Universal technology of language

Proceedings of ‏The 2nd World Conference on Social Sciences

Year: 2021

DOI: https://www.doi.org/10.33422/2nd.worldcss.2021.02.50

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Universal technology of language

Dan M. Mrejeru

 

ABSTRACT: 

This paper aims to demonstrate that in nature exist several fundamental mechanisms, which regulate the complexities. Such a regulatory process introduces two phases of complex systems evolution: the adaptability of undifferentiation and the efficiency of the differentiation that causes stability. The same two steps regulate neurogenesis by producing undifferentiation and differentiation. It transforms unstructured information into structured data. This paper discusses why the neurogenesis manifests distinctly in humans versus other animals and how this aspect makes an immense difference in achievement.

Further on, I demonstrate that the mechanism above complexity participated in generating a language-ready brain in humans that serves as a mental blueprint and textbook that can assemble language and factual technologies. The language-ready brain developed in-depth the processing of linearity that is specific to all life forms. The language is initially processed in mental mapping as a complex nonlinear outcome. Further on, such nonlinear data can be naturally reduced to linearity by decreasing dimensionality.
Every reduction causes loss of its initial variances that represents information. Every process of diversification produces a loss of the initial data. During most of the Paleolithic era, the brain used a prevalence of nonlinearly structured data (like olfactive, sensory, and emotional outcomes)—such type of data required nonlinear processing. The language development highlighted the linear transformation mechanism that also increased the diversification process, significantly extending the consciousness. Every consciousness is linear, but it became vastly developed in humans, paralleling the linear path of language and giving rise to linear technologies.

Keywords: codimensionality, covariance, correlation, context.