Rate My Professor Ken Norman

Norman's research employs computational modeling to elucidate brain mechanisms underlying learning and memory. His lab tests these models using neuroimaging—fMRI and EEG—to decode thoughts as individuals learn and remember, investigating learning rules for memory modification, sleep's role in learning, experience segmentation into events, collaboration among episodic, semantic, and working memory systems, and intentional forgetting. He develops machine learning techniques for neural pattern analysis, data mining algorithms to isolate signatures of thoughts and memories, and real-time neurofeedback methods. Norman has earned the Psychonomic Society Mid-Career Award (2023), Fellow of the Society of Experimental Psychologists (2018), Fellow of the Association for Psychological Science (2018), Fellow of the Psychonomic Society, Princeton Graduate Mentoring Award (2016), President’s Award for Distinguished Teaching (2007), and others. Key publications include “Beyond mind-reading: multi-voxel pattern analysis of fMRI data” (Trends in Cognitive Sciences, 2006), “Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach” (Psychological Review, 2003), “Discovering event structure in continuous narrative perception and memory” (Neuron, 2017), “A context maintenance and retrieval model of organizational processes in free recall” (Psychological Review, 2009), and “Category-specific cortical activity precedes retrieval during memory search” (Science, 2005). His work has profoundly influenced cognitive neuroscience, with over 28,000 citations.