Predictive Processing: an introduction to the unifying proposal of human cognition




Predictive Processing, Free Energy Principle, Active Inference, Perception, Action, Philosophy of Cognitive Science


This article aims to provide a critical, comprehensive, and previously unprecedented, Portuguese presentation of Predictive Processing (PP) – a theoretical framework for comprehending cognition that proposes an inversion of our standard understanding of action, perception, sensation, and their relation. Here, our primary objective is to introduce the main concepts and ideas behind PP, treating it as a moderately embodied model of cognition and analysing its credentials as a unifying theoretical proposal. In order to do so, we will start from a historical contextualization of some currents of thought that might have fostered its initial development, then we will offer a non-mathematical description of the Free Energy Principle, which underlies and substantiates the activity of its specificities, and finally clarify the role that, according to PP, Bayesian inference, prediction error minimization and the so-called Active Inference have in the homeostatic maintenance of our predictive brains and bodies. As a conclusion, we will provide a synthesis of some of the consequences PP could bring to our current understanding of the human brain and behaviour, alleging that, although its description of cognition as a single and continuous predictive process has the potential to eventually unify different explanatory paradigms and levels of analysis, for now, perhaps it is better to think of it in more modest terms, as a tool or heuristic to help us rethink many of those topics that are central to the scientific and philosophical study of the mind.


Author Biographies

Maria Luiza Iennaco, Universidade de São Paulo

Bacharel em Psicologia pela Universidade Federal de Juiz de Fora e doutoranda em Filosofia pelo Programa de Pós-Graduação da Universidade de São Paulo, com ênfase em Filosofia das Ciências Cognitivas. Membro do Active Inference Institute (EUA) e fundadora de um grupo de estudos em Processamento Preditivo.

Thales Maia, Universidade Federal de Juiz de Fora

Bacharel em História com formação complementar e especialização em Antropologia pela Faculdade de Filosofia e Ciências Humanas da Universidade Federal de Minas Gerais. Mestre em Antropologia Cognitiva da Religião pelo Programa de Pós-Graduação em Ciência da Religião da Universidade Federal de Juiz de Fora. Doutorando em Psicologia pela USP.

Paulo Sayeg, Universidade de São Paulo

Mestrando em Filosofia pela Universidade de São Paulo.


Atal, B. 2006. The history of linear prediction. IEEE Signal Processing Magazine 23(2): 154-158;

Bruineberg, J. & Rietveld. E. 2014. Self-organization, free energy minimization, and optimal grip on a field of affordances. Front. Hum. Neurosci. 8(599): 1-14;

Bruineberg, J.; Kiverstein, J.; Rietveld, E. 2018. The anticipating brain is not a scientist: the free-energy principle from an ecological-enactive perspective. Synthese 195(6): 2417-2444;

Burr, C. 2017. Embodied decisions and the predictive brain. In: T. Metzinger & W. Wiese (orgs.), Philosophy and Predictive Processing, 7. Frankfurt am Main: MIND Group;

Clark, A. 2013. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and brain sciences 36(3): 181-204;

Clark, A. 2016. Surfing uncertainty: Prediction, action, and the embodied mind. Oxford: Oxford University Press;

Clark, A. 2019. Replies to Critics. In: M. Colombo; E. Irvine; M. Stapleton (orgs.), Andy Clark and His Critics, p.266-302. Oxford: Oxford University Press;

Colombo, M. & Wright. C. 2018. First principles in the life sciences: the free-energy principle, organicism, and mechanism. Synthese 198: 3463-3488;

Corcoran, A.; Pezzulo, G.; Hohwy, J. 2020. From allostatic agents to counterfactual cognisers: active inference, biological regulation, and the origins of cognition. Biol. Philos. 35(32): 1-45;

Craik, K. 1943. The nature of explanation. Cambridge: Cambridge University Press;

Di Paolo, E. et al. 2017. Sensorimotor Life: an enactive proposal. Oxford: Oxford University Press;

Engel, A.; Fries, P.; Singer, W. 2001. Dynamic predictions: Oscillations and synchrony in top-down processing. Nature Review Neuroscience 2(10): 704-716;

Fletcher, P. & Frith. C. 2009. Perceiving is believing: A Bayesian approach to explaining the positive symptoms of schizophrenia. Nature Reviews: Neuroscience 10: 48-58;

Friston, K. 2003. Learning and inference in the brain. Neural Networks 6(9): 1325-1352;

Friston, K. 2005. A theory of cortical responses. Philosophical transactions of the Royal Society of London. Series B, Biological sciences 360(1456): 815-36;

Friston, K. 2009. The free-energy principle: A rough guide to the brain? Trends in Cognitive Sciences 13(7): 293-301;

Friston, K. 2010. The free-energy principle: a uni?ed brain theory? Nature reviews. Neuroscience 11(2): 127-138;

Friston, K.; Mattout, J.; Kilner, J. 2011. Action understanding and active inference. Biological Cybernetics 104(1-2): 137-160;

Friston, K.; Samothrakis, S.; Montague, R. 2012. Active inference and agency: Optimal control without cost functions. Biological Cybernetics 106(8): 523-541;

Friston, K. et al. 2010. Action and behavior: a free energy formulation. Biological Cybernetics 102(3): 227-260;

Friston, K. et al. 2017. Active Inference: A Process Theory. Neural. Comput. 29(1): 1-49;

Gadsby, S. & Hohwy, J. 2021. Predictive Processing and Body Representation. PsyArXiv. Acesso: 09.09.2022;

Gallagher, S. 2020. Action and Interaction. Oxford: Oxford University Press;

Gibson. J. 1979. The Ecological Approach to Visual Perception. Boston: Houghton Mifflin;

Gladziejewski, P. 2019. Mechanistic unity of the predictive mind. Theory & Psychology 29(5): 657-675;

Gregory, R. 1980. Perceptions as hypotheses. Philosophical Transactions of the Royal Society B: Biological Sciences 290(1038): 181-97;

Harkness, D. & Keshava, A. 2017. Moving from the What to the How and Where – Bayesian Models and Predictive Processing. In: T. Metzinger & W. Wiese (orgs.), Philosophy and Predictive Processing, 16. Frankfurt am Main: MIND Group;

Helmholtz. H. 1867. Handbuch der physiologischen Optik. Leipzig: Voss;

Hinton, G. 2007. Learning multiple layers of representation. Trends in cognitive sciences 11(10): 428-434;

Hipólito, I. et al. 2021. Embodied skillful performance: where the action is. Synthese 199: 4457-4481;

Hirsh, J.; Mar, R.; Peterson, J. 2013. Personal narratives as the highest level of cognitive integration. Behavioral and Brain Sciences 36(3): 216-217;

Hohwy, J. 2013. The predictive mind. Oxford: Oxford University Press;

Hohwy, J. 2014. The self-evidencing brain. Noûs 50(2): 259-285;

Hohwy, J. 2020. New directions in predictive processing, Mind and Language 35(2): 209-223;

Hohwy, J. & Kallestrup, J. (org.). 2008. Being reduced: New essays on reduction, explanation, and causation. Oxford: Oxford University Press;

Hume. D. 1748. Philosophical Essays Concerning Human Understanding. London: A. Millar;

Kant, I. 1787. Critik der reinen Vernunft. Zweyte hin und wieder verbesserte Auflage. Riga: Johann Friedrich Hartknoch;

Kelso J. 2012. Multistability and metastability: understanding dynamic coordination in the brain. Philos. Trans. R. Soc. Lond. B. Biol Sci. 367(1591): 906-918;

Körding, K.; Tenenbaum, J.; Shadmehr, R. 2007. The dynamics of memory as a consequence of optimal adaptation to a changing body. Nature neuroscience 10(6): 779-786;

Körding, K. & Wolpert, D. 2004. Bayesian integration in sensorimotor learning. Nature 427(6971): 244;

Linson, A. et al. 2018. The Active Inference Approach to Ecological Perception: General Information Dynamics for Natural and Artificial Embodied Cognition. Front. Robot. AI 5(21): 1-22;

Litwin, P. & Mi?kowski, M. 2020. Unification by Fiat: Arrested Development of Predictive Processing. Cogn. Sci. 44: e12867;

Mackay, D. 1956. Towards an information-flow model of human behavior. British Journal of Psychology 47(1): 30-43;

Makoshi, Z.; Kroliczak, G.; Donkelaar, P. 2011. Human supplementary motor area contribution to predictive motor planning. Journal of motor behavior 43(4): 303-309;

Marr, D. 1982. Vision: a computational investigation into the human representation and processing of visual information. San Francisco: W. H. Freeman;

Metzinger, T. 2004. Being no one: The self-model theory of subjectivity. Cambridge: MIT Press;

Muckli, L. 2010. What are we missing here? Brain imaging evidence for higher cognitive functions in primary visual cortex V1. International Journal of Imaging Systems Technology 20: 131-139;

Nave, K. 2022. Everybody's gotta eat: why autonomous systems can’t live on prediction-error minimization alone. Edinburgh: The University of Edinburgh;

Neisser, U. 1967. Cognitive psychology. New York: Appleton-Century-Crofts;

Newen, A. et al. (orgs.). 2018. The Oxford Handbook of 4E Cognition. Oxford: Oxford University Press;

Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Burlington: Morgan Kaufmann;

Piekarski, M. 2021. Understanding Predictive Processing: a review. AVANT: trends in interdisciplinary studies 7(1): 1-48;

Ramstead, M. 2022. Easy as 1, 2, 3: On the Short History of the Use of Affordance in Active Inference. In: Z. Djebbara (org.). Affordances in Everyday Life: A Multidisciplinary Collection of Essays, p.193-202. Cham: Springer;

Ramstead, M.; Badcock P.; Friston K. 2018. Answering Schrödinger's question: A free-energy formulation. Phys. Life Rev. 24: 1-16;

Ramstead M.; Kirchhoff M.; Friston K. 2019. A tale of two densities: active inference is enactive inference. Adapt. Behav. 28(4): 225-239;

Ramstead, M. et al. 2023. On Bayesian mechanics: a physics of and by beliefs. Interface Focus 13(29): 1-27;

Rao, R. & Ballard, D. 1999. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-?eld e?ects. Nature neuroscience 2(1): 79-87;

Rauss, K.; Schwartz, S.; Pourtois, G. 2011. Top-down effects on early visual processing in humans: A predictive coding framework. Neuroscience and Biobehavioral Reviews 35(5): 1237-1253;

Rolla, G. 2019. Processamento Preditivo: a representação nos olhos de quem vê. Voluntas – Revista Internacional de Filosofia 10(1): 85-92;

Seth, A. 2014. Interoceptive inference: From decision-making to organism integrity. Trends in Cognitive Sciences 18(6): 270-271;

Seth, A. 2015. The Cybernetic Bayesian Brain: From Interoceptive Inference to Sensorimotor Contingencies. In: T. Metzinger & J. Windt (orgs.), Open MIND, 35(T). Frankfurt am Main: MIND Group;

Seth, A. 2020. The Brain as a Prediction Machine. In: D. Mendonça.; M. Curado.; S. Gouveia (orgs.), The Philosophy and Science of Predictive Processing, p.XIV-XVII. London: Bloomsbury;

Seth, A. 2021. Being You. London: Faber & Faber;

Silva, T. et al. 2021. Da habituação individual à construção de nichos por meio da ritualização de grupos: uma descrição partindo da Inferência Ativa. In: J. Pinheiro & S. Di Marco (orgs.), EVOLUÇÃO BIO-CULTURAL, MORAL E POLÍTICA, p.47-49. Lisboa: CFCUL;

Sims, M. 2021. Strong continuity of life and mind: the free energy framework, predictive processing and ecological psychology. Edinburgh: The University of Edinburgh;

Spratling, M. 2008. Predictive coding as a model of biased competition in visual attention. Vision research 48(12): 1391-1408;

Spratling, M. 2016. A review of predictive coding algorithms. Brain and cognition 112: 92-97;

Sprevak, M. 2021. Predictive coding I: Introduction. Philsci-Archive. Acesso: 09.09.2022;

Stock, A. & Stock, C. 2004. A short history of ideo-motor action. Psychological Research 68: 176-188;

Sun, Z. & Firestone, C. 2020. The Dark Room Problem. Trends in Cognitive Sciences 24(5): 346-348;

Thompson, E. 2010. Mind in Life: Biology, Phenomenology and the Sciences of Mind. Cambridge: Harvard University Press;

Tribus, M. 1961. Thermodynamics and thermostatics: An introduction to energy, information and states of matter, with engineering applications. New York: D. Van Nostrand;

Uexküll, J. 1957. A stroll through the worlds of animals and men: a picture book of invisible worlds. In: C. Schiller (org.), Instinctive behavior: The Development of a Modern Concept, p.5-80. New York: International Universities Press;

Vasconcelos, M. 2023. A inter-relação entre o Modelo dos Múltiplos Esboços e o Processamento Preditivo para o estudo da consciência. São Paulo: Universidade de São Paulo.

Waade, P. 2020. Confucian Free Energy: The Predictive Mind in Ancient China. PsyArXiv. Acesso: 09.09.2022;

Wiese, W. & Metzinger, T. 2017. Vanilla PP for Philosophers: A Primer on Predictive Processing. In: T. Metzinger & W. Wiese (orgs.), Philosophy and Predictive Processing, 1. Frankfurt am Main: MIND Group;

Williams, D. 2018. Predictive Processing and the Representation Wars. Minds & Machines 28: 141-172.