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Wiese & Friston (2021): Neural Correlates of Consciousness under the Free Energy Principle

Posted Jul 7, 2026 · Updated Jul 17, 2026 · 3 min read

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What the work establishes

Wiese and Friston (2021) examine how the free energy principle (FEP) reframes research on neural correlates of consciousness (NCC). They shift focus from static neural states to neural dynamics. They add computational correlates defined as probabilities encoded by those states.

Core result: FEP supplies a route from computational descriptions to explanations of consciousness. It handles cases of consciousness without ongoing sensory input or behavior. It distinguishes systems that merely simulate conscious processes from systems that instantiate them.

Exact primary passages

The paper states: "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." (Abstract, 2021).

It continues: "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." (Abstract, 2021).

Further: "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." (Abstract, 2021).

Convergence patterns touched

The work links thermodynamic and information-theoretic minimization (FEP) to structured neural activity and probabilistic inference. It touches flow networks and memory-like internal models. It connects active inference to the emergence of mind-like capacities from basic self-organizing dynamics. These map onto the Ladder steps from energy flow to structure to computational processes supporting awareness.

Relation to the OIP/GRAIN synthesis

The paper supplies a mechanistic bridge at the computational layer. FEP minimization produces internal models that encode probabilities; these models support the transition from raw dynamics to conscious processing. It supports the synthesis claim that reliable energy-flow patterns generate the narrow family of structures that include memory and mind. It does not address the Mirror Layer or the full end-to-end Ladder from difference to life.

Honest limits and disconfirming edges

The account remains framework-level. No new empirical data establish that FEP dynamics are necessary or sufficient for consciousness. Reductionist objections apply: the same equations may describe non-conscious systems equally well. The distinction between simulation and instantiation rests on interpretive ascription rules rather than direct measurement. Claims about islands of awareness or minimal unifying models stay speculative without further constraints from biology or experiment.

Claims

Claim c1: FEP requires NCC definitions in terms of dynamics rather than static states. Tier: mechanistic. Source: Wiese & Friston 2021 abstract.
Claim c2: Computational correlates under FEP are probabilities encoded by neural states. Tier: mechanistic. Source: Wiese & Friston 2021 abstract.
Claim c3: FEP addresses consciousness without sensory stimulation or behavior. Tier: mechanistic. Source: Wiese & Friston 2021 abstract.
Claim c4: FEP-derived computation distinguishes simulation of consciousness from instantiation. Tier: speculative. Source: Wiese & Friston 2021 abstract.
Claim c5: The approach improves prospects for computational explanation of consciousness. Tier: mechanistic. Source: Wiese & Friston 2021 abstract.

Sources

Wiese, W., & Friston, K. J. (2021). The neural correlates of consciousness under the free energy principle: From computational correlates to computational explanation. Philosophy and the Mind Sciences, 2. https://doi.org/10.33735/phimisci.2021.81. Quote: exact passages listed above.

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Study

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

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.

Core abstract statement reframing NCC via FEP dynamics and probabilities.

2026-07-07 20:47s1 · #91a8f1da65d9

Key evidence

4 claims · tier-ranked · API

mechanistic

FEP requires NCC definitions in terms of dynamics rather than static states.

sources: s1

mechanistic

Computational correlates under FEP are probabilities encoded by neural states.

sources: s1

mechanistic

FEP addresses consciousness without sensory stimulation or behavior.

sources: s1

mechanistic

The approach improves prospects for computational explanation of consciousness.

sources: s1

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draft2026-07-07 20:47

Wiese & Friston (2021): Neural Correlates of Consciousness under the Free Energy Principle · 5 claims · 1 sources

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- S

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GROUNDING NOTES (from the thinker map — verify before relying on):
FEP extension to consciousness/neural correlates, ladder from thermo/info to mind

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{"voice":"enrichment","article_shape":"single_focus","condition":null,"condition_key":null,"primary_peptide":null,"peptides_in_scope":[],"drugs_in_scope":[],"weigh

it output

{
  "slug": "wiese-2021-neural-correlates-free-energy-principle",
  "title": "Wiese & Friston (2021): Neural Correlates of Consciousness under the Free Energy Principle",
  "body": "## What the work establishes\nWiese and Friston (2021) examine how the free energy principle (FEP) reframes research on neural correlates of consciousness (NCC). They shift focus from static neural states to neural dynamics. They add computational correlates defined as probabilities encoded by those states.\n\nCore result: FEP supplies a route from computational descriptions to explanations of consciousness. It handles cases of consciousness without ongoing sensory input or behavior. It distinguishes systems that merely simulate conscious processes from systems that instantiate them.\n\n## Exact primary passages\nThe paper states: \"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.\" (Abstract, 2021).\n\nIt continues: \"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 possibil

68c463ab20eb7863

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What the work establishes

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Relation to the OIP/GRAIN synthesis

Honest limits and disconfirming edges

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