, Volume 195, Issue 6, pp 2541–2557 | Cite as

What do predictive coders want?

  • Colin KleinEmail author
S.I. : Predictive Brains


The so-called “dark room problem” makes vivd the challenges that purely predictive models face in accounting for motivation. I argue that the problem is a serious one. Proposals for solving the dark room problem via predictive coding architectures are either empirically inadequate or computationally intractable. The Free Energy principle might avoid the problem, but only at the cost of setting itself up as a highly idealized model, which is then literally false to the world. I draw at least one optimistic conclusion, however. Real-world, real-time systems may embody motivational states in a variety of ways consistent with idealized principles like FEP, including ways that are intuitively embodied and extended. This may allow predictive coding theorists to reconcile their account with embodied principles, even if it ultimately undermines loftier ambitions.


Predictive coding Free energy principle Homeostasis Good regulator theorem Extended mind Explanation 



Research on this work was funded by Australian Research Council Grant FT140100422. For helpful discussions, thanks to Esther Klein, Julia Staffel, Wolfgang Schwartz, the ANU 2013 reading group on predictive coding, and participants at the 2015 CAVE “Predictive Coding, Delusions, and Agency” workshop at Macquarie University. For feedback on earlier drafts of this work, additional thanks to Peter Clutton, Jakob Hohwy, Max Coltheart, Michael Kirchhoff, Bryce Huebner, Luke Roelofs, Daniel Stoljar, two anonymous referees, the ANU Philosophy of Mind work in progress group, and an audience at the “Predictive Brains and Embodied, Enactive Cognition” workshop at the University of Wollongong.


  1. Anderson, M. L., & Chemero, T. (2013). The problem with brain GUTs: Conflation of different senses of ‘prediction’ threatens metaphysical disaster. Behavioral and Brain Sciences, 36(3), 204–205.CrossRefGoogle Scholar
  2. Anscombe, G. E. M. (1957). Intention. Cambridge: Harvard University Press.Google Scholar
  3. Ashby, W. R. (1956). An introduction to cybernetics. London: Chapman & Hall Ltd.CrossRefGoogle Scholar
  4. Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–253.CrossRefGoogle Scholar
  5. Clark, A. (2015). Surfing uncertainty: Prediction, action, and the embodied mind. New York: Oxford University Press.Google Scholar
  6. Conant, R. C., & Ashby, W. R. (1970). Every good regulator of a system must be a model of that system. International Journal of Systems Science, 1(2), 89–97.CrossRefGoogle Scholar
  7. Craver, C. (2007). Explaining the brain. New York: Oxford University Press.CrossRefGoogle Scholar
  8. Dahlberg, M. D. (1979). A review of survival rates of fish eggs and larvae in relation to impact assessments. Marine Fisheries Review, 41(3), 1–12.Google Scholar
  9. Fabiani, A., Galimberti, F., Sanvito, S., & Hoelzel, A. R. (2004). Extreme polygyny among southern elephant seals on Sea Lion Island, Falkland Islands. Behavioral Ecology, 15(6), 961–969.CrossRefGoogle Scholar
  10. Feldman, J. (2013). Tuning your priors to the world. Topics in Cognitive Science, 5(1), 13–34.CrossRefGoogle Scholar
  11. Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138.CrossRefGoogle Scholar
  12. Friston, K. (2013). Active inference and free energy. Behavioral and Brain Sciences, 36(3), 212–213.CrossRefGoogle Scholar
  13. Friston, K., Samothrakis, S., & Montague, R. (2012a). Active inference and agency: optimal control without cost functions. Biological Cybernetics, 106(8–9), 523–541.CrossRefGoogle Scholar
  14. Friston, K., Thornton, C., & Clark, A. (2012b). Free-energy minimization and the dark-room problem. Frontiers in Psychology, 3, 1–7.Google Scholar
  15. Gawande, A. (2010). The checklist manifesto: How to get things right. New York: Henry Holt and Company.Google Scholar
  16. Godfrey-Smith, P. (2009). Darwinian populations and natural selection. New York: Oxford University Press.CrossRefGoogle Scholar
  17. Griffiths, P. E., & Gray, R. D. (1994). Developmental systems and evolutionary explanation. The Journal of Philosophy, 91(6), 277–304.CrossRefGoogle Scholar
  18. Hohwy, J. (2013). The predictive mind. New York: Oxford University Press.CrossRefGoogle Scholar
  19. Huang, Y., & Rao, R. P. (2011). Predictive coding. Wiley Interdisciplinary Reviews: Cognitive Science, 2(5), 580–593.Google Scholar
  20. Huebner, B. (2012). Surprisal and valuation in the predictive brain. Frontiers in Psychology, 3(415), 1–2.Google Scholar
  21. Klein, C. (2008). An ideal solution to disputes about multiply realized kinds. Philosophical Studies, 140(2), 161–177.CrossRefGoogle Scholar
  22. Klein, C. (2015). What the body commands: The imperative theory of pain. Cambridge, MA: MIT Press.Google Scholar
  23. Larson, E. (2004). The Devil in the White City. New York: Vintage Books.Google Scholar
  24. McMullin, E. (1985). Galilean idealization. Studies in the History and Philosophy of Science, 16(3), 247–273.CrossRefGoogle Scholar
  25. Melzack, R. (1999). From the gate to the neuromatrix. Pain, 82, S121–S126.CrossRefGoogle Scholar
  26. Melzack, R., & Wall, P. (1965). Pain mechanisms: A new theory. Science, 150(699), 971–979.CrossRefGoogle Scholar
  27. Minsky, M. L. (1967). Computation: Finite and infinite machines. Englewood Cliffs, NJ: Prentice-Hall Inc.Google Scholar
  28. Mumford, D. (1992). On the computational architecture of the neocortex. Biological Cybernetics, 66(3), 241–251.CrossRefGoogle Scholar
  29. Putnam, H. (1967/1991). The nature of mental states. In Rosenthal DM (Ed.), The nature of mind (pp. 197–210). New York: Oxford University Press.Google Scholar
  30. Rao, R. P., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1), 79–87.CrossRefGoogle Scholar
  31. Seth, A. K. (2014a). The cybernetic Bayesian brain: From interoceptive inference to sensorimotor contingencies. In T. K. Metzinger & J. M. Windt (Eds.), Open mind. Frankfurt am Main: MIND Group.Google Scholar
  32. Seth, A. K. (2014b). Response to Gu and FitzGerald: Interoceptive inference: From decision-making to organism integrity. Trends in Cognitive Sciences, 18(6), 270–271.CrossRefGoogle Scholar
  33. Simon, H. A. (1996). The sciences of the artificial (3rd ed.). Cambridge: MIT Press.Google Scholar
  34. Sterelny, K., & Griffiths, P. E. (1999). Sex and death. Chicago: University of Chicago Press.Google Scholar
  35. Woodward, J. (2003). Making things happen. New York: Oxford University Press.Google Scholar

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© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Department of PhilosophyMacquarie UniversitySydneyAustralia

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