Cognitive Action Laws: The Case of Visual Features.

Area of Science:

• Computational Neuroscience
• Artificial Intelligence
• Machine Learning Theory

Background:

• Perceptual learning is crucial for intelligent agents interacting with complex environments.
• Existing models often lack a unified theoretical framework grounded in fundamental principles.
• Artificial neural networks (ANNs) offer a powerful paradigm for modeling learning but require theoretical underpinnings.

Purpose of the Study:

• To propose a novel theoretical framework for understanding perceptual learning.
• To integrate principles of natural laws into the study of ANNs and cognitive processes.
• To develop a new computational model for feature extraction in computer vision.

Main Methods:

• Formulating ANNs as systems with Lagrangian variables (time-dependent connections).
• Defining a 'cognitive action' functional to measure agent-environment interactions.
• Deriving fourth-order differential equations, termed Cognitive Action Laws (CAL), from a specific functional choice.
• Implementing an 'information overloading control' policy for system dynamics.

Main Results:

• The CAL framework exhibits a structure mirroring classic machine learning regularization.
• The stationarity condition in CAL corresponds to a global minimum, unlike in classical mechanics.
• Asymptotic learning conditions on weights can coexist with initialization under information overloading control.
• The theory was successfully applied to the problem of feature extraction in computer vision.

Conclusions:

• The proposed Cognitive Action Laws provide a principled, physics-inspired framework for perceptual learning in ANNs.
• This approach unifies aspects of machine learning regularization and system dynamics.
• The findings demonstrate the potential of CAL for advancing artificial intelligence, particularly in feature extraction and cognitive modeling.