Journal of Biological Physics

, Volume 37, Issue 1, pp 1–38 | Cite as

Information processing, computation, and cognition

Perspective

Abstract

Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both – although others disagree vehemently. Yet different cognitive scientists use ‘computation’ and ‘information processing’ to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In this paper, we address this unsatisfactory state of affairs by presenting a general and theory-neutral account of computation and information processing. We also apply our framework by analyzing the relations between computation and information processing on one hand and classicism, connectionism, and computational neuroscience on the other. We defend the relevance to cognitive science of both computation, at least in a generic sense, and information processing, in three important senses of the term. Our account advances several foundational debates in cognitive science by untangling some of their conceptual knots in a theory-neutral way. By leveling the playing field, we pave the way for the future resolution of the debates’ empirical aspects.

Keywords

Classicism Cognitivism Computation Computational neuroscience Computational theory of mind Computationalism Connectionism Information processing Meaning Neural computation Representation 

References

  1. 1.
    Perkel, D.H.: Computational neuroscience: scope and structure. In: Schwartz, E.L. (ed.) Computational Neuroscience, pp. 38–45. MIT Press, Cambridge (1990)Google Scholar
  2. 2.
    Edelman, G.M.: Bright Air, Brilliant Fire: on the Matter of the Mind. Basic Books, New York (1992)Google Scholar
  3. 3.
    Globus, G.G.: Towards a noncomputational cognitive neuroscience. J. Cogn. Neurosci. 4(4), 299–310 (1992)CrossRefGoogle Scholar
  4. 4.
    Port, R.F., van Gelder, T.: Mind as Motion: Explorations in the Dynamics of Cognition. MIT Press, Cambridge (1995)Google Scholar
  5. 5.
    Freeman, W.J.: How Brains Make Up Their Minds. Columbia University Press, New York (2001)Google Scholar
  6. 6.
    Wallace, B., Ross, A., David, J., Anderson, T. (eds.): The Mind, The Body and the World: Psychology after Cognitivism? Imprint Academic, Exeter (2007)Google Scholar
  7. 7.
    Spivey, M.: The Continuity of Mind. Oxford University Press, Oxford (2007)Google Scholar
  8. 8.
    Miller, G.A., Galanter, E.H., Pribram, K.H.: Plans and the Structure of Behavior. Holt, New York (1960)CrossRefGoogle Scholar
  9. 9.
    Newell, A., Simon, H.A.: Computer science as an empirical enquiry: symbols and search. Commun. ACM 19, 113–126 (1976)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Fodor, J.A., Pylyshyn, Z.W.: Connectionism and cognitive architecture. Cognition 28, 3–71 (1988)CrossRefGoogle Scholar
  11. 11.
    Newell, A.: Unified Theories of Cognition. Harvard University Press, Cambridge (1990)Google Scholar
  12. 12.
    Pinker, S.: How the Mind Works. Norton, New York (1997)Google Scholar
  13. 13.
    Gallistel, C.R., King, A.P.: Memory and the Computational Brain: Why Cognitive Science will Transform Neuroscience. Wiley-Blackwell, Malden (2008)Google Scholar
  14. 14.
    Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386–408 (1958)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Feldman, J.A., Ballard, D.H.: Connectionist models and their properties. Cogn. Sci. 6, 205–254 (1982)CrossRefGoogle Scholar
  16. 16.
    Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. U.S.A. 79, 2554–2558 (1982)MathSciNetADSCrossRefGoogle Scholar
  17. 17.
    Rumelhart, D.E., McClelland, J.M., et al.: Parallel Distributed Processing: Explorations in the Microstructure of Cognition. MIT Press, Cambridge (1986)Google Scholar
  18. 18.
    Smolensky, P.: On the proper treatment of connectionism. Behav. Brain Sci. 11(1), 1–23 (1988)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Churchland, P.S., Sejnowski, T.J.: The Computational Brain. MIT Press, Cambridge (1992)Google Scholar
  20. 20.
    O’Reilly, R.C., Munakata, Y.: Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain. MIT Press, Cambridge (2000)Google Scholar
  21. 21.
    Rogers, T.T., McClelland, J.L.: Semantic Cognition: a Parallel Distributed Processing Approach. MIT Press, Cambridge (2006)Google Scholar
  22. 22.
    Dale, R.: The possibility of a pluralist cognitive science. J. Exp. Theor. Artif. Intell. 20(3), 155–179 (2008)CrossRefGoogle Scholar
  23. 23.
    Edelman, S.: On the nature of minds, or: truth and consequences. J. Exp. Theor. Artif. Intell. 20(3), 181–196 (2008)CrossRefGoogle Scholar
  24. 24.
    Fodor, J.A.: LOT 2: the Language of Thought Revisited. Oxford University Press, Oxford (2008)Google Scholar
  25. 25.
    Pylyshyn, Z.W.: Computation and Cognition. MIT Press, Cambridge (1984)Google Scholar
  26. 26.
    Shagrir, O.: Why we view the brain as a computer. Synthese 153(3), 393–416 (2006)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Piccinini, G.: Computing mechanisms. Philos. Sci. 74(4), 501–526 (2007)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Piccinini, G.: Computational modeling vs. computational explanation: is everything a Turing machine, and does it matter to the philosophy of mind? Australas. J. Philos. 85(1), 93–115 (2007)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Piccinini, G.: Computationalism, the Church-Turing thesis, and the Church-Turing fallacy. Synthese 154(1), 97–120 (2007)MATHMathSciNetCrossRefGoogle Scholar
  30. 30.
    Piccinini, G.: Some neural networks compute, others don’t. Neural Netw. 21(2–3), 311–321 (2008)CrossRefGoogle Scholar
  31. 31.
    Piccinini, G.: Computers. Pac. Philos. Q. 89(1), 32–73 (2008)CrossRefGoogle Scholar
  32. 32.
    Piccinini, G.: Computation without representation. Philos. Stud. 137(2), 205–241 (2008)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Piccinini, G.: Computationalism in the philosophy of mind. Philos. Comp. 4(3), 515–532 (2009)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Piccinini, G.: The mind as neural software? Understanding functionalism, computationalism, and computational functionalism. Philos. Phenomenol. Res. 81(2) (2010)Google Scholar
  35. 35.
    Piccinini, G.: The resilience of computationalism. Philos. Sci. (2010, in press)Google Scholar
  36. 36.
    Scarantino, A.: A theory of probabilistic information. UnpublishedGoogle Scholar
  37. 37.
    Scarantino, A., Piccinini, G.: Information without truth. Metaphilosophy 41, 313–330 (2010)CrossRefGoogle Scholar
  38. 38.
    Edelman, S.: Computing the Mind: How the Mind Really Works. Oxford University Press, Oxford (2008)Google Scholar
  39. 39.
    Shannon, C.E.: A mathematical theory of communication. ATT Tech. J. 27(379–423), 623–656 (1948)MathSciNetGoogle Scholar
  40. 40.
    Turing, A.: On computable numbers, with an application to the Entscheidungsproblem. Proc. Lond. Math. Soc., ser. 2, 42, 230–265 (1936–37)CrossRefGoogle Scholar
  41. 41.
    Wiener, N.: Cybernetics: or Control and Communication in the Animal and the Machine. MIT Press, Cambridge (1948)Google Scholar
  42. 42.
    McCulloch, W.S.: The brain as a computing machine. Electr. Eng. 68, 492–497 (1949)Google Scholar
  43. 43.
    McCulloch, W.S., Pitts, W.H.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biol. 7, 115–133 (1943)MathSciNetGoogle Scholar
  44. 44.
    Dayan, P., Abbott, L.F.: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press, Cambridge (2001)MATHGoogle Scholar
  45. 45.
    Eliasmith, C., Anderson, C.H.: Neural Engineering: Computation, Representation, and Dynamics in Neurobiological systems. MIT Press, Cambridge (2003)Google Scholar
  46. 46.
    Dretske, F.: Knowledge and the Flow of Information. Blackwells, Oxford (1981)Google Scholar
  47. 47.
    Millikan, R.G.: Varieties of Meaning. MIT Press, Cambridge (2004)Google Scholar
  48. 48.
    Floridi, L.: Is semantic information meaningful data? Philos. Phenomenol. Res. 70(2), 351–370 (2005)CrossRefGoogle Scholar
  49. 49.
    Cohen, J., Meskin, A.: An objective counterfactual theory of information. Australas. J. Philos. 84, 333–352 (2006)CrossRefGoogle Scholar
  50. 50.
    Piccinini, G.: Functionalism, computationalism, and mental contents. Can. J. Philos. 34(3), 375–410 (2004)Google Scholar
  51. 51.
    Baum, E.B.: What is Thought? MIT Press, Cambridge (2004)Google Scholar
  52. 52.
    Churchland, P.M.: Neurophilosophy at Work. Cambridge University Press, Cambridge (2007)CrossRefGoogle Scholar
  53. 53.
    Church, A.: An unsolvable problem in elementary number theory. Am. J. Math. 58, 345–363 (1936)MathSciNetCrossRefGoogle Scholar
  54. 54.
    Kleene, S.C.: Introduction to Metamathematics. Elsevier, New York (1952)MATHGoogle Scholar
  55. 55.
    Copeland, B.J.: Narrow versus wide mechanism: including a re-examination of Turing’s views on the mind-machine issue. J. Philos. XCVI(1), 5–32 (2000)MathSciNetCrossRefGoogle Scholar
  56. 56.
    Wolfram, S.: A New Kind of Science. Wolfram Media, Champaign (2002)MATHGoogle Scholar
  57. 57.
    Lloyd, S.: Programming the Universe: a Quantum Computer Scientist Takes on the Cosmos. Knopf, New York (2006)Google Scholar
  58. 58.
    Pitowsky, I.: The physical Church thesis and physical computational complexity. Iyyun 39, 81–99 (1990)Google Scholar
  59. 59.
    Jilk, D.J., Lebiere, C., O’Reilly, R.C., Anderson, J.R.: SAL: an explicitly pluralistic cognitive architecture. J. Exp. Theor. Artif. Intell. 20, 197–218 (2008)CrossRefGoogle Scholar
  60. 60.
    Gödel, K.: On undecidable propositions of formal mathematical systems. In: Davis, M. (ed.) The Undecidable, pp. 41–71. Raven, Ewlett (1934)Google Scholar
  61. 61.
    Post, E.: Finite combinatory processes - formulation I. J. Symb. Log. 1, 103–105 (1936)MATHCrossRefGoogle Scholar
  62. 62.
    Davis, M.D., Sigal, R., Weyuker, E.J.: Computability, Complexity, and Languages. Academic Press, Boston (1994)Google Scholar
  63. 63.
    Piccinini, G.: The first computational theory of mind and brain: a close look at McCulloch and Pitts’s “Logical calculus of ideas immanent in nervous activity”. Synthese 141(2), 175–215 (2004)MathSciNetCrossRefGoogle Scholar
  64. 64.
    Penrose, R.: Shadows of the Mind. Oxford University Press, Oxford (1994)Google Scholar
  65. 65.
    Gerard, R.W.: Some of the problems concerning digital notions in the central nervous system. Cybernetics. In: Foerster, H.V., Mead, M., Teuber, H.L. (eds.) Circular Causal and Feedback Mechanisms in Biological and Social Systems. Transactions of the Seventh Conference, pp. 11–57. Macy Foundation, New York (1951)Google Scholar
  66. 66.
    Rubel, L.A.: The brain as an analog computer. J. Theor. Neurobiol. 4, 73–81 (1985)Google Scholar
  67. 67.
    Pour-El, M.B.: Abstract computability and its relation to the general purpose analog computer (some connections between logic, differential equations and analog computers). Trans. Am. Math. Soc. 199, 1–28 (1974)MATHMathSciNetCrossRefGoogle Scholar
  68. 68.
    Fodor, J.A.: The mind-body problem. Sci. Am. 244, 124–132 (1981)CrossRefGoogle Scholar
  69. 69.
    Block, N.: Troubles with functionalism. In: Savage, C.W. (ed.) Perception and Cognition: Issues in the Foundations of Psychology, vol. 6, pp. 261–325. University of Minnesota Press, Minneapolis (1978)Google Scholar
  70. 70.
    Baars, B.J., Banks, W.P., Newman, J.B. (eds.): Essential Sources in the Scientific Study of Consciousness. MIT Press, Cambridge (2003)Google Scholar
  71. 71.
    Harman, G.: Thought. Princeton University Press, Princeton (1973)Google Scholar
  72. 72.
    Fodor, J.A.: The Language of Thought. Harvard University Press, Cambridge (1975)Google Scholar
  73. 73.
    Marr, D.: Vision. W.H. Freeman, San Francisco (1982)Google Scholar
  74. 74.
    Bechtel, W., Abrahamsen, A.: Connectionism and the Mind: Parallel Processing, Dynamics, and Evolution in Networks. Blackwell, Malden (2002)Google Scholar
  75. 75.
    Koch, C.: Biophysics of Computation: Information Processing in Single Neurons. Oxford University Press, New York (1999)Google Scholar
  76. 76.
    Marr, D., Poggio, T.: Cooperative computation of stereo disparity. Science 194(4262), 283–287 (1976)ADSCrossRefGoogle Scholar
  77. 77.
    Smolensky, P., Legendre, G.: The Harmonic Mind: From Neural Computation to Optimality-Theoretic Grammar, vol. 1: Cognitive Architecture; vol. 2: Linguistic and Philosophical Implications. MIT Press, Cambridge (2006)Google Scholar
  78. 78.
    Horgan, T., Tienson, J.: Connectionism and the Philosophy of Psychology. MIT Press, Cambridge (1996)Google Scholar
  79. 79.
    Thorndike, E.: The Fundamentals of Learning. Columbia University Press, New York (1932)CrossRefGoogle Scholar
  80. 80.
    Hebb, D.: The Organization of Behavior: a Neuropsychological Theory. Wiley, New York (1949)Google Scholar
  81. 81.
    Fodor, J.A.: The Modularity of Mind. MIT Press, Cambridge (1983)Google Scholar
  82. 82.
    Turing, A.M.: Intelligent Machinery. Mechanical Intelligence, pp. 117–127. D. Ince. Amsterdam, North-Holland (1948)Google Scholar
  83. 83.
    Copeland, B.J., Proudfoot, D.: On Alan Turing’s anticipation of connectionism. Synthese 113, 361–377 (1996)MathSciNetCrossRefGoogle Scholar
  84. 84.
    Trehub, A.: The Cognitive Brain. Cambridge, MIT Press, Cambridge (1991)Google Scholar
  85. 85.
    Marcus, G.F.: The Algebraic Mind: Integrating Connectionism and Cognitive Science. MIT Press, Cambridge (2001)Google Scholar
  86. 86.
    Miller, G.A.: Language and Communication. McGraw-Hill, New York (1951)CrossRefGoogle Scholar
  87. 87.
    Minsky, M.: Semantic Information Processing. MIT Press, Cambridge (1968)MATHGoogle Scholar
  88. 88.
    Fano, R.M.: Transmission of Information; a Statistical Theory of Communications. MIT Press, New York (1961)Google Scholar
  89. 89.
    Pierce, J.R.: An Introduction to Information Theory. Dover Publications, New York (1980)MATHGoogle Scholar
  90. 90.
    Smith, J.M.: The concept of information in biology. Philos. Sci. 67(2), 177–194 (2000)MathSciNetCrossRefGoogle Scholar
  91. 91.
    Godfrey-Smith, P.: On the theoretical role of “genetic coding”. Philos. Sci. 67, 26–44 (2000)MathSciNetCrossRefGoogle Scholar
  92. 92.
    Griffiths, P.E.: Genetic information: a metaphor in search of a theory. Philos. Sci. 68(3), 394–412 (2001)CrossRefGoogle Scholar
  93. 93.
    Bradbury, J.W., Vehrencamp, S.L.: Economic models of animal communication. Anim. Behav. 59(2), 259–268 (2000)CrossRefGoogle Scholar
  94. 94.
    Hartley, R.V.: Transmission of information. ATT Tech. J. 7, 535–563 (1928)Google Scholar
  95. 95.
    Baddeley, R., Hancock, P., et al. (eds.): Information Theory and the Brain. Cambridge University Press, Cambridge (2000)Google Scholar
  96. 96.
    Grice, P.: Meaning. Philos. Rev. 66, 377–88 (1957)CrossRefGoogle Scholar
  97. 97.
    Struhsaker, T.T.: Auditory communication among vervet monkeys (Cercopithecus aethiops). In: Altmann, S.A. (ed.) Social Communication Among Primates, pp. 281–324. University of Chicago Press, Chicago (1967)Google Scholar
  98. 98.
    Seyfarth, R.M., Cheney, D.L.: Meaning and mind in monkeys. Sci. Am. 267, 122–129 (1992)CrossRefGoogle Scholar
  99. 99.
    Scarantino, A.: Shell games, information, and counterfactuals. Australas. J. Philos. 86(4), 629–634 (2008)CrossRefGoogle Scholar
  100. 100.
    Fisher, R.: The Design of Experiments. Oliver and Boyd, Edinburgh (1935)Google Scholar
  101. 101.
    Li, M., Vitányi, P.: An Introduction to Kolmogorov Complexity and its Applications, 2nd edn. Springer, New York (1997)MATHGoogle Scholar
  102. 102.
    Winograd, S., Cowan, J.D.: Reliable Computation in the Presence of Noise. MIT Press, Cambridge (1963)MATHGoogle Scholar
  103. 103.
    Margolis, E., Laurence, S. (eds.): Concepts: Core Readings. MIT Press, Cambridge (1999)Google Scholar
  104. 104.
    Quine, W.V.O.: Word and Object. MIT Press, Cambridge (1960)MATHGoogle Scholar
  105. 105.
    Stich, S.: From Folk Psychology to Cognitive Science. MIT Press, Cambridge (1983)Google Scholar
  106. 106.
    Egan, F.: A modest role for content. Stud. Hist. Philos. Sci. (2010, in press)Google Scholar
  107. 107.
    Rupert, R.D.: Causal theories of mental content. Philos. Comp. 3(2), 353–380 (2008)MathSciNetCrossRefGoogle Scholar
  108. 108.
    Dretske, F.: Explaining Behaviour. Bradford Press, Cambridge (1988)Google Scholar
  109. 109.
    Barwise, J., Seligman, J.: Information Flow: The Logic of Distributed Systems. Cambridge University Press, Cambridge (1997)MATHCrossRefGoogle Scholar
  110. 110.
    Fodor, J.: A Theory of Content and Other Essays. MIT Press, Cambridge (1990)Google Scholar
  111. 111.
    Millikan, R.G.: Language, Thought, and Other Biological Categories. MIT Press, Cambridge (1984)Google Scholar
  112. 112.
    Harman, G.: (Nonsolipsistic) conceptual role semantics. In: LePore, E. (ed.) New Directions in Semantics, pp. 55–81. Academic Press, London (1987)Google Scholar
  113. 113.
    Papineau, D.: Reality and Representation. Blackwell, Oxford (1987)Google Scholar
  114. 114.
    Grush, R.: The emulation theory of representation: motor control, imagery, and perception. Behav. Brain Sci. 27(3), 377–442 (2004)Google Scholar
  115. 115.
    Ryder, D.: SINBAD neurosemantics: a theory of mental representation. Mind Lang. 19(2), 211–240 (2004)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Department of Philosophy, Center for Neurodynamics, and Department of PsychologyUniversity of Missouri – St. LouisSt. LouisUSA
  2. 2.Department of Philosophy and Neuroscience InstituteGeorgia State UniversityAtlantaUSA

Personalised recommendations