ACT-R » ACT-R Theory

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

Rice, P., & Stocco, A. (2019). The role of dorsal premotor cortex in resolving abstract motor rules: Converging evidence from Transcranial Magnetic Stimulation and cognitive modeling. Topics in Cognitive Science.

Ritter, F. E., Tehranchi, F., & Oury, J. D. (2018). ACT‐R: A cognitive architecture for modeling cognition. Wiley Interdisciplinary Reviews: Cognitive Science, e1488.

Juvina, I., Larue, O., & Hough, A. (2018). Modeling valuation and core affect in a cognitive architecture: The impact of arousal and valence on memory and decision-making. Cognitive Systems Research 48: 4-24. http://dx.doi.org/10.1016/j.cogsys.2017.06.002

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.

Gartenberg, D., Veksler, B., Gunzelmann, G., & Trafton, J. G. (2014). An ACT-R process model of the signal duration phenomenon of vigilance. In Proceedings of 58th annual meeting of the Human Factors and Ergonomics Society.

Gunzelmann, G. (2013). Motivations and goals in developing integrative models of human cognition [Workshop Organizer]. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th Annual Meeting of the Cognitive Science Society (pp. 30-31). Austin, TX: Cognitive Science Society.

Borst, J.P., Nijboer, M., Taatgen, N.A., & Anderson, J.R. (2013). A Data-Driven Mapping of Five ACT-R Modules on the Brain. In R. West & T. Stewart (Eds.), Proceedings of the 12th International Conference on Cognitive Modeling. Ottawa: Carleton University.

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.

Cao, S., & Liu, Y. (2013). Queueing network- adaptive control of thought rational (QN-ACTR): An integrated cognitive architecture for modelling complex cognitive and multi-task performance. International Journal of Human Factors Modelling and Simulation, 4(1), 63–86.

Schneider, D. W., & Anderson, J. R. (2012). Modeling fan effects on the time course of associative recognition. Cognitive Psychology, 64, 127-160.

Gunzelmann, G., & Lyon, D. R. (2011). Representations and processes of human spatial competence. Topics in Cognitive Science, 3(4), 741-759. (DOI: 10.1111/j.1756-8765.2011.01153.x)

Gunzelmann, G. (2011). Introduction to the Topic on Modeling Spatial Cognition. Topics in Cognitive Science, 3(4), 628-631. (DOI: 10.1111/j.1756-8765.2011.01160.x)

Marewski, J. N., & Schooler, L. J. (2011). Cognitive Niches: An ecological model of strategy selection. Psychological Review, 118, 393-437.

Kennedy, W. G. (2011). Notes on teaching ACT-R modeling. Retrieved from http://act-r.psy.cmu.edu/publications/pubinfo.php?id=1006

Stocco, A., Lebiere, C., & Anderson, J. R. (2010). Conditional routing of information to the cortex: A model of the basal ganglia’s role in cognitive coordination. Psychological Review, 117(2), 540-574.

Reitter, D. & Lebiere, C. (2010). Accountable modeling in ACT-UP, a sclable, rapid-prototyping ACT-R implementation. In D. D. Salvucci & G. Gunzelmann (Eds.), Proceedings of the 10th International Conference on Cognitive Modeling (pp. 199-204). Philadelphia, PA: Drexel University.

Anderson, J. R., Betts, S. A., Ferris, J. L., & Fincham, J. M. (2009). Can neural imaging investigate learning in an educational task? In proceedings of the 36th Annual Carnegie Symposium on Cognition, June 2-3, Carnegie Mellon University.

Stocco, A., Lebiere, C., & Anderson, J. R. (2009). Dopamine, learning, and production rules: The basal ganglia and the flexible control of information transfer in the brain. In A. Samsonovich (Ed.) Biologically Inspired Cognitive Architectures II (pp. 169-175), AAAI Press.

Gunzelmann, G., & Gluck, K. A. (2009). An Integrative approach to understanding and predicting the consequences of fatigue on cognitive performance. Cognitive Technology, 14(1), 14-25.

Anderson, J.R., Fincham, J. M., Qin, Y., & Stocco, A. (2008). A Central circuit of the mind. Trends in Cognitive Science. 12(4), 136-143.

Stocco, A., Lebiere, C., & Anderson, J. R. (2008). Procedural Learning and Sequential Control of Behavior in a Neural Network Model of the Basal Ganglia. In Proceedings of the Annual Meeting of the Cognitive Neuroscience Society, San Francisco, CA, pp. 210-211.

Taatgen, N.A. & Anderson, J.R. (2008). ACT-R. In R. Sun (ed.), Constraints in Cognitive Architectures. Cambridge University Press, pp 170-185.

Jilk, D.J., Lebiere, C., O’Reilly, R. C., & Anderson, J. R. (2008) SAL: An explicitly pluralistic cognitive architecture. Journal of Experimental and Theoretical Artificial Intelligence, 20, 197-218.

Qin, Y., Bothell, D., & Anderson, J. R. (2007). ACT-R meets fMRI. In Proceedings of LNAI 4845 (pp.. 205-222). Berlin, Germany: Springer-Verlag.

Gunzelmann, G. & Lyon, D. R. (2007). Mechanisms for human spatial competence. In T. Barkowsky, M. Knauff, G. Ligozat, & D. Montello (Eds.), Spatial Cognition V: Reasoning, Action, Interaction. Lecture Notes in Artificial Intelligence #4387 (pp. 288-307). Berlin, Germany: Springer-Verlag.

Anderson, J. R., Qin, Y., Jung, K-W., & Carter, C. S. (2007). Information-Processing Modules and their Relative Modality Specificity, Cognitive Psychology, 54(3), 185-217

Anderson, J. R. (2007) How Can the Human Mind Occur in the Physical Universe? New York: Oxford University Press.

Anderson, J. R. (2007). Using brain imaging to guide the development of a cognitive architecture. In W. D. Gray (Ed.), Integrated Models of Cognitive Systems (pp. 49-62).New York, NY: Oxford University Press.

Gunzelmann, G. & Lyon, D. R (2007). Cognitive architectures: Valid control mechanisms for spatial information processing. In H. Schultheis, T. Barkowsky, B. Kuipers, and B. Hommel (Eds.), Technical Report #SS07-01: AAAI Spring Symposium Series: Control Mechanisms for Spatial Knowledge Processing in Cognitive/Intelligent Systems (pp. 23-28). Menlo Park, CA: AAAI Press.

Anderson, J. R. (2007). The image of complexity. In The 29th Annual Conference of the Cognitive Science Society. Nashville, Tennessee, USA.

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.

Kennedy, W. G., & Trafton, J.G. (2007) Long-term symbolic learning. Cognitive Systems Research, 8(3), 237-247.

Bothell, D. (2007). ACT-R 6.0 Reference Manual. From the ACT-R Web site: http://act-r.psy.cmu.edu/wordpress/wp-content/themes/ACT-R/actr6/reference-manual.pdf.

Taatgen, N.A., Lebiere, C. & Anderson, J.R. (2006). Modeling Paradigms in ACT-R. In R. Sun (ed.), Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press; 29-52.

Kennedy, W.G., & Trafton, J.G. (2006) Long-term Symbolic Learning in Soar and ACT-R. In Proceedings of the Seventh International Conference on Cognitive Modeling(pp. 166-171). Trieste, Italy.

Stewart, T. C. & West, R. L. (2006). Deconstructing ACT-R. In Proceedings of the Seventh International Conference on Cognitive Modeling (pp. 298-303). Trieste, Italy.

Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y . (2004). An integrated theory of the mind. Psychological Review 111, (4). 1036-1060.

Young, R. M. (2004). The data learning problem arises also in cognitive architectures other than Soar. In Proceedings of the sixth International Conference on Cognitive Modeling (pp. 331-336). Pittsburgh, PA: Carnegie Mellon University/University of Pittsburgh.

Taatgen, N.A. (winter 2004). Production compilation: a versatile cognitive mechanism. AISB Quarterly p. 7.

Taatgen, N.A. (2003). Learning rules and productions. In: L. Nadel (Ed.), Encyclopedia of Cognitive Science, vol. 2 (pp. 822-830). London: MacMillan.

Schoppek, W. (2003). Emerging structure in ACT-R: Explorations in competitive chunking In F. Detje, D. Doerner, & H. Schaub (Eds.), In Proceedings of the Fifth International Conference on Cognitive Modeling (pp. 297-298). Bamberg, Germany: Universitats-Verlag Bamberg.

Belavkin, R. V. (2003). Conflict Resolution by Random Estimated Costs. In D. Al-Dabass (Ed.), Proceedings of 17th European Simulation Multiconference (pp. 105--110). Nottingham, England. ISBN 3-936150-25-7

Anderson, J. R. & Schooler, L. J. (2000). The adaptive nature of memory. In E. Tulving and F. I. M. Craik (Eds.) Handbook of memory, 557-570. New York: Oxford University Press.

Anderson, J. R. & Lebiere, C. (1998). The atomic components of thought. Mahwah, NJ: Erlbaum.

Byrne, M. D. & Anderson J. A. (1997) Enhancing ACT-R's perceptual-motor abilites. In Proceedings of the Nineteenth Annual Conference of the Cognitive Science Society. Stanford University, p. 880. Mahwah, NJ: Erlbaum.

Anderson, J. R. (1996). ACT: A simple theory of complex cognition. American Psychologist, 51, 355-365.

Anderson, J. R., John, B. E., Just, M. A., Carpenter, P. A., Kieras, D. E., & Meyer, D. E. (1995). Production system models of complex cognition. In Proceedings of the Seventeenth Annual Conference of the Cognitive Science Society (pp. 9-12). Hillsdale, NJ: Lawrence Erlbaum Associates.

Lebiere, C. & Anderson, J. R. (1993). A Connectionist Implementation of the ACT-R Production System. In Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society, pp. 635-640.

Anderson, J. R. (1993). Rules of the Mind. Hillsdale, NJ: Erlbaum.

Anderson, J. R. (1993). Problem solving and learning. American Psychologist, 48, 35-44.

Anderson, J. R. (1992). Automaticity and the ACT* theory. American Journal of Psychology, 105, 165-180.

Anderson, J. R. (1991). Is human cognition adaptive? Behavioral and Brain Sciences, 14, 471-484.

Anderson, J. R. (1991). The place of cognitive architectures in a rational analysis. In K. Van Len (Ed.), Architectures for Intelligence. Hillsdale, NJ: Erlbaum.

Anderson, J. R. & Schooler, L. J. (1991). Reflections of the environment in memory. Psychological Science, 2, 396-408.

Anderson, J. R. (1990). The Adaptive Character of Thought. Hillsdale, NJ: Erlbaum.

Anderson, J. R. (1989). A theory of human knowledge. Artificial Intelligence, 40, 313-351.

Anderson, J. R. & Milson, R. (1989). Human Memory: An Adaptive Perspective. Psychological Review, 96, 703-719.

Anderson, J. R. (1989). A rational analysis of human memory. In H. L. Roediger, III and F. I. M. Craik (Eds.) Varieties of Memory and Consciousness: Essays in Honor of Endel Tulving. Hillsdale, NJ: Erlbaum, 195-210.

Anderson, J. R. (1989). Human Memory: An Adaptive Perspective. Psychological Review, 96, 703-719.

Anderson, J. R. (1988). The place of cognitive architectures in a rational analysis. Cognitive Science Meetings, 1-10.

Anderson, J. R. (1987). Methodologies for the study of human knowledge. The Behavioral and Brain Sciences, 10, 467-505.

Anderson, J. R. (1987). Skill acquisition: Compilation of weak-method problem solutions. Psychological Review, 94, 192-210.

Anderson, J. R. (1986). Knowledge compilation: The general learning mechanism. In R. Michalski, J. Carbonell, & T. Mitchell (Eds.), Machine learning II. Los Altos, CA: Morgan Kaufmann.

Anderson, J. R., & Pirolli, P. L. Spread of activation. (1984). Journal of Experimental Psychology: Learning, Memory, & Cognition, 10, 791-799.

Anderson, J. R. (1983). The Architecture of Cognition. Cambridge, MA: Harvard University Press.

Anderson, J. R. (1983). A spreading activation theory of memory. Journal of Verbal Learning and Verbal Behavior, 22, 261-295.

Anderson, J. R. (1983). Knowledge compilation: The general learning mechanisms. In Proceedings of the 1983 Machine Learning Workshop, 203-212.

Anderson, J. R. (1983). A general learning theory and its application to the acquisition of proof skills in geometry. In R. Michalski, J. Carbonell, and T. Mitchell (Eds.), Machine Learning: An Artificial Intelligence Approach. Palo Alto, CA: Tioga Publishing.

Anderson, J. R. & Kline, P. (1977). Design of a Production System. In Proceedings of the Workshop on Pattern-Directed Inference Systems, 60-65.

Anderson, J. R. (1976). Language, memory, and thought. Hillsdale, NJ: Erlbaum.

Anderson, J. R. & Bower, G. H. (1973). Human Associative memory. Washington: Winston and Sons.

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