Category Learning

Category learning research investigates how humans and machines acquire and utilize knowledge about categories, aiming to understand the underlying cognitive mechanisms and build more effective learning systems. Current research focuses on developing computational models that integrate attentional and memory factors, incorporating hierarchical structures and ecological priors to better mimic human-like learning, and leveraging techniques like semi-supervised learning and virtual categories to address data limitations. These advancements have implications for improving machine learning algorithms, particularly in low-shot and few-shot learning scenarios, and offer valuable insights into human cognitive development and learning processes.