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
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
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.
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
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.
Gennari, J. H., Langley, P., & Fisher, D. H. (1989).
Models of incremental concept formation.
Artificial Intelligence, 40, 11-61.
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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