Publications & Models
Publications in Reinforcement Learning
Stocco, A. Murray, N. L., Yamasaki, B. L., Renno, T., Nguyen, J., & Prat, C. S. (2017). Individual differences in the Simon effect are underpinned by differences in the competitive dynamics in the basal ganglia: An experimental verification and a computational model. Cognition, 164, 31-45.
Arslan, B., Taatgen, N., & Verbrugge, R. (2017). Five-year-olds’ systematic errors in second-order false belief tasks are due to first-order theory of mind strategy selection: A computational modeling study. Frontiers in Psychology, 8:275.
Cao, S., Qin, Y., Zhao, L., & Shen, M. (2015). Modeling the development of vehicle lateral control skills in a cognitive architecture. Transportation Research Part F: Traffic Psychology and Behaviour, 32, 1-10.
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.
Janssen, C.P. & Gray, W.D. (2012) When, What, and How Much to Reward in Reinforcement Learning based Models of Cognition. Accepted for Cognitive Science, 36(2), 333-358.
Walsh, M. M. & Anderson, J. R. (2012). Learning from experience: Event-related potential correlates of reward processing, neural adaptation, and behavioral choice. Neuroscience and Biobehavioral Reviews, 36, 1870-1894.
Walsh, M.M., & Anderson, J.R. (2011). Learning from delayed feedback: Neural responses in temporal credit assignment. Cognitive, Affective, and Behavioral Neuroscience, 11, 131-143.
Walsh, M. M. & Anderson, J. R. (2011) Modulation of the feedback-related negativity by instruction and experience. Proceedings of the National Academy of Science U.S.A, 108 (47), 19048-19053.
Napoli, A. & Fum, D. (2010). Rewards and punishments in iterated decision making: An explanation for the frequency of the contingent event effect. In D. D. Salvucci & G. Gunzelmann (Eds.), Proceedings of the 10th International Conference on Cognitive Modeling (pp. 175-180). Philadelphia, PA: Drexel University.
Walsh, M. M. & Anderson, J. R. (2010). Neural correlates of temporal credit assignment. In D. D. Salvucci & G. Gunzelmann (Eds.), Proceedings of the 10th International Conference on Cognitive Modeling (pp. 265-270). Philadelphia, PA: Drexel University.
Halbrügge, M. (2010). Keep it simple - A case study of model development in the context of the Dynamic Stocks and Flows (DSF) task. Journal of Artificial General Intelligence, 2(2):38-51
Dutt, V., Yamaguchi, M., Gonzalez, C., & Proctor, R. W. (2009). In Proceedings of the 9th International Conference of Cognitive Modeling (paper 115), Manchester, United Kingdom.
Napoli, A. & Fum, D. (2009). Applying Occam's razor to paper (and rock and scissors, too): Why simpler models are sometimes better. In Proceedings of the 9th International Conference of Cognitive Modeling (paper 203), Manchester, United Kingdom.
Lebiere, C. & Best, B. J. (2009). Balancing Long-Term Reinforcement and Short-Term Inhibition. In Proceedings of the 31st Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
Tamborello, F. P., II, & Byrne, M. D. (2007). Fast Learning in a Simple Probabilistic Visual Environment: A Comparison of ACT-R’s Old PG-C and New Reinforcement Learning Algorithms. In Proceedings of the 8th International Conference on Cognitive Modeling. Ann Arbor, Michigan, USA.
Copyright © 2002-2013 ACT-R Research Group
Department of Psychology
Carnegie Mellon University