Publications in Declarative Memory

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

Anderson, J. R., Betts, S., Byrne, M. D., Schooler, L. J., & Stanley, C. (2022, December 22). The Environmental Basis of Memory. Psychological Review. Advance online publication. https://dx.doi.org/10.1037/rev0000409

Wirzberger, M., Borst, J. P., Krems, J. F., & Rey, G. D. (2020). Memory-related cognitive load effects in an interrupted learning task: A model-based explanation. Trends in Neuroscience and Education, 20, 100139. https://doi.org/10.1016/j.tine.2020.100139

Itabashi, K., Morita, J., Hirayama, T., Mase, K., and Yamada, K. (2020). Interactive Model-based Reminiscence Using a Cognitive Model and Physiological Indices. in Proceedings of the 18th International Conference on Cognitive Modelling.

Anderson, J.R., Borst, J.P., Fincham, J.M., Ghuman, A.S., Tenison, C., & Zhang, Q. (2018). The Common Time Course of Memory Processes Revealed. Psychological Science, 29(9), 1463-1474.

Moon,J., Fincham,J.M., Betts,S.A., & Anderson,J.R.,(2015). End effects and cross-dimensional interference in identification of time and length: Evidence for a common memory mechanism. Cognitive, Affective, & Behavioral Neuroscience, 15, 680-695.

Borst, J.P. & Anderson, J.R. (2015). The Discovery of Processing Stages: Analyzing EEG data with Hidden Semi-Markov Models. NeuroImage 108, 60-73.

Halbrügge, M., Quade, M., & Engelbrecht, K.-P. (2015) How can Cognitive Modeling Benefit from Ontologies? Evidence from the HCI Domain. In Bieger, J., Goertzel, B., & Potapov, A. (Eds.) Proceedings of Artificial General Intelligence 2015, pp. 261–271. Berlin: Springer.

Moon, J., & Anderson, J. R. (submitted). Is temporal memory special? The choose-short effect in time and length.

Peebles, D. & Jones, C. (2014). A model of object location memory. 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.

Borst, J. P., & Anderson, J. R. (2013). Using model-based functional MRI to locate working memory updates and declarative memory retrievals in the fronto-parietal network. Proceedings of the National Academy of Sciences U.S.A., 110(5), 1628-1633.

Das, A., & Stuerzlinger, W. (2013). Unified Modeling of Proactive Interference and Memorization Effort: A new mathematical perspective within ACT-R theory. In Knauff M., Pauen M., Sebanz N., & Wachsmuth I. (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 358-363). Austin, TX: Cognitive Science Society.

Derbinsky, N., Laird, J. E., & Smith, B. (2010). Towards efficiently supporting large symbolic declarative memories. In D. D. Salvucci & G. Gunzelmann (Eds.), Proceedings of the 10th International Conference on Cognitive Modeling (pp. 49-54). Philadelphia, PA: Drexel University.

Douglass, S. A. & Myers, C. W. (2010). Concurrent knowledge activation calculation in large declarative memories. In D. D. Salvucci & G. Gunzelmann (Eds.), Proceedings of the 10th International Conference on Cognitive Modeling (pp. 55-60). Philadelphia, PA: Drexel University.

Taatgen, N. A. & van Rijn, H. (2010). Nice Graphs, Good R2, but still a poor fit? How to be more sure your model explains your data. In D. D. Salvucci & G. Gunzelmann (Eds.), Proceedings of the 10th International Conference on Cognitive Modeling (pp. 247-252). Philadelphia, PA: Drexel University.

Rosenbloom, P. S. (2010). Combining procedural and declarative knowledge in a graphical architecture. In D. D. Salvucci & G. Gunzelmann (Eds.), Proceedings of the 10th International Conference on Cognitive Modeling (pp. 205-2010). Philadelphia, PA: Drexel University.

Das, A. & Stuerzlinger, W. (2010). Proactive interference in location learning: A new closed-form approximation. In D. D. Salvucci & G. Gunzelmann (Eds.), Proceedings of the 10th International Conference on Cognitive Modeling (pp. 37-42). Philadelphia, PA: Drexel University.

Douglass, S., Ball, J., & Rodgers, S. (2009).Large declarative memories in ACT-R. In Proceedings of the 9th International Conference of Cognitive Modeling (paper 234), Manchester, United Kingdom.

Chikhaoui, B., Pigot, H., Beaudoin, M., Pratte, G., Bellefeuille, P. & Laudares, F. (2009). Learning a song: an ACT-R Model. In proceedings of the International Conference on Computational Intelligence (pp. 405-410), Oslo, Norway.

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Department of Psychology, Carnegie Mellon University