Levels of Computational Explanation

  • Michael RescorlaEmail author
Part of the Philosophical Studies Series book series (PSSP, volume 128)


It is widely agreed that one can fruitfully describe a computing system at various levels. Discussion typically centers on three levels: the representational level, the syntactic level, and the hardware level. I will argue that the three-level picture works well for artificial computing systems (i.e. computing systems designed and built by intelligent agents) but less well for natural computing systems (i.e. computing systems that arise in nature without design or construction by intelligent agents). Philosophers and cognitive scientists have been too hasty to extrapolate lessons drawn from artificial computation to the much different case of natural computation.


Levels of explanation Representation Syntax The computational theory of mind Intentionality Functionalism Abstraction Bayesianism 



I presented an earlier version of this material at the 2015 annual meeting of the International Association for Computing and Philosophy, held at the University of Delaware. I am grateful to all participants for their feedback, especially Gualtiero Piccinini, Thomas Powers, and William Rapaport. Thanks also to Tyler Burge and Mark Greenberg for many helpful discussions of these ideas.


  1. Abelson, H., Sussman, G., & Sussman, J. (1996). The structure and interpretation of computer programs. Cambridge: MIT Press.Google Scholar
  2. Bays, P., & Wolpert, D. (2007). Computational principles of sensorimotor control that minimize uncertainty and variability. The Journal of Physiology, 578, 387–396.CrossRefGoogle Scholar
  3. Burge, T. (1982). Other bodies. In A. Woodfield (Ed.), Thought and object. Oxford: Oxford University Press.Google Scholar
  4. Burge, T. (2007). Foundations of mind. Oxford: Oxford University Press.Google Scholar
  5. Burge, T. (2010). Origins of objectivity. Oxford: Oxford University Press.CrossRefGoogle Scholar
  6. Chalmers, D. (2011). A computational foundation for the study of cognition. The Journal of Cognitive Science, 12, 323–357.Google Scholar
  7. Chalmers, D. (2012). The varieties of computation: A reply. The Journal of Cognitive Science, 13, 213–248.Google Scholar
  8. Chemero, A. (2009). Radical embodied cognitive science. Cambridge: MIT Press.Google Scholar
  9. Cheng, K., Shuttleworth, S., Huttenlocher, J., & Rieser, J. (2007). Bayesian integration of spatial information. Psychological Bulletin, 13, 625–637.CrossRefGoogle Scholar
  10. Churchland, P. M. (1981). Eliminative materialism and the propositional attitudes. The Journal of Philosophy, 78, 67–90.Google Scholar
  11. Davidson, D. (1980). Essays on actions and events. Oxford: Clarendon Press.Google Scholar
  12. Dennett, D. (1971). Intentional systems. The Journal of Philosophy, 68, 87–106.CrossRefGoogle Scholar
  13. Dennett, D. (1987). The intentional stance. Cambridge: MIT Press.Google Scholar
  14. Egan, F. (2003). Naturalistic inquiry: Where does mental representation fit in? In L. Antony & N. Hornstein (Eds.), Chomsky and his critics. Malden: Blackwell.Google Scholar
  15. Evans, G. (1982). The varieties of reference. Oxford: Clarendon Press.Google Scholar
  16. Evans, T., Bicanski, A., Bush, D., & Burgess, N. (2016). How environment and self-motion combine in neural representations of space. The Journal of Physiology, 594, 6535–6546.CrossRefGoogle Scholar
  17. Feldman, J. (2015). Bayesian models of perceptual organization. In J. Wagemans (Ed.), The Oxford handbook of perceptual organization. Oxford: Oxford University Press.Google Scholar
  18. Field, H. (2001). Truth and the absence of fact. Oxford: Clarendon Press.CrossRefGoogle Scholar
  19. Fodor, J. (1975). The language of thought. New York: Thomas Y. Crowell.Google Scholar
  20. Fodor, J. (1981). Representations. Cambridge: MIT Press.Google Scholar
  21. Fodor, J. (1987). Psychosemantics. Cambridge: MIT Press.Google Scholar
  22. Fodor, J. (1994). The elm and the expert. Cambridge: MIT Press.Google Scholar
  23. Fodor, J. (2008). LOT2. Oxford: Clarendon Press.Google Scholar
  24. Gallistel, C. R. (1990). The organization of learning. Cambridge: MIT Press.Google Scholar
  25. Gallistel, C. R., & King, A. (2009). Memory and the computational brain. Malden: Wiley-Blackwell.CrossRefGoogle Scholar
  26. Giacomo, L., Moser, M.-B., & Moser, E. (2011). Computational models of grid cells. Neuron, 71, 589–603.CrossRefGoogle Scholar
  27. Goodwin, R. (1967). A growth cycle. In C. Feinstein (Ed.), Socialism, capitalism and economic growth. Cambridge: Cambridge University Press.Google Scholar
  28. Haugeland, J. (1985). Artificial intelligence: The very idea. Cambridge: MIT Press.Google Scholar
  29. Horgan, T., & Tienson, J. (1996). Connectionism and the philosophy of psychology. Cambridge: MIT Press.Google Scholar
  30. Jeanerrod, M. (2006). Motor cognition. Oxford: Oxford University Press.CrossRefGoogle Scholar
  31. Kermack, W., & McKendrick, A. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London, 115, 700–721.CrossRefGoogle Scholar
  32. Knill, D., & Richards, W. (Eds.). (1996). Perception as Bayesian inference. Cambridge: Cambridge University Press.Google Scholar
  33. Lotka, A. J. (1910). Contribution to the theory of periodic reaction. Journal of Physical Chemistry, 14, 271–274.CrossRefGoogle Scholar
  34. Madl, T. (2016). Towards real-world capable spatial memory in the LIDA architecture. Biologically Inspired Cognitive Architectures, 16, 87–104.CrossRefGoogle Scholar
  35. Madl, T., Franklin, S., Chen, K., Montaldi, D., & Trappl, R. (2014). Bayesian integration of information in hippocampal place cells. PloS One, 9, e89762.CrossRefGoogle Scholar
  36. McCulloch, W., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 7, 115–133.CrossRefGoogle Scholar
  37. Morrison, M. (2000). Unifying scientific theories. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  38. Nowak, M. (2006). Evolutionary dynamics: Exploring the equations of life. Harvard: Belknap Press.Google Scholar
  39. O’Keefe, J., & Nadel, L. (1978). The hippocampus as a cognitive map. Oxford: Clarendon University Press.Google Scholar
  40. Pacherie, E. (2006). Towards a dynamic theory of intentions. In S. Pockett, W. P. Banks, & S. Gallagher (Eds.), Does consciousness cause behavior? An investigation of the nature of volition. Cambridge: MIT Press.Google Scholar
  41. Palmer, S. (1999). Vision science. Cambridge: MIT Press.Google Scholar
  42. Peacocke, C. (1992). A study of concepts. Cambridge: MIT Press.Google Scholar
  43. Penny, W., Zeidman, P., & Burgess, N. (2013). Forward and backward inference in spatial cognition. PLoS Computational Biology, 9, e1003383.CrossRefGoogle Scholar
  44. Piccinini, G. (2008). Computation without representation. Philosophical Studies, 137, 205–241.CrossRefGoogle Scholar
  45. Piccinini, G. (2009). Computationalism in the philosophy of mind. Philosophy Compass, 4, 512–532.CrossRefGoogle Scholar
  46. Piccinini, G. (2015). Physical computation: A mechanistic account. Oxford: Oxford University Press.CrossRefGoogle Scholar
  47. Potochnik, A. (2010). Levels of explanation reconceived. Philosophy of Science, 77, 59–72.CrossRefGoogle Scholar
  48. Pouget, A., Beck, J., Ma, W. J., & Latham, P. (2013). Probabilistic brains: Knowns and unknowns. Nature Neuroscience, 16, 1170–1178.CrossRefGoogle Scholar
  49. Putnam, H. (1975). Mind, language, and reality: Philosophical papers (Vol. 2). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  50. Pylyshyn, Z. (1984). Computation and cognition. Cambridge: MIT Press.Google Scholar
  51. Quine, W. V. (1960). Word and object. Cambridge: MIT Press.Google Scholar
  52. Ramsey, W. (2007). Representation reconsidered. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  53. Rescorla, M. (2009). Cognitive maps and the language of thought. The British Journal for the Philosophy of Science, 60, 377–407.CrossRefGoogle Scholar
  54. Rescorla, M. (2012). How to integrate representation into computational modeling, and why we should. The Journal of Cognitive Science, 13, 1–38.CrossRefGoogle Scholar
  55. Rescorla, M. (2013a). Against Structuralist theories of computational implementation. The British Journal for the Philosophy of Science, 64, 681–707.CrossRefGoogle Scholar
  56. Rescorla, M. (2013b). Millikan on honeybee navigation and communication. In D. Ryder, J. Kingsbury, & K. Williford (Eds.), Millikan and her critics. Malden: Wiley-Blackwell.Google Scholar
  57. Rescorla, M. (2014a). The causal relevance of content to computation. Philosophy and Phenomenological Research, 88, 173–208.CrossRefGoogle Scholar
  58. Rescorla, M. (2014b). Computational modeling of the mind: What role for mental representation? Wiley Interdisciplinary Reviews: Cognitive Science, 6, 65–73.Google Scholar
  59. Rescorla, M. (2014c). A theory of computational implementation. Synthese, 191, 1277–1307.CrossRefGoogle Scholar
  60. Rescorla, M. (2015a). Bayesian perceptual psychology. In M. Matthen (Ed.), The Oxford handbook of the philosophy of perception. Oxford: Oxford University Press.Google Scholar
  61. Rescorla, M. (2015b). The computational theory of mind. In E. Zalta (Ed.), (2015, Fall) The Stanford encyclopedia of philosophy. Scholar
  62. Rescorla, M. (2015c). The representational foundations of computation. Philosophia Mathematica, 23, 338–366.CrossRefGoogle Scholar
  63. Rescorla, M. (2016a). Bayesian sensorimotor psychology. Mind and Language, 31, 3–36.CrossRefGoogle Scholar
  64. Rescorla, M. (2016b). Review of Gualtiero Piccinini’s Physical Computation. BJPS Review of Books.Google Scholar
  65. Rescorla, M. (2017). From Ockham to Turing—and Back Again. In A. Bokulich & J. Floyd (Eds.), In Turing 100: Philosophical explorations of the legacy of Alan Turing. Cham: Springer.Google Scholar
  66. Rescorla, M. (in press). Maps in the head? In K. Andrews & J. Beck (Eds.), The Routledge handbook of philosophy of animal minds. Basingstoke: Taylor & Francis.Google Scholar
  67. Rosenbaum, D. (2002). Motor Control. In H. Pashler & S. Yantis (Eds.), Stevens’ Handbook of experimental psychology (Vol. 1, 3rd ed.). New York: Wiley.Google Scholar
  68. Rumelhart, D., McClelland, J., & The PDP Research Group. (1986). Parallel distributed processing (Vol. 1). Cambridge: MIT Press.Google Scholar
  69. Searle, J. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3, 417–424.CrossRefGoogle Scholar
  70. Semenov, N. (1935). Chemical kinematics and chain reactions. Oxford: Clarendon Press.Google Scholar
  71. Shadmehr, R., & Mussa-Ivaldi, S. (2012). Biological learning and control. Cambridge: MIT Press.CrossRefGoogle Scholar
  72. Skinner, B. F. (1938). The behavior of organisms. New York: Appleton-Century-Crofts.Google Scholar
  73. Stich, S. (1983). From folk psychology to cognitive science. Cambridge: MIT Press.Google Scholar
  74. Strevens, M. (2008). Depth. Cambridge: Harvard University Press.Google Scholar
  75. Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. Cambridge: MIT Press.Google Scholar
  76. Tolman, E. (1948). Cognitive maps in rats and men. Psychological Review, 55, 189–208.CrossRefGoogle Scholar
  77. Turing, A. (1936). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, 42, 230–265.Google Scholar
  78. van Gelder, T. (1992). What might cognition be, if not computation? The Journal of Philosophy, 92, 345–381.CrossRefGoogle Scholar
  79. Weihrauch, K. (2000). Computable analysis: An introduction. Berlin: Springer.CrossRefGoogle Scholar
  80. Williamson, T. (2000). Knowledge and its limits. Oxford: Oxford University Press.Google Scholar

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Authors and Affiliations

  1. 1.Department of PhilosophyUniversity of CaliforniaLos AngelesUSA

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