Publications in Implicit Learning

Morita, J., Konno, T., Okuda, J., Samejima, K., Li, G., Fujiwara, M., and Hashimoto, T. (2017). Implicit Memory Processing in the Formation of a Shared Communication System. in Proceedings of the 15th International Conference on Cognitive Modeling.

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., 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.

Kennedy, W.G. & Patterson, R.E. (2012) Modeling Intuitive Decision Making in ACT-R. In Proceedings of the 11th International Conference on Cognitive Modeling (ICCM). Berlin, Germany. 12-15 April 2012.

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.

Moore, L. R., Gunzelmann, G., & Brown, J. W. (2010). Modeling statistical learning and response inhibition with the change signal task. In D. D. Salvucci & G. Gunzelmann (Eds.), Proceedings of the 10th International Conference on Cognitive Modeling (pp. 169-174). Philadelphia, PA: Drexel University.

Guhe, M. (2009). Generating referring expressions with a cognitive model. In Proceedings of PRE-CogSci 2009: Production of Referring Expressions: Bridging the gap between computational and empirical approaches to reference. 29th July 2009, Amsterdam.

Janssen, C.P., Gray, W.D., & Schoelles, M.J. (2008). How a Modeler's Conception of Rewards Influences a Model's Behavior: Investigating ACT-R 6's Utility Learning Mechanism. In Proceedings of the 15th Annual ACT-R Workshop (p. 42), Pittsburgh, PA.

Guhe, M. & Bard, E. G. (2008). Adapting the use of attributes to the task environment in joint action: Results and a model. In Proceedings of Londial – The 12th Workshop on the Semantics and Pragmatics of Dialogue, 91–98.

Fu, W.-T. & Anderson, J. (2008), Solving the Credit Assignment Problem: Explicit and Implicit Learning of Action Sequences with Probabilistic Outcomes. Psychological Research, 72 (3), 321-330.

Fu, W. T. & Anderson, J. R. (2006) Solving the credit assignment problem: The interaction of explicit and implicit learning with internal and external state information. In proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 238-243 ) . Vancouver, British Columbia, Canada.

Fum, D. & Stocco, A. (2003) Outcome Evaluation and Procedural Knowledge in Implicit Learning. In Proceedings of the 25th Annual Meeting of the Cognitive Science Society. pg 456-431.

Fum, D. & Stocco, A. (2003) Instance vs. rule based learning in controlling a dynamic system. In Proceeedings of the Fifth International Conference on Cognitive Modelling, Bamberg, Germany, 10-12 April 2003.

Lebiere, C. & Wallach, D. (2002). Explicit or implicit learning? An integrative theory of sequence learning. In Proceedings of the 43rd conference of the German Psychological Association.

Taatgen, N. A. & Wallach, D. (2002). Whether skill acquisition is rule or instance based is determined by the structure of the task. Cognitive Science Quarterly, 2(2), 163-204.

Lebiere, C. & Wallach, D. (2001). Sequence Learning in the ACT-R Cognitive Architecture: Empirical Analysis of a Hybrid Model. In R. Sun & C. L. Gilles (Eds.). Sequence Learning: Paradigms, Algorithms, and Applications (pp. 188-212). Berlin: Spinger Lecture Notes in Computer Science.

Wallach, D. & C. Lebiere, C. (2000). Learning of event sequences: An architectural approach. In N. Taatgen (Ed.). In Proceedings of the Third International Conference on Cognitive Modeling, pp. 271-279. Groningen: Universal Press.

Lebiere, C., Wallach, D. & Taatgen, N. (1998). Implicit and explicit learning in ACT-R. In F. Ritter & R. Young (Eds.). Cognitive Modeling II (pp. 183-193). Nottingham: Nottingham University Press.

Anderson, J. R. (1996). Implicit memory and metacognition: Why is the glass half-full? In L. M. Reder (Ed.) Implicit memory and metacognition, 123-136. Mahwah, NJ: Erlbaum.

Servan-Schreiber, E. & Anderson, J. R. (1990). Chunking as a mechanism of implicit learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16, 592-608.

Copyright © 2002-2013 ACT-R Research Group


Department of Psychology, Carnegie Mellon University