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Updated: Jun 27, 2026

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

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Dialectics for Artificial Intelligence.

¹Adobe Research, Adobe Inc., 345 Park Avenue, San Jose, CA 95110-2704, USA.

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|June 26, 2026

Summary

This summary is machine-generated.

Artificial intelligence can discover human concepts by defining them as information objects. This algorithmic approach uses determination and excess information to enable concept revision, transmission, and alignment across agents.

Keywords:

Kolmogorov complexity algorithmic information theory compression concept formation concept transmission multi-agent alignment

Related Experiment Videos

Last Updated: Jun 27, 2026

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

Published on: September 26, 2025

Area of Science:

Artificial Intelligence
Cognitive Science
Information Theory

Background:

Human concepts are fluid and dynamic, posing challenges for AI.
Existing definitions of concepts lack structural revision capabilities.
A new framework is needed for AI to discover and manage concepts.

Purpose of the Study:

To define concepts algorithmically for AI discovery.
To develop a method for revising, comparing, and aligning concepts across agents.
To enable AI to discover concepts from raw experience without human supervision.

Main Methods:

Proposed an algorithmic information viewpoint defining concepts as information objects.
Introduced determination as a core constraint for concept reversibility.
Defined excess information to measure the naturalness of concept decomposition.
Formulated dialectics as an optimization dynamic for concept evolution.
Formalized concept transmission and multi-agent alignment using grounds.

Main Results:

Demonstrated that concepts can be defined as reversible information objects.
Quantified concept naturalness using excess information.
Showcased dialectics driving concept expansion, contraction, splitting, and merging.
Enabled low-cost concept transmission and alignment between agents.

Conclusions:

AI can discover and dynamically manage concepts from raw experience.
The proposed framework supports concept revision and inter-agent alignment.
This work provides a computational basis for understanding concept formation and communication.