Causal relationship is no doubt one of the most important targets for scientific research as well as for philosophical study. But science and philosophy don’t have much in common in the search for causality. The reason is very simple. While in science the emphasis is usually put on how to detect a causal relationship of events, in philosophy it’s more important how to define causality. Some philosophers of science have attempted to bridge the gap. For example, SGS (Spirtes, Glymour, Scheines) attempted to give philosophical basis for statistical analysis of causality based on Bayesian Network. But in this attempt, they have almost abandoned developing traditional arguments of causality, i.e. turned away from providing a proper reductive definition of causality. So this is not exactly an attempt to fill in the gap. Another attempt is to take a pluralistic stance for the definition of causality. This can help in a way breaking the deadlock of reductionism in philosophy, but it has not been so successful in supplying the missing link with science, either in its two major forms, ontological or conceptual one. In this paper, I will consider the possibility of the third kind of pluralism of causality, which can be derived from Broadbent’s argument. His argument originally aimed to relieve the tension between two attitudes toward causality, which are generally found in scientific researches. However, I think we can see another possibility of this argument as a different kind of pluralism of causality, which will not only bridge the gap, but will also change the direction of reductionistic arguments without discarding pursuit of strict definition of causal items.

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The philosophical foundation of statistical methods has long been discussed among statisticians, scientists, and philosophers. While traditional accounts have assumed epistemological differences between Frequentist and subjective Bayesian methods, some recent practitioners are adopting the Bayesian method from different aspects. This paper aims to give a brief review of the epistemological justification behind Frequentist and subjective Bayesian methods and of how the prior distribution is chosen in the current practice of statistics. Then, I propose further examination .n the philosophical aspects of these activities.

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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.

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[in Japanese]

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[in Japanese]

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