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Related Experiment Videos

Chaotic itinerancy in coupled dynamical recognizers.

Takashi Ikegami1, Gentaro Morimoto

  • 1Department of General Systems Sciences, The Graduate School of Arts and Sciences, University of Tokyo, 3-8-1 Komaba, Tokyo 153-8902, Japan.

Chaos (Woodbury, N.Y.)
|August 30, 2003
PubMed
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Human interaction dynamics exhibit chaotic itinerancy due to fluctuating predictions from unstable learning models. This study proposes a simulation to explore this "hot prediction" phenomenon in cognitive interactions.

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Human Interaction Dynamics

Background:

  • Human interactions, such as conversational turn-taking, often display chaotic itinerancy.
  • This phenomenon is linked to the nonconvergent nature of learning dynamics and prediction fluctuations.

Purpose of the Study:

  • To investigate the origins of chaotic itinerancy in human interaction.
  • To explore the role of fluctuating predictions, termed "hot prediction," in cognitive dynamics.

Main Methods:

  • A simulation model, the coupled dynamical recognizer, was developed to study chaotic itinerancy.
  • The model explores the relationship between unstable prediction models and interaction dynamics.

Main Results:

Related Experiment Videos

  • The simulation demonstrates how fluctuating predictions from nonconvergent learning can lead to chaotic itinerancy.
  • Identified dynamic characteristics of cognitive interactions influenced by "hot prediction."

    • Conclusions:

    • Chaotic itinerancy in human interaction stems from unstable prediction fluctuations in learning dynamics.
    • The "hot prediction" concept offers a framework for understanding cognitive interaction complexities.