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Are we predictive engines? Perils, prospects, and the puzzle of the porous perceiver

Published online by Cambridge University Press:  10 May 2013

Andy Clark*
School of Philosophy, Psychology, and Language Sciences, University of Edinburgh, Edinburgh EH12 5AY, Scotland, United Kingdom.


The target article sketched and explored a mechanism (action-oriented predictive processing) most plausibly associated with core forms of cortical processing. In assessing the attractions and pitfalls of the proposal we should keep that element distinct from larger, though interlocking, issues concerning the nature of adaptive organization in general.

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Copyright © Cambridge University Press 2013 

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Barrett, L. F. & Bar, M. (2009) See it with feeling: Affective predictions during object perception. Philosophical Transactions of the Royal Society of London B: Biological Sciences 364(1521):1325–34.Google Scholar
Bengio, Y. (2009) Learning deep architectures for AI. Foundations and Trends in Machine Learning 2(1):1127.Google Scholar
Brayanov, J. B. & Smith, M. A. (2010) Bayesian and “anti-Bayesian” biases in sensory integration for action and perception in the size–weight illusion. Journal of Neurophysiology 103(3):1518–31.Google Scholar
Carrasco, M., Ling, S. & Read, S. (2004) Attention alters appearance. Nature Neuroscience 7:308–13.Google Scholar
Churchland, P. M. (1989) The neurocomputational perspective. MIT/Bradford Books.Google Scholar
Churchland, P. M. (2012) Plato's camera: How the physical brain captures a landscape of abstract universals. MIT Press.Google Scholar
Clark, A. (1989) Microcognition: Philosophy, cognitive science and parallel distributed processing. MIT Press/Bradford Books.Google Scholar
Clark, A. (1993) Minimal rationalism. Mind 102(408):587610.Google Scholar
Clark, A. (2006a) Language, embodiment and the cognitive niche. Trends in Cognitive Sciences 10(8):370–74.Google Scholar
Clark, A (2008) Supersizing the mind: Action, embodiment, and cognitive extension. Oxford University Press.Google Scholar
Clark, A. (2012) Dreaming the whole cat: Generative models, predictive processing, and the enactivist conception of perceptual experience. Mind 121(483):753–71.Google Scholar
Clark, A. (forthcoming) Perceiving as predicting, In: Perception and its modalities, ed. Mohan, M., Biggs, S. & Stokes, D.. Oxford University Press.Google Scholar
Clark, A. & Thornton, C. (1997) Trading spaces: Computation, representation, and the limits of uninformed learning. Behavioral and Brain Sciences 20(1):5766.Google Scholar
Dayan, P., Hinton, G. E. & Neal, R. M. (1995) The Helmholtz machine. Neural Computation 7:889904.Google Scholar
Dennett, D. C. (2009) Darwin's “Strange Inversion of Reasoning”. Proceedings of the National Academy of Sciences USA 106 (Suppl. 1):10061–65.Google Scholar
den Ouden, H. E. M., Daunizeau, J., Roiser, J., Friston, K. J. & Stephan, K. E. (2010) Striatal prediction error modulates cortical coupling. Journal of Neuroscience 30:3210–19.Google Scholar
den Ouden, H. E. M, Friston, K. J., Daw, N. D., McIntosh, A. R. & Stephan, K. E. (2009) A dual role for prediction error in associative learning. Cerebral Cortex 19:1175–85.Google Scholar
Egner, T., Monti, J. M. & Summerfield, C. (2010) Expectation and surprise determine neural population responses in the ventral visual stream. Journal of Neuroscience 30(49):16601–608.Google Scholar
Eliades, S. J. & Wang, X. (2008) Neural substrates of vocalization feedback monitoring in primate auditory cortex. Nature 453:1102–106.Google Scholar
Feldman, H. & Friston, K. J. (2010) Attention, uncertainty, and free-energy. Frontiers in Human Neuroscience 4:215. doi:10.3389/fnmuh.2010.00215.Google Scholar
Friston, K. (2005) A theory of cortical responses. Philosophical Transactions of the Royal Society of London B: Biological Sciences 360(1456):815–36.Google Scholar
Friston, K. (2011a) Embodied inference: Or I think therefore I am, if I am what I think. In: The implications of embodiment (Cognition and Communication), ed. Tschacher, W. & Bergomi, C., pp. 89125. Imprint Academic.Google Scholar
Friston, K. (2011b) What is optimal about motor control? Neuron 72:488–98.Google Scholar
Friston, K., Adams, R. A., Perrinet, L. & Breakspear, M. (2012) Perceptions as hypotheses: Saccades as experiments. Frontiers in Psychology 3:151. doi: 10.3389/fpsyg.2012.00151.Google Scholar
Friston, K. & Kiebel, S. (2009) Cortical circuits for perceptual inference. Neural Networks 22:1093–104.Google Scholar
Frith, C. D. & Wentzer, T. S. (in press) Neural hermeneutics. In: Encyclopedia of philosophy and the social sciences, vol. 1, ed. Kaldis, B.. Sage.Google Scholar
Glimcher, P. (2010) Foundations of neuroeconomic analysis. Oxford University Press.Google Scholar
Hinton, G. E. & Nair, V. (2006) Inferring motor programs from images of handwritten digits. In: Advances in neural information processing systems 18: Proceedings of the 2005 NIPS Conference, ed. Weiss, Y., Scholkopf, B. & Platt, J., pp. 515–22. MIT Press.Google Scholar
Hohwy, J. (2012) Attention and conscious perception in the hypothesis testing brain. Frontiers in Psychology 3:96, 114. doi: 10.3389/fpsyg.2012.00096.Google Scholar
Hutchins, E. (1995) Cognition in the wild. MIT Press.Google Scholar
Kay, J. & Phillips, W. A. (2010) Coherent Infomax as a computational goal for neural systems. Bulletin of Mathematical Biology 73:344–72. doi: 10.1007/s11538-010-9564-x.Google Scholar
Keller, G. B., Bonhoeffer, T. & Hubener, M. (2012) Sensorimotor mismatch signals in primary visual cortex of the behaving mouse. Neuron 74:809–15.Google Scholar
Kok, P., Rahnev, D., Jehee, J. F., Lau, H. C. & de Lange, F. P. (2011) Attention reverses the effect of prediction in silencing sensory signals. Cerebral Cortex 22:2197–206.Google Scholar
Lee, D. & Wang, X.-J. (2009) Mechanisms for stochastic decision making in the primate frontal cortex: Single-neuron recording and circuit modeling. In: Neuroeconomics: Decision making and the brain, ed. Glimcher, P., Camerer, C., Fehr, E. & Poldrack, R., pp. 481501. Elsevier.Google Scholar
Meyer, T. & Olson, C. R. (2011) Statistical learning of visual transitions in monkey inferotemporal cortex. Proceedings of the National Academy of Sciences USA 108:19401–406.Google Scholar
Mumford, D. (1992) On the computational architecture of the neocortex. II. The role of cortico-cortical loops. Biological Cybernetics 66(3):241–51.Google Scholar
Murray, S. O., Kersten, D., Olshausen, B. A., Schrater, P. & Woods, D. L. (2002) Shape perception reduces activity in human primary visual cortex. Proceedings of the National Academy of Sciences USA 99(23):15164–69.Google Scholar
Olshausen, B. A. & Field, D. J. (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583):607609.Google Scholar
Pfeifer, R., Lungarella, M., Sporns, O. & Kuniyoshi, Y. (2007) On the information theoretic implications of embodiment – principles and methods. Lecture Notes in Computer Science (LNCS), vol. 4850. Springer.Google Scholar
Phillips, W. A., Kay, J. & Smyth, D. (1995) The discovery of structure by multistream networks of local processors with contextual guidance. Network: Computation in Neural Systems 6:225–46.Google Scholar
Phillips, W. A., von der Malsburg, C. & Singer, W. (2010) Dynamic coordination in brain and mind. In: Strüngmann Forum Report, vol. 5: Dynamic coordination in the brain: From neurons to mind, ed. von der Malsburg, C., Phillips, W. A. & Singer, W., Chapter 1, pp. 124. MIT Press.Google Scholar
Pickering, M. J. & Garrod, S. (2007) Do people use language production to make predictions during comprehension? Trends in Cognitive Sciences (11):105110.Google Scholar
Posner, M. (1980) Orienting of attention. Quarterly Journal of Experimental Psychology 32:33.Google Scholar
Reichert, D., Seriès, P. & Storkey, A. (2010) Hallucinations in Charles Bonnet Syndrome induced by homeostasis: A Deep Boltzmann Machine model. Advances in Neural Information Processing Systems 23:2020–28.Google Scholar
Roepstorff, A., Niewohner, J. & Beck, S. (2010) Enculturing brains through patterned practices. Neural Networks 23(8–9):1051–59.Google Scholar
Salakhutdinov, R. & Hinton, G. E. (2009) Deep Boltzmann machines. Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS), vol. 5, ed. D. van Dyk & M. Welling, pp. 448–55. The Journal of Machine Learning Research, published online, at Google Scholar
Schenk, T. & McIntosh, R. (2010) Do we have independent visual streams for perception and action? Cognitive Neuroscience 1:5278.Google Scholar
Seth, A. K., Suzuki, K. & Critchley, H. D. (2011) An interoceptive predictive coding model of conscious presence. Frontiers in Psychology 2:395.Google Scholar
Stephan, K., Friston, K. & Frith, C. (2009) Dysconnection in schizophrenia: From abnormal synaptic plasticity to failures of self-monitoring. Schizophrenia Bulletin 35(3):509–27.Google Scholar
von der Malsburg, C., Phillips, W. A. & Singer, W., eds. (2010) Strungmann Forum Report, Vol. 5. Dynamic coordination in the brain: From neurons to mind. MIT Press.Google Scholar
Wyart, V., Nobre, A. C. & Summerfield, C. (2012) Dissociable prior influences of signal probability and relevance on visual contrast sensitivity. Proceedings of the National Academy of Sciences USA 109:3593–98.Google Scholar
Zhu, Q. & Bingham, G. P. (2011) Human readiness to throw: The size-weight illusion is not an illusion when picking the best objects to throw. Evolution and Human Behavior 32(4):288–93.Google Scholar