Comparing the complexity of written and molecular symbolic systems

Area of Science:

• Biosemiotics
• Information Theory
• Linguistics
• Biochemistry

Background:

• Symbolic systems (SSs) are integral to life, with symbol complexity influencing functional optimization.
• Understanding how SSs sample latent symbol space is crucial for fields like biochemistry and linguistics.
• Existing research lacks quantitative comparisons of symbol complexity across diverse biosemiotic systems.

Purpose of the Study:

• To quantitatively explore and compare the graphic complexity of two distinct biosemiotic systems: genetically encoded amino acids (GEAAs) and written language.
• To investigate the relationship between molecular/graphical complexity and symbol set distribution within these systems.
• To identify universal selection pressures that may govern optimal symbol set complexity.

Main Methods:

• Quantitative analysis of graphic complexity for GEAAs and written language.
• Comparison of symbol complexity distributions relative to their respective latent symbol spaces.
• Correlation analysis between molecular and graphical complexity metrics.

Main Results:

• Molecular and graphical complexity measures are highly correlated for both GEAAs and written language.
• Symbol sets do not exhibit minimal or maximal complexity but fall within an objectively definable distribution.
• Genetically encoded amino acids demonstrate notably low complexity compared to their latent space.

Conclusions:

• Selection pressures related to symbol production and disambiguation efficiency explain observed complexity distributions.
• These pressures may be universal, providing a quantifiable metric for comparing SSs.
• Optimal symbol set complexity distribution, rather than individual symbol complexity, may be a more significant biomarker for life across the universe.