Mind as Space

Abstract

The present article describes a possible method for the automatic discovery of a universal human semantic-affective hyperspatial approximation of the human subcognitive substrate – the associative network which French (1990) asserts is the ultimate foundation of the human ability to pass the Turing Test – that does not require a machine to have direct human experience or a physical human body. This method involves automatic programming – such as Koza’s genetic programming (1992) – guided in the discovery of the proposed universal hypergeometry by feedback from a Minimum Intelligent Signal Test or MIST (McKinstry, 1997) constructed from a very large number of human validated probabilistic propositions collected from a large population of Internet users. It will be argued that though a lifetime of human experience is required to pass a rigorous Turing Test, a probabilistic propositional approximation of this experience can be constructed via public participation on the Internet, and then used as a fitness function to direct the artificial evolution of a universal hypergeometry capable of classifying arbitrary propositions. A model of this hypergeometry will be presented; it predicts Miller’s “Magical Number Seven” (1956) as the size of human short-term memory from fundamental hypergeometric properties. A system that can lead to the generation of novel propositions or “artificial thoughts” will also be described.