Cognitive Transfer Learning: Leveraging Human-Like Learning Strategies for Machine Intelligence

Abstract

Transfer of Cognitive States Learning is a paradigm change in the field of artificial intelligence that improves the efficiency and flexibility of AI systems by taking inspiration from human-like learning mechanisms. In order to capitalise on the vast amount of knowledge that people have accumulated over time, this study investigates the incorporation of cognitive principles from human learning processes into machine learning algorithms. By imitating how people use their acquired abilities and knowledge to different tasks. The suggested method aims to improve machine intelligence’s capacity for generalisation across a variety of areas. We explore the theoretical underpinnings of cognitive transfer learning, clarifying the essential processes that promote the transfer and retention of knowledge. Furthermore, we showcase real-world applications and case studies that show how cognitive transfer learning can enhance machine learning models’ performance.

The findings point to a viable path for raising the bar for artificial intelligence, opening the door for more adaptable and versatile systems that can learn and use information on their own for a variety of purposes. By providing insights into the potential of cognitive transfer learning to completely transform the field of AI applications, this research adds to the continuing conversation about closing the gap between human and machine intelligence.

DOI - https://doi.org/10.65525/SVUP.9788199778009.2026.26-33