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Our exploration of both naturally and artificially intelligent systems dates back to the 1960s when psychology professor Herbert A. Simon articulated foundational concepts to help create the field of artificial intelligence.

In this same tradition, in the 1980s, psychology professor James L. McClelland articulated a new way of thinking about natural and artificial intelligence using parallel distributed computational methods based on the architecture of the brain.

As part of the diverse and robust Carnegie Mellon community studying intelligence, researchers and trainees in our department continue to pursue the study of intelligence in both directions: leveraging our increasing understanding of natural intelligence to develop better artificial models and leveraging state-of-the-art artificial intelligence models to explicate core underlying computational and representational principles underlying intelligent behavior and it neural bases.

Our faculty and trainees collaborate closely with scholars from wide range of other disciplines at Carnegie Mellon including the Neuroscience Institute, the Robotics Institute, the Machine Learning Department, the Language Technology Institute, the Human Computer Interaction Institute, and the Department of Statistics and Data Science. In addition, we are active participants in the Center for the Neural Basis of Cognition.

Recent Projects and Papers

• , Laurie Heller
• , Roberta L. Klatzky & Mahadev Satyanarayanan
• , Brian MacWhinney & Houjun Liiud ()
• , Brian MacWhinney & Houjun Liiu
• ,  Andrew Luo, Margaret Henderson, Leila Wehbe, & Michael J Tarr ()
• , Andrew Luo, Margaret Henderson, Michael J Tarr, & Leila Wehbe
• ,  Gabriel Sarch, Michael J Tarr, Katerina Fragkiadaki, & Leila Wehbe ()
• , Aria Wang, Michael J Tarr, & Leila Wehbe
• , Omisa Jinsi, Margaret Henderson, & Michael J Tarr ()
• , Yuchen Zhou, Michael J Tarr, & Daniel Yurovsky