Journal of Computer Engineering & Information TechnologyISSN : 2324-9307

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Model human thinking by replicating the mind by applying a juxtaposition of freudian and jungian concepts to the dual process theory


Niklas Hageback

The Virtual Mind, Hong Kong

: J Comput Eng Inf Technol

Abstract


This paper proposes an approach to replicate human thinking through approximating the mind as it provides for less design issues given its relatively simplistic abstract concepts with regard to the complexities attempting to model the brain. Albeit recognizing the tenets of the Dual Process Theory, shortcomings of the amalgamation problem by bringing the associative and rule-based systems together has prevented the establishment of an integrated model. However, by re-introducing the notion of a mind with a conscious and an unconscious part and applying a juxtaposition of Freudian and Jungian concepts, consolidating the two systems into a unified framework becomes possible. While the century old theories of Freud and Jung might appear antiquated, recent findings in neuropsychoanalysis, is breathing new life in them by confirming the existence of an unconscious; playing an active part in decision-making and how perceptions are interpreted. To Freud, the unconscious stored perceptions repressed by the conscious and Jung theorized that the unconscious organized itself in innate patterns, archetypes that activates when an accumulation of repressed perceptions overextends and starts to influence conscious thought patterns. Empirical studies highlighted that activities in the unconscious can be tracked through figurative language. By extracting, categorizing, and statistically analyzing these, through a public media big data approach, it is possible to determine the interactions, how unconscious reasoning intermittently supersedes conscious reasoning and determines any dynamic constants between the two systems that allow establishing a rule-based mechanism to forecast thought patterns, which can be standardized and translated into machine-generated thinking.

Biography


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