Axiomatization of the Background Knowledge in Bayesian Theory of Perception

Abstract

Recently, Bayesian theories of human object perception are widely studied. Helmholtz’s idea of perception as unconscious inference is formalized by Bayes’ theorem. Human object perception is now called Bayesian inference or statistical inference, and to obtain a Bayesian quantitative model of human object perception has now become a primary goal for the consciousness scientists utilizing Helmholtz’s idea. An adequate Bayesian theory of perception seems to require the axiomatization of the so-called “background knowledge”. This paper argues the axiomatization. We deal with the problem of the ambiguity of the Necker cube as a special case. The difficulty of the duplication or modeling of human object perception arises partly because of the problem of the so-called ambiguity of an image. The probabilistic account of the ambiguity of an image is wanting in recent constructivist approaches to the duplication of human object perception. In this paper, a reframing of the problem of the ambiguity of the Necker cube is proposed and a deficiency of the prevailing statement of the problem that the underlying task of the theory of visual processes is to derive properties of the three-dimensional world from two-dimensional images of it is pointed out. A linguistic definition of sense modalities and a language setup are proposed as the basis of the axiomatization of background knowledge in Bayesian theory of perception.

Journal

• Linkage: Studies in Applied Philosophy of Science

  Linkage: Studies in Applied Philosophy of Science 1 (0), 15-25, 2021

  Hokkaido University Graduate School of Science Department of Philosophy of Science

Details

• CRID: 1390850501238718464
• NII Article ID: 130008021131
• DOI: 10.50824/linkage.1.0_15
• ISSN: 24359084
• Text Lang: en
• Data Source:
  • JaLC
  • CiNii Articles
• Abstract License Flag: Disallowed