Computational Models of Human Behavior

I was trained at Carnegie Mellon as a cognitive psychologist in the tradition of Herbert Simon and Allen Newell. This paradigm involved selecting some task of interest, analyzing how an agent might solve that task, collecting verbal protocols of humans in the domain, developing a computational model of their behavior (an AI system designed to reflect their strategies), and comparing the model and human behavior.

My interactions with Allen Newell and John Anderson also convinced me of the advantages of working within a strong theory of the human cognitive architecture. To many of us, the most attractive class of architectures at the time were production systems, and many of us developed our own production system architectures that incorporated various psychological constraints. Bob Neches and I went one step further and developed PRISM, a flexible formalism that supported the creation of many different production system architectures.

I continued to work with PRISM after moving to UCI, but I gradually became convinced that the detailed analyses of the Newell and Simon tradition, although enlightening, were not always worth the effort they involved. Instead, my focus in computational modeling turned to domains like categorization and motor behavior where there were already clear empirical generalizations that held across subjects. My work with Doug Fisher on Cobweb and with Wayne Iba on Maeander explored this approach to computational modeling.

More recently, I have become convinced that, in many domains, the psychological results do not provide enough evidence to distinguish between classes of architectures like production systems and neural networks. Stellan Ohlsson has proposed another level of computational simulations, which he calls abstract models, that let one fit the data and make predictions without making unnecessary commitments or even building an AI system to carry out the task. My recent work in this area has incorporated many of his ideas on abstract models.

The list below does not include all my publications on computational models of human behavior. Rather, it highlights those papers that include comments on the methodology of computational modeling.

Related Publications

Jones, R. M., & Langley, P. (2005). A constrained architecture for learning and problem solving. Computational Intelligence, 21, 480-502.

Langley, P. (1999). Concrete and abstract models of category learning. Proceedings of the Twenty-First Annual Conference of the Cognitive Science Society (pp. 288-293). Vancouver, BC: Lawrence Erlbaum.

Langley, P. (1996). An abstract computational model of learning selective sensing skills. Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society (pp. 385-390). San Diego: Lawrence Erlbaum Publishers.

Langley, P. (1995). Order effects in incremental learning. In P. Reimann & H. Spada (Eds.), Learning in humans and machines: Towards an interdisciplinary learning science. Oxford: Elsevier.

Langley, P., & Allen, J. A. (1991). Learning, memory, and search in planning. Proceedings of the Thirteenth Conference of the Cognitive Science Society (pp. 364-369). Chicago: Lawrence Erlbaum.

Billman, D., Fisher, D., Gluck, M., Langley, P., & Pazzani, M. (1990). Computational models of category learning. Proceedings of the Twelfth Conference of the Cognitive Science Society (pp. 989-996). Cambridge, MA: Lawrence Erlbaum.

Langley, P., Gennari, J. H., & Iba, W. (1987). Hill-climbing theories of learning. Proceedings of the Fourth International Workshop on Machine Learning (pp. 312-323). Irvine, CA: Morgan Kaufmann.

Langley, P. (1986). Human and machine learning. Machine Learning, 1, 243-248.

Langley, P., Ohlsson, S., Thibadeau, R., & Walter, R. (1984). Cognitive architectures and principles of behavior. Proceedings of the Sixth Conference of the Cognitive Science Society (pp. 244-247). Boulder, CO: Lawrence Erlbaum.

Langley, P. (1983). Exploring the space of cognitive architectures. Behavior Research Methods and Instrumentation, 15, 289-299.

Langley, P., & Neches, R. T. (1981). PRISM user's manual (Technical Report). Pittsburgh, PA: Carnegie-Mellon University, Department of Computer Science.

Langley, P., & Simon, H. A. (1981). The central role of learning in cognition. In J. R. Anderson (Ed.), Cognitive skills and their acquisition. Hillsdale, NJ: Lawrence Erlbaum.

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