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The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
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Introduction to Cognitive Psychology01:20

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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
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Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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Functional Brain Systems: Limbic System01:15

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The limbic system, often called the "emotional brain," is a complex set of structures located deep within the brain. The intricate network of the limbic system supports a wide range of psychological functions, from emotional regulation to memory formation and sensory processing. This functional brain region encompasses specific parts of the diencephalon and the cerebrum, integrating the higher mental functions of the cerebral cortex with the primitive emotional responses of the deep brain...
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Reason and Intuition01:37

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Related Experiment Video

Updated: Feb 28, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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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
PubMed
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.