Cognitive Taskonomy

Cognitive taskonomy aims to map the relationships between different cognitive tasks, revealing their underlying similarities and differences. Current research focuses on leveraging machine learning, particularly transfer learning and multi-task learning frameworks, often employing architectures like masked autoencoders, mixture-of-experts models, and differentiable multi-task grouping methods, to analyze both behavioral and neuroimaging data (e.g., fMRI). This research is significant for advancing our understanding of cognitive processes and for improving the efficiency and performance of artificial intelligence systems, particularly in areas like brain-computer interfaces and personalized learning.