philosophy Study 2 (10):689-703 (2012)
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Abstract

One of the main challenges in consciousness research is widely known as the hard problem of consciousness. In order to tackle this problem, I utilize an approach from theoretical physics, called stochastic electrodynamics (SED), which goes one step beyond quantum theory and sheds new light on the reality behind matter. According to this approach, matter is a resonant oscillator that is orchestrated by an all-pervasive stochastic radiation field, called zero-point field (ZPF). The properties of matter are not intrinsic but acquired by dynamic interaction with the ZPF, which in turn picks up information about the material system as soon as an ordered state, i.e., a stable attractor, is reached. I point out that these principles apply also to macroscopic biological systems. From this perspective, long-range correlations in the brain, such as neural gamma synchrony, can be interpreted in terms of order phenomena induced and stabilized by the ZPF, suggesting that every attractor in the brain goes along with an information state in the ZPF. In order to build the bridge to consciousness, I employ additional input from Eastern philosophy. From a comparison between SED and Eastern philosophy I draw the conclusion that the ZPF is an appropriate candidate for the substrate of consciousness, implying that information states in the ZPF are associated with conscious states. On this basis I develop a conceptual framework for consciousness that is fully consistent with physics, neurophysiology, and Eastern philosophy. I argue that this conceptual framework has many interesting features and opens a door to a theory of consciousness. Particularly, it solves the problem of how matter and consciousness communicate in a causally closed functional chain, it gives a physical grounding to existing approaches regarding the connection between consciousness and information, and it gives clear direction to future models and experiments.

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