Mind as Space

Toward the Automatic Discovery of a Universal Human Semantic-affective Hyperspace – A Possible Subcognitive Foundation of a Computer Program Able to Pass the Turing Test
  • Chris Mckinstry


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.


Affective body consciousness corpus fitness test genetic programming geometric models Internet lexical decision lexical priming measurement Mindpixel Minimum Intelligent Signal Test proposition robot semantic subcognition tagging Turing Test World Wide Web 


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© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Chris Mckinstry
    • 1
  1. 1.Mindpixel Digital Mind Modeling Project  

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