Abstract
A putative form of vagueness in perceptual presentation/representation is blur. From the standpoint of accuracy—in representationalism or intentionalism—blurred vision is like hallucinations, an instance of inaccuracy, of misrepresentation. Things are not the way they are perceived or represented as being. Alternatively, it has been argued that along with error, blur has its own distinctive phenomenological conditions of presentation, in a broader sense, and recognition. For authors such as Crane, Pace and Smith, blur challenges the transparency of visual representation and its content. An alternative view to misrepresentation or underrepresentation is that the phenomenological property of blur is a form of overrepresentation. I want to suggest a different approach.
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Notes
- 1.
Ibid., 49.
- 2.
Smith [1].
- 3.
- 4.
Allen [4].
- 5.
Zadeh’s distinction between opaque and transparent algorithms is, as is every distinction, too simple. This diversity is relevant to different types of situations and features, for instance, in cognitive approaches to the viewing and understanding of film images. Interestingly, the recognition capacity that is exercised in film viewing along with any acquired competence in the conventions and choices of authors, genres or styles is itself based on combinations of perceptual disabilities; see Thomson-Jones [9].
- 6.
Gibson [5].
- 7.
For a useful survey of more specific issues and situations see Gong et al. [6].
- 8.
Marr [7]. Some of the research also adopts assumptions from Gibson’s ecological approach. The notion of perceptual learning as a model of vision and cognition in general was effectively introduced by Helmholtz in the late nineteenth century; it was part of a project of naturalizing some of the tenets in Kant’s epistemology.
- 9.
References
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Pace, M. (2007). Blurred vision and the transparency of experience. Pacific Philosophical Quarterly, 88(3), 283–309.
Allen, K. (2013). Blur. Philosophical Studies, 162, 257–273.
Gibson, J. J. (1979). The ecological approach to visual perception. Boston: Houghton Mifflin.
Gong, S., McKenna, S. J., & Psarrou, A. (2000). Dynamic vision. London: Imperial College Press.
Marr, D. (1982). Vision. San Francisco: Freeman.
Bishop, C. M. (2006). Pattern recognition and machine learning. New York: Springer.
Thomson-Jones, K. (2010). Aesthetics & Film. New York: Continuum.
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Cat, J. (2017). Vague Pictures: Scientific Epistemology, Aesthetics and Pragmatics of Fuzziness; From Fuzzy Perception to Fuzzy Pictures. In: Fuzzy Pictures as Philosophical Problem and Scientific Practice. Studies in Fuzziness and Soft Computing, vol 348. Springer, Cham. https://doi.org/10.1007/978-3-319-47190-7_7
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