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.

Highlights

  • The free energy principle (FEP, Friston, 2010) provides an information-theoretic analysis of the concept of existence of self-organising systems (Hohwy, 2020)

  • We will argue that FEP supports the following three observations with respect to the debate on neural correlates of consciousness (NCCs) and correlates of consciousness (CCCs). (i) According to FEP, NCCs must be defined in terms of neural dynamics, not neural states. (ii) According to FEP, there is a relevant distinction to be made between the probabilities of neural states and the probabilities encoded by neural states

  • A computational explanation of consciousness requires more than a formal description of neural dynamics

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Summary

Introduction

The free energy principle (FEP, Friston, 2010) provides an information-theoretic analysis of the concept of existence of self-organising systems (Hohwy, 2020). In Friston, Wiese, et al (2020), the information geometries – associated with the probabilities of states and with probabilities encoded by states – are called intrinsic and extrinsic information geometries, respectively In line with this distinction, neural dynamics (with NCCs as a special case) pertain to movements on the intrinsic statistical manifold, whereas neural computations (with CCCs as a special case) pertain to movements on the extrinsic manifold. Since empirical evidence suggests that activity in conscious systems has high dynamical complexity, we briefly discuss how these notions relate to one another and suggest that it should be possible to define dynamical complexity in terms of a system’s extrinsic information geometry (section 3.5) The benefit of such a definition is that it would provide not just a fundamental theoretical motivation for associating consciousness with dynamical complexity, and the basis for a computational explanation of consciousness.

From neural correlates to computational correlates of consciousness
Challenge 1
Challenge 2
Challenge 3
Computational correlates of consciousness and the free energy principle
CCCs and computational principles
Deriving computational principles from first principles
The challenge of islands of awareness
Dynamical complexity as a CCC?
From computational correlates to a computational explanation of consciousness
The scope of computational explanations
Distinguishing simulation from instantiation
Computational explanations of consciousness and minimal unifying models
Conclusion
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