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Subsymmetries predict auditory and visual pattern complexity.

Godfried T Toussaint1, Juan F Beltran2

  • 1Faculty of Science, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates. gt42@nyu.edu

Perception
|February 6, 2014
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Summary

A new mathematical measure of pattern complexity, based on subsymmetries, accurately predicts cognitive complexity in both visual and auditory domains. This finding suggests a universal principle underlying pattern perception and production.

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Area of Science:

  • Cognitive Science
  • Music Cognition
  • Pattern Recognition

Background:

  • A mathematical measure of pattern complexity, derived from subsymmetries, has shown high correlation with visual cognitive complexity.
  • Understanding complexity in auditory patterns is crucial for fields like music cognition and speech perception.

Purpose of the Study:

  • To investigate the correlation between the subsymmetry-based complexity measure and empirical measures of auditory temporal and musical rhythmic pattern complexity.
  • To determine if the subsymmetry measure can predict the difficulty of reproducing auditory rhythms.

Main Methods:

  • Utilized a pre-existing mathematical measure of pattern complexity based on subsymmetries.
  • Collected empirical data on the perception and production of auditory temporal and musical rhythmic patterns.
  • Correlated the subsymmetry measure with empirical measures of reproduction difficulty.

Main Results:

  • The subsymmetry-based complexity measure significantly correlated with empirical measures of auditory pattern complexity.
  • The measure also correlated highly with the difficulty of reproducing auditory rhythms by tapping.
  • Both visual and auditory empirical complexity measures showed similar trends related to the number of subsymmetries.

Conclusions:

  • The subsymmetry measure of pattern complexity is a robust predictor across both visual and auditory domains.
  • This suggests a unified mathematical framework for understanding cognitive complexity in pattern processing.
  • The findings have implications for theories of perception, cognition, and potentially artificial intelligence pattern recognition.