Forms of prediction in the nervous system

A Publisher Correction to this article was published on 27 March 2020

This article has been updated


The idea that predictions shape how we perceive and comprehend the world has become increasingly influential in the field of systems neuroscience. It also forms an important framework for understanding neuropsychiatric disorders, which are proposed to be the result of disturbances in the mechanisms through which prior information influences perception and belief, leading to the production of suboptimal models of the world. There is a widespread tendency to conceptualize the influence of predictions exclusively in terms of ‘top-down’ processes, whereby predictions generated in higher-level areas exert their influence on lower-level areas within an information processing hierarchy. However, this excludes from consideration the predictive information embedded in the ‘bottom-up’ stream of information processing. We describe evidence for the importance of this distinction and argue that it is critical for the development of the predictive processing framework and, ultimately, for an understanding of the perturbations that drive the emergence of neuropsychiatric symptoms and experiences.

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Fig. 1: Possible neural network implementation of predictive coding.
Fig. 2: Context-dependent and context-independent predictions.

Change history

  • 27 March 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.


  1. 1.

    Conant, R. C. & Ashby, W. R. Every good regulator of a system must be a model of that system. Int. J. Syst. Sci. 1, 89–97 (1970).

    Article  Google Scholar 

  2. 2.

    Lee, T. S. & Mumford, D. Hierarchical Bayesian inference in the visual cortex. J. Opt. Soc. Am. A Opt. Image. Sci. Vis. 20, 1434–1448 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Friston, K. The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Kersten, D., Mamassian, P. & Yuille, A. Object perception as Bayesian inference. Annu. Rev. Psychol. 55, 271–304 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Neri, P. Object segmentation controls image reconstruction from natural scenes. PLoS Biol. 15, e1002611 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Teufel, C., Dakin, S. C. & Fletcher, P. C. Prior object-knowledge sharpens properties of early visual feature-detectors. Sci. Rep. 8, 1–12 (2018).

    Article  CAS  Google Scholar 

  7. 7.

    Liang, H. et al. Interactions between feedback and lateral connections in the primary visual cortex. Proc. Natl Acad. Sci. USA 114, 8637–8642 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Li, W., Piëch, V. & Gilbert, C. D. Learning to link visual contours. Neuron 57, 442–451 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Nurminen, L., Merlin, S., Bijanzadeh, M., Federer, F. & Angelucci, A. Top-down feedback controls spatial summation and response amplitude in primate visual cortex. Nat. Commun. 9, 1–13 (2018).

    Article  CAS  Google Scholar 

  10. 10.

    Keller, G. B. & Mrsic-Flogel, T. D. Predictive processing: a canonical cortical computation. Neuron 100, 424–435 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Kok, P., Failing, M. F. & de Lange, F. P. Prior expectations evoke stimulus templates in the primary visual cortex. J. Cognit. Neurosci. 26, 1546–1554 (2014).

    Article  Google Scholar 

  12. 12.

    Muckli, L. et al. Contextual feedback to superficial layers of V1. Curr. Biol. 25, 2690–2695 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Rao, R. & Ballard, D. H. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 2, 79–87 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Spratling, M. W. A review of predictive coding algorithms. Brain Cogn. 112, 92–97 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Powers, A. R., Mathys, C. & Corlett, P. R. Pavlovian conditioning-induced hallucinations result from overweighting of perceptual priors. Science 357, 596–600 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Fletcher, P. C. & Frith, C. D. Perceiving is believing: a Bayesian approach to explaining the positive symptoms of schizophrenia. Nat. Rev. Neurosci. 10, 48–58 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Teufel, C. et al. Shift toward prior knowledge confers a perceptual advantage in early psychosis and psychosis-prone healthy individuals. Proc. Natl Acad. Sci. USA 112, 13401–13406 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Homan, P. et al. Neural computations of threat in the aftermath of combat trauma. Nat. Neurosci. 22, 470–476 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Schutter, D. J. L. G. A cerebellar framework for predictive coding and homeostatic regulation in depressive disorder. Cerebellum 15, 30–33 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Barrett, L. F., Quigley, K. S. & Hamilton, P. An active inference theory of allostasis and interoception in depression. Philos. Trans. R. Soc. Lond. B Biol. Sci. 371, 20160011 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Palmer, C. J., Seth, A. K. & Hohwy, J. The felt presence of other minds: predictive processing, counterfactual predictions, and mentalising in autism. Conscious. Cogn. 36, 376–389 (2015).

    Article  Google Scholar 

  22. 22.

    Reichert, D. P., Seriès, P. & Storkey, A. J. Charles Bonnet syndrome: evidence for a generative model in the cortex? PLoS Comput. Biol. 9, e1003134 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Bar, M. The proactive brain: memory for predictions. Philos. Trans. R. Soc. Lond. B Biol. Sci. 364, 1235–1243 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Summerfield, C. & de Lange, F. P. Expectation in perceptual decision making: neural and computational mechanisms. Nat. Rev. Neurosci. 15, 745–756 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Lupyan, G. & Clark, A. Words and the world: predictive coding and the language–perception–cognition interface. Curr. Dir. Psychol. 24, 279–284 (2015).

    Article  Google Scholar 

  26. 26.

    Gilbert, C. D. & Li, W. Top-down influences on visual processing. Nat. Rev. Neurosci. 14, 350–363 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Hochstein, S. & Ahissar, M. View from the top. Neuron 36, 791–804 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Berger, J. O. Statistical Decision Theory and Bayesian Analysis, (Springer, 1985).

  29. 29.

    Kording, K. Decision theory: what ‘should’ the nervous system do? Science 318, 606–610 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Huang, Y. & Rao, R. P. N. Predictive coding. Wiley Interdiscip. Rev. Cogn. Sci. 2, 580–593 (2011).

    Article  Google Scholar 

  31. 31.

    Lee, T. S. The visual system’s internal model of the world. Proc. IEEE 103, 1359–1378 (2015).

    Article  Google Scholar 

  32. 32.

    Aitchison, L. & Lengyel, M. With or without you: predictive coding and Bayesian inference in the brain. Curr. Opin. Neurobiol. 46, 219–227 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Clark, A. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain. Sci. 36, 181–253 (2013).

    Article  Google Scholar 

  34. 34.

    Pickering, A. The Cybernetic Brain (Univ. of Chicago Press, 2010).

  35. 35.

    Ashby, W. R. Requisite variety and its implications for the control of complex systems. Cybernetica 2, 83–99 (1958).

    Google Scholar 

  36. 36.

    Mumford, D. On the computational architecture of the neocortex. Biol. Cybern. 66, 241–251 (1992).

    Article  CAS  Google Scholar 

  37. 37.

    Marr, D. Vision (MIT Press, 1982).

  38. 38.

    Geisler, W. S. Visual perception and the statistical properties of natural scenes. Annu. Rev. Psychol. 59, 167–192 (2008).

    Article  Google Scholar 

  39. 39.

    Torralba, A. & Oliva, A. Statistics of natural image categories. Network: Comput. Neural Syst. 14, 391–412 (2003).

    Article  Google Scholar 

  40. 40.

    Shi, L. & Griffiths, T. L. in Advances in Neural Information Processing Systems (NIPS 2009) (eds Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C. K. I. & Culotta, A.) Vol. 22, 1669–1677 (NIPS, 2009).

  41. 41.

    Ganguli, D. & Simoncelli, E. P. Efficient sensory encoding and Bayesian inference with heterogeneous neural populations. Neural Comp. 26, 2103–2134 (2014).

    Article  Google Scholar 

  42. 42.

    Girshick, A. R., Landy, M. S. & Simoncelli, E. P. Cardinal rules: visual orientation perception reflects knowledge of environmental statistics. Nat. Neurosci. 14, 926–932 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Fischer, B. J. & Peña, J. L. Owl’s behavior and neural representation predicted by Bayesian inference. Nat. Neurosci. 14, 1061–1066 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Li, B., Peterson, M. R. & Freeman, R. D. Oblique effect: a neural basis in the visual cortex. J. Neurophysiol. 90, 204–217 (2003).

    Article  Google Scholar 

  45. 45.

    Furmanski, C. S. & Engel, S. A. An oblique effect in human primary visual cortex. Nature 3, 535–536 (2000).

    CAS  Google Scholar 

  46. 46.

    Seriès, P. & Seitz, A. R. Learning what to expect (in visual perception). Front. Hum. Neurosci. 7, 1–14 (2013).

    Article  Google Scholar 

  47. 47.

    Geisler, W. S., Perry, J. S., Super, B. J. & Gallogly, D. P. Edge co-occurrence in natural images predicts contour grouping performance. Vis. Res. 41, 711–724 (2001).

    Article  CAS  Google Scholar 

  48. 48.

    Hess, R. F., May, K. A. & Dumoulin, S. O. in Oxford Handbook of Perceptual Organization (ed. Wagemans, J.) 189–206 (Oxford Univ. Press, 2015).

  49. 49.

    Geisler, W. S. & Perry, J. S. Contour statistics in natural images: grouping across occlusions. Vis. Neurosci. 26, 109–121 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Bosking, W. H., Zhang, Y., Schofield, B. & Fitzpatrick, D. Orientation selectivity and the arrangement of horizontal connections in tree shrew striate cortex. J. Neurosci. 17, 2112–2127 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Gerard-Mercier, F., Carelli, P. V., Pananceau, M., Troncoso, X. G. & Frégnac, Y. Synaptic correlates of low-level perception in V1. J. Neurosci. 36, 3925–3942 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Fowlkes, C. C., Martin, D. R. & Malik, J. Local figure-ground cues are valid for natural images. J. Vis. 7, 1–9 (2007).

    Article  Google Scholar 

  53. 53.

    Wagemans, J. et al. A century of gestalt psychology in visual perception: I. Perceptual grouping and figure–ground organization. Psychol. Bull. 138, 1172–1217 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Peterson, M. A. & Gibson, B. S. Must figure-ground organization precede object recognition? An assumption in peril. Psychol. Sci. 5, 253–259 (1994).

    Article  Google Scholar 

  55. 55.

    Cacciamani, L., Scalf, P. E. & Peterson, M. A. Neural evidence for competition-mediated suppression in the perception of a single object. Cortex 72, 124–139 (2015).

    Article  Google Scholar 

  56. 56.

    Felleman, D. J. & Van Essen, D. C. Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex 1, 1–47 (1991).

    Article  CAS  Google Scholar 

  57. 57.

    Markov, N. T. et al. Anatomy of hierarchy: feedforward and feedback pathways in macaque visual cortex. J. Comp. Neurol. 522, 225–259 (2014).

    Article  Google Scholar 

  58. 58.

    Self, M. W., van Kerkoerle, T., Supèr, H. & Roelfsema, P. R. Distinct roles of the cortical layers of area V1 in figure–ground segregation. Curr. Biol. 23, 2121–2129 (2013).

    Article  CAS  Google Scholar 

  59. 59.

    Zhaoping, L. Border ownership from intracortical interactions in visual area V2. Neuron 47, 143–153 (2005).

    Article  CAS  Google Scholar 

  60. 60.

    Kogo, N., Strecha, C., Van Gool, L. & Wagemans, J. Surface construction by a 2-D differentiation–integration process: a neurocomputational model for perceived border ownership, depth, and lightness in Kanizsa figures. Psychol. Rev. 117, 406–439 (2010).

    Article  Google Scholar 

  61. 61.

    Le, R., Witthoft, N., Ben-Shachar, M. & Wandell, B. The field of view available to the ventral occipito-temporal reading circuitry. J. Vis. 17, 6 (2017).

    Article  Google Scholar 

  62. 62.

    Kaiser, D., Quek, G. L., Cichy, R. M. & Peelen, M. V. Object vision in a structured world. Trends Cog Sci 23, 672–685 (2019).

    Article  Google Scholar 

  63. 63.

    Silson, E. H., Groen, I. I. A., Kravitz, D. J. & Baker, C. I. Evaluating the correspondence between face-, scene-, and object-selectivity and retinotopic organization within lateral occipitotemporal cortex. J. Vis. 16, 14–14 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Shettleworth, S. J. Cognition, Evolution and Behavior (Oxford Univ. Press, 2009).

  65. 65.

    Dunlap, A. S. & Stephens, D. W. Experimental evolution of prepared learning. Proc. Natl Acad. Sci. USA 111, 11750–11755 (2014).

    Article  CAS  Google Scholar 

  66. 66.

    White, L. E. & Fitzpatrick, D. Vision and cortical map development. Neuron 56, 327–338 (2007).

    Article  CAS  Google Scholar 

  67. 67.

    Blakemore, C. & Cooper, G. F. Development of the brain depends on the visual environment. Nature 228, 477–478 (1970).

    Article  CAS  Google Scholar 

  68. 68.

    Gandhi, T., Kalia, A., Ganesh, S. & Sinha, P. Immediate susceptibility to visual illusions after sight onset. Curr. Biol. 25, R358–R359 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Hershberger, W. Attached-shadow orientation perceived as depth by chickens reared in an environment illuminated from below. J. Comp. Physiol. Psych. 78, 407–411 (1970).

    Article  Google Scholar 

  70. 70.

    Svensson, L., Grant, P. J., Mullarney, K. & Zetterström, D. Bird Guide (Harper Collins, 2001).

  71. 71.

    Bar, M. Visual objects in context. Nat. Rev. Neurosci. 5, 617–629 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Bar, M. & Ullman, S. Spatial context in recognition. Perception 25, 343–352 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Brandman, T. & Peelen, M. V. Interaction between scene and object processing revealed by human fMRI and MEG decoding. J. Neurosci. 37, 7700–7710 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Sillito, A. M., Cudeiro, J. & Jones, H. E. Always returning: feedback and sensory processing in visual cortex and thalamus. Trends Neurosci. 29, 307–316 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Jones, H. E. et al. Figure–ground modulation in awake primate thalamus. Proc. Natl Acad. Sci. USA 112, 7085–7090 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Lupyan, G. Objective effects of knowledge on visual perception. J. Exp. Psychol. Hum. Percept. Perform. 43, 794–806 (2017).

    Article  Google Scholar 

  77. 77.

    Neri, P. Semantic control of feature extraction from natural scenes. J. Neurosci. 34, 2374–2388 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Christensen, J. H., Bex, P. J. & Fiser, J. Prior implicit knowledge shapes human threshold for orientation noise. J. Vis. 15, 24–24 (2015).

    Article  Google Scholar 

  79. 79.

    Hsieh, P. J., Vul, E. & Kanwisher, N. Recognition alters the spatial pattern of fMRI activation in early retinotopic cortex. J. Neurophysiol. 103, 1501–1507 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Flounders, M. W., Gonzalez-Garcia, C., Hardstone, R. & He, B. J. Neural dynamics of visual ambiguity resolution by perceptual prior. eLife 8, 1–25 (2019).

    Article  Google Scholar 

  81. 81.

    Wyart, V., Nobre, A. C. & Summerfield, C. Dissociable prior influences of signal probability and relevance on visual contrast sensitivity. Proc. Natl Acad. Sci. USA 109, 3593–3598 (2012).

    Article  Google Scholar 

  82. 82.

    Griffin, J. D. & Fletcher, P. C. Predictive processing, source monitoring, and psychosis. Annu. Rev. Clin. Psychol. 13, 265–289 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Neri, P., Luu, J. Y. & Levi, D. M. Meaningful interactions can enhance visual discrimination of human agents. Nat. Neurosci. 9, 1186–1192 (2006).

    Article  CAS  Google Scholar 

  84. 84.

    Moore, J. W., Teufel, C., Subramaniam, N., Davis, G. & Fletcher, P. C. Attribution of intentional causation influences the perception of observed movements: behavioral evidence and neural correlates. Front. Psychol. 4, 1–11 (2013).

    Article  CAS  Google Scholar 

  85. 85.

    Teufel, C. et al. Social cognition modulates the sensory coding of observed gaze direction. Curr. Biol. 19, 1274–1277 (2009).

    Article  CAS  Google Scholar 

  86. 86.

    Liepelt, R., Cramon, von, D. Y. & Brass, M. What is matched in direct matching? Intention attribution modulates motor priming. J. Exp. Psychol. Hum. Percept. Perform. 34, 578–591 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  87. 87.

    Teufel, C. et al. What is social about social perception research? Front. Integr. Neurosci. 6, 1–9 (2012).

    Google Scholar 

  88. 88.

    Solomon, S. G. & Kohn, A. Moving sensory adaptation beyond suppressive effects in single neurons. Curr. Biol. 24, R1012–R1022 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Webster, M. A. Visual adaptation. Ann. Rev. Vis. Sci. 1, 547–567 (2015).

    Article  Google Scholar 

  90. 90.

    Grill-Spector, K., Henson, R. & Martin, A. Repetition and the brain: neural models of stimulus-specific effects. Trends Cog. Sci. 10, 14–23 (2006).

    Article  Google Scholar 

  91. 91.

    Vogels, R. Sources of adaptation of inferior temporal cortical responses. Cortex 80, 185–195 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  92. 92.

    Stocker, A. A. & Simoncelli, E. P. Sensory adaptation within a Bayesian framework for perception. Adv. Neural Inf. Process. Syst. 18, 1–8 (2006).

    Google Scholar 

  93. 93.

    Summerfield, C., Trittschuh, E. H., Monti, J. M., Mesulam, M.-M. & Egner, T. Neural repetition suppression reflects fulfilled perceptual expectations. Nat. Neurosci. 11, 1004–1006 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Vinken, K., de Beeck, H. P. O. & Vogels, R. Face repetition probability does not affect repetition suppression in macaque inferotemporal cortex. J. Neurosci. 38, 7492–7504 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Chopin, A. & Mamassian, P. Predictive properties of visual adaptation. Curr. Biol. 22, 622–626 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. 96.

    Wacongne, C., Changeux, J.-P. & Dehaene, S. A neuronal model of predictive coding accounting for the mismatch negativity. J. Neurosci. 32, 3665–3678 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. 97.

    Todorovic, A., van Ede, F., Maris, E. & de Lange, F. P. Prior expectation mediates neural adaptation to repeated sounds in the auditory cortex: an MEG study. J. Neurosci. 31, 9118–9123 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. 98.

    Wacongne, C. et al. Evidence for a hierarchy of predictions and prediction errors in human cortex. Proc. Natl Acad. Sci. USA 108, 20754–20759 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  99. 99.

    Schwiedrzik, C. M. & Freiwald, W. A. High-level prediction signals in a low-level area of the macaque face-processing hierarchy. Neuron 96, 89–97 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. 100.

    Parras, G. G. et al. Neurons along the auditory pathway exhibit a hierarchical organization of prediction error. Nat. Commun. 8, 2148 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. 101.

    Ewbank, M. P. et al. Changes in ‘top-down’ connectivity underlie repetition suppression in the ventral visual pathway. J. Neurosci. 31, 5635–5642 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. 102.

    Ewbank, M. P., Henson, R. N., Rowe, J. B., Stoyanova, R. S. & Calder, A. J. Different neural mechanisms within occipitotemporal cortex underlie repetition suppression across same and different-size faces. Cereb. Cortex 23, 1073–1084 (2013).

    Article  Google Scholar 

  103. 103.

    de Lange, F. P., Heilbron, M. & Kok, P. How do expectations shape perception? Trends Cog. Sci. 22, 764–779 (2018).

    Article  Google Scholar 

  104. 104.

    Brown, H. & Friston, K. J. Free-energy and illusions: the Cornsweet effect. Front. Psychol. 3, 1–13 (2012).

    Google Scholar 

  105. 105.

    Corlett, P. R. et al. Hallucinations and strong priors. Trends Cog. Sci. 23, 114–127 (2019).

    Article  Google Scholar 

  106. 106.

    Sterzer, P. et al. The predictive coding account of psychosis. Biol. Psychiatry 84, 634–643 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  107. 107.

    Adams, R. A., Stephan, K. E., Brown, H. R., Frith, C. D. & Friston, K. J. The computational anatomy of psychosis. Front. Integr. Neurosci. 4, 1–26 (2013).

    CAS  Google Scholar 

  108. 108.

    Notredame, C.-E., Denève, S. & Jardri, R. What visual illusions teach us about schizophrenia. Front. Integr. Neurosci. 8, 1–16 (2014).

    Article  Google Scholar 

  109. 109.

    Cornsweet, T. N. Visual Perception (HBJ, 1970).

  110. 110.

    Purves, D., Shimpi, A. & Lotto, R. B. An empirical explanation of the Cornsweet effect. J. Neurosci. 19, 8542–8551 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. 111.

    Dakin, S. C. & Bex, P. J. Natural image statistics mediate brightness filling in. Proc. Biol. Sci. 270, 2341–2348 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  112. 112.

    Anderson, E. J., Dakin, S. C. & Rees, G. Monocular signals in human lateral geniculate nucleus reflect the Craik–Cornsweet–O’Brien effect. J. Vis. 9, 1–18 (2009).

    PubMed  PubMed Central  Google Scholar 

  113. 113.

    Ramachandran, V. S. Perception of shape from shading. Nature 331, 163–166 (1988).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. 114.

    Olshausen, B. A. & Field, D. J. Sparse coding with an overcomplete basis set: a strategy employed by V1? Vis. Res. 37, 3311–3325 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. 115.

    Tulver, K., Aru, J., Rutiku, R. & Bachmann, T. Individual differences in the effects of priors on perception: a multi-paradigm approach. Cognition 187, 167–177 (2019).

    Article  Google Scholar 

  116. 116.

    Adams, W. J., Graf, E. W. & Ernst, M. O. Experience can change the ‘light-from-above’ prior. Nat. Neurosci. 7, 1057–1058 (2004).

    Article  CAS  Google Scholar 

  117. 117.

    Panichello, M. F., Cheung, O. S. & Bar, M. Predictive feedback and conscious visual experience. Front. Psychol. 3, 620 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  118. 118.

    Adams, W. J., Kerrigan, I. S. & Graf, E. W. Efficient visual recalibration from either visual or haptic feedback: the importance of being wrong. J. Neurosci. 30, 14745–14749 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. 119.

    Kerrigan, I. S. & Adams, W. J. Learning different light prior distributions for different contexts. Cognition 127, 99–104 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  120. 120.

    Knill, D. C. Learning Bayesian priors for depth perception. J. Vis. 7, 13 (2007).

    Article  Google Scholar 

  121. 121.

    Mamassian, P., Jentzsch, I., Bacon, B. A. & Schweinberger, S. R. Neural correlates of shape from shading. Neuroreport 14, 971–975 (2003).

    Article  Google Scholar 

  122. 122.

    Cuthbert, B. N. & Insel, T. R. Toward the future of psychiatric diagnosis: the seven pillars of RDoC. BMC Med. 11, 126 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  123. 123.

    Xia, C. H. et al. Linked dimensions of psychopathology and connectivity in functional brain networks. Nat. Commun. 9, 3003 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. 124.

    Pollak, T. A. et al. Autoimmune psychosis: an international consensus on an approach to the diagnosis and management of psychosis of suspected autoimmune origin. Lancet Psychiatry 7, 93–108 (2020).

    Article  Google Scholar 

  125. 125.

    O’Callaghan, C. et al. Visual hallucinations are characterised by impaired sensory evidence accumulation: insights from hierarchical drift diffusion modelling in Parkinson’s disease. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 2, 680–688 (2017).

    Article  Google Scholar 

  126. 126.

    Urwyler, P. et al. Visual hallucinations in eye disease and Lewy body disease. Am. J. Geriat. Psychiatry 24, 350–358 (2016).

    Article  Google Scholar 

  127. 127.

    Waters, F. & Fernyhough, C. Hallucinations: a systematic review of points of similarity and difference across diagnostic classes. Schizophr. Bull. 1, 32–43 (2017).

    Article  Google Scholar 

  128. 128.

    McGrath, J. J. et al. Psychotic experiences in the general population: a cross-national analysis based on 31261 respondents from 18 countries. JAMA Psychiatry 72, 697–705 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  129. 129.

    Carhart-Harris, R. L., Friston, K. J. & Barker, E. L. REBUS and the anarchic brain: toward a unified model of the brain action of psychedelics. Pharmacol. Rev. 71, 316–344 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. 130.

    Corlett, P. R., Frith, C. D. & Fletcher, P. C. From drugs to deprivation: a Bayesian framework for understanding models of psychosis. Psychopharmacology 206, 515–530 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. 131.

    Cassidy, C. M. et al. A perceptual inference mechanism for hallucinations linked to striatal dopamine. Curr. Biol. 28, 503–514 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. 132.

    Schmack, K. et al. Delusions and the role of beliefs in perceptual inference. J. Neurosci. 33, 13701–13712 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. 133.

    Grzeczkowski, L. et al. Is the perception of illusions abnormal in schizophrenia? Psychiatry Res. 270, 929–939 (2018).

    Article  Google Scholar 

  134. 134.

    Keane, B. P., Cruz, L. N., Paterno, D. & Silverstein, S. M. Self-reported visual perceptual abnormalities are strongly associated with core clinical features in psychotic disorders. Front. Integr. Neurosci. 9, 646 (2018).

    Google Scholar 

  135. 135.

    McGhie, A. & Chapman, J. Disorders of attention and perception in early schizophrenia. Brit. J. Med. Psychol. 34, 103–116 (1961).

    Article  CAS  Google Scholar 

  136. 136.

    Geisler, W. S. & Diehl, R. L. Bayesian natural selection and the evolution of perceptual systems. Philos. Trans. R. Soc. Lond. B Biol. Sci. 357, 419–448 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  137. 137.

    Uexküll, J. Theoretische Biologie (Paetel, 1920).

  138. 138.

    Seth, A. K. in Open MIND (eds Metzinger, T. & Windt, J.) (MIND Group, 2015).

  139. 139.

    Srinivasan, M. V., Laughlin, S. B. & Dubs, A. Predictive coding: a fresh view of inhibition in the retina. Proc. R. Soc. Lond. B Biol. Sci. 216, 427–459 (1982).

    Article  CAS  Google Scholar 

  140. 140.

    Heeger, D. J. Theory of cortical function. Proc. Natl Acad. Sci. USA 114, 1773–1782 (2017).

    Article  CAS  Google Scholar 

  141. 141.

    Blank, H. & Davis, M. H. Prediction errors but not sharpened signals simulate multivoxel fMRI patterns during speech perception. PLoS Biol. 14, e1002577 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. 142.

    Richter, D., Ekman, M. & de Lange, F. P. Suppressed sensory response to predictable object stimuli throughout the ventral visual stream. J. Neurosci. 38, 7452–7461 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. 143.

    Han, B., Mostert, P. & de Lange, F. P. Predictable tones elicit stimulus-specific suppression of evoked activity in auditory cortex. NeuroImage 200, 242–249 (2019).

    Article  CAS  Google Scholar 

  144. 144.

    Meijs, E. L., Slagter, H. A., de Lange, F. P. & van Gaal, S. Dynamic interactions between top-down expectations and conscious awareness. J. Neurosci. 38, 2318–2327 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. 145.

    Kok, P., Jehee, J. F. M. & de Lange, F. P. Less is more: expectation sharpens representations in the primary visual cortex. Neuron 75, 265–270 (2012).

    Article  CAS  Google Scholar 

  146. 146.

    Carrasco, M. Visual attention: the past 25 years. Vis. Res. 51, 1484–1525 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  147. 147.

    Desimone, R. & Duncan, J. Neural mechanisms of selective visual-attention. Annu. Rev. Neurosci. 18, 193–222 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. 148.

    Summerfield, C. & Egner, T. Expectation (and attention) in visual cognition. Trends Cog. Sci. 13, 403–409 (2009).

    Article  Google Scholar 

  149. 149.

    Jiang, J., Summerfield, C. & Egner, T. Attention sharpens the distinction between expected and unexpected percepts in the visual brain. J. Neurosci. 33, 18438–18447 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. 150.

    Kok, P., Rahnev, D., Jehee, J. F. M., Lau, H. C. & de Lange, F. P. Attention reverses the effect of prediction in silencing sensory signals. Cereb. Cortex 22, 2197–2206 (2012).

    Article  Google Scholar 

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C.T. and P.C.F. are funded by the Wellcome Trust and P.C.F. is funded by the Bernard Wolfe Health Neuroscience Fund.

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The authors contributed equally to all aspects of the article.

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Correspondence to Christoph Teufel or Paul C. Fletcher.

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Nature Reviews Neuroscience thanks M. Bar, F. de Lange and P. Sterzer for their contribution to the peer review of this work.

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In the fields of cybernetics, reinforcement learning and artificial intelligence, an agent is an entity that is capable of acting autonomously to self-regulate in the face of changes in its environment.

Bayesian decision theory

A theory that describes how decisions are optimized by application of principles from Bayesian probability; that is, by drawing on probability distributions that quantify prior probabilities of events or states. These probabilities are referred to as priors and reflect beliefs about a state before new evidence is taken into account.

Information theory

The mathematical formulation of how information is coded, transmitted and processed. Informally, information can be thought of as a measure of the reduction of uncertainty. The field of information theory emerged from attempts to solve the problem of how to transfer large datasets within limited-capacity systems and has proven useful in thinking about how neural systems deal with a similar problem.

Perceptual and cognitive inference

The process by which perceptions and beliefs arise from the combination of sensory evidence and information based on prior experience or knowledge. The process of inference may be optimized by using prior knowledge according to Bayes’ theorem.


Estimates of unobserved or missing information on the basis of a model. Within the predictive processing framework, the model is provided by prior knowledge of the world. Note that predictions can be (but are not necessarily) future-oriented.

Predictive coding

Within neuroscience, a family of algorithms aiming to capture how the brain performs probabilistic inference using the mismatch between the predicted and the actual magnitude of a signal.


In Bayesian models of perception, action and cognition, the term is used as shorthand for ‘prior probability distributions’, which model the system’s information about a world state before current evidence is assessed. Importantly, priors provide information that is the basis of the formation of predictions. It is important to note that the term is agnostic as to how this prior information is implemented, making combined terms, such as ‘top-down prior’, which implies a specific mechanism, confusing.


In Bayesian decision theory, a function that determines the value of a possible situation or outcome.

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Teufel, C., Fletcher, P.C. Forms of prediction in the nervous system. Nat Rev Neurosci 21, 231–242 (2020).

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