Human Concept

Understanding how humans form and utilize concepts is a central challenge in cognitive science and artificial intelligence. Current research focuses on how large language models and artificial neural networks represent and reason with concepts, often employing methods to analyze feature attribution and develop concept-aware architectures. This work aims to bridge the gap between human cognition and machine learning, improving the interpretability and performance of AI systems while simultaneously advancing our understanding of human conceptualization. Ultimately, this research has implications for both the development of more robust and explainable AI and a deeper understanding of the human mind.