Computational theories of learning

This research paper, authored by Disha (2025), introduces a novel theoretical framework titled Computational Theories of Learning, which seeks to unify cognitive science, artificial intelligence, and mathematical computation into a coherent understanding of how learning emerges, evolves, and self-optimizes within dynamic environments.

The paper explores the computational nature of cognition, proposing that learning is not merely a biological or algorithmic process but an evolving information system that minimizes entropy through structured adaptation. Drawing upon principles from machine learning, complexity theory, and quantum-inspired models of consciousness, the study presents a hierarchical approach to understanding learning systems — from data representation to self-referential abstraction.

The work also formulates new perspectives on:

• Learning as computation of uncertainty — where intelligence is viewed as a process that collapses informational possibilities into stable predictions.

• Algorithmic symmetry in neural and artificial systems — identifying deep mathematical parallels between human thought and computational models.

• Conscious generalization — suggesting that higher learning arises when a system develops internal representations capable of reprogramming its own learning process.

By bridging philosophy, computation, and the mathematics of cognition, Computational Theories of Learning opens a new direction in theoretical AI — envisioning learning not as code or cognition alone, but as a universal pattern of computational evolution.