Publications in Reinforcement Learning

Xu, Y., Stocco, A. Recovering Reliable Idiographic Biological Parameters from Noisy Behavioral Data: the Case of Basal Ganglia Indices in the Probabilistic Selection Task. Comput Brain Behav (2021). https://doi.org/10.1007/s42113-021-00102-5

Xu, Y. Reliable Idiographic Parameters From Noisy Behavioral Data: The Case of Individual Differences in a Reinforcement Learning Task.

Nishikawa, J., and Morita, J. (2020). Modeling The Cognitive Modeling of Errors During the Japanese Phonological Awareness Formation Process. in Proceedings of the 18th Annual Meeting of the International Conference on Cognitive Modelling.

Nagashima, K., Morita, J., and Takeuchi, Y. (2020). Modeling Intrinsic Motivation in ACT-R: Focusing on the Relation Between Pattern Matching and Intellectual Curiosity. in Proceedings of the 18th Annual Meeting of the International Conference on Cognitive Modelling.

Morita, J., Miwa, K., Maehigashi, A., Terai, H., Kojima, K., and Ritter, F. E. (2020). Cognitive Modeling of Automation Adaptation in a Time Critical Task. Frontiers in Psychology (section Cognitive Science), doi: 10.3389/fpsyg.2020.02149.

Stocco, A. (2018). A Biologically Plausible Action Selection System for Cognitive Architectures: Implications of Basal Ganglia Anatomy for Learning and Decision‐Making Models. Cognitive science, 42(2), 457-490.

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.

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Department of Psychology, Carnegie Mellon University