The Understanding Capacity and Information Dynamics in the Human Brain
1Virtual Structures Research, Inc., Potomac, MD 20854, USA.
Entropy (Basel, Switzerland)
|December 3, 2020
Summary
This study introduces a theory of neuronal processes for human understanding, emphasizing mental modeling. This cognitive mechanism allows learning and prediction beyond given information, crucial for novel situations.
Area of Science:
- Neuroscience
- Cognitive Science
- Artificial Intelligence
Background:
- Human understanding relies on complex neuronal processes.
- Current artificial intelligence often lacks true understanding, relying on machine learning.
- Bridging the gap between biological and artificial understanding is a key challenge.
Purpose of the Study:
- To propose a theory of neuronal mechanisms underlying human understanding.
- To define understanding as a product of mental modeling within the neuronal system.
- To explore how mental modeling extends cognitive reach beyond immediate information.
Main Methods:
- Theoretical framework development.
- Analysis of neuronal self-organization and emergent properties.
- Conceptual comparison with machine learning paradigms.
Main Results:
- Understanding is framed as information production via mental modeling.
- Mental modeling facilitates unique human learning and prediction capabilities.
- This theory offers a new perspective on cognition beyond "the information given".
Conclusions:
- The proposed theory offers a novel perspective on cognition and understanding.
- It highlights the role of self-organization and emergent properties in the brain.
- The theory provides a foundation for developing more advanced intelligent artifacts capable of machine understanding.
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