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Indispensability of Computational Modeling in Cognitive Science

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The concept of computation remains a frequently discussed topic in cognitive science, but there is no consensus about its meaning and the role in this field. I discuss this concept in wider sense, also including nonclassical computation, in the light of Marr’s three levels of analysis and their relevance for main modeling frameworks pursued in cognitive science – symbolic, connectionist, dynamic and probabilistic. I point to differences between these approaches and argue, providing empirical and theoretical arguments, that connectionism, out of the existing approaches, holds the promise of providing the most plausible and detailed accounts of human cognition. Connectionism also benefits from the emerging field of cognitive developmental robotics that aims at designing autonomous cognitive robots using the synthetic bottom-up approach. I conclude with emphasizing the key role of computational modeling that will help advance the field of computational cognitive science as an indispensable core component.

Abstract

The concept of computation remains a frequently discussed topic in cognitive science, but there is no consensus about its meaning and the role in this field. I discuss this concept in wider sense, also including nonclassical computation, in the light of Marr’s three levels of analysis and their relevance for main modeling frameworks pursued in cognitive science – symbolic, connectionist, dynamic and probabilistic. I point to differences between these approaches and argue, providing empirical and theoretical arguments, that connectionism, out of the existing approaches, holds the promise of providing the most plausible and detailed accounts of human cognition. Connectionism also benefits from the emerging field of cognitive developmental robotics that aims at designing autonomous cognitive robots using the synthetic bottom-up approach. I conclude with emphasizing the key role of computational modeling that will help advance the field of computational cognitive science as an indispensable core component.

참고문헌 (81)

  1. Addyman, C., "Computational modeling in cognitive Science : A Manifesto for change", Topics in Cognitive Science, 4, pp.332~341 (2012)
  2. Asada, M., "Cognitive developmental Robotics: A Survey", IEEE Transactions on Autonomous Mental Development, 1(1), pp.12~34 (2009)
  3. Barsalou, L., "Grounded cognition", Annual Reviews of Psychology, 59, pp.617~645 (2008)
  4. beim Graben, P., "Incompatible implementations of Physical Symbol Systems", Mind and Matter, 2(2), pp.29~51 (2004)
  5. Bell, A., "Levels and Loops: The Future of Artificial Intelligence and neuroscience", Philosophical Transactions of the Royal Society London B, 354, pp.2013~2020 (1999)
  6. Bengio, Y., "Learning Deep Architectures for AI", Foundations and Trends in Machine Learning, 2(1), pp.11~27 (2009)
  7. Box, G. E. P., "Empirical Model Building and Response Surfaces" (1987), John Wiley & Sons
  8. Elisabetta Ježek, "Nominals, Polysemy, and Co-predication", Journal of Cognitive Science, 12(1), pp.1~31 (2011), 인지과학연구소
  9. Clark, A., "Mindware: An Introduction to the Philosophy of Cognitive Science" (2001), Oxford University Press
  10. Christiansen, M., "Toward a Connectionist Model of recursion in Human Linguistic Performance", Cognitive Science, 23, pp.157~205 (1999)
  11. Christiansen, M., "Connectionist Psycholinguistics" (2001), Ablex Publishing
  12. Cooper, S. B., "Turing’s Titanic machine?", Communications of the ACM, 55(3), pp.74~83 (2012)
  13. Craenen, B., "Computational intelligence, In: Encyclopedia of Life Support Sciences" (2003), EOLSS Publishers Co
  14. Chomsky, N., "Aspects of the Theory of Syntax" (1965), MIT Press
  15. Churchland, P., "The Computational Brain" (1992), MIT Press
  16. "Cognitive modeling greatest hits" (2012)[URL]
  17. Davies, J., "Semantic Web Technologies: Trends and Research in Ontology based Systems" (2006), John Wiley & Sons
  18. Doya, K., "What are the computations of the cerebellum, the Basal Ganglia, and the Cerebral Cortex?", Neural Networks, 12, pp.961~974 (1999)
  19. Duch, W., "Towards comprehensive foundations of computational intelligence", Lecture Notes in Computer Science, 63, pp.261~316 (2007), Springer
  20. Elman, J., "Finding structure in time", Cognitive Science, 14, pp.179~211 (1990)
  21. Engelbrecht, A., "Computational Intelligence: An Introduction (2nd ed.)" (2007), John Willey & Sons
  22. Farkaš, I., "Syntactic systematicity in Sentence Processing with a Recurrent Selforganizing Network", Neurocomputing, 71, pp.1172~1179 (2008)
  23. Feldman, J., "The Neural Binding Problem(s)", Cognitive Neurodynamics (2012)[DOI]
  24. Fodor, J., "Connectionism and cognitive Architecture : A Critical Analysis", Cognition, 28, pp.3~71 (1988)
  25. Fodor, J., "The Mind Doesn’t Work That Way" (2000), MIT Press
  26. Frank, S., "Connectionist Semantic Systematicity", Cognition, 110, pp.358~379 (2009)
  27. French, R., "Catastrophic forgetting in Connectionist Networks", Trends in Cognitive Science, 3(4), pp.128~135 (1999)
  28. French, R., "Dusting off the Turing test", Science, 336(6078), pp.164~165 (2012)
  29. Gardner, H., "The Minds New Science: A History of the Cognitive Revolution" (1987), Basic Books
  30. Gärdenfors, P., "Conceptual spaces as a basis for Cognitive Semantics, In Philosophy and Cognitive Science", pp.159~180 (1996), Kluwer
  31. Gallese, V., "Mirror Neuron Forum", Perspectives on Psychological Science, 6(4), pp.369~407 (2011)
  32. Gentner, D., "Psychology in Cognitive Science: 1978-2038", Topics in Cognitive Science, 2, pp.328~344 (1978)
  33. Greco, A., "Connectionist models can Reveal Good Analogies", Psycoloquy, 9(47) (1998)
  34. Greco, A., "Compositional Symbol Grounding for Motor Patterns", Frontiers in Neurorobotics, 4, pp.111~ (2010)[DOI]
  35. Griffiths, T., "Probabilistic models of Cognition: Exploring Representations and Inductive Biases", Topics in Cognitive Science, 14, pp.357~364 (2010)
  36. Harnad, S., "The Symbol Grounding Problem", Physica D, 42, pp.335~346 (1990)
  37. Hornik, K., "Multilayer Feedforward Networks are Universal Approximators", Neural Networks, 2(5), pp.359~366 (1989)
  38. Kaczmarek, L., "Neuromodulation: The Biochemical Control of Neuronal Excitability" (1987), Oxford University Press
  39. Ma, W. J., "Bayesian inference with Probabilistic Population Codes", Nature Neuroscience, 9(11), pp.1432~1438 (2006)
  40. Maass, W., "Pulsed Neural Networks" (1999), MIT Press
  41. MacKay, D. J. C., "Probable networks and Plausible Predictions – A Review of Practical Bayesian Methods for Supervised Neural Networks", Network : Computation in Neural Systems, 6, pp.469~505 (1995)
  42. Marr, D., "Vision" (1982), W.H. Freeman
  43. Maturana, H., "The Tree of Knowledge: The Biological Roots of Human Understanding (Rev. ed.)" (1992), Shambhala
  44. McClelland, J., "The place of modeling in Cognitive Science", Topics in Cognitive Science, 1(1), pp.11~38 (2009)
  45. "Cognitive Modeling Repository. Columbus" (2010)[URL]
  46. Newell, A., "Computer science as Empirical Inquiry: Symbols and search", Communications of the ACM, 19(3), pp.113~126 (1976)
  47. O’Brien, G., "How do Connectionist Networks Compute?", Cognitive Processing, 7, pp.30~41 (2006)
  48. O’Reilly, R., "Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain" (2000), MIT Press
  49. Page, M., "Connectionist modelling in Psychology: A Localist Manifesto", Behavioral and Brain Sciences, 23(4), pp.443~467 (2000)
  50. Perfors, A., "A Tutorial Introduction to Bayesian models of Cognitive Development", Cognition, 120, pp.302~321 (2011)
  51. Pfeifer, R., "Understanding Intelligence" (1999), MIT Press
  52. Piccinini, G., "Computing mechanisms", Philosophy of Science, 74, pp.501~526 (2007)
  53. Piccinini, G., "Some Neural Networks Compute, Others don’t", Neural Networks, 21(23), pp.311~321 (2008)
  54. Piccinini, G, "Information Processing, Computation, and cognition", Journal of Biological Physics, 37, pp.1~38 (2011)
  55. Pinker, S., "How the Mind Works" (2009), W.W. Norton and Co
  56. Pinker, S., "The Past Tense Debate", Trends in Cognitive Sciences, 6(11), pp.456~474 (2002)
  57. Port, R., "Mind as Motion: Explorations in the Dynamics of Cognition" (1995), MIT Press
  58. Prinz, W., "Modes of linkage between perception and action, In Cognition and Motor Processes", pp.185~193 (1984), Springer
  59. Pylyshyn, Z., "Computation and Cognition: Toward a Foundation for Cognitive Science" (1984), MIT Press
  60. Rieke, F., "Spikes: Exploring the Neural Code" (1999), MIT Press
  61. J. Brendan Ritchie, "Chalmers on Implementation and Computational Sufficiency", Journal of Cognitive Science, 12(4), pp.407~425 (2011), 인지과학연구소
  62. Rosenblatt F., "Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms" (1962), Spartan Books
  63. Russell, S., "Artificial Intelligence: A Modern Approach (3rd ed.)" (2009), Prentice Hall
  64. Scheutz, M., "When Physical Systems Realize Functions...", Minds and Machines, 9, pp.161~196 (1999)
  65. Schoener, G., "Dynamical Systems Approaches to cognition, In Cambridge Handbook of Computational Psychology", pp.101~126 (2008), Cambridge University Press
  66. Siegelmann, H., "Turing computability with Neural Nets", Applied Mathematics Letters, 4(6), pp.77~80 (1991)
  67. Siegelmann, H., "Neural networks and Analog Computation: Beyond the Turing limit" (1999), Birkhauser
  68. Sloman, A., "The irrelevance of Turing machines to Artificial Intelligence, In Computationalism: New Directions", pp.87~128 (2002), MIT Press
  69. Spivey, M., "The Continuity of Mind" (2007), Oxford University Press
  70. Sporns, O., "Networks of the Brain" (2011), MIT Press
  71. Sprevak, M., "Computation and Cognitive Science", Studies in History and Philosophy of Science, 41, pp.223~334 (2010)
  72. Tabor, W., "A dynamical systems perspective on the relationship between symbolic and Nonsymbolic Computation", Cognitive Neurodynamics, 3(4), pp.415~427 (2009)
  73. Takáč, M., "Mapping sensorimotor sequences to word sequences : A connectionist model of language acquisition and Sentence Generation", Cognition, 125(2), pp.288~308 (2012)
  74. Thorisson, K. R., "Cognitive Architectures and Autonomy : A Comparative Review", Journal of Artificial General Intelligence, 3(2), pp.1~30 (2012)
  75. Tschacher, W., "The Dynamical Systems Approach To Cognition: Concepts and Empirical Paradigms Based on SelfOrganization, Embodiment, and Coordination Dynamics" (2003), World Scientific
  76. Turing, A., "On computable numbers, with an application to the Entscheidungs problem", Proceedings of the London Mathematical Society, 2(42), pp.230~265 (1936)
  77. Turing, A., "Computing machinery and intelligence", Mind, 59, pp.33~460 (1950)
  78. Van Gelder, T., "Compositionality: A connectionist variation on a classical theme", Cognitive Science, 14, pp.355~384 (1990)
  79. Varela, F., "The Embodied Mind: Cognitive Science and Human Experience" (1991), MIT Press
  80. Vernon, D., "A survey of Artificial Cognitive Systems: Implications for the autonomous development of mental capabilities in computational agents", IEEE Transactions on Evolutionary Computation, 11(2), pp.151~180 (2007)
  81. Weng, J., "Symbolic models and emergent models", IEEE Transactions on Mental Development, 4(1), pp.29~54 (2012)

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