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Information processing and dynamics in minimally cognitive agents.

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This study compares information processing and dynamical systems theories in cognitive science. By analyzing an evolved agent, it reveals how information flows and interrelationships contribute to categorization decisions.

Keywords:
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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Ongoing debate in cognitive science regarding information processing vs. dynamical systems approaches.
  • Need for a unified framework to bridge these explanatory styles.

Purpose of the Study:

  • To explore the relationship between information processing and dynamical approaches to cognition.
  • To analyze a model agent's cognitive processes using both theoretical frameworks.
  • To propose a framework for integrating these explanations.

Main Methods:

  • Utilized an evolved agent tasked with relational categorization.
  • Applied information theory to analyze information flow and combination.
  • Employed dynamical systems theory to examine geometrical and temporal interrelationships.

Main Results:

  • Information-theoretic analysis elucidated how task-relevant information is processed for decisions.
  • Dynamical analysis identified key spatio-temporal dynamics underlying categorization.
  • Demonstrated a method to connect information flow with system dynamics.

Conclusions:

  • Information processing and dynamical systems offer complementary insights into cognitive processes.
  • The proposed framework facilitates a more integrated understanding of cognition.
  • Findings contribute to resolving long-standing debates in cognitive science.