Advertisement

Modularity, Schemas and Neurons: A Critique of Fodor

  • Michael A. Arbib
Part of the Australasian Studies in History and Philosophy of Science book series (AUST, volume 7)

Abstract

It is a standard notion that a complex system may be analyzed by being decomposed into a set of interacting subsystems. Such a decomposition succeeds insofar as we can understand the relation between the inputs and outputs of each individual subsystem, and insofar as the interactions between the subsystems can be explained via suitable connections between various of their inputs and outputs, without further analysis of variables internal to the subsystems. Suci a decomposition is structural to the extent that the subsystems can be mapped onto physical substructures of a physical structure embodying the overall system. In this section, I show that neuroscientists have long sought structural decompositions of the brain, and in some cases referred to the physical substructures as modules. Recently, Fodor has popularized the use of the term ‘module’ to denote a unit in a functional decomposition of a cognitive system, but a subsystem that meets constraints beyond those specified above. I shall argue that Fodor’s analysis of cognitive systems is flawed and that the restrictions he introduces are not useful. Consequently, I shall use the term ‘module’ as a synonym for the term ‘subsystem’ defined above.

Keywords

Depth Perception Input Process Optic Tectum Functional Decomposition Brain Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

  1. 1.
    M.E. Scheibel and A.B. Scheibel (1958) `Structural Substrates for Integrative Patterns in the Brain Stem Reticular Core’, Reticular Formation of the Brain, H.H. Jasper et al. (eds.), Little, Brown and Company, Boston, pp. 407–434.Google Scholar
  2. 2.
    W.L. Kilmer, W.S. McCulloch and J. Blum (1969) `A Model of the Vertebrate Central Command System’, International Journal of Man-Machine Studies I, pp. 279–309.Google Scholar
  3. 3.
    W.H. Pitts and W.S. McCulloch (1947) `How We Know Universals, the Perception of Auditory and Visual Forms’, Bulletin of Mathematical BiophysicsIX, pp. 127–147.CrossRefGoogle Scholar
  4. 4.
    J.Y. Lettvin, H. Maturana, W.S. McCulloch and W.H. Pitts (1959) `What the Frog’s Eye Tells the Frog’s Brain’, Proceedings of the Institute of Radio Engineers 1959 XLVII, pp. 1940–1959.Google Scholar
  5. 5.
    T.P.S. Powell and V.B. Mountcastle (1959) `Some Aspects of the Functional Organization of the Cortex of the Post Central Gyrus of the Monkey: A Correlation of Findings Obtained in a Single Unit Analysis with Cytoarchitecture’, Bulletin of Johns Hopkins Hospital CV, pp. 133–162.Google Scholar
  6. 6.
    D.H. Hubel and T.N. Wiesel (1974) `Sequence Regularity and Geometry of Orientation Columns in the Monkey Striate Cortex’, Journal of Comparative Neurology CLVIII, pp. 267–294.CrossRefGoogle Scholar
  7. 7.
    J. Szentâgothai and M.A. Arbib (1974) `Conceptual Models of Neural Organization’, Neuroscience Research Program BulletinXII, No. 3, pp. 310–479.Google Scholar
  8. 8.
    V.B. Mountcastle (1978) ‘An Organizing Principle for Cerebral Function: The Unit Module and the Distributed System’, The Mindful Brain, G.M. Edelman and V.B. Mountcastle, MIT Press, Cambridge, Mass.Google Scholar
  9. 9.
    J. Fodor (1983) Modularity of Mind, MIT, Cambridge, Mass., p. 13.Google Scholar
  10. 10.
    Ibid. p. 40.Google Scholar
  11. 11.
    Ibid. p. 21 and p. 37.Google Scholar
  12. 12.
    Ibid. p. 21.Google Scholar
  13. 13.
    Ibid. Section III. 5.Google Scholar
  14. 14.
    Ibid. p. 41.Google Scholar
  15. 10.
    Ibid. p. 42.Google Scholar
  16. 16.
    Ibid. p. 45.Google Scholar
  17. 17.
    Ibid. p. 101.Google Scholar
  18. 18.
    Ibid. p. 102.Google Scholar
  19. 19.
    Ibid. p. 103.Google Scholar
  20. 20.
    Ibid. p. 20.Google Scholar
  21. 21.
    M.A. Arbib (1972) The Metaphorical Brain: An Introduction to Cybernetics as Artificial Intelligence and Brain Theory, Wiley-interscience, New York.Google Scholar
  22. 22.
    M.A. Arbib (1972) The Metaphorical Brain: An Introduction to Cybernetics as Artificial Intelligence and Brain Theory, Wiley-interscience, New York.Google Scholar
  23. 23.
    M.A. Arbib, T. Iherall and D.M. Lyons (1985) `Coordinated Control Programs for Movements of the Hand’ Experimental Brain Research SupplementX, pp. 111–129.Google Scholar
  24. 24.
    M.A. Arbib, E.J. Conklin and J.C. Hill (1986) From Schema Theory to Language, Oxford University Press.Google Scholar
  25. 25.
    M.A. Arbib, C.C. Boylls and P. Dev (1974) `Neural Models of Spatial Perception and the Control of Movement’, Cybernetics and Bionics, W.D. Keidel, W. Handler and M. Spreng (eds.), Oldenbourg, Munich, pp. 216–231.Google Scholar
  26. 26.
    P. Dev (1975) `Perception of Depth Surfaces in Random-Dot Stereograms: A Neural Model’, International Journal of Man-Machine Studies VII, pp. 141–202.Google Scholar
  27. 27.
    S. Amari and M.A. Arbib (1977) `Competition and Cooperation in Neural Nets’, Sstems Neuroscience, J. Metzler (ed.), Academic Press, New York, pp. 119–165.Google Scholar
  28. 28.
    D. Marr and T. Poggio (1976) `Cooperative Computation of Stereo Disparity’, Science CXCIV, pp. 283–287.Google Scholar
  29. 29.
    D. Marr (1982) Vision: A Computational Investigation into the Human Representation and Processing of Visual Information, W.H. Freeman and Co.Google Scholar
  30. 30.
    D.H. House (1984) `Neural Models of Depth Perception in Frog and Toad; Ph. D. Dissertation, Department of Computer and Information Science, Technical Report 82–16, University of Massachusetts at Amherst.Google Scholar
  31. 31.
    See op. cit. (note 4).Google Scholar
  32. 32.
    H. Barlow (1953) `Summation and Inhibition in the Frog’s Retina’, Journal of Physiol-ogy CXIX, pp. 69–88.Google Scholar
  33. 33.
    J.P. Ewert (1976) `The Visual System of the Toad: Behavioral and Physiological Studies on a Pattern Recognition System’, The Amphibian Visual System: A Multidisciplinary Approach, K. Fite (ed.), Academic Press, New York, pp. 141–202.Google Scholar
  34. 34.
    J.P. Ewert and W. von Seelen (1974) `Neurobiologie und System-Theorie eines visuellen Muster-Erkennungsmechanismus bei Kröten’, Kybernetic, XIV, pp. 167–183.Google Scholar
  35. 35.
    F. Cervantes-Perez, R. Lara and M.A. Arbib (1985) ‘A Neural Model of Interactions Subserving Prey-Predator Discrimination and Size Preference in Anurans’, Journal of Theoretical Biology CXIII, pp. 117–152.Google Scholar
  36. 36.
    R. Lara, M.A. Arbib and A.S. Cromarty (1982) ‘The Role of the Tectal Column in Facilitation of Amphibian Prey-Catching Behavior: A Neural Model’, Journal of Neuroscience II, pp. 521–530.Google Scholar
  37. 37.
    G. Székely and G. Lâzâr (1976) ‘Cellular and Synaptic Architecture of the Optic Tectum’, Frog Neurobiology, R. Llinas and W. Precht (eds.), Springer-Verlag, pp. 407–434.Google Scholar
  38. 38.
    See op. cit. (note 35), See also M.A. Arbib (1981) Perceptual Structures and Distributed Motor Control’, Handbook of Physiology - The Nervous System. Il. Motor Control. V.B. Brooks (ed.) American Physiological Society, pp. 1449–1480.Google Scholar
  39. 39.
    M.A. Arbib (1987) ‘Levels of Modelling of Neural Mechanism Underlying Visuomotor Coordination’, Behavioural and Brain Sciences, X, pp. 407–465.CrossRefGoogle Scholar
  40. 40.
    See op. cit. (note 39).Google Scholar
  41. 41.
    E.M. Riseman and A.R. Hanson (1987) ‘A Methodology for the Development of General Knowledge-Based Systems’, Vision, Brain and Cooperative Computation, M.A. Arbib and A.R. Hanson (eds.), MIT, Mass., pp. 285–328.Google Scholar
  42. 42.
    See op. cit. (note 9), p. 104.Google Scholar
  43. 43.
    Ibid. p. 104.Google Scholar
  44. 44.
    Ibid. pp. 107–8.Google Scholar
  45. 45.
    Ibid. pp. 110–111.Google Scholar
  46. 46.
    Ibid. p. 112.Google Scholar
  47. 47.
    Ibid. pp. 94–5.Google Scholar
  48. 48.
    Ibid. p. 103; p. 139 note 43.Google Scholar
  49. 49.
    Ibid. p. 137.Google Scholar

Copyright information

© Kluwer Academic Publishers 1989

Authors and Affiliations

  • Michael A. Arbib
    • 1
  1. 1.Departments of Computer Science and NeurobiologyUniversity of Southern CaliforniaLos AngelesUSA

Personalised recommendations