DOI: 10.4324/9780415249126-W047-2
Version: v2,  Published online: 2010
Retrieved July 07, 2020, from

References and further reading

  • Berkeley, G. (1709)‘An Essay towards a New Theory of Vision’, inThe Works of George Berkeley, Bishop of Cloyne, vol. 1, ed. A. A. Luce and T. E. Jessop, Edinburgh: Thomas Nelson, 9 vols,1948–57.

    (Referred to in §1.)

  • Biederman, I. (1995)‘Higher-Level Vision’, in S. M. Kosslyn and D. N. Osherson (eds) An Invitation to Cognitive Science, 2nd edn, vol. 2, Visual Cognition, Cambridge, MA: MIT Press.

    (A discussion of the decomposition approach to object recognition.)

  • Descartes, R. (1637) ‘Optics’, in The Philosophical Writings of Descartes, trans. J. Cottingham, R. Stoothoff and D. Murdoch, Cambridge: Cambridge University Press, 1985, vol. 1, 152–175.

    (Discourses 5 and 6 are particularly relevant.)

  • Descartes, R. (1641)‘Author’s Replies to the Sixth Set of Objections’, inThe Philosophical Writings of Descartes, trans. J. Cottingham, R. Stoothoff and D. Murdoch, Cambridge: Cambridge University Press,1984, vol. 2, esp. §9: 294–296.

    (Referred to in §1 – Descartes’ ‘intellectualist’ theory of vision.)

  • Fodor, J. A. and Pylyshyn, Z. (1981)‘How Direct is Visual Perception?: Some Reflections on Gibson’s “Ecological Approach”’, Cognition 9: 139–196.

    (A critical discussion of Gibson’s direct theory of perception. Includes detailed argument but no technicality.)

  • Gibson, J. (1979) The Ecological Approach to Visual Perception, Boston: Houghton Mifflin.

    (The most developed statement of Gibson’s theory of perception.)

  • Helmholtz, H. von (1950) Treatise on Physiological Optics, ed. J. Southall, New York: Dover, 3 vols.

    (Influential nineteenth-century account of perceptual processing as a species of inference.)

  • Hinton, G. E. (1992)‘How Neural Networks Learn from Experience’, Scientific American 267 (3): 144.

    (Includes a discussion of connectionist models of shape recognition.)

  • Kersten, D., Mamassian, P., and Yuille, A. (2004)‘Object Perception as Bayesian Inference’, Annual Review of Psychology 55: 271–304.

    (A general discussion of the Bayesian framework applied to object perception.)

  • Kersten, D. and Yuille, A. (2003)‘Bayesian Models of Object Perception’, Current Opinion in Neurobiology 13: 1–9.

    (A useful short introduction to Bayesian models of vision.)

  • Marr, D. (1982) Vision, New York: Freeman Press.

    (Somewhat technical, but includes a clear account of the rationale behind the computational approach to vision.)

  • Paragios, N., Chen, Y. and Faugeras, O. (2006) Handbook of Mathematical Models in Computer Vision, New York: Springer.

    (A comprehensive survey of recent work in computational vision. Very technical.)

  • Rao, R., Olshausen, B. and Lewicki, M. (2002) Probabilistic Models of the Brain, Cambridge, MA: MIT Press.

    (A survey of probabilistic models of perception and neural function, including Bayesian models.)

  • Rock, I. (1983) The Logic of Perception, Cambridge, MA: MIT Press.

    (An account of perceptual processing as a form of hypothesis formation and testing.)

  • Schwartz, R. (1994) Vision: Variations on Some Berkelian Themes, Oxford: Blackwell.

    (A useful discussion of historical work on the problems of vision. Also includes a chapter on Gibson’s theory.)

  • Ullman, S. (1979) The Interpretation of Visual Motion, Cambridge, MA: MIT Press.

    (A detailed analysis of the computations involved in visual motion perception. Cited in §4.)

Citing this article:
Egan, Frances and Nico Orlandi. Bibliography. Vision, 2010, doi:10.4324/9780415249126-W047-2. Routledge Encyclopedia of Philosophy, Taylor and Francis,
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