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Related Concept Videos

Language01:16

Language

171
Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
171

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Local Compositional Complexity: How to Detect a Human-Readable Message.

Louis Mahon1

  • 1School of Informatics, Edinburgh University, Edinburgh EH8 9YL, UK.

Entropy (Basel, Switzerland)
|April 26, 2025
PubMed
Summary

This study introduces a computable definition for data complexity, focusing on its potential for communication. The new method quantizes structured data, distinguishing meaningful signals from noise across various domains.

Keywords:
complexityextraterrestrial communicationinformation theory

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Area of Science:

  • Information Theory
  • Data Science
  • Statistical Mechanics

Background:

  • Data complexity lacks a rigorous, computable definition in natural sciences.
  • Existing measures do not fully capture complexity related to data's communicative potential.
  • High complexity is associated with structured data (e.g., language, images), low complexity with uniform or random data.

Purpose of the Study:

  • To develop a general framework for measuring data complexity based on its shortest description.
  • To define complexity as the size of the structured portion of a data's shortest description.
  • To derive a computable definition for human communication using local compositionality.

Main Methods:

  • Decomposition of the shortest data description into structured and unstructured components.
  • Application of the framework to statistical mechanics for characterizing physical systems.
  • Derivation of a communication-oriented complexity measure based on local compositionality.

Main Results:

  • A general framework for quantifying data complexity is proposed.
  • The method successfully distinguishes meaningful signals from noise in auditory, visual, and text data.
  • Potential application in identifying messages within complex data, including potential extraterrestrial signals.

Conclusions:

  • The proposed framework offers a computable measure of data complexity, particularly for communicative data.
  • Local compositionality serves as a robust structural element for defining communication-oriented complexity.
  • This approach has broad applicability in signal processing, data analysis, and the search for extraterrestrial intelligence.