An attractor lattice model with latent emergent order

Area of Science:

• Computational neuroscience
• Theoretical physics
• Complex systems science

Background:

• The theory of primary mental models posits that conscious experience is an emergent macrostate of the neural substrate.
• Understanding intrinsic emergent order is crucial for explaining complex phenomena in various scientific domains.

Purpose of the Study:

• To develop and analyze a lattice model clarifying latent or intrinsic emergent order.
• To investigate the emergence of macroscopic order and its underlying latent structure.

Main Methods:

• Development of a lattice model where elements interact to reduce energy based on cluster relationships.
• Conducting simulations to observe emergent behavior and macroscopic order.
• Utilizing descriptive analyses, including singular value decomposition, to detect latent dimensions.

Main Results:

• Simulations demonstrated the emergence of macroscopic order with two attractors at a critical noise level.
• The emergent macrostate exhibited self-predictive capabilities.
• Descriptive analyses revealed degenerate state vectors and two latent dimensions characterizing the attractor states.

Conclusions:

• The lattice model successfully clarifies the concept of latent emergent order.
• The findings support the theory of primary mental models by demonstrating emergent order in a simulated system.
• The identified latent dimensions provide insights into the structure of emergent macrostates.