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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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A quantitative evolutionary theory of adaptive behavior dynamics.

J J McDowell1

  • 1Emory University.

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|November 14, 2013
PubMed
Summary
This summary is machine-generated.

Behavioral selection mirrors organic evolution. A genetic algorithm models this, generating behaviors matching matching theory equations across species and environments.

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Area of Science:

  • Behavioral science
  • Evolutionary biology
  • Computational neuroscience

Background:

  • The concept of behavior selection by consequences has been explored for over a century.
  • Behavioral principles are known to describe diverse vertebrate species, including humans.

Purpose of the Study:

  • To instantiate the idea of behavior selection via consequences using a genetic algorithm.
  • To test if this evolutionary model can reproduce established behavioral laws.

Main Methods:

  • A genetic algorithm was employed, representing behaviors as phenotypic (decimal) and genotypic (binary bit strings).
  • The algorithm simulated selection based on behavioral consequences, reproduction via recombination, and mutation.
  • Behavioral populations were evolved to equilibrium states.

Main Results:

  • The generated behaviors precisely matched all empirically valid equations of matching theory.
  • The model accurately predicted instantaneous dynamics and preference changes in fluctuating environments.
  • Equilibrium states conform to matching theory without systematic error.

Conclusions:

  • The evolutionary theory of behavior provides a powerful framework for understanding behavioral dynamics.
  • This model supports the assertion that observed behavior arises from evolutionary dynamics.
  • Findings are consistent across various species and environmental contexts.