Hierarchical active inference: A theory of motivated control
Open accessAbstract
Motivated control refers to the coordination of behaviour to achieve affectively valenced outcomes or goals. The study of motivated control traditionally assumes a distinction between control and motivational processes, which map to distinct (dorsolateral versus ventromedial) brain systems. However, the respective roles and interactions between these processes remain controversial. We offer a novel perspective that casts control and motivational processes as complementary aspects - goal propagation and prioritization, respectively - of active inference and hierarchical goal processing under deep generative models. We propose that the control hierarchy propagates prior preferences or goals, but their precision is informed by the motivational context, inferred at different levels of the motivational hierarchy. The ensuing integration of control and motivational processes underwrites action and policy selection and, ultimately, motivated behaviour, by enabling deep inference to prioritize goals in a context-sensitive way.
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
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works
The Ecological Approach to Visual Perception
Bornstein, M. H., & Gibson, J. J. (1980) · The Journal of Aesthetics and Art Criticism · doi:10.2307/429816 · Cited by 22,935 · see citing works
An Integrative Theory of Prefrontal Cortex Function
Miller, E. K., & Cohen, J. D. (2001) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.24.1.167 · Cited by 12,803 · see citing works
Neural Mechanisms of Selective Visual Attention
Desimone, R., & Duncan, J. (1995) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.18.030195.001205 · Cited by 8,388 · see citing works
The free-energy principle: a unified brain theory?
Friston, K. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2787 · Cited by 8,120 · cited 3× · see citing works
“Active inference views the brain as a statistical organ that forms internal generative models of the (hidden) states and contingencies in the world, and uses these models to continuously generate predictions in the service of perception and adaptive behaviour [15,34,36,37].”
Self-citationCybernetics or Control and Communication in the Animal and the Machine
Wiener, N. (2019) · The MIT Press eBooks · doi:10.7551/mitpress/11810.001.0001 · Cited by 5,045 · see citing works
Cybernetics. Or Control and Communication in the Animal and the Machine
N., E., & Wiener, N. (1949) · The Journal of Philosophy · doi:10.2307/2020260 · Cited by 3,590 · see citing works
Behavior: The Control of Perception
Kuhn, A., & Powers, W. T. (1975) · Contemporary Sociology · doi:10.2307/2063243 · Cited by 2,398 · see citing works
The Prefrontal Cortex: Anatomy, Physiology, and Neuropsychology of the Frontal Lobe
Fuster, J. M. (1980) · Cited by 2,367 · see citing works
The expected value of control: an integrative theory of anterior cingulate cortex function
Shenhav, A., Botvinick, M. M., & Cohen, J. D. (2013) · Neuron · doi:10.1016/j.neuron.2013.07.007 · Cited by 2,321 · see citing works
Self-Control in Decision-Making Involves Modulation of the vmPFC Valuation System
Hare, T. A., Camerer, C. F., & Rangel, A. (2009) · Science · doi:10.1126/science.1168450 · Cited by 1,928 · see citing works
The Architecture of Cognitive Control in the Human Prefrontal Cortex
Koechlin, E., Ody, C., & Kouneiher, F. (2003) · Science · doi:10.1126/science.1088545 · Cited by 1,922 · see citing works
Hohwy, J. (2013) · doi:10.1093/acprof:oso/9780199682737.001.0001 · Cited by 1,914 · see citing works
‘What’ and ‘where’ in the human brain
Ungerleider, L. G., & Haxby, J. V. (1994) · Current Opinion in Neurobiology · doi:10.1016/0959-4388(94)90066-3 · Cited by 1,913 · cited 1× · see citing works
“, ‘what’ and ‘where’ [39] or ‘what’ and ‘when’ [38]).”
Interoceptive predictions in the brain
Barrett, L. F., & Simmons, W. K. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn3950 · Cited by 1,766 · see citing works
Allostasis: A New Paradigm to Explain Arousal Pathology
Sterling, P., & Eyer, J. (1988) · Zenodo · doi:10.5281/zenodo.13537700 · Cited by 1,450 · see citing works
(1952) · Medical Entomology and Zoology · Cited by 1,436 · see citing works
Bayesian model selection for group studies
Stephan, K. E., Penny, W. D., Daunizeau, J., Moran, R. J., & Friston, K. J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.03.025 · Cited by 1,423 · see citing works
Self-citationHandbook of Life Stress, Cognition and Health
PASTOR, L. H. (1989) · American Journal of Psychiatry · doi:10.1176/ajp.146.10.1347 · Cited by 1,402 · see citing works
Attention, uncertainty, and free-energy
Feldman, H., & Friston, K. J. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00215 · Cited by 1,365 · see citing works
Self-citationCognitive and attentional mechanisms in delay of gratification.
Mischel, W., Ebbesen, E. Β., & Zeiss, A. R. (1972) · Journal of Personality and Social Psychology · doi:10.1037/h0032198 · Cited by 1,364 · see citing works
An information theoretical approach to prefrontal executive function
Koechlin, E., & Summerfield, C. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.04.005 · Cited by 1,183 · see citing works
Active inference: a process theory
Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., & Pezzulo, G. (2017) · Neural Computation · doi:10.1162/neco_a_00912 · Cited by 1,156 · see citing works
Self-citation(2015) · Cited by 1,141 · see citing works
Cohen, J. D., McClure, S. M., & Yu, A. J. (2007) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2007.2098 · Cited by 1,121 · see citing works
Cortical mechanisms of action selection: the affordance competition hypothesis
Cisek, P. (2007) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2007.2054 · Cited by 1,077 · see citing works
Medial prefrontal cortex as an action-outcome predictor
Alexander, W. H., & Brown, J. W. (2011) · Nature Neuroscience · doi:10.1038/nn.2921 · Cited by 1,000 · see citing works
Cognitive control, hierarchy, and the rostro–caudal organization of the frontal lobes
Badre, D. (2008) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2008.02.004 · Cited by 980 · see citing works
Is the rostro-caudal axis of the frontal lobe hierarchical?
Badre, D., & D'Esposito, M. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2667 · Cited by 946 · see citing works
On the ability to inhibit thought and action: General and special theories of an act of control.
Logan, G. D., Zandt, T. V., Verbruggen, F., & Wagenmakers, E. (2014) · Psychological Review · doi:10.1037/a0035230 · Cited by 878 · see citing works
Predictions not commands: Active inference in the motor system
Adams, R. A., Shipp, S., & Friston, K. J. (2013) · Brain Structure and Function · doi:10.1007/s00429-012-0475-5 · Cited by 765 · see citing works
Self-citationThe Executive Functions and Self-Regulation: An Evolutionary Neuropsychological Perspective
Barkley, R. A. (2001) · Neuropsychology Review · doi:10.1023/a:1009085417776 · Cited by 734 · see citing works
Active inference and epistemic value
Friston, K., Rigoli, F., Ognibene, D., Mathys, C., Fitzgerald, T., & Pezzulo, G. (2015) · Cognitive Neuroscience · doi:10.1080/17588928.2015.1020053 · Cited by 731 · see citing works
Self-citationHow unrealistic optimism is maintained in the face of reality
Sharot, T., Korn, C. W., & Dolan, R. J. (2011) · Nature Neuroscience · doi:10.1038/nn.2949 · Cited by 729 · see citing works
Neuroeconomics: The Consilience of Brain and Decision
Glimcher, P. W., & Rustichini, A. (2004) · Science · doi:10.1126/science.1102566 · Cited by 725 · see citing works
A Hierarchy of Time-Scales and the Brain
Kiebel, S. J., Daunizeau, J., & Friston, K. J. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000209 · Cited by 694 · see citing works
Self-citationActive Inference, homeostatic regulation and adaptive behavioural control
Pezzulo, G., Rigoli, F., & Friston, K. (2015) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2015.09.001 · Cited by 650 · cited 10× · see citing works
“In other words, cognitive (or executive) functions can be considered as hierarchical contextualizations of sensorimotor decisions, affording more sophisticated forms of control; for example, self-regulation over extended time periods [10,14,18,21,34,42,47].”
InfluentialSelf-citationHierarchically organized behavior and its neural foundations: A reinforcement learning perspective
Botvinick, M. M., Niv, Y., & Barto, A. G. (2009) · Cognition · doi:10.1016/j.cognition.2008.08.011 · Cited by 646 · see citing works
See it with feeling: affective predictions during object perception
Barrett, L. F., & Bar, M. (2009) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2008.0312 · Cited by 632 · see citing works
Motivation of extended behaviors by anterior cingulate cortex
Holroyd, C. B., & Yeung, N. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.12.008 · Cited by 618 · see citing works
Frontal Cortex and the Hierarchical Control of Behavior
Badre, D., & Nee, D. E. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.11.005 · Cited by 611 · see citing works
Motivation and cognitive control in the human prefrontal cortex
Kouneiher, F., Charron, S., & Koechlin, E. (2009) · Nature Neuroscience · doi:10.1038/nn.2321 · Cited by 590 · see citing works
Hierarchical models of behavior and prefrontal function
Botvinick, M. M. (2008) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2008.02.009 · Cited by 532 · see citing works
Computational psychiatry: the brain as a phantastic organ
Friston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014) · The Lancet Psychiatry · doi:10.1016/s2215-0366(14)70275-5 · Cited by 531 · see citing works
Self-citationA fronto–striato–subthalamic–pallidal network for goal-directed and habitual inhibition
Jahanshahi, M., Obeso, I., Rothwell, J. C., & Obeso, J. A. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn4038 · Cited by 521 · see citing works
The prefrontal cortex: Anatomy, physiology and neuropsychology of the frontal lobe
Andy, O. J. (1981) · Surgical Neurology · doi:10.1016/0090-3019(81)90062-8 · Cited by 455 · see citing works
Reinforcement learning or active inference?
Friston, K. J., Daunizeau, J., & Kiebel, S. J. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006421 · Cited by 443 · see citing works
Self-citationCerebral hierarchies: predictive processing, precision and the pulvinar
Kanai, R., Komura, Y., Shipp, S., & Friston, K. (2015) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2014.0169 · Cited by 432 · see citing works
Self-citationNavigating the Affordance Landscape: Feedback Control as a Process Model of Behavior and Cognition
Pezzulo, G., & Cisek, P. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.03.013 · Cited by 419 · see citing works
Self-citationFoundations of human reasoning in the prefrontal cortex
Donoso, M., Collins, A. G. E., & Koechlin, É. (2014) · Science · doi:10.1126/science.1252254 · Cited by 405 · see citing works
The 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 352 · see citing works
Active 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 347 · see citing works
Self-citationFrank, M. J., & Badre, D. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhr114 · Cited by 330 · see citing works
The Cognitive-Emotional Brain: From Interactions to Integration
Pessoa, L. (2013) · Cited by 328 · see citing works
Emotional Valence and the Free-Energy Principle
Joffily, M., & Coricelli, G. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003094 · Cited by 322 · see citing works
The Neurobiology of the Prefrontal Cortex: Anatomy, Evolution, and the Origin of Insight
Passingham, R. E., & Wise, S. P. (2012) · Cited by 307 · see citing works
Botvinick, M., & Toussaint, M. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.08.006 · Cited by 295 · see citing works
The anatomy of choice: dopamine and decision-making.
Friston, K., Schwartenbeck, P., FitzGerald, T., Moutoussis, M., Behrens, T., & Dolan, R. J. (2014) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2013.0481 · Cited by 289 · see citing works
Self-citationPhysiological utility theory and the neuroeconomics of choice
Glimcher, P. W., Dorris, M. C., & Bayer, H. M. (2004) · Games and Economic Behavior · doi:10.1016/j.geb.2004.06.011 · Cited by 283 · see citing works
The What and How of prefrontal cortical organization
O’Reilly, R. C. (2010) · Trends in Neurosciences · doi:10.1016/j.tins.2010.05.002 · Cited by 271 · see citing works
Deep temporal models and active inference
Friston, K. J., Rosch, R. E., Parr, T., Price, C. J., & Bowman, H. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.04.009 · Cited by 240 · see citing works
Self-citationExecutive control and decision-making in the prefrontal cortex
Domenech, P., & Koechlin, É. (2014) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2014.10.007 · Cited by 237 · see citing works
Working 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 · see citing works
Self-citationThe dopaminergic midbrain encodes the expected certainty about desired outcomes
Schwartenbeck, P., FitzGerald, T. H. B., Mathys, C., Dolan, R., & Friston, K. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhu159 · Cited by 219 · 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
Self-citationEmbodied choice: how action influences perceptual decision making
Lepora, N. F., & Pezzulo, G. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004110 · Cited by 209 · see citing works
Self-citationPlanning by Probabilistic Inference.
Attias, H. T. (2003) · Cited by 204 · see citing works
On the challenges and mechanisms of embodied decisions
Cisek, P., & Pastor‐Bernier, A. (2014) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2013.0479 · Cited by 196 · see citing works
Hierarchical control over effortful behavior by rodent medial frontal cortex: A computational model.
Holroyd, C. B., & McClure, S. M. (2014) · Psychological Review · doi:10.1037/a0038339 · Cited by 192 · see citing works
The Functional Anatomy of Time: What and When in the Brain
Friston, K. J., & Buzsáki, G. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.05.001 · Cited by 179 · 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-citationNeural Mechanisms of Hierarchical Planning in a Virtual Subway Network
Balaguer, J., Spiers, H., Hassabis, D., & Summerfield, C. (2016) · Neuron · doi:10.1016/j.neuron.2016.03.037 · Cited by 163 · see citing works
Model-based hierarchical reinforcement learning and human action control
Botvinick, M., & Weinstein, A. (2014) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2013.0480 · Cited by 153 · see citing works
Beyond the “Bereitschaftspotential”: Action preparation behind cognitive functions
Russo, F. D., Berchicci, M., Bozzacchi, C., Perri, R. L., Pitzalis, S., & Spinelli, D. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.04.019 · Cited by 149 · see citing works
Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics
Bishop, C. (2003) · Cited by 143 · see citing works
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 142 · see citing works
Self-citationThinking as the control of imagination: a conceptual framework for goal-directed systems
Pezzulo, G., & Castelfranchi, C. (2009) · Psychological Research Psychologische Forschung · doi:10.1007/s00426-009-0237-z · Cited by 141 · see citing works
Self-citationShould I stay or should I go? Conceptual underpinnings of goal-directed actions
Mirabella, G. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00206 · Cited by 137 · see citing works
Prefrontal–parietal function: from foraging to foresight
Genovesio, A., Wise, S. P., & Passingham, R. E. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.11.007 · Cited by 137 · see citing works
The why, what, where, when and how of goal-directed choice: neuronal and computational principles
Verschure, P. F. M. J., Pennartz, C. M. A., & Pezzulo, G. (2014) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2013.0483 · Cited by 131 · cited 1× · see citing works
“Calling on early cybernetic models [16–18], the view that the brain uses control hierarchies has inspired many recent proposals [19–25]; for example, hierarchical temporal structures [26], hierarchical reinforcement learning [9], hierarchical mixture of experts [23], distributed adaptive control [8,27] and hierarchica…”
Self-citationWhy do you fear the bogeyman? An embodied predictive coding model of perceptual inference
Pezzulo, G. (2014) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/s13415-013-0227-x · Cited by 128 · see citing works
Self-citationSilvetti, M., Alexander, W., Verguts, T., & Brown, J. W. (2013) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.11.003 · Cited by 114 · see citing works
Computational models of cognitive control
O’Reilly, R. C., Herd, S. A., & Pauli, W. M. (2010) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2010.01.008 · Cited by 113 · see citing works
Dopamine, reward learning, and active inference
FitzGerald, T. H. B., Dolan, R. J., & Friston, K. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00136 · Cited by 112 · see citing works
Self-citationAlexander, W. H., & Brown, J. W. (2015) · Neural Computation · doi:10.1162/neco_a_00779 · Cited by 111 · see citing works
Distributed Adaptive Control: A theory of the Mind, Brain, Body Nexus
Verschure, P. F. M. J. (2012) · Biologically Inspired Cognitive Architectures · doi:10.1016/j.bica.2012.04.005 · Cited by 106 · see citing works
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 93 · see citing works
Self-citationMeder, D., Kolling, N., Verhagen, L., Wittmann, M. K., Scholl, J., Madsen, K. H., Hulme, O. J., Behrens, T. E. J., & Rushworth, M. F. S. (2017) · Nature Communications · doi:10.1038/s41467-017-02169-w · Cited by 84 · see citing works
Strategic control in decision‐making under uncertainty
Venkatraman, V., & Huettel, S. A. (2012) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2012.08009.x · Cited by 81 · see citing works
Rewards and Cognitive Control in the Human Prefrontal Cortex
Duverne, S., & Koechlin, É. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx210 · Cited by 79 · see citing works
Active Inference, epistemic value, and vicarious trial and errorPreprint
Pezzulo, G., Cartoni, E., Rigoli, F., Pio‐Lopez, L., & Friston, K. J. (2016) · Learning & Memory · doi:10.1101/lm.041780.116 · Cited by 75 · see citing works
Self-citationFatigue increases the perception of future effort during decision making
Iodice, P., Calluso, C., Barca, L., Bertollo, M., Ripari, P., & Pezzulo, G. (2017) · Psychology of sport and exercise · doi:10.1016/j.psychsport.2017.08.013 · Cited by 67 · see citing works
Self-citationMaisto, D., Donnarumma, F., & Pezzulo, G. (2015) · Journal of The Royal Society Interface · doi:10.1098/rsif.2014.1335 · Cited by 59 · see citing works
Self-citationDonnarumma, F., Maisto, D., & Pezzulo, G. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004864 · Cited by 58 · see citing works
Self-citationEncoding Goals but Not Abstract Magnitude in the Primate Prefrontal Cortex
Genovesio, A., Tsujimoto, S., & Wise, S. P. (2012) · Neuron · doi:10.1016/j.neuron.2012.02.023 · Cited by 57 · see citing works
Caching mechanisms for habit formation in Active Inference
Maisto, D., Friston, K. J., & Pezzulo, G. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.05.083 · Cited by 46 · see citing works
Self-citationGoal-Driven Cognition in the Brain: A Computational FrameworkPreprint
O’Reilly, R. C., Hazy, T. E., Mollick, J., Mackie, P., & Herd, S. A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1404.7591 · Cited by 45 · see citing works
Prefrontal Goal Codes Emerge as Latent States in Probabilistic Value Learning
Stoianov, I., Genovesio, A., & Pezzulo, G. (2016) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00886 · Cited by 44 · see citing works
Self-citationActive inference and cognitive-emotional interactions in the brain
Pezzulo, G., Barca, L., & Friston, K. J. (2015) · Behavioral and Brain Sciences · doi:10.1017/s0140525x14001009 · Cited by 32 · see citing works
Self-citationNonparametric Problem-Space Clustering: Learning Efficient Codes for Cognitive Control Tasks
Maisto, D., Donnarumma, F., & Pezzulo, G. (2016) · Entropy · doi:10.3390/e18020061 · Cited by 20 · see citing works
Self-citationToyArchitecture: Unsupervised learning of interpretable models of the environment
Vítků, J., Dluhoš, P., Davidson, J., Nikl, M., Andersson, S., Paška, P., Šinkora, J., Hlubuček, P., Stránský, M., Hyben, M., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230432 · Cited by 7 · see citing works
Goals reconfigure cognition by modulating predictive processes in the brain
Pezzulo, G. (2014) · Behavioral and Brain Sciences · doi:10.1017/s0140525x13002148 · Cited by 4 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
A 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 · see citing works
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 250 · see citing works
Self-citationWords 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 · see citing works
Self-citationGershman, S. J., & Uchida, N. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0220-7 · Cited by 236 · see citing works
The hierarchically mechanistic mind: A free-energy formulation of the human psyche
Badcock, P. B., Friston, K. J., & Ramstead, M. J. D. (2019) · Physics of Life Reviews · doi:10.1016/j.plrev.2018.10.002 · Cited by 217 · see citing works
Self-citationTheory of Event Coding (TEC) V2.0: Representing and controlling perception and action
Hommel, B. (2019) · Attention Perception & Psychophysics · doi:10.3758/s13414-019-01779-4 · Cited by 199 · see citing works
Neuroimaging the consciousness of self: Review, and conceptual-methodological framework
Frewen, P., Schroeter, M. L., Riva, G., Cipresso, P., Fairfield, B., Padulo, C., Kemp, A. H., Palaniyappan, L. K., Owolabi, M. O., Kusi‐Mensah, K., et al. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.01.023 · Cited by 177 · see citing works
The secret life of predictive brains: what’s spontaneous activity for?
Pezzulo, G., Zorzi, M., & Corbetta, M. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.05.007 · Cited by 164 · see citing works
Self-citationInteroception of breathing and its relationship with anxiety
Harrison, O. K., Köchli, L., Marino, S., Luechinger, R., Hennel, F., Brand, K., Hess, A. J., Frässle, S., Iglesias, S., Vinckier, F., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.045 · Cited by 163 · see citing works
Corcoran, A. W., Pezzulo, G., & Hohwy, J. (2020) · Biology & Philosophy · doi:10.1007/s10539-020-09746-2 · Cited by 161 · see citing works
Self-citationThe whole prefrontal cortex is premotor cortex
Fine, J. M., & Hayden, B. Y. (2021) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2020.0524 · Cited by 139 · see citing works
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 142 · see citing works
Self-citationThe 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 133 · see citing works
Moretta, T., Buodo, G., Demetrovics, Z., & Potenza, M. N. (2022) · Comprehensive Psychiatry · doi:10.1016/j.comppsych.2021.152286 · Cited by 125 · see citing works
Collective intelligence: a unifying concept for integrating biology across scales and substrates
McMillen, P., & Levin, M. (2024) · Communications Biology · doi:10.1038/s42003-024-06037-4 · Cited by 122 · see citing works
The 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 126 · see citing works
Self-citationBioelectric networks: the cognitive glue enabling evolutionary scaling from physiology to mind
Levin, M. G. (2023) · Animal Cognition · doi:10.1007/s10071-023-01780-3 · Cited by 116 · see citing works
Sandved-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 116 · see citing works
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 · see citing works
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 · see citing works
An interoceptive illusion of effort induced by false heart-rate feedback
Iodice, P., Porciello, G., Bufalari, I., Barca, L., & Pezzulo, G. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1821032116 · Cited by 100 · see citing works
Self-citationCognitive control, motivation and fatigue: A cognitive neuroscience perspective
Kok, A. (2022) · Brain and Cognition · doi:10.1016/j.bandc.2022.105880 · Cited by 118 · see citing works
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 · see citing works
Embodied cognition: dimensions, domains and applications
Farina, M. (2020) · Adaptive Behavior · doi:10.1177/1059712320912963 · Cited by 100 · see citing works
Livneh, Y., & Andermann, M. L. (2021) · Neuron · doi:10.1016/j.neuron.2021.08.036 · Cited by 106 · see citing works
The epistemic and pragmatic value of non-action: a predictive coding perspective on meditation
Antoine Lutz, A., Mattout, J., & Pagnoni, G. (2019) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2018.12.019 · Cited by 97 · see citing works
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 101 · see citing works
Self-citationTschantz, 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 105 · see citing works
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 · see citing works
Embodying addiction: A predictive processing account
Miller, M., Kiverstein, J. D., & Rietveld, E. (2019) · Brain and Cognition · doi:10.1016/j.bandc.2019.105495 · Cited by 85 · see citing works
“Stopping for knowledge”: The sense of beauty in the perception-action cycle
Sarasso, P., Neppi-Mòdona, M., Sacco, K., & Ronga, I. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.09.004 · Cited by 84 · see citing works
Generating meaning: active inference and the scope and limits of passive AI
Pezzulo, G., Parr, T., Cisek, P., Clark, A., & Friston, K. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.10.002 · Cited by 80 · see citing works
Self-citationBeyond Desire? Agency, Choice, and the Predictive Mind
Clark, A. (2020) · Australasian Journal of Philosophy · doi:10.1080/00048402.2019.1602661 · Cited by 78 · see citing works
Tea With Milk? A Hierarchical Generative Framework of Sequential Event Comprehension
Kuperberg, G. R. (2020) · Topics in Cognitive Science · doi:10.1111/tops.12518 · Cited by 79 · see citing works
Learning in Infancy Is Active, Endogenously Motivated, and Depends on the Prefrontal Cortices
Raz, G., & Saxe, R. R. (2020) · Annual Review of Developmental Psychology · doi:10.1146/annurev-devpsych-121318-084841 · Cited by 71 · see citing works
Zink, N., Lenartowicz, A., & Markett, S. A. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.02.011 · Cited by 75 · see citing works
Stoianov, I. P., Maisto, D., & Pezzulo, G. (2022) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2022.102329 · Cited by 68 · see citing works
Self-citationTherapeutic Alliance as Active Inference: The Role of Therapeutic Touch and Synchrony
McParlin, Z., Cerritelli, F., Friston, K. J., & Esteves, J. E. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.783694 · Cited by 71 · see citing works
Self-citationKim, D., Park, G. Y., O’Doherty, J. P., & Lee, S. W. (2019) · Nature Communications · doi:10.1038/s41467-019-13632-1 · Cited by 64 · see citing works
Reinforcement Learning through Active InferencePreprint
Tschantz, A., Millidge, B., Seth, A. K., & Buckley, C. L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.12636 · Cited by 72 · see citing works
Deep active inference agents using Monte-Carlo methods
Fountas, Z., Sajid, N., Mediano, P. A. M., & Friston, K. (2020) · arXiv · doi:10.48550/arxiv.2006.04176 · Cited by 64 · see citing works
Self-citationThe computational neurology of movement under active inference
Parr, T., Limanowski, J., Rawji, V., & Friston, K. J. (2021) · Brain · doi:10.1093/brain/awab085 · Cited by 59 · see citing works
Self-citationExplainable reinforcement learning for broad-XAI: a conceptual framework and survey
Dazeley, R., Vamplew, P. W., & Cruz, F. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08423-1 · Cited by 68 · see citing works
A goal-centric outlook on learning
Molinaro, G., & Collins, A. G. E. (2023) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.08.011 · Cited by 63 · see citing works
Symptom Perception From a Predictive Processing Perspective
Pezzulo, G., Maisto, D., Barca, L., & den Bergh, O. V. (2019) · Clinical Psychology in Europe · doi:10.32872/cpe.v1i4.35952 · Cited by 55 · see citing works
Self-citationCellular signaling pathways as plastic, proto-cognitive systems: Implications for biomedicine
Mathews, J., Chang, A. (., Devlin, L., & Levin, M. G. (2023) · Patterns · doi:10.1016/j.patter.2023.100737 · Cited by 59 · see citing works
Arnaldo, I., Corcoran, A. W., Friston, K. J., & Ramstead, M. J. D. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104590 · Cited by 56 · see citing works
Self-citationCuriosity, information demand and attentional priority
Gottlieb, J. P., Cohanpour, M., Li, Y., Singletary, N. M., & Zabeh, E. (2020) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2020.07.016 · Cited by 54 · see citing works
A neuro-cognitive process model of emotional intelligence
Smith, R. S., Killgore, W. D. S., Alkozei, A., & Lane, R. D. (2018) · Biological Psychology · doi:10.1016/j.biopsycho.2018.10.012 · Cited by 59 · see citing works
The Predictive Dynamics of Happiness and Well-Being
Miller, M. M., Kiverstein, J. D., & Rietveld, E. (2021) · Emotion Review · doi:10.1177/17540739211063851 · Cited by 55 · see citing works
Safron, A. (2021) · Entropy · doi:10.3390/e23060783 · Cited by 52 · see citing works
Functional Oxides for Photoneuromorphic Engineering: Toward a Solar Brain
Pérez‐Tomás, A. (2019) · Advanced Materials Interfaces · doi:10.1002/admi.201900471 · Cited by 50 · see citing works
Keep your interoceptive streams under control: An active inference perspective on anorexia nervosa
Barca, L., & Pezzulo, G. (2020) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-020-00777-6 · Cited by 54 · see citing works
Self-citationTheriault, J. E., Shaffer, C., Dienel, G. A., Sander, C. Y. M., Hooker, J. M., Dickerson, B. C., Barrett, L. F., & Quigley, K. S. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105373 · Cited by 55 · see citing works
Pezzulo, G., LaPalme, J., Durant, F., & Levin, M. G. (2021) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2019.0765 · Cited by 45 · see citing works
Self-citationA Step-by-Step Tutorial on Active Inference and its Application to Empirical DataPreprint
Smith, R. S., Friston, K. J., & Whyte, C. J. (2021) · doi:10.31234/osf.io/b4jm6 · Cited by 51 · see citing works
Self-citationExpecting some action: predictive processing and the construction of conscious experience
Nave, K., Deane, G., Miller, M., & Clark, A. (2022) · Review of Philosophy and Psychology · doi:10.1007/s13164-022-00644-y · Cited by 50 · see citing works
Hezemans, F. H., Wolpe, N., O’Callaghan, C., Ye, R., Rua, C., Jones, P. S., Murley, A. G., Holland, N., Regenthal, R., Tsvetanov, K. A., et al. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010079 · Cited by 48 · see citing works
Maisto, D., Barca, L., den Bergh, O. V., & Pezzulo, G. (2021) · Psychological Review · doi:10.1037/rev0000290 · Cited by 56 · see citing works
Self-citationAn active inference model of hierarchical action understanding, learning and imitation
Proietti, R., Pezzulo, G., & Tessari, A. (2023) · Physics of Life Reviews · doi:10.1016/j.plrev.2023.05.012 · Cited by 47 · see citing works
Self-citationCaching mechanisms for habit formation in Active Inference
Maisto, D., Friston, K. J., & Pezzulo, G. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.05.083 · Cited by 46 · see citing works
Self-citationPrefrontal contributions to the stability and variability of thought and conscious experience
Zamani, A., Carhart-Harris, R. L., & Kalina Christoff, A. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01147-7 · Cited by 45 · see citing works
Active inference models do not contradict folk psychology
Smith, R. S., Ramstead, M. J. D., & Kiefer, A. B. (2022) · Synthese · doi:10.1007/s11229-022-03480-w · Cited by 50 · see citing works
Unraveling the Mysteries of Motivation
O’Reilly, R. C. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.03.001 · Cited by 48 · see citing works
Commentary: Respiration-Entrained Brain Rhythms Are Global but Often Overlooked
Corcoran, A. W., Pezzulo, G., & Hohwy, J. (2018) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2018.00025 · Cited by 45 · see citing works
Self-citationThe Free Energy Principle for Perception and Action: A Deep Learning Perspective
Mazzaglia, P., Verbelen, T., Çatal, O., & Dhoedt, B. (2022) · Entropy · doi:10.3390/e24020301 · Cited by 47 · see citing works
Toward the unity of pathological and exertional fatigue: A predictive processing model
Greenhouse‐Tucknott, A., Butterworth, J. B., Wrightson, J. G., Smeeton, N. J., Critchley, H., Dekerle, J., & Harrison, N. A. (2021) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-021-00958-x · Cited by 46 · 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
Deane, G. (2021) · Neuroscience of Consciousness · doi:10.1093/nc/niab024 · Cited by 38 · see citing works
Motor invariants in action execution and perception
Torricelli, F., Tomassini, A., Pezzulo, G., Pozzo, T., Fadiga, L., & D’Ausilio, A. (2022) · Physics of Life Reviews · doi:10.1016/j.plrev.2022.11.003 · Cited by 41 · see citing works
Self-citationMachine learning meets advanced robotic manipulation
Nahavandi, S., Alizadehsani, R., Nahavandi, D., Lim, C. P., Kelly, K., & Bello, F. (2024) · Information Fusion · doi:10.1016/j.inffus.2023.102221 · Cited by 42 · see citing works
Realigning Models of Habitual and Goal-Directed Decision-Making
Miller, K. J., Ludvig, E. A., Pezzulo, G., & Shenhav, A. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-812098-9.00018-8 · Cited by 41 · see citing works
Self-citationModel-based spatial navigation in the hippocampus-ventral striatum circuit: A computational analysis
Stoianov, I. P., Pennartz, C. M. A., Lansink, C. S., & Pezzulo, G. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006316 · Cited by 37 · see citing works
Self-citationFrom filters to fillers: an active inference approach to body image distortion in the selfie era
Tremblay, S. C., Tremblay, S. E., & Poirier, P. (2020) · AI & Society · doi:10.1007/s00146-020-01015-w · Cited by 38 · see citing works
Safron, A., & DeYoung, C. G. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-819200-9.00010-7 · Cited by 34 · see citing works
Understanding, Explanation, and Active Inference
Parr, T., & Pezzulo, G. (2021) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2021.772641 · Cited by 38 · see citing works
Self-citationMatsumoto, T., & Tani, J. (2020) · Entropy · doi:10.3390/e22050564 · Cited by 34 · see citing works
Wiese, W., & Friston, K. J. (2021) · Philosophies · doi:10.3390/philosophies6010018 · Cited by 36 · see citing works
Self-citationThe Problem of Meaning: The Free Energy Principle and Artificial Agency
Kiverstein, J. D., Kirchhoff, M. D., & Froese, T. (2022) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2022.844773 · Cited by 37 · see citing works
Aesthetics and predictive processing: grounds and prospects of a fruitful encounter
Frascaroli, J., Leder, H., Brattico, E., & de Cruys, S. V. (2023) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2022.0410 · Cited by 33 · see citing works
Binocular rivalry reveals an out-of-equilibrium neural dynamics suited for decision-making
Cao, R., Pastukhov, A., Aleshin, S. E., Mattia, M., & Braun, J. (2021) · eLife · doi:10.7554/elife.61581 · Cited by 33 · see citing works
Gómez, C. M., Arjona, A., Donnarumma, F., Maisto, D., Rodríguez‐Martínez, E. I., & Pezzulo, G. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.01424 · Cited by 31 · see citing works
Self-citationOhata, W., & Tani, J. (2020) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2020.00061 · Cited by 31 · see citing works
Parvizi-Wayne, D., Sandved-Smith, L., Pitliya, R. J., Limanowski, J., Tufft, M. R. A., & Friston, K. J. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1354719 · Cited by 34 · see citing works
Self-citationActive inference, morphogenesis, and computational psychiatry
Pio‐Lopez, L., Kuchling, F., Tung, A., Pezzulo, G., & Levin, M. G. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.988977 · Cited by 27 · see citing works
Self-citationDissolving the self:Active inference, psychedelics, and ego-dissolution
Deane, G. (2020) · Philosophy and the Mind Sciences · doi:10.33735/phimisci.2020.i · Cited by 37 · see citing works
Deep Active Inference and Scene Construction
Heins, R. C., Mirza, M. B., Parr, T., Friston, K. J., Kagan, I., & Pooresmaeili, A. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.509354 · Cited by 27 · see citing works
Self-citationTabor, A., Ryckeghem, D. M. L. V., & Hasenbring, M. I. (2019) · Clinical Journal of Pain · doi:10.1097/ajp.0000000000000786 · Cited by 28 · see citing works
Motivation in the Service of Allostasis: The Role of Anterior Mid-Cingulate Cortex
Touroutoglou, A., Andreano, J. M., Adebayo, M., Lyons, S., & Barrett, L. F. (2019) · Advances in Motivation Science · doi:10.1016/bs.adms.2018.09.002 · Cited by 25 · see citing works
“More recent models of motivated behavior have suggested that multiple components of motivational computations span multiple levels of complexity, with higher levels regulating lower ones (Pezzulo et al., 2018).”
BackgroundInfluentialGoekoop, R., & de Kleijn, R. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.12.021 · Cited by 27 · see citing works
Anderson, T., Petranker, R., Lin, H., & Farb, N. A. S. (2020) · Attention Perception & Psychophysics · doi:10.3758/s13414-020-02131-x · Cited by 28 · see citing works
Hierarchical predictive coding in distributed pain circuits
Chen, Z. S. (2023) · Frontiers in Neural Circuits · doi:10.3389/fncir.2023.1073537 · Cited by 34 · see citing works
The amygdala instructs insular feedback for affective learning
Kargl, D., Kaczanowska, J., Ulonska, S., Groessl, F., Piszczek, Ł., Lazović, J., Buehler, K., & Haubensak, W. E. (2020) · eLife · doi:10.7554/elife.60336 · Cited by 25 · see citing works
Hierarchical generative modelling for autonomous robots
Yuan, K., Sajid, N., Friston, K., & Li, Z. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00752-z · Cited by 23 · see citing works
Self-citationInterpersonal Motor Interactions Shape Multisensory Representations of the Peripersonal Space
Fanghella, M., Era, V., & Candidi, M. (2021) · Brain Sciences · doi:10.3390/brainsci11020255 · Cited by 22 · see citing works
Embodied decisions as active inference
Priorelli, M., Stoianov, I. P., & Pezzulo, G. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013180 · Cited by 21 · see citing works
Self-citationBudaev, S. V., Kristiansen, T. S., Giske, J., & Eliassen, S. (2020) · Royal Society Open Science · doi:10.1098/rsos.201886 · Cited by 23 · see citing works
Visual detection is locked to the internal dynamics of cortico-motor control
Tomassini, A., Maris, E., Hilt, P. M., Fadiga, L., & D’Ausilio, A. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000898 · Cited by 22 · see citing works
Greenhouse‐Tucknott, A., Butterworth, J. B., Wrightson, J. G., Harrison, N. A., & Dekerle, J. (2022) · PLoS ONE · doi:10.1371/journal.pone.0262303 · Cited by 24 · see citing works
Pezzulo, G., D’Amato, L., Mannella, F., Priorelli, M., de Maele, T. V., Stoianov, I. P., & Friston, K. J. (2024) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.15118 · Cited by 23 · see citing works
Self-citation
- Authors
- Pezzulo, G., Rigoli, F., & Friston, K. J.
- Affiliations
- National Research Council (IT)
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Pezzulo, Giovanni
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 22
- Issue
- 4
- Pages
- 294-306
- ISSN
- 1364-6613 (ISSN-L)
- PMC
- PMC5870049
- OpenAlex
- W2787984105
- Cited by
- 334
- References
- 103
- Citations by year
- 2026: 50 · 2025: 52 · 2024: 38 · 2023: 40 · 2022: 34 · 2021: 43 · 2020: 43 · 2019: 24 · 2018: 9 · 2017: 1
- FWCI
- 3.97
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Trends in Cognitive Sciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- City Research Online (City University London) (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Wellcome Trust Principal Research Fellowship (088130/Z/09/Z)
- HFSP (RGY0088/2014)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Free Will and Agency
- Keywords
- active inference, goal prioritization, deep generative models, motivation, action selection, policy choice
- MeSH terms
- Humans, Motivation, Perception, Psychological Theory, Thinking, Prefrontal Cortex, Executive Function
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
