Robert H Thomson

robert.thomsonusma.edu

United States Military Academy

Publications:

Stocco, A., Rice, P., Thomson, R., Smith, B. M., Morrison, D., & Lebiere, C. (2023). An Integrated Computational Framework for the Neurobiology of Memory Based on the ACT-R Declarative Memory System. Computational Brain and Behavior. https://doi.org/10.1007/s42113-023-00189-y
Lebiere, C., Bothell, D., Morrison, D., Oltramari, A., Romero, O., Thomson, R., & Vinokurov, J. (2015). Strong CogSci: Guidance from Cognitive Science On the Design of a Test of Artificial Intelligence. In Rossi, F (Ed), Beyond The Turing Test Workshop Proceedings of the 29th AAAI Conference. Austin, TX.
Lebiere, C., Bennati, S., Thomson, R., Shakarian, P., & Nunes, E. (2015). Functional Cognitive Models of Malware Identification. In N. Taatgen (Ed), Proceedings of the 13th Annual Conference on Cognitive Modeling. Groningen, Netherlands.
Thomson, R. Lebiere, C., Bennati, S., Shakarian, P., & Nunes, E. (2015). Malware Identification Using Cognitively-Inspired Inference. In Proceedings of the 24th Annual Behavior Representation in Modeling and Simulation Conference. Washington, DC.
Thomson, R., Pyke, A., Trafton, J. G., & Hiatt, L. M. (2015). An Account of Associative Learning in Memory Recall. In Proceedings of the 37th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
Thomson, R., Lebiere, C., Anderson, J. R., & Staszewski, J. (2015). A General Instance-Based Learning Framework for Studying Intuitive Decision Making in a Cognitive Architecture. Journal of Applied Research in Memory and Cognition; Special Issue on Modeling and Aiding Intuitions in Organizational Decision Making.
Thomson, R. Lebiere, C., & Bennati, S. (2014). Human, Model, and Machine: A Complementary Approach to Big Data. In Association for Computing Machinery Proceedings of the IARPA Workshop on Human Centered Big Data Research. Raleigh, NC. doi:10.1145/2609876.2609883
Thomson, R. Lebiere, C., & Bennati, S. (2014). A General Instance-Based Model of Sensemaking in a Functional Architecture. In Proceedings of the 23rd Annual Behavior Representation in Modeling and Simulation Conference. Washington, DC.
Thomson, R., Bennati, S., & Lebiere, C. (2014). Extending the Influence of Contextual Information in ACT-R using Buffer Decay. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
Thomson, R. & Lebiere, C. (2013). A Balanced Hebbian Algorithm for Associative Learning in ACT-R. R. West & T. Stewart (Eds.) Proceedings of the 12th International Conference on Cognitive Modeling. Ottawa: Canada.
Lebiere, C., Pirolli, P., Thomson, R., Paik, J., Rutledge-Taylor, M., Staszewski, J., & Anderson, J. (2013). A Functional Model of Sensemaking in a Neurocognitive Architecture. Computational Intelligence and Neuroscience; Special Issue on Neurocognitive Models of Sense Making.
Paik, J., Pirolli, P., Dong, W., Lebiere, C., & Thomson, R. (2013). An ACT-R Model of Sensemaking in a Geospatial Intelligence Task. In Proceedings of the 22nd Annual Conference on Behavior Representation in Modeling and Simulation.
Thomson, R., Lebiere, C., Rutledge-Taylor, M., Staszewski, J., & Anderson, J. R. (2012). Understanding Sensemaking Using Functional Architectures. In T. Jambrowski (Ed.) Proceedings of the 21st Annual Behavior Representation in Modeling and Simulation Conference. Dayton, OH.
Rutledge-Taylor, M., Lebiere, C., Thomson, R., Staszewski, J., & Anderson, J. R. (2012). A Comparison of Rule-Based versus Exemplar-Based Categorization Using the ACT-R Architecture. In Proceedings of the 21st Annual Behavior Representation in Modeling and Simulation Conference.

Copyright © 2002-2013 ACT-R Research Group


Department of Psychology, Carnegie Mellon University