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Collective predictive coding hypothesis: symbol emergence as decentralized Bayesian inference.

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Summary
This summary is machine-generated.

This study introduces the collective predictive coding (CPC) hypothesis to model symbol emergence. It links environmental interaction, social meaning, and predictive coding for understanding language evolution and AI knowledge.

Keywords:
Bayesian inferenceemergent communicationlanguage evolutionmulti-agent systemspredictive codingprobabilistic generative modelssymbol emergence

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Area of Science:

  • Computational neuroscience
  • Cognitive science
  • Linguistics

Background:

  • Symbol emergence, particularly language, requires models of both individual learning and historical evolution.
  • Existing models often lack integration of physical interaction and social meaning in symbol systems.

Purpose of the Study:

  • Introduce and detail the collective predictive coding (CPC) hypothesis for symbol emergence.
  • Model the interdependence of internal representations and social semiotic interactions.
  • Explore connections to the free-energy principle and explain AI knowledge acquisition.

Main Methods:

  • Theoretical modeling based on predictive coding and probabilistic generative models.
  • Utilizing concepts from language games and decentralized Bayesian inference.
  • Connecting computational models to the free-energy principle.

Main Results:

  • The CPC hypothesis models symbol emergence through environmental interaction and social meaning sharing.
  • Symbol emergence can be viewed as decentralized Bayesian inference in multi-agent systems.
  • A novel explanation for large language models' world knowledge is proposed.

Conclusions:

  • The CPC hypothesis offers a unified framework for understanding symbol emergence.
  • Symbol emergence aligns with a society-wide free-energy principle.
  • This work opens avenues for cross-disciplinary research in AI and cognitive science.