The DOI matches its Crossref record; 113 of the 113 references it cites were found in a registry or an index.
Active Inference: A Process Theory
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Prospect Theory: An Analysis of Decision under Risk
Kahneman, D., & Tversky, A. (1979) · Econometrica · doi:10.2307/1914185 · Cited by 48,524
Prospect theory: An analysis of decision under risk
Kahneman, D. J., & Tversky, A. (1988) · Cambridge University Press eBooks · doi:10.1017/cbo9780511609220.014 · Cited by 32,720
Information theory and statistical mechanics
Jaynes, E. T. (1957) · Physical Review · doi:10.1103/physrev.106.620 · Cited by 12,931
Whatever next? Predictive brains, situated agents, and the future of cognitive science
Clark, A. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12000477 · Cited by 5,924
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
Rao, R. P. N., & Ballard, D. H. (1999) · Nature Neuroscience · doi:10.1038/4580 · Cited by 5,483 · cited 2×
“There has been a paradigm shift in the cognitive neurosciences over the past decade toward the Bayesian brain and predictive coding (Ballard, Hin-ton, & Sejnowski, 1983; Rao & Ballard, 1999; Knill & Pouget, 2004; Yuille & Kersten, 2006; De Bruin & Michael, 2015).”
BackgroundMethodThe Neural Basis of Decision Making
Gold, J. I., & Shadlen, M. N. (2007) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.113038 · Cited by 3,764
A sensorimotor account of vision and visual consciousness
O'Regan, J. K., & Noë, A. (2001) · Behavioral and Brain Sciences · doi:10.1017/s0140525x01000115 · Cited by 3,126
Advances in Neural Information Processing Systems 20
Bonilla, E. V., Chai, K. M. A., & Williams, C. K. I. (2008) · neural information processing systems · Cited by 2,701
The Bayesian brain: The role of uncertainty in neural coding and computation
Knill, D. C., & Pouget, A. (2004) · Trends in Neurosciences · doi:10.1016/j.tins.2004.10.007 · Cited by 2,588
Flagged first, then by citations
Research works printed but not linked: 9
- Attias H., 2003, Proc. of the 9th Int. Workshop on Artificial Intelligence and Statistics
- Barto A., 2004, Proceedings of the 3rd International Conference on Development and Learning
- FitzGerald T., 2014, Front. Hum. Neurosci., 8
- Friston K., 2012, Frontiers in Neurorobotics, 6
- Jaynes E. T., 1957, Physical Review Series II, 106, 620
- Klyubin A. S., 2005, Proc. CEC 2005. IEEE, 128
- Mirza M. B., 2016, Frontiers in Computational Neuroscience, 10
- Schmidhuber J., 1991, Proc. International Joint Conference on Neural Networks, 1458
- van den Broek J. L., 2010, UAI, 6, 1
Cited by
Sorted by citations
Seth, A. K., & Bayne, T. (2022) · Nature Reviews Neuroscience · doi:10.1038/s41583-022-00587-4 · Cited by 880
Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., & Elger, G. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.107864 · Cited by 581
Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation
Scellier, B., & Bengio, Y. (2017) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2017.00024 · Cited by 504
Towards a neuroscience of active sampling and curiosity
Gottlieb, J., & Oudeyer, P.-Y. (2018) · Nature Reviews Neuroscience · doi:10.1038/s41583-018-0078-0 · Cited by 494
The graphical brain: Belief propagation and active inference
Friston, K. J., Parr, T., & de Vries, B. (2017) · Network Neuroscience · doi:10.1162/netn_a_00018 · Cited by 415
Self-citationBeing a Beast Machine: The Somatic Basis of Selfhood
Seth, A. K., & Tsakiris, M. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.08.008 · Cited by 371
Emotion words, emotion concepts, and emotional development in children: A constructionist hypothesis
Hoemann, K., Xu, F., & Barrett, L. F. (2019) · Developmental Psychology · doi:10.1037/dev0000686 · Cited by 354
Answering Schrödinger's question: A free-energy formulation
Ramstead, M. J. D., Badcock, P. B., & Friston, K. J. (2018) · Physics of Life Reviews · doi:10.1016/j.plrev.2017.09.001 · Cited by 354
Self-citationThe free energy principle for action and perception: A mathematical review
Buckley, C. L., Kim, C. S., McGregor, S., & Seth, A. K. (2017) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2017.09.004 · Cited by 387
Thinking through other minds: A variational approach to cognition and culture
Veissière, S. P. L., Constant, A., Ramstead, M. J. D., Friston, K. J., & Kirmayer, L. J. (2020) · Behavioral and Brain Sciences · doi:10.1017/s0140525x19001213 · Cited by 348
Self-citationActive Inference, Curiosity and Insight
Friston, K. J., Lin, M., Frith, C., Pezzulo, G., Hobson, J. A., & Ondobaka, S. (2017) · Neural Computation · doi:10.1162/neco_a_00999 · Cited by 372
Self-citationHierarchical active inference: A theory of motivated control
Pezzulo, G., Rigoli, F., & Friston, K. J. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.01.009 · Cited by 334
Self-citationInterpreting and Utilising Intersubject Variability in Brain Function
Seghier, M. L., & Price, C. J. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.03.003 · Cited by 306
Words as social tools: language, sociality and inner grounding in abstract concepts
Borghi, A. M., Barca, L., Binkofski, F., Castelfranchi, C., Pezzulo, G., & Tummolini, L. (2019) · Physics of Life Reviews · doi:10.1016/j.plrev.2018.12.001 · Cited by 253
Self-citationUncertainty, epistemics and active inference
Parr, T., & Friston, K. J. (2017) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0376 · Cited by 252
Self-citationComputational mechanisms of curiosity and goal-directed exploration
Schwartenbeck, P., Passecker, J., Hauser, T. U., FitzGerald, T. H. B., Kronbichler, M., & Friston, K. J. (2019) · eLife · doi:10.7554/elife.41703 · Cited by 278
Self-citationA step-by-step tutorial on active inference and its application to empirical data
Smith, R., Friston, K. J., & Whyte, C. J. (2022) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2021.102632 · Cited by 246
Self-citationDeeply Felt Affect: The Emergence of Valence in Deep Active Inference
Hesp, C., Smith, R., Parr, T., Allen, M., Friston, K. J., & Ramstead, M. J. D. (2021) · Neural Computation · doi:10.1162/neco_a_01341 · Cited by 246
Self-citationGershman, S. J., & Uchida, N. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0220-7 · Cited by 236
Generalised free energy and active inference
Parr, T., & Friston, K. J. (2019) · Biological Cybernetics · doi:10.1007/s00422-019-00805-w · Cited by 236
Self-citationA tale of two densities: active inference is enactive inference
Ramstead, M. J. D., Kirchhoff, M. D., & Friston, K. J. (2019) · Adaptive Behavior · doi:10.1177/1059712319862774 · Cited by 250
Self-citationActive inference on discrete state-spaces: A synthesis
Da Costa, L., Parr, T., Sajid, N., Veselic, S., Neacsu, V., & Friston, K. (2020) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2020.102447 · Cited by 226
“For the purpose of this paper we use a particular (structured) mean-field approximation (c.f., Figure 2): This choice is driven by didactic purposes and since this factorisation has been used extensively in the active inference literature (e.g., [2, 27, 43]).”
BackgroundMethodResultInfluentialSelf-citationWorking memory, attention, and salience in active inference
Parr, T., & Friston, K. J. (2017) · Scientific Reports · doi:10.1038/s41598-017-15249-0 · Cited by 226
Self-citationA free energy principle for a particular physics
Friston, K. (2019) · arXiv · doi:10.48550/arxiv.1906.10184 · Cited by 222
Self-citationThe power of predictions: An emerging paradigm for psychological research
Hutchinson, J. B., & Barrett, L. F. (2019) · Current Directions in Psychological Science · doi:10.1177/0963721419831992 · Cited by 216
Planning and navigation as active inference
Kaplan, R., & Friston, K. J. (2018) · Biological Cybernetics · doi:10.1007/s00422-018-0753-2 · Cited by 203
Self-citationThe sense of should: A biologically-based framework for modeling social pressure
Theriault, J. E., Young, L., & Barrett, L. F. (2021) · Physics of Life Reviews · doi:10.1016/j.plrev.2020.01.004 · Cited by 201
Am i self-conscious? (or does self-organization entail self-consciousness?)
Friston, K. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00579 · Cited by 197
Self-citationThe Anatomy of Inference: Generative Models and Brain Structure
Parr, T., & Friston, K. J. (2018) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2018.00090 · Cited by 215
Self-citationSmith, R. S., Kuplicki, R., Feinstein, J. S., Forthman, K. L., Stewart, J. L., Paulus, M. P., Tulsa 1000 investigators, & Khalsa, S. S. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008484 · Cited by 207
In the body's eye: the computational anatomy of interoceptive inference
Allen, M., Levy, A., Parr, T., & Friston, K. J. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010490 · Cited by 185
Self-citationActive Inference: Demystified and Compared
Sajid, N., Ball, P. J., Parr, T., & Friston, K. J. (2021) · Neural Computation · doi:10.1162/neco_a_01357 · Cited by 197
Self-citationSmith, R., Badcock, P., & Friston, K. J. (2021) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.13138 · Cited by 174
Self-citationRegimes of Expectations: An Active Inference Model of Social Conformity and Human Decision Making
Constant, A., Ramstead, M. J. D., Veissière, S. P. L., & Friston, K. J. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.00679 · Cited by 184
Self-citationPredictive processing as a systematic basis for identifying the neural correlates of consciousness
Hohwy, J., & Seth, A. (2020) · Philosophy and the Mind Sciences · doi:10.33735/phimisci.2020.ii.64 · Cited by 170
Badcock, P. B., Friston, K. J., Ramstead, M. J. D., Ploeger, A., & Hohwy, J. (2019) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-019-00721-3 · Cited by 175
Self-citationThe free energy principle made simpler but not too simple
Friston, K., Da Costa, L., Sajid, N., Heins, C., Ueltzhöffer, K., Pavliotis, G. A., & Parr, T. (2023) · Physics Reports · doi:10.1016/j.physrep.2023.07.001 · Cited by 163
“These beliefs are then used to evaluate the expected free energy of allowable policies, from which actions can be selected [123–125].”
InfluentialSelf-citationCOVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks
Makris, A., Kontopoulos, I., & Tserpes, K. (2020) · Hellenic Conference on Artificial Intelligence · doi:10.1145/3411408.3411416 · Cited by 165
Better Safe Than Sorry: A Common Signature of General Vulnerability for Psychopathology
den Bergh, O. V., BROSSCHOT, J. F., Critchley, H., Thayer, J. F., & Ottaviani, C. (2020) · Perspectives on Psychological Science · doi:10.1177/1745691620950690 · Cited by 170
Corcoran, A. W., Pezzulo, G., & Hohwy, J. (2020) · Biology & Philosophy · doi:10.1007/s10539-020-09746-2 · Cited by 161
Self-citationComputational psychiatry: from synapses to sentience
Friston, K. J. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01743-z · Cited by 168
Self-citationDiverse motives for human curiosity
Kobayashi, K., Ravaioli, S., Baranès, A., Woodford, M., & Gottlieb, J. P. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0589-3 · Cited by 157
Limanowski, J., & Friston, K. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00643 · Cited by 154
“Hence the agent’s model must be able to entertain ‘counterfactually rich’ representations; i.e., beliefs about several alternative potential actions and the states of affairs that these actions would bring about (Friston et al., 2014, 2017c; Seth, 2014; Pezzulo, 2017; cf. Powers, 1973/2005).”
BackgroundMethodInfluentialSelf-citationPredictive Processing and the Representation Wars
Williams, D. (2017) · Minds and Machines · doi:10.1007/s11023-017-9441-6 · Cited by 153
Computational Neuropsychology and Bayesian Inference
Parr, T., Rees, G. E., & Friston, K. J. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00061 · Cited by 158
Self-citationSales, A. C., Friston, K. J., Jones, M. W., Pickering, A. E., & Moran, R. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006267 · Cited by 157
Self-citationMultiscale integration: beyond internalism and externalism
Ramstead, M. J. D., Kirchhoff, M. D., Constant, A., & Friston, K. J. (2019) · Synthese · doi:10.1007/s11229-019-02115-x · Cited by 156
Self-citationThe feeling of grip: novelty, error dynamics, and the predictive brain
Kiverstein, J., Miller, M., & Rietveld, E. (2019) · Synthese · doi:10.1007/s11229-017-1583-9 · Cited by 143
Neuronal oscillations: unavoidable and useful?
Singer, W. (2017) · European Journal of Neuroscience · doi:10.1111/ejn.13796 · Cited by 153
An information-theoretic perspective on the costs of cognition
Zénon, A., Solopchuk, O., & Pezzulo, G. (2017) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2018.09.013 · Cited by 153
Self-citationFriston, K. J. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000426 · Cited by 141
Self-citationVariational ecology and the physics of sentient systems
Ramstead, M., Constant, A., Badcock, P., & Friston, K. J. (2019) · Physics of Life Reviews · doi:10.1016/j.plrev.2018.12.002 · Cited by 132
Self-citationA landscape of consciousness: Toward a taxonomy of explanations and implications
Kuhn, R. L. (2024) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2023.12.003 · Cited by 135
Safron, A. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.00030 · Cited by 133
“Beginning from this fundamental principle of nature (Hohwy, 2020), the FEP and Active Inference (FEP-AI) framework (Friston et al., 2017a) proscribes means of satisfying this imperative through minimizing prediction-error (or “free energy”) with respect to the models by which systems preserve…”
BackgroundThe Emperor's New Markov Blankets
Bruineberg, J. P., Dołęga, K., Dewhurst, J., & Baltieri, M. (2021) · Behavioral and Brain Sciences · doi:10.1017/s0140525x21002351 · Cited by 142
Neuronal message passing using mean-field, bethe, and marginal approximations
Parr, T., Markovic, D., Kiebel, S. J., & Friston, K. J. (2019) · Scientific Reports · doi:10.1038/s41598-018-38246-3 · Cited by 128
Self-citationSmith, R. S., Parr, T., & Friston, K. J. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02844 · Cited by 133
Self-citationThe evolution of brain architectures for predictive coding and active inference
Pezzulo, G., Parr, T., & Friston, K. J. (2021) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2020.0531 · Cited by 136
Self-citationFriston, K. J., Costa, L. D., Hafner, D., Hesp, C., & Parr, T. (2021) · Neural Computation · doi:10.1162/neco_a_01351 · Cited by 150
Self-citationPlay in predictive minds: A cognitive theory of play.
Andersen, M. M., Kiverstein, J., Miller, M., & Roepstorff, A. (2023) · Psychological Review · doi:10.1037/rev0000369 · Cited by 122
Path integrals, particular kinds, and strange things
Friston, K. J., Costa, L. D., Sakthivadivel, D. A. R., Heins, C., Pavliotis, G. A., Ramstead, M. J. D., & Parr, T. (2023) · Physics of Life Reviews · doi:10.1016/j.plrev.2023.08.016 · Cited by 145
Self-citationFields, C., & Levin, M. (2022) · Entropy · doi:10.3390/e24060819 · Cited by 121
A mathematical model of embodied consciousness.
Rudrauf, D., Bennequin, D., Granic, I., Landini, G., Friston, K., & Williford, K. (2017) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2017.05.032 · Cited by 118
Self-citationKuchling, F., Friston, K. J., Georgiev, G., & Levin, M. G. (2019) · Physics of Life Reviews · doi:10.1016/j.plrev.2019.06.001 · Cited by 123
Self-citationSandved-Smith, L., Hesp, C., Mattout, J., Friston, K. J., Antoine Lutz, A., & Ramstead, M. J. D. (2021) · Neuroscience of Consciousness · doi:10.1093/nc/niab018 · Cited by 136
Self-citationOn markov blankets and hierarchical self-organisation
Palacios, E. R., Razi, A., Parr, T., Kirchhoff, M., & Friston, K. (2020) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2019.110089 · Cited by 115
Self-citationFrontal cortex function as derived from hierarchical predictive coding
Alexander, W. H., & Brown, J. W. (2018) · Scientific Reports · doi:10.1038/s41598-018-21407-9 · Cited by 121
The math is not the territory: navigating the free energy principle
Andrews, M. (2021) · Biology & Philosophy · doi:10.1007/s10539-021-09807-0 · Cited by 124
Generative models, linguistic communication and active inference
Friston, K. J., Parr, T., Yufik, Y. M., Sajid, N., Price, C. J., & Holmes, E. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.07.005 · Cited by 120
Self-citationAn Active Inference Approach to Modeling Structure Learning: Concept Learning as an Example Case
Smith, R. S., Schwartenbeck, P., Parr, T., & Friston, K. J. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.00041 · Cited by 119
Self-citationActive inference as a theory of sentient behavior
Pezzulo, G., Parr, T., & Friston, K. (2024) · Biological Psychology · doi:10.1016/j.biopsycho.2023.108741 · Cited by 109
Self-citationInteroception as modeling, allostasis as control
Sennesh, E., Theriault, J., Brooks, D., van de Meent, J.-W., Barrett, L. F., & Quigley, K. S. (2022) · Biological Psychology · doi:10.1016/j.biopsycho.2021.108242 · Cited by 107
Predictive Coding: a Theoretical and Experimental Review
Millidge, B., Seth, A., & Buckley, C. L. (2021) · arXiv · doi:10.48550/arxiv.2107.12979 · Cited by 106
The first prior: From co-embodiment to co-homeostasis in early life
Ciaunica, A., Constant, A., Preissl, H., & Fotopoulou, K. (2021) · Consciousness and Cognition · doi:10.1016/j.concog.2021.103117 · Cited by 105
Tschantz, A., Barca, L., Maisto, D., Buckley, C. L., Seth, A. K., & Pezzulo, G. (2022) · Biological Psychology · doi:10.1016/j.biopsycho.2022.108266 · Cited by 112
Self-citationFriston, K. J., Sajid, N., Quiroga‐Martinez, D. R., Parr, T., Price, C. J., & Holmes, E. (2020) · Hearing Research · doi:10.1016/j.heares.2020.107998 · Cited by 113
Self-citationThe neurobiology of interoception and affect
Feldman, M. J., Bliss-Moreau, E., & Lindquist, K. A. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.01.009 · Cited by 101
A World Unto Itself: Human Communication as Active Inference
Vasil, J., Badcock, P. B., Constant, A., Friston, K. J., & Ramstead, M. J. D. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.00417 · Cited by 114
Self-citationDecision-making in schizophrenia: A predictive-coding perspective
Sterzer, P., Voss, M., Schlagenhauf, F., & Heinz, A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.074 · Cited by 105
Ueltzhöffer, K. (2018) · Biological Cybernetics · doi:10.1007/s00422-018-0785-7 · Cited by 98
Free-energy minimization in joint agent-environment systems: A niche construction perspective
Bruineberg, J. P., Rietveld, E., Parr, T., van Maanen, L., & Friston, K. J. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.07.002 · Cited by 98
Self-citationThe active construction of the visual world
Parr, T., & Friston, K. J. (2017) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2017.08.003 · Cited by 101
Self-citationRepresentation Wars: Enacting an Armistice Through Active Inference
Constant, A., Clark, A., & Friston, K. J. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.598733 · Cited by 102
Self-citationWhence the Expected Free Energy?
Millidge, B., Tschantz, A., & Buckley, C. L. (2021) · Neural Computation · doi:10.1162/neco_a_01354 · Cited by 114
Internally generated hippocampal sequences as a vantage point to probe future‐oriented cognition
Pezzulo, G., Kemere, C. T., & van der Meer, M. A. A. (2017) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13329 · Cited by 98
Self-citationLinson, A., Clark, A., Ramamoorthy, S., & Friston, K. J. (2018) · Frontiers in Robotics and AI · doi:10.3389/frobt.2018.00021 · Cited by 102
Self-citationSmith, R., Lane, R. D., Parr, T., & Friston, K. J. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.09.002 · Cited by 89
Self-citationPerceptual awareness and active inference
Parr, T., Corcoran, A. W., Friston, K. J., & Hohwy, J. (2019) · Neuroscience of Consciousness · doi:10.1093/nc/niz012 · Cited by 91
Self-citationAction perception as hypothesis testing
Donnarumma, F., Costantini, M., Ambrosini, E., Friston, K., & Pezzulo, G. (2017) · Cortex · doi:10.1016/j.cortex.2017.01.016 · Cited by 90
Self-citationConcepts, goals and the control of survival-related behaviors
Feldman Barrett, L., & Finlay, B. L. (2018) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2018.10.001 · Cited by 89
World model learning and inference
Friston, K. J., Moran, R., Nagai, Y., Taniguchi, T., Gomi, H., & Tenenbaum, J. B. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.09.011 · Cited by 99
Self-citationAllostasis as a core feature of hierarchical gradients in the human brain
Katsumi, Y., Theriault, J. E., Quigley, K. S., & Barrett, L. F. (2022) · Network Neuroscience · doi:10.1162/netn_a_00240 · Cited by 88
How particular is the physics of the free energy principle?
Aguilera, M., Millidge, B., Tschantz, A., & Buckley, C. L. (2021) · Physics of Life Reviews · doi:10.1016/j.plrev.2021.11.001 · Cited by 88
Precision and False Perceptual Inference
Parr, T., Benrimoh, D. A., Vincent, P., & Friston, K. J. (2018) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2018.00039 · Cited by 87
Self-citationLosing Ourselves: Active Inference, Depersonalization, and Meditation
Deane, G., Miller, M., & Wilkinson, S. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.539726 · Cited by 87
Conscious artificial intelligence and biological naturalism
Seth, A. K. (2026) · Behavioral and Brain Sciences · doi:10.1017/s0140525x25000032 · Cited by 87
Leptourgos, P., Fortier, M., Carhart-Harris, R. L., Corlett, P. R., Dupuis, D., Halberstadt, A. L., Kometer, M., Kozáková, E., Larøi, F., Noorani, T. N., et al. (2020) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaa117 · Cited by 90
A free energy principle for generic quantum systems
Fields, C., Friston, K. J., Glazebrook, J. F., & Levin, M. (2022) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2022.05.006 · Cited by 90
Self-citationCiaunica, A., Seth, A. K., Limanowski, J., Hesp, C., & Friston, K. J. (2022) · Consciousness and Cognition · doi:10.1016/j.concog.2022.103320 · Cited by 91
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
