John Anderson

John Anderson is R.K. Mellon University Professor of Psychology and Computer Science at Carnegie Mellon University, jointly appointed in the Department of Psychology and School of Computer Science. He directs the Anderson Lab focused on cognitive architectures and unified theories of cognition through the ACT-R framework. His research integrates computational modeling with neuroimaging to understand knowledge organization, problem-solving, and skill acquisition.

Anderson's research examines how humans organize knowledge to produce intelligent behavior, with emphasis on mathematical learning, memory systems, and cognitive skill development. His work combines behavioral experiments, computational modeling (ACT-R), and neural imaging techniques (fMRI, EEG, MEG) to identify cognitive processes and their neural correlates. Recent investigations focus on discovering hidden cognitive stages through brain activation patterns and developing methods to map cognitive models to neural data.

His publication trends show consistent focus on: 1) Developing methodologies to link cognitive models with neuroimaging data, 2) Analyzing stages of skill acquisition and problem solving, 3) Investigating the neural basis of mathematical cognition, and 4) Refining the ACT-R cognitive architecture. Recent work emphasizes temporal dynamics of cognition and educational applications of cognitive modeling.

Scientific Awards:

• Heineken Prize for Cognitive Science
• Franklin Award in Computer and Cognitive Science

Anderson advises graduate students including Qiong Zhang (PhD candidate), and leads a research team comprising postdoctoral fellows and research scientists. The lab has secured funding from NSF and other agencies for projects on mathematical learning, cognitive architectures, and neuroimaging methodologies.

The Anderson Lab collaborates with researchers in psychology, computer science, and neuroscience to develop cognitive models that simulate human performance across diverse domains. Current projects include modeling inventive mathematical thinking and integrating deep learning with cognitive architectures for explainable AI systems.