LEARNING FROM THE PERSPECTIVE OF THE BAYESIAN BRAIN
DOI:
https://doi.org/10.4025/imagenseduc.v16i1.81907Keywords:
Learning. Bayesian Brain. Predictive Coding. Inference. Predictive Processing. Bayes’ Theorem.Abstract
This narrative review presents the theoretical construct of the Bayesian Brain for the field of learning. The objective is to contribute to the expansion and consolidation of learning processes. The review presents the history of the Bayesian Brain framework and the definition of its basic nomenclature. It also makes explicit its core concepts, such as predictive processing and Bayesian inference. To exemplify learning, we analyzed constructs related to how oral language is learned for greater clarity in the exploration of the possibilities of understanding that the framework enables within the scientific method. This implies the observation and experimentation of phenomena based on a principle that integrates perception, cognition and action. This new approach can feed the cycle of hypotheses and empiricism on which science is based so that scientific evidence in the area of learning can be aligned with recent advances in the understanding of brain functioning.
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References
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Brown, E. C., & Brüne, M. (2012). The role of prediction in social neuroscience. Frontiers in Human Neuroscience, 6, 147.
Brown, H., Friston, K., & Bestmann, S. (2011). Active inference, attention, and motor preparation. Frontiers in Psychology, 2, 218.
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 University Press.
Conway, C. M. (2020). How does the brain learn environmental structure? Ten core principles for understanding the neurocognitive mechanisms of statistical learning. Neuroscience & Biobehavioral Reviews, 112, 279-299.
Craik, K. J. W. (1943). The nature of explanation. Cambridge University Press, Cambridge, UK.
Doya, K. (1999). What are the computations of the cerebellum, the basal ganglia and the cerebral cortex?. Neural Networks, 12(7-8), 961-974.
Feldman, H.; Friston, K. (2010). Attention, uncertainty, and free-energy. Frontiers of Human Neuroscience, 4(215).
Friston, K. (2003). Learning and inference in the brain. Neural Networks, 16(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 uniï¬ed brain theory? Nature Reviews. Neuroscience 11(2): 127-138.
Friston, K., & Kiebel, S. (2009). Predictive coding under the free-energy principle. Philosophical Transactions of the Royal Society B: Biological sciences, 364(1521), 1211-1221.
Friston, K., & Frith, C. (2015) A Duet for one, Consciousness and Cognition, (36), 390-405.
Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., O’Doherty, J., & Pezzulo, G. (2016). Active inference and learning, Neuroscience & Biobehavioral Reviews, 68, 862-879.
Fuster, J. M., & Bressler, S. L. (2015). Past makes future: role of pFC in prediction. Journal of Cognitive Neuroscience, 27(4), 639-654.
Gallagher, S. (2023). Surprise! Why enactivism and predictive processing are parting ways: The case of improvisation. Possibility Studies & Society, 1(3), 269-278.
Giraud, A. L., & Poeppel, D. (2012). Cortical oscillations and speech processing: emerging computational principles and operations. Nature Neuroscience, 15(4), 511-517.
Goswami, U. (2022). Language acquisition and speech rhythm patterns: an auditory neuroscience perspective. Royal Society Open Science, 9(7), 211855.
Gregory, R. L. (1997). Knowledge in perception and illusion. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 352(1358), 1121-1127.
Gregory R. L. (2005). The Medawar Lecture 2001 Knowledge for vision: vision for knowledge. Philosophical Transactions of the Royal Society of London. Series B, Biological sciences, 360(1458), 1231–1251. https://doi.org/10.1098/rstb.2005.1662
Helmholtz. (2000). Treatise on Physiological Optics 3 Volumes (Reprinted from the 1924-5 edition). Introduction text and illustrations (J. P. C. Southhall, Ed.). Thoemmes Press. ISBN 1 85506 831 1
Hipólito, I. et. al. (2021) Markov blankets in the brain. Neuroscience & Biobehavioral Reviews, 125, 88-97.
Hipólito, I., & Kirchhoff, M. (2023) Breaking boundaries: The Bayesian Brain Hypothesis for perception and prediction. Consciousness and Cognition, 111(103510).
Hodson, R., Mehta, M., & Smith, R. (2024). The empirical status of predictive coding and active inference. Neuroscience and biobehavioral reviews, 157, 105473.
Hohwy, J. (2013). The predictive mind. Oxford: Oxford University Press.
Hohwy, J. (2016). The selfâ€evidencing brain. Noûs, 50(2), 259-285.
Hohwy, J. (2020). New directions in predictive processing. Mind & Language, 35(2), 209-223.
Hume. D. (1748). Philosophical Essays Concerning Human Understanding. London: A. Millar.
Iennaco, M. L., Maia, T., & Sayeg, P. (2023). Processamento Preditivo: uma introdução à proposta de unificação da cognição humana. Principia 27(3), 425–452.
Jones, N. A., Ross, H., Lynam, T., Perez, P. & Leitch, A. (2011). Mental models: an interdisciplinary synthesis of theory and methods. Ecology and Society, 16 (1), 46
Kawano, A. e Paiva, J. L. (2023). Probabilidades: um curso básico de reoferecimento. São Paulo: Epusp. 1116 k [E-book]. ISBN: 978-65-89190-25-7. DOI: 10.11606/9786589190257. Disponível em https://www.livrosabertos.abcd.usp.br/portaldelivrosUSP/catalog/download/1012/926/3414?inline=1
Kirchhoff, M., Kiverstein, J. (2018) Extended Consciousness and Predictive Processing: A Third Wave View. Routledge.
Koizumi, H. (2008). Developing the brain: A functional imaging approach to learning and educational sciences. In A.M. Battro, K. W. Fischer, & P. J. Léna. The educated brain. Cambridge University Press., 166- 180.
Kuhl, P. K., Conboy, B. T., Padden, D., Nelson, T., & Pruitt, J. (2005). Early speech perception and later language development: Implications for the" critical period". Language Learning and Development, 1(3-4), 237-264.
Kuhl, U., Neef, N. E., Kraft, I., Schaadt, G., Dörr, L., Brauer, J., ... & Skeide, M. A. (2020). The emergence of dyslexia in the developing brain. Neuroimage, 211, 116633.
Lehongre, K., Ramus, F., Villiermet, N., Schwartz, D., & Giraud, A. L. (2011). Altered low-gamma sampling in auditory cortex accounts for the three main facets of dyslexia. Neuron, 72(6), 1080-1090.
Mampe, B., Friederici, A. D., Christophe, A., & Wermke, K. (2009). Newborns' cry melody is shaped by their native language. Current Biology, 19(23), 1994-1997.
Manjaly, Z. M., & Iglesias, S. (2020). A computational theory of mindfulness based cognitive therapy from the “bayesian brain” perspective. Frontiers in Psychiatry, 11, 404.
Meyer, L. (2018). The neural oscillations of speech processing and language comprehension: state of the art and emerging mechanisms. European Journal of Neuroscience, 48(7), 2609-2621.
Otten, M., Seth, A. K., & Pinto, Y. (2017). A social Bayesian brain: How social knowledge can shape visual perception. Brain and Cognition, 112, 69-77.
Parr, T., Mirza, M. B., Cagnan, H., & Friston, K. J. (2019). Dynamic causal modelling of active vision. Journal of Neuroscience, 39(32), 6265-6275.
Parr, T., Pezzulo, G., & Friston, K. (2022). Active Inference: The Free Energy Principle in Mind, Brain, and Behavior. 1ªed. The MIT Press.
Pearl, J. (1988) Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann.
Piekarski, M. (2021). Understanding predictive processing. A review. AVANT. Pismo Awangardy Filozoficzno-Naukowej, (1), 1-48.
Ramacciotti, M.C.C., Bailer, C., Hedlund, A., Mattiello, R. (2024). Multilinguismo e Neurociência. In: Relvas, M. Baum, S. (eds.). O Cérebro que se comunica: diálogo entre a neurociência e as diversas ciências. ISBN: 978-85-7854-650-2
Ramacciotti, M. C., Bailer, C., & Noro, G. (2023). Overview of Language Acquisition for an Efficient Inclusion. Organon, 38(76), 1-20.
Rao, R.; Ballard, D. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2, 79–87.
Saxton, M. (2017). Child language: Acquisition and development. Sage Publications.
Seth, A. K. (2013). Interoceptive inference, emotion, and the embodied self. Trends in Cognitive Sciences, 17(11), 565-573.
Seth, A. K. (2015). The Cybernetic Bayesian brain: From Interoceptive Inference to Sensorimotor Contingencies. In T. Metxinger & J. M. Windt (Eds.). Open MIND: 35(T). Frankfurt am Main: MIND Group.
Smith, R.; Badcock, P.; Friston, K. (2020). Recent advances in the application of predictive coding and active inference models within clinical neuroscience. Psychiatry and Clinical Neurosciences, 75(1), 3-13.
Souza Filho, O. M. (1995). A física de Helmholtz e suas bases filosóficas. Revista da Sociedade Brasileira de História da Ciência, (13), 53-64.
Szokolszky, A. (2013). Interview with Ulric Neisser, Ecological Psychology, 25:2, 182-199, DOI: 10.1080/10407413.2013.780498
Thornton, C. (2017). Predictive processing simplified: The infotropic machine. Brain and Cognition, 112, 13-24.
Vasconcelos, M. L. I. de. (2023). A inter-relação entre o modelo dos múltiplos esboços e o processamento preditivo para o estudo da consciência (Dissertação de Mestrado). Universidade de São Paulo, São Paulo. Recuperado de https://www.teses.usp.br/teses/disponiveis/8/8133/tde-17052023-114613/
Walsh, K. S., McGovern, D. P., Clark, A., & O'Connell, R. G. (2020). Evaluating the neurophysiological evidence for predictive processing as a model of perception. Annals of the new York Academy of Sciences, 1464(1), 242-268.
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.
Brown, E. C., & Brüne, M. (2012). The role of prediction in social neuroscience. Frontiers in Human Neuroscience, 6, 147.
Brown, H., Friston, K., & Bestmann, S. (2011). Active inference, attention, and motor preparation. Frontiers in Psychology, 2, 218.
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 University Press.
Conway, C. M. (2020). How does the brain learn environmental structure? Ten core principles for understanding the neurocognitive mechanisms of statistical learning. Neuroscience & Biobehavioral Reviews, 112, 279-299.
Craik, K. J. W. (1943). The nature of explanation. Cambridge University Press, Cambridge, UK.
Doya, K. (1999). What are the computations of the cerebellum, the basal ganglia and the cerebral cortex?. Neural Networks, 12(7-8), 961-974.
Feldman, H.; Friston, K. (2010). Attention, uncertainty, and free-energy. Frontiers of Human Neuroscience, 4(215).
Friston, K. (2003). Learning and inference in the brain. Neural Networks, 16(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 uniï¬ed brain theory? Nature Reviews. Neuroscience 11(2): 127-138.
Friston, K., & Kiebel, S. (2009). Predictive coding under the free-energy principle. Philosophical Transactions of the Royal Society B: Biological sciences, 364(1521), 1211-1221.
Friston, K., & Frith, C. (2015) A Duet for one, Consciousness and Cognition, (36), 390-405.
Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., O’Doherty, J., & Pezzulo, G. (2016). Active inference and learning, Neuroscience & Biobehavioral Reviews, 68, 862-879.
Fuster, J. M., & Bressler, S. L. (2015). Past makes future: role of pFC in prediction. Journal of Cognitive Neuroscience, 27(4), 639-654.
Gallagher, S. (2023). Surprise! Why enactivism and predictive processing are parting ways: The case of improvisation. Possibility Studies & Society, 1(3), 269-278.
Giraud, A. L., & Poeppel, D. (2012). Cortical oscillations and speech processing: emerging computational principles and operations. Nature Neuroscience, 15(4), 511-517.
Goswami, U. (2022). Language acquisition and speech rhythm patterns: an auditory neuroscience perspective. Royal Society Open Science, 9(7), 211855.
Gregory, R. L. (1997). Knowledge in perception and illusion. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 352(1358), 1121-1127.
Gregory R. L. (2005). The Medawar Lecture 2001 Knowledge for vision: vision for knowledge. Philosophical Transactions of the Royal Society of London. Series B, Biological sciences, 360(1458), 1231–1251. https://doi.org/10.1098/rstb.2005.1662
Helmholtz. (2000). Treatise on Physiological Optics 3 Volumes (Reprinted from the 1924-5 edition). Introduction text and illustrations (J. P. C. Southhall, Ed.). Thoemmes Press. ISBN 1 85506 831 1
Hipólito, I. et. al. (2021) Markov blankets in the brain. Neuroscience & Biobehavioral Reviews, 125, 88-97.
Hipólito, I., & Kirchhoff, M. (2023) Breaking boundaries: The Bayesian Brain Hypothesis for perception and prediction. Consciousness and Cognition, 111(103510).
Hodson, R., Mehta, M., & Smith, R. (2024). The empirical status of predictive coding and active inference. Neuroscience and biobehavioral reviews, 157, 105473.
Hohwy, J. (2013). The predictive mind. Oxford: Oxford University Press.
Hohwy, J. (2016). The selfâ€evidencing brain. Noûs, 50(2), 259-285.
Hohwy, J. (2020). New directions in predictive processing. Mind & Language, 35(2), 209-223.
Hume. D. (1748). Philosophical Essays Concerning Human Understanding. London: A. Millar.
Iennaco, M. L., Maia, T., & Sayeg, P. (2023). Processamento Preditivo: uma introdução à proposta de unificação da cognição humana. Principia 27(3), 425–452.
Jones, N. A., Ross, H., Lynam, T., Perez, P. & Leitch, A. (2011). Mental models: an interdisciplinary synthesis of theory and methods. Ecology and Society, 16 (1), 46
Kawano, A. e Paiva, J. L. (2023). Probabilidades: um curso básico de reoferecimento. São Paulo: Epusp. 1116 k [E-book]. ISBN: 978-65-89190-25-7. DOI: 10.11606/9786589190257. Disponível em https://www.livrosabertos.abcd.usp.br/portaldelivrosUSP/catalog/download/1012/926/3414?inline=1
Kirchhoff, M., Kiverstein, J. (2018) Extended Consciousness and Predictive Processing: A Third Wave View. Routledge.
Koizumi, H. (2008). Developing the brain: A functional imaging approach to learning and educational sciences. In A.M. Battro, K. W. Fischer, & P. J. Léna. The educated brain. Cambridge University Press., 166- 180.
Kuhl, P. K., Conboy, B. T., Padden, D., Nelson, T., & Pruitt, J. (2005). Early speech perception and later language development: Implications for the" critical period". Language Learning and Development, 1(3-4), 237-264.
Kuhl, U., Neef, N. E., Kraft, I., Schaadt, G., Dörr, L., Brauer, J., ... & Skeide, M. A. (2020). The emergence of dyslexia in the developing brain. Neuroimage, 211, 116633.
Lehongre, K., Ramus, F., Villiermet, N., Schwartz, D., & Giraud, A. L. (2011). Altered low-gamma sampling in auditory cortex accounts for the three main facets of dyslexia. Neuron, 72(6), 1080-1090.
Mampe, B., Friederici, A. D., Christophe, A., & Wermke, K. (2009). Newborns' cry melody is shaped by their native language. Current Biology, 19(23), 1994-1997.
Manjaly, Z. M., & Iglesias, S. (2020). A computational theory of mindfulness based cognitive therapy from the “bayesian brain” perspective. Frontiers in Psychiatry, 11, 404.
Meyer, L. (2018). The neural oscillations of speech processing and language comprehension: state of the art and emerging mechanisms. European Journal of Neuroscience, 48(7), 2609-2621.
Otten, M., Seth, A. K., & Pinto, Y. (2017). A social Bayesian brain: How social knowledge can shape visual perception. Brain and Cognition, 112, 69-77.
Parr, T., Mirza, M. B., Cagnan, H., & Friston, K. J. (2019). Dynamic causal modelling of active vision. Journal of Neuroscience, 39(32), 6265-6275.
Parr, T., Pezzulo, G., & Friston, K. (2022). Active Inference: The Free Energy Principle in Mind, Brain, and Behavior. 1ªed. The MIT Press.
Pearl, J. (1988) Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann.
Piekarski, M. (2021). Understanding predictive processing. A review. AVANT. Pismo Awangardy Filozoficzno-Naukowej, (1), 1-48.
Ramacciotti, M.C.C., Bailer, C., Hedlund, A., Mattiello, R. (2024). Multilinguismo e Neurociência. In: Relvas, M. Baum, S. (eds.). O Cérebro que se comunica: diálogo entre a neurociência e as diversas ciências. ISBN: 978-85-7854-650-2
Ramacciotti, M. C., Bailer, C., & Noro, G. (2023). Overview of Language Acquisition for an Efficient Inclusion. Organon, 38(76), 1-20.
Rao, R.; Ballard, D. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2, 79–87.
Saxton, M. (2017). Child language: Acquisition and development. Sage Publications.
Seth, A. K. (2013). Interoceptive inference, emotion, and the embodied self. Trends in Cognitive Sciences, 17(11), 565-573.
Seth, A. K. (2015). The Cybernetic Bayesian brain: From Interoceptive Inference to Sensorimotor Contingencies. In T. Metxinger & J. M. Windt (Eds.). Open MIND: 35(T). Frankfurt am Main: MIND Group.
Smith, R.; Badcock, P.; Friston, K. (2020). Recent advances in the application of predictive coding and active inference models within clinical neuroscience. Psychiatry and Clinical Neurosciences, 75(1), 3-13.
Souza Filho, O. M. (1995). A física de Helmholtz e suas bases filosóficas. Revista da Sociedade Brasileira de História da Ciência, (13), 53-64.
Szokolszky, A. (2013). Interview with Ulric Neisser, Ecological Psychology, 25:2, 182-199, DOI: 10.1080/10407413.2013.780498
Thornton, C. (2017). Predictive processing simplified: The infotropic machine. Brain and Cognition, 112, 13-24.
Vasconcelos, M. L. I. de. (2023). A inter-relação entre o modelo dos múltiplos esboços e o processamento preditivo para o estudo da consciência (Dissertação de Mestrado). Universidade de São Paulo, São Paulo. Recuperado de https://www.teses.usp.br/teses/disponiveis/8/8133/tde-17052023-114613/
Walsh, K. S., McGovern, D. P., Clark, A., & O'Connell, R. G. (2020). Evaluating the neurophysiological evidence for predictive processing as a model of perception. Annals of the new York Academy of Sciences, 1464(1), 242-268.
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.
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2026-04-25
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Ensino, Aprendizagem e Formação de Professores
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I Declare that current article is original and has not been submitted for publication, in part or in whole, to any other national or international journal. I also declare that once published in the Imagens da Educação, a publication of the IES (UEM, UEL, UFSM, Univali, Unioeste and UEPG), it will not be submitted by me or by any co-author to any other journal. In my name and in the name of co-authors, I shall cede the copyright of the above mentioned article to the Universidade Estadual de Maringá and I declare that I know that the non-observance of this norm may make me liable for the penalties contemplated in the Law for the Protection of Authors' Rights (Act 9609 of the 19th February 1998).
How to Cite
LEARNING FROM THE PERSPECTIVE OF THE BAYESIAN BRAIN . (2026). Imagens Da Educação , 16(1), e81907. https://doi.org/10.4025/imagenseduc.v16i1.81907
