Christian Lebiere

Summary

Cognitive architectures are computational instantiations of unified theories of cognition that reproduce the structure, mechanisms and representations of the human brain. Cognitive models constructed using those architectures provide a natural interface between human and artificial intelligence. This course provides an introduction to cognitive modeling and its applications to human-AI teaming.

Cognitive models can be used as indicators of human activity such as cognitive load, trust and salience, as oracles that predict human activity to optimize interaction, and as surrogate agents that can act as cognitive twins. Applications of cognitive models to human-AI teaming include explainable AI, where dual models of common ground informed by analytical introspection can drive adaptive explanations, cybersecurity agents that use personalized models to optimize interaction and adaptive training, socially intelligent agents that leverage theory of mind to optimize workload and information flows, and trust in automation where models of automation reliability can calibrate trust and predict teaming vulnerabilities.

Syllabus

• Common model of cognition
  • Structural assumptions
  • Mechanisms and representations
  • Neuroimaging validation
• ACT-R cognitive architecture
  • Procedural-declarative distinction
  • Symbolic-subsymbolic duality
  • Instance-based learning
• Cognitive model applications
  • Indicators: cognitive load, trust and salience
  • Oracles: optimizing interaction
  • Surrogates: explainable cognitive twins
• Explainable artificial intelligence
  • Models of common ground
  • Introspection methods
  • Adaptive explanations
• Cybersecurity agents
  • Personalized cognitive models
  • Optimized interaction
  • Adaptive training
• Socially intelligent agents
  • Theory of Mind
  • Adaptive workload
  • Intelligent environment
• Trust in automation
  • Automation reliability
  • Trust calibration
  • Vulnerability prediction

References

Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y. (2004). An Integrated Theory of the Mind. Psychological Review, 111(4), 1036-1060.

Anderson, J. R. & Lebiere, C. (2003). The Newell test for a theory of cognition. Behavioral & Brain Sciences, 26, 587-637.

Cranford, E. A., Lebiere, C., Gonzalez, C., Aggarwal, P., Somers, S., Mitsopoulos, K., & Tambe, M. (2024). Personalized model-driven interventions for decisions from experience. Topics in Cognitive Science, 00, 1-24.

Gonzalez, C., Admoni, H., Brown, S., & Woolley, A. W. (2025). COHUMAIN: Building the Socio‐Cognitive Architecture of Collective Human–Machine Intelligence. Topics in Cognitive Science, 17(2), 180-188.

Gonzalez, C., Lerch, F. J., & Lebiere, C. (2003). Instance-based learning in dynamic decision making. Cognitive Science, 27(4), 591-635.

Laird, J. E., Lebiere, C. & Rosenbloom, P. S. (2017). A Standard Model of the Mind: Toward a Common Computational Framework across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics. AI Magazine, 38(4).

Lebiere, C., Blaha, L. M., Fallon, C. K., & Jefferson, B. (2021). Adaptive Cognitive Mechanisms to Maintain Calibrated Trust and Reliance in Automation. Frontiers in Robotics and AI, 8.

Lebiere, C., Cranford, E. A., Aggarwal, P., Cooney, S., Tambe, M., & Gonzalez, C. (2023). Cognitive modeling for personalized, adaptive signaling for cyber deception. In Cyber Deception: Techniques, Strategies, and Human Aspects (pp. 59-82). Cham: Springer International Publishing.

Lebiere, C., Cranford, E. A., Martin, M., Morrison, D., & Stocco, A. (2022, December). Cognitive architectures and their applications. In 2022 IEEE 8th International Conference on Collaboration and Internet Computing (CIC) (pp. 55-60). IEEE.

Lebiere, C., Pirolli, P., Johnson, M., Martin, M., & Morrison, D. (2025). Cognitive Models for Machine Theory of Mind. Topics in Cognitive Science, 17(2), 268-290.

Mitsopoulos, K., Somers, S., Schooler, J., Lebiere, C., Pirolli, P., & Thomson, R. (2022). Toward a psychology of deep reinforcement learning agents using a cognitive architecture. Topics in Cognitive Science, 14(4), 756-779.

Stocco, A., Sibert, C., Steine-Hanson, Z., Koh, N., Laird, J. E., Lebiere, C. J., & Rosenbloom, P. (2021). Analysis of the human connectome data supports the notion of a “Common Model of Cognition” for human and human-like intelligence across domains. NeuroImage, 235, 118035.

Pre-requisites

None.

Short bio

Christian Lebiere is a Research Professor in the Psychology Department at Carnegie Mellon University. He received his B.S. in Computer Science from the University of Liege (Belgium) and his M.S. and Ph.D. from the School of Computer Science at Carnegie Mellon University. During his graduate career, he studied connectionist models and algorithms and was the co-developer of the Cascade-Correlation neural network deep learning algorithm. Since 1991, he has worked on the development of the ACT-R cognitive architecture and was co-author of the book The Atomic Components of Thought. Most recently he has been involved with the specification of the Common Model of Cognition, a community-wide effort to consolidate and formalize the scientific progress resulting from the 50-year research program in cognitive architectures. He is a founding member of the Biologically Inspired Cognitive Architectures Society, of the International Conference on Cognitive Modeling, and of the Journal of Artificial General Intelligence. His main research interests are cognitive architectures and their applications to cognitive psychology, artificial intelligence, human-computer interaction, decision making, network science, cognitive robotics and human-machine teaming.