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Neural Dynamic Principles for an Intentional Embodied Agent.

Jan Tekülve1, Gregor Schöner1

  • 1Institute for Neural Computation, Ruhr-University Bochum.

Cognitive Science
|September 3, 2024
PubMed
Summary
This summary is machine-generated.

This study proposes a neural theory for embodied cognition, explaining how agents use knowledge to achieve goals. It models intentional states and dynamic neural principles for autonomous learning and action in embodied agents.

Keywords:
Autonomous agentsCognitive systemsDynamical systems modelingEmbodied cognitionIntentionalityNeural network modelingSituated cognition

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Artificial Intelligence

Background:

  • The classical question of intelligence concerns how situated embodied agents use knowledge to achieve goals.
  • A central challenge for a neural theory of embodied cognition is understanding how organisms achieve this with their nervous systems.

Purpose of the Study:

  • To structure the challenge of embodied cognition by borrowing from Searle's analysis of intentionality.
  • To postulate neural mechanisms for intentional states and their dynamic organization.

Main Methods:

  • Utilized Searle's analysis of intentionality (directions of fit, psychological modes).
  • Postulated neural activation patterns stabilized by neural interaction for intentional states.
  • Modeled dynamic instabilities for initiating/terminating intentional states and organizing sequences.
  • Developed a toy scenario with a robotic agent demonstrating learned color transformation rules.

Main Results:

  • Neural activation patterns stabilized by interaction instantiate intentional states.
  • Dynamic neural instabilities are key to initiating, terminating, and sequencing intentional states.
  • Beliefs are autonomously learned and activated in response to desired outcomes.

Conclusions:

  • The proposed neural dynamic principles provide a framework for understanding intentionality in embodied agents.
  • This model demonstrates how robotic agents can learn and achieve goals in an environment.
  • The study bridges concepts from philosophy of mind and computational neuroscience.