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Related Experiment Video

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Understanding the Brain as a Mentalizing Machine with a Symbol Grounding Function.

Takaaki Kaneko1,2, Taihei Ninomiya3,2, Masaki Isoda1,2

  • 1Division of Behavioral Development, Department of System Neuroscience, National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki, Aichi 444-8585, Japan kanekot@nips.ac.jp isodam@nips.ac.jp.

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
|February 25, 2026
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Summary
This summary is machine-generated.

This review proposes computational mechanisms for mentalizing, the ability to infer others' mental states. Symbol grounding enhances this process, improving predictions of others' actions.

Keywords:
artificial neural networkmentalizingpredictive codingsocial cognitionsymbol grounding

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Artificial Intelligence

Background:

  • Mentalizing, the inference of others' hidden mental states from sensory information, is crucial for social cognition.
  • Predictive coding frameworks explain how the brain infers hidden states by minimizing prediction errors.

Purpose of the Study:

  • To propose computational mechanisms underlying mentalizing.
  • To review the neural substrates involved in these computational processes.
  • To explore the role of symbol grounding in enhancing mentalizing efficiency and reliability.

Main Methods:

  • Review of existing literature on mentalizing and predictive coding.
  • Theoretical proposal of computational mechanisms for mentalizing.
  • Preliminary simulation of an artificial mentalizing system with and without symbol grounding.

Main Results:

  • Mentalizing may uniquely involve representing others' states in a subjectively experienceable mental space, solving the symbol grounding problem.
  • Symbol grounding improves the efficiency and reliability of inferring others' mental states.
  • Simulated artificial mentalizing system with symbol grounding outperformed a pure Bayesian observer in predicting virtual agent actions.

Conclusions:

  • The computational framework of predictive coding, augmented by symbol grounding, offers a promising model for understanding mentalizing.
  • Future research integrating artificial and biological neural networks can elucidate the neural algorithms of social cognition.