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  5. The neural correlates of consciousness under the free energy principle: From computational correlates to computational explanation

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en
2020

The neural correlates of consciousness under the free energy principle: From computational correlates to computational explanation

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en
2020
DOI: 10.31234/osf.io/7gefk

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Karl Friston
Karl Friston

University College London

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Wanja Wiese
Karl Friston

Abstract

How can the free energy principle contribute to research on neural correlates of consciousness, and to the scientific study of consciousness more generally? Under the free energy principle, neural correlates should be defined in terms of neural *dynamics*, not neural states, and should be complemented by research on *computational* correlates of consciousness -- defined in terms of probabilities encoded by neural states. We argue that these restrictions brighten the prospects of a computational explanation of consciousness, by addressing two central problems. The first is to account for consciousness in the absence of sensory stimulation and behaviour. The second is to allow for the possibility of systems that implement computations associated with consciousness, without being conscious, which requires differentiating between computational systems that merely simulate conscious beings and computational systems that are conscious in and of themselves. Given the notion of computation entailed by the free energy principle, we derive constraints on the ascription of consciousness in controversial cases (e.g., in the absence of sensory stimulation and behaviour). We show that this also has implications for what it means to *be*, as opposed to merely *simulate* a conscious system.

How to cite this publication

Wanja Wiese, Karl Friston (2020). The neural correlates of consciousness under the free energy principle: From computational correlates to computational explanation. , DOI: https://doi.org/10.31234/osf.io/7gefk.

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Publication Details

Type

Preprint

Year

2020

Authors

2

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.31234/osf.io/7gefk

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