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Active Inference: A Process Theory
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This article describes a process theory based on active inference and belief propagation. Starting from the premise that all neuronal processing (and action selection) can be explained by maximizing Bayesian model evidence—or minimizing variational free energy—we ask whether neuronal responses can be described as a gradient descent on variational free energy. Using a standard (Markov decision process) generative model, we derive the neuronal dynamics implicit in this description and reproduce a remarkable range of well-characterized neuronal phenomena. These include repetition suppression, mismatch negativity, violation responses, place-cell activity, phase precession, theta sequences, theta-gamma coupling, evidence accumulation, race-to-bound dynamics, and transfer of dopamine responses. Furthermore, the (approximately Bayes’ optimal) behavior prescribed by these dynamics has a degree of face validity, providing a formal explanation for reward seeking, context learning, and epistemic foraging. Technically, the fact that a gradient descent appears to be a valid description of neuronal activity means that variational free energy is a Lyapunov function for neuronal dynamics, which therefore conform to Hamilton’s principle of least action.
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“The perception-action cycle (Tishby and Polani, 2010) and active inference (Friston et al., 2017) both make evaluation emergent from the agent's ongoing interaction with the environment, arising either through optimization of information flow or through minimization of expected surprise.”
From Toward Enactive Artificial Intelligence (2026) · cites this paper as Friston et al., 2017
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“This marginal likelihood scores the plausibility of a particle pursuing this kind of path and is usually interpreted in terms of expected loss (i.e., negative expected reward or utility) [66, 63], and pragmatic affordance [153, 123].”
From The free energy principle made simpler but not too simple (2023) · cites this paper as [123]
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“A prevailing account of human perception is that the brain builds a generative model of the world, constantly explaining its observations (Friston et al., 2017), i.e. active inference.”
From Object-Centric Scene Representations Using Active Inference (2024) · cites this paper as Friston et al., 2017
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“These beliefs are then used to evaluate the expected free energy of allowable policies, from which actions can be selected [123, 124, 125].”
From The free energy principle made simpler but not too simple (2023) · cites this paper as [123]
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“The conditional independencies among states implicit in partially observed Markov decision processes entail the above functional forms for variational and expected free energies [124, 123].”
From The free energy principle made simpler but not too simple (2023) · cites this paper as [123]
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“The ensuing minimisation of free energy can be formulated as gradient flows following (17)—between the discrete arrival of new sensory input—in a way that relates comfortably to neuronal dynamics [123, 124, 42].”
From The free energy principle made simpler but not too simple (2023) · cites this paper as [123]
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“AI-C can similarly be described as presenting a predictive processing account of conscious experience; however, unlike NREP, it is specifically committed to the process theory developed under the free energy principle (Friston et al., 2017; Parr et al., 2022) -with the caveat that the precise implementational details described at the process theory level are themselves subject to development (e.g.”
From Integrated information and predictive processing theories of consciousness: An adversarial collaborative review (2026) · cites this paper as Friston et al., 2017
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“it also implies that organisms can minimise uncertainty through action, actively sampling the environment to reduce ambiguity and confirm or disconfirm their predictions, as formalised under active inference (17).”
From Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders (2026) · cites this paper as [17]
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“Under active inference, actions are selected to minimise expected free energy, reducing uncertainty and maintaining preferred states (17).”
From Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders (2026) · cites this paper as [17]
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Self-citation
Metadata
- Authors
- Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., & Pezzulo, G.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- National Research Council (IT)
- Corresponding author
- Friston, Karl
- FitzGerald, Thomas
- Rigoli, Francesco
- Schwartenbeck, Philipp
- Pezzulo, Giovanni
- Venue
- Neural Computation
- Publisher
- MIT Press
- Published
- 2017-01
- Volume
- 29
- Issue
- 1
- Pages
- 1-49
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2552810632
- Cited by
- 1,156
- References
- 113
- Citations by year
- 2026: 142 · 2025: 145 · 2024: 124 · 2023: 129 · 2022: 145 · 2021: 147 · 2020: 155 · 2019: 73 · 2018: 61 · 2017: 35
- FWCI
- 35.50
- Type
- journal article
- Language
- en
- Open access
- Locations
- Neural Computation (publishedVersion)
- PubMed (publishedVersion)
- City Research Online (City University London) (submittedVersion)
- UEA Digital Repository (University of East Anglia) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Wellcome Trust (088130/Z/09/Z)
- Österreichischen Akademie der Wissenschaften
- Universität Salzburg
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- active inference, variational free energy, marginal likelihood, gradient descent, neural dynamics, Markov decision process, repetition suppression, mismatch negativity, place cells, phase precession, theta sequences, theta-gamma coupling, evidence accumulation, dopamine, reward seeking, contextual learning, Lyapunov function, Hamilton's principle of least action, action selection
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