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Quantum-Tunnelling Oscillators for Cognitive Modelling and Neural Computation: Foundations, Machine-Vision
1Seymour Research Laboratories, Seymour, VIC 3660, Australia.
Entropy (Basel, Switzerland)
|June 26, 2026
Summary
This study introduces a quantum-tunnelling oscillator model to explain cognitive processes like perception and decision-making. It demonstrates how networked quantum agents can form a neural system that models complex human behaviors, offering a new framework for quantum cognition.
Area of Science:
- Cognitive Science
- Quantum Physics
- Computational Neuroscience
Background:
- Classical probability models struggle to explain complex cognitive phenomena.
- Quantum cognition theory offers alternative frameworks for understanding decision-making and perception.
- Neural network models are widely used but often lack a direct physical grounding.
Purpose of the Study:
- To present a novel quantum-tunnelling oscillator model as a universal dynamical engine.
- To apply this model to paradigmatic problems in quantum cognition: optical illusion perception and group decision-making.
- To bridge quantum cognition theory with neural network approaches for a physically grounded description of cognition.
Main Methods:
- Modeling individuals as quantum-mechanical agents with context-dependent choice transitions.
- Networking these quantum agents to form a quantum-cognitive neural system.
- Analyzing the system's ability to reproduce perceptual and collective phenomena.
Main Results:
- The quantum-tunnelling oscillator model successfully explains optical illusion perception and group decision-making.
- Networked quantum agents reproduce familiar collective and perceptual phenomena.
- The model accommodates counterintuitive processes that challenge classical cognitive models.
Conclusions:
- The proposed model provides a compact and physically grounded approach to understanding cognition.
- It offers a unified framework for describing how individuals and groups think, perceive, and decide.
- This work advances the integration of quantum mechanics principles into cognitive science and neural network research.
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