Action understanding and active inference
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We have suggested that the mirror-neuron system might be usefully understood as implementing Bayes-optimal perception of actions emitted by oneself or others. To substantiate this claim, we present neuronal simulations that show the same representations can prescribe motor behavior and encode motor intentions during action-observation. These simulations are based on the free-energy formulation of active inference, which is formally related to predictive coding. In this scheme, (generalised) states of the world are represented as trajectories. When these states include motor trajectories they implicitly entail intentions (future motor states). Optimizing the representation of these intentions enables predictive coding in a prospective sense. Crucially, the same generative models used to make predictions can be deployed to predict the actions of self or others by simply changing the bias or precision (i.e. attention) afforded to proprioceptive signals. We illustrate these points using simulations of handwriting to illustrate neuronally plausible generation and recognition of itinerant (wandering) motor trajectories. We then use the same simulations to produce synthetic electrophysiological responses to violations of intentional expectations. Our results affirm that a Bayes-optimal approach provides a principled framework, which accommodates current thinking about the mirror-neuron system. Furthermore, it endorses the general formulation of action as active inference.
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Cortical signals that drive movement are descending predictions about the proprioceptive consequences of movement and are therefore better considered as corollary discharge as opposed to efference copy.
“In other words, cortical signals that drive movement are descending predictions about the proprioceptive consequences of movement and are therefore better considered as corollary discharge (the predictions of the sensory consequences of movement) as opposed to efference copy (copies of the motor commands used to form corollary discharge).”
From The free-energy self: a predictive coding account of self-recognition (2014) · cites this paper as Friston et al., 2011
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Friston, K., & Frith, C. (2015) · Consciousness and Cognition · doi:10.1016/j.concog.2014.12.003 · Cited by 359 · see citing works
Self-citationWhat is optimal about motor control?
Friston, K. (2011) · Neuron · doi:10.1016/j.neuron.2011.10.018 · Cited by 332 · see citing works
Self-citationActive inference, communication and hermeneutics
Friston, K. J., & Frith, C. D. (2015) · Cortex · doi:10.1016/j.cortex.2015.03.025 · Cited by 266 · see citing works
Self-citationMinimal self-models and the free energy principle.
Limanowski, J., & Blankenburg, F. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00547 · Cited by 263 · see citing works
Reflections on agranular architecture: predictive coding in the motor cortex
Shipp, S., Adams, R. A., & Friston, K. J. (2013) · Trends in Neurosciences · doi:10.1016/j.tins.2013.09.004 · Cited by 239 · see citing works
Self-citationActive inference and agency: optimal control without cost functions
Friston, K., Samothrakis, S., & Montague, R. (2012) · Biological Cybernetics · doi:10.1007/s00422-012-0512-8 · Cited by 241 · see citing works
Self-citationInternally generated sequences in learning and executing goal-directed behavior
Pezzulo, G., van der Meer, M. A. A., Lansink, C. S., & Pennartz, C. M. A. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.06.011 · Cited by 217 · see citing works
What is mood? A computational perspective
Clark, J. E., Watson, S., & Friston, K. J. (2018) · Psychological Medicine · doi:10.1017/s0033291718000430 · Cited by 204 · see citing works
Virtual reality and consciousness inference in dreaming
Hobson, J. A., Hong, C. C.-H., & Friston, K. J. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.01133 · Cited by 197 · see citing works
“This embodied perspective is referred to as active inference (Friston et al., 2011).”
BackgroundSelf-citationThe role of prediction in social neuroscience
Brown, E. C., & Brüne, M. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00147 · Cited by 190 · see citing works
Sentience and the Origins of Consciousness: From Cartesian Duality to Markovian Monism
Friston, K. J., Wiese, W., & Hobson, J. A. (2020) · Entropy · doi:10.3390/e22050516 · Cited by 186 · see citing works
Self-citationScene Construction, Visual Foraging, and Active Inference
Mirza, M. B., Adams, R. A., Mathys, C. D., & Friston, K. J. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00056 · Cited by 165 · see citing works
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 · see citing works
“Please see [97] for further details. view, the solenoidal flow can be regarded as circumnavigating the contours of the steady-state density to find a path of steepest descent.”
Self-citationActive Inference, Attention, and Motor Preparation
Brown, H., Friston, K., & Bestmann, S. (2011) · Frontiers in Psychology · doi:10.3389/fpsyg.2011.00218 · Cited by 165 · see citing works
Self-citationClark, A. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00270 · Cited by 108 · see citing works
Beta- and gamma-band activity reflect predictive coding in the processing of causal events
van Pelt, S., Heil, L., Kwisthout, J., Ondobaka, S., van Rooij, I., & Bekkering, H. (2016) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsw017 · Cited by 98 · see citing works
Action 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 · see citing works
Self-citationUeltzhöffer, K. (2018) · Biological Cybernetics · doi:10.1007/s00422-018-0785-7 · Cited by 98 · see citing works
Palmer, C. E., Davare, M., & Kilner, J. M. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.1694-16.2016 · Cited by 96 · see citing works
Self-citationSmooth pursuit and visual occlusion: active inference and oculomotor control in schizophrenia
Adams, R. A., Perrinet, L. U., & Friston, K. (2012) · PLoS ONE · doi:10.1371/journal.pone.0047502 · Cited by 91 · see citing works
Self-citationA Novel Adaptive Controller for Robot Manipulators Based on Active Inference
Pezzato, C., Ferrari, R., & Corbato, C. H. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2974451 · Cited by 104 · see citing works
Interactional leader–follower sensorimotor communication strategies during repetitive joint actions
Candidi, M., Curioni, A., Donnarumma, F., Sacheli, L. M., & Pezzulo, G. (2015) · Journal of The Royal Society Interface · doi:10.1098/rsif.2015.0644 · Cited by 92 · see citing works
Heartfelt imitation: high interoceptive awareness is linked to greater automatic imitation
Ainley, V., Brass, M., & Tsakiris, M. (2014) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2014.05.010 · Cited by 87 · see citing works
Caligiore, D., Pezzulo, G., Miall, R. C., & Baldassarre, G. (2013) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.07.016 · Cited by 87 · see citing works
The overfitted brain: Dreams evolved to assist generalization
Hoel, E. (2021) · Patterns · doi:10.1016/j.patter.2021.100244 · Cited by 70 · see citing works
“Prediction error can be minimized by changes in action and behavior, a process called "active inference" (Friston et al., 2011).”
BackgroundA Multi-scale View of the Emergent Complexity of Life: A Free-Energy Proposal
Hesp, C., Ramstead, M., Constant, A., Badcock, P., Kirchhoff, M., & Friston, K. (2019) · Springer Proceedings in Complexity · doi:10.1007/978-3-030-00075-2_7 · Cited by 63 · see citing works
Self-citationAction is enabled by systematic misrepresentations
Wiese, W. (2017) · Erkenntnis · doi:10.1007/s10670-016-9867-x · Cited by 60 · see citing works
Bayesian Learning Models of Pain: A Call to Action
Tabor, A., & Burr, C. (2019) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2018.10.006 · Cited by 57 · see citing works
Predictive processing in cognitive robotics: A review
Ciria, A., Schillaci, G., Pezzulo, G., Hafner, V. V., & Lara, B. (2021) · Neural Computation · doi:10.1162/neco_a_01383 · Cited by 51 · see citing works
Generative models for active vision
Parr, T., Sajid, N., Da Costa, L., Mirza, M. B., & Friston, K. J. (2021) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2021.651432 · Cited by 46 · see citing works
Self-citationPain Asymbolia as Depersonalization for Pain Experience. An Interoceptive Active Inference Account
Gerrans, P. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.523710 · Cited by 43 · see citing works
Affective experience in the predictive mind: a review and new integrative account
Fernandez Velasco, P., & Loev, S. (2021) · Synthese · doi:10.1007/s11229-020-02755-4 · Cited by 43 · see citing works
Knowing when to stop: aberrant precision and evidence accumulation in schizophrenia
Limongi, R., Bohaterewicz, B., Nowicka, M., Plewka, A., & Friston, K. J. (2018) · Schizophrenia Research · doi:10.1016/j.schres.2017.12.018 · Cited by 38 · see citing works
Self-citationInteroceptive technologies for psychiatric interventions: From diagnosis to clinical applications
Schoeller, F., Horowitz, A. H., Jain, A., Maes, P., Reggente, N., Christov-Moore, L., Pezzulo, G., Barca, L., Allen, M., Salomon, R., et al. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105478 · Cited by 42 · see citing works
Self-citationArtificial cognition vs. artificial intelligence for next-generation autonomous robotic agents
Sandini, G., Sciutti, A., & Morasso, P. (2024) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2024.1349408 · Cited by 40 · see citing works
Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics
Pezzato, C., Corbato, C. H., Bonhof, S., & Wisse, M. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3226144 · Cited by 44 · see citing works
“This theory has been initially developed for continuous processes [14], [15], [16], where the main idea is that the brain’s cognition and motor control functions could be described in terms of free-energy minimization [17].”
BackgroundBoukarras, S., Era, V., Aglioti, S. M., & Candidi, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-84280-z · Cited by 25 · see citing works
Fitting predictive coding to the neurophysiological data
Spratling, M. W. (2019) · Brain Research · doi:10.1016/j.brainres.2019.146313 · Cited by 23 · see citing works
Fornaro, S., & Vallesi, A. (2024) · Current Psychology · doi:10.1007/s12144-023-04312-x · Cited by 21 · see citing works
Fault-tolerant Control of Robot Manipulators with Sensory Faults using Unbiased Active Inference
Baioumy, M., Pezzato, C., Ferrari, R., Corbato, C. H., & Hawes, N. (2021) · 2021 European Control Conference (ECC) · doi:10.23919/ecc54610.2021.9654913 · Cited by 19 · see citing works
Les trois cultures de la psychiatrie computationnelle
Gauld, C., Dumas, G., Fakra, É., Mattout, J., & Micoulaud-Franchi, J.-A. (2021) · Annales Médico-psychologiques, revue psychiatrique · doi:10.1016/j.amp.2020.11.011 · Cited by 14 · see citing works
Self-citationActive Inference for Fault Tolerant Control of Robot Manipulators with Sensory Faults
Pezzato, C., Baioumy, M., Corbato, C. H., Hawes, N., Wisse, M., & Ferrari, R. (2020) · Communications in Computer and Information Science · doi:10.1007/978-3-030-64919-7_3 · Cited by 14 · see citing works
The myth of the Bayesian brain
Mangalam, M. (2025) · European Journal of Applied Physiology · doi:10.1007/s00421-025-05855-6 · Cited by 14 · see citing works
“Active inference extends these ideas to account for decision-making and planning, suggesting that agents select actions that minimize expected free energy or maximize expected information gain (Friston et al. 2011, 2017; Parr et al. 2020).”
A survey on neuro-mimetic deep learning via predictive coding
Salvatori, T., Mali, A., Buckley, C. L., Lukasiewicz, T., Rao, R. P. N., Friston, K., & Ororbia, A. (2026) · Neural Networks · doi:10.1016/j.neunet.2025.108161 · Cited by 2 · see citing works
Self-citation
- Authors
- Friston, K., Mattout, J., & Kilner, J.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- Inserm (FR)
- Corresponding author
- Friston, Karl
- Venue
- Biological Cybernetics
- Published
- 2011-02
- Volume
- 104
- Issue
- 1-2
- Pages
- 137-160
- ISSN
- 0340-1200 (ISSN-L)
- 1432-0770
- PMC
- PMC3491875
- OpenAlex
- W2111947800
- Cited by
- 665
- References
- 96
- Citations by year
- 2026: 34 · 2025: 41 · 2024: 35 · 2023: 76 · 2022: 55 · 2021: 64 · 2020: 44 · 2019: 68 · 2018: 39 · 2017: 40 · 2016: 38 · 2015: 29 · 2014: 38 · 2013: 26 · 2012: 29
- FWCI
- 44.52
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Biological Cybernetics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Wellcome Trust (081048, 088130)
- Topics
- Social Sciences › Psychology › Social Psychology › Action Observation and Synchronization
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › Free Will and Agency
- Keywords
- mirror neuron system, action understanding, active inference, free energy principle, predictive coding, action observation, proprioception, attention, handwriting, electrophysiological responses, generative models
- MeSH terms
- Bayes Theorem, Cybernetics, Handwriting, Humans, Models, Neurological, Motor Neurons, Perception, Psychomotor Performance, Sensation, Nonlinear Dynamics
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