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Entropy 2010, 12(3), 528-553;

Information and Signs: The Language of Images

Faculty of Education and Arts, Research Academic, The University of Newcastle, NSW, Australia
Received: 30 November 2009 / Revised: 25 February 2010 / Accepted: 1 March 2010 / Published: 12 March 2010
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Since time immemorial, philosophers and scientists were searching for a “machine code” of the so-called Mentalese language capable of processing information at the pre-verbal, pre-expressive level. In this paper I suggest that human languages are only secondary to the system of primitive extra-linguistic signs which are hardwired in humans and serve as tools for understanding selves and others; and creating meanings for the multiplicity of experiences. The combinatorial semantics of the Mentalese may find its unorthodox expression in the semiotic system of Tarot images, the latter serving as the ”keys” to the encoded proto-mental information. The paper uses some works in systems theory by Erich Jantsch and Erwin Laszlo and relates Tarot images to the archetypes of the field of collective unconscious posited by Carl Jung. Our subconscious beliefs, hopes, fears and desires, of which we may be unaware at the subjective level, do have an objective compositional structure that may be laid down in front of our eyes in the format of pictorial semiotics representing the universe of affects, thoughts, and actions. Constructing imaginative narratives based on the expressive “language” of Tarot images enables us to anticipate possible consequences and consider a range of future options. The thesis advanced in this paper is also supported by the concept of informational universe of contemporary cosmology. View Full-Text
Keywords: anticipation; computational universe; ethics; evolution; information; logic of the included middle; memes; Peirce’s semiotics; Tarot images anticipation; computational universe; ethics; evolution; information; logic of the included middle; memes; Peirce’s semiotics; Tarot images

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Semetsky, I. Information and Signs: The Language of Images. Entropy 2010, 12, 528-553.

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