Metacognition and Multiple Strategies in a Cognitive Model of Online Control
We present a cognitive model performing the Dynamic Stocks&Flows control task, in which subjects control a system by counteracting a systematically changing external variable. The model uses a metacognitive layer that chooses a task strategy drawn from of two classes of strategies: precise calculation and imprecise estimation. The model, formulated within the ACT-R theory, monitors the success of each strategy continuously using instance-based learning and blended retrieval from declarative memory. The model underspecifies other portions of the task strategies, whose timing was determined as unbiased estimate from empirical data. The model's predictions were evaluated on data collected from novel experimental conditions, which did not inform the model's development and included discontinuous and noisy environmental change functions and a control delay. The model as well as the data show sudden changes in subject error and general learning of control; the model also correctly predicted oscillations of plausible magnitude. With its predictions, the model ranked first among the entries to the 2009 Dynamic Stocks&Flows modeling challenge.
If the inline PDF is not rendering correctly, you can download the PDF file here.
Anderson, J. R.; Bothell, D.; Byrne, M. D.; Douglass, S.; Lebiere, C.; and Quin, Y. 2004. An integrated theory of mind. Psychological Review 111:1036-1060.
Anderson, J. R. 1991. The place of cognitive architectures in a rational analysis. Hillsdale (NJ): Lawrence Erlbaum Associates. 1-24.
Anderson, J. R. 1996. Implicit Memory and Metacognition: Why is the glass half full? In Reder, L. M., ed., Implicit Memory and Cognition. Hillsdale (NJ): Lawrence Erlbaum Associates. 123-136.
Ball, J. T.; Gluck, K. A.; Kursmark, M. A.; and Rodgers, S. M. 2003. Comparing three variants of a computational process model of basic aircraft maneuvering. In Proceedings of the 12th Conference on Behavior Representations in Modeling and Simulations, 87-98.
Berry, D. C., and Broadbent, D. E. 1987. The combination of explicit and implicit learning processes in task control. Psychological Research 49:7-15.
Bott, L., and Heit, E. 2004. Nonmonotonic extrapolation in function learning. Journal of Experimental Psychology 30:38-50.
Campbell, J. D. 2008. Addition by Subtraction. Memory and Cognition 36:1094-1102.
Fink, E.; Kaplowitz, S.; and Hubbard, S. M. 2002. Oscillation in Beliefs and Decisions. In The persuasion handbook: developments in theory and practice. Thousand Oaks, CA: Sage.
Gluck, K.; Halbruegge, M.; Moore, R.; Reitter, D.; and Stanley, C. 2010. Parameter space exploration in cognitive models. Journal of Artificial General Intelligence 2(2).
Gonzalez, C., and Lebiere, C. 2005. Instance-based cognitive models of decision making. In Zizzo, D., and Courakis, A., eds., Transfer of knowledge in economic decision making. New York: Palgrave McMillan.
Gonzalez, C.; Lerch, F.; and Lebiere, C. 2003. Instance-based learning in dynamic decision making. Cognitive Science 27(4):591-635.
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 (this issue).
Jones, R. M.; Laird, J. E.; Nielsen, P. E.; Coulter, K. J.; Kenny, P.; and Koss, F. V. 1999. Automated intelligent pilots for combat flight simulation. AI Magazine 20:27-42.
Lebiere, C.; Gonzalez, C.; and Warwick, W. 2009. A comparative approach to understanding general intelligence: Predicting cognitive performance in an open-ended dynamic task. In Proceedings of the Second Conference on Artificial General Intelligence, 103-107. Amsterdam/Paris: Atlantis Press.
Lebiere, C. 1999. The dynamics of cognition: An ACT-R model of cognitive arithmetic. Kognitionswissenschaft 8(1):5-19.
Mané, A. M., and Donchin, E. 1989. The Space Fortress game. Acta Psychologica 71(1-3):17-22.
McCloskey, M., and Lindemann, M. 1992. MATHNET: preliminary results from a distributed model of arithmetic fact retrieval. In Campbell., ed., The Nature and Origin of Mathematical Skills. Amsterdam, Netherlands: Elsevier.
Reder, L. M., and Schunn, C. D. 1996. Metacognition does not imply awareness: Strategy choice is governed by implicit learning and memory. In Reder, L. M., ed., Implicit Memory and Cognition. Lawrence Erlbaum Associates. 45-78.
Reitter, D., and Lebiere, C. 2010. Accountable Modeling in ACT-UP, a Scalable, Rapid-Prototyping ACT-R Implementation. In Proceedings of the 10th International Conference on Cognitive Modeling (ICCM), 199-204. Philadelphia, PA.
Reitter, D.; Juvina, I.; Stocco, A.; and Lebiere, C. 2010. Resistance is Futile: Winning Lemonade Market Share through Metacognitive Reasoning in a Three-Agent Cooperative Game. In Proceedings of the 19th Behavior Representation in Modeling & Simulation (BRIMS). Charleston, SC.
Salvucci, D. D. 2006. Modeling driver behavior in a cognitive architecture. Human Factors 48(2):362-380.
Taatgen, N., and Lee, F. J. 2003. Production Compilation: A Simple Mechanism to Model Complex Skill Acquisition. Human Factors 45(1):61-76.
Vicuso, S. R.; Anderson, J. A.; and Spoehr, K. T. 1989. Representing simple arithmetic in neural networks. In Tiberghien, G., ed., Advanced Cognitive Science: Theory and Applications. Chichester, England: Horwood. 144-164.