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8 - Constraints in Cognitive Architectures

from Part II - Cognitive Modeling Paradigms

Published online by Cambridge University Press:  21 April 2023

By
Niels Taatgen  and
John Anderson
Edited by
Ron Sun
Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
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Summary

Cognitive architectures are computational theories that attempt to cover as much of cognition as possible. Developers of cognitive architectures need to balance functionality of the theory against its explanatory and predictive power, which are often at odds with one another. This chapter discusses this balance with respect to working memory, cognitive performance, perceptual and motor systems, learning and neuroscience. It discusses which solutions six cognitive architectures offer to these areas, and illustrates this with a number of successful models.

Keywords

cognitive architectureslearningperformanceneuroscienceneural networkssymbolic theories
Type
Chapter
Information
The Cambridge Handbook of Computational Cognitive Sciences , pp. 275 - 300
Publisher: Cambridge University Press
Print publication year: 2023

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References

Anderson, J. R. (1976). Language, Memory and Thought. Mahwah, NJ: Erlbaum.Google Scholar
Anderson, J. R. (1990). The Adaptive Character of Thought. Mahwah, NJ: Erlbaum.Google Scholar
Anderson, J. R. (2005). Human symbol manipulation within an integrated cognitive architecture. Cognitive Science, 29(3), 313–341.CrossRefGoogle ScholarPubMed
Anderson, J. R. (2007). How Can the Human Mind Occur in the Physical Universe. Oxford: Oxford University Press.CrossRefGoogle Scholar
Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., & Qin, Y. (2004). An integrated theory of mind. Psychological Review, 111(4), 1036–1060.CrossRefGoogle Scholar
Anderson, J. R., & Lebiere, C. L. (1998). The Atomic Components of Thought. Mahwah, NJ: Erlbaum.Google Scholar
Anderson, J. R., & Lebiere, C. L. (2003). The Newell test for a theory of cognition. Behavioral & Brain Sciences, 26, 587–637.CrossRefGoogle ScholarPubMed
Anderson, J. R., & Matessa, M. P. (1997). A production system theory of serial memory. Psychological Review, 104, 728–748.Google Scholar
Anderson, J. R., Taatgen, N. A., & Byrne, M. D. (2005). Learning to achieve perfect time sharing: architectural implications of Hazeltine, Teague, & Ivry (2002). Journal of Experimental Psychology: Human Perception and Performance, 31(4), 749–761.Google Scholar
Berry, D. C., & Broadbent, D. E. (1984). On the relationship between task performance and associated verbalizable knowledge. The Quarterly Journal of Experimental Psychology, 36A, 209–231.Google Scholar
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, 110(5), 1628–1633.CrossRefGoogle ScholarPubMed
Byrne, M. D., & Anderson, J. R. (2001). Serial modules in parallel: the psychological refractory period and perfect time-sharing. Psychological Review, 108, 847–869.Google Scholar
Chater, N., & Vitányi, P. (2003). Simplicity: a unifying principle in cognitive science? Trends in Cognitive Sciences, 7(1), 19–22.Google Scholar
Choi, H., Chang, L. H., Shibata, K., Sasaki, Y., & Watanabe, T. (2012). Resetting capacity limitations revealed by long-lasting elimination of attentional blink through training. Proceedings of the National Academy of Sciences, 109(30), 12242–12247.Google Scholar
Chong, R. S. (1999). Modeling dual-task performance improvement: casting executive process knowledge acquisition as strategy refinement. Unpublished dissertation. University of Michigan.Google Scholar
Cooper, R., & Fox, J. (1998). COGENT: a visual design environment for cognitive modelling. Behavior Research Methods, Instruments, & Computers, 30, 553–564.Google Scholar
Eliasmith, C. (2013). How to Build a Brain: A Neural Architecture for Biological Cognition. Oxford: Oxford University Press.CrossRefGoogle Scholar
Eliasmith, C., Stewart, T. C., Choo, X., et al. (2012). A large-scale model of the functioning brain. Science, 338(6111), 1202–1205.CrossRefGoogle ScholarPubMed
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14, 179–211.CrossRefGoogle Scholar
Ferlazzo, F., Lucido, S., Di Nocera, F., Fagioli, S., & Sdoia, S. (2007). Switching between goals mediates the attentional blink effect. Experimental Psychology, 54(2), 89–98.CrossRefGoogle ScholarPubMed
Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: a critical analysis. Cognition, 28, 3–71.CrossRefGoogle ScholarPubMed
Forgy, C. L. (1982). Rete: a fast algorithm for the many object pattern match problem. Artificial Intelligence, 19, 17–37.Google Scholar
Hoekstra, C., Martens, S., & Taatgen, N. A. (2020). A skill-based approach to modeling the attentional blink. Topics in Cognitive Science, 12(3), 1030–1045.CrossRefGoogle ScholarPubMed
Hornof, A. J., & Kieras, D. E. (1997). Cognitive modeling reveals menu search is both random and systematic. Proceedings of CHI-97 (pp. 107–114). New York, NY: Association for Computing Machinery.Google Scholar
Huffman, S. B., & Laird, J. E. (1995). Flexibly instructable agents. Journal of Artificial Intelligence Research, 3, 271–324.Google Scholar
Huijser, S., van Vugt, M. K., & Taatgen, N. A. (2018). The wandering self: tracking distracting self-generated thought in a cognitively demanding context. Consciousness and Cognition, 58, 170–185.Google Scholar
Just, M. A., & Carpenter, P. A. (1992). A capacity theory of comprehension: individual differences in working memory. Psychological Review, 99, 122–149.Google Scholar
Kieras, D. E., Meyer, D. E., Mueller, S. T., & Seymour, T. L. (1999). Insights into working memory from the perspective of The EPIC Architecture for modeling skilled perceptual-motor and cognitive human performance. In: Miyaki, A. & Shah, P. (Eds.), Models of Working Memory. New York, NY: Cambridge University Press.Google Scholar
Kirk, J. R., & Laird, J. E. (2019). Learning hierarchical symbolic representations to support interactive task learning and knowledge transfer. Proceedings of IJCAI-19 (pp. 6095–6102).Google Scholar
Laird, J. E. (2012). The Soar Cognitive Architecture. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Laird, J. E., Newell, A., & Rosenbloom, P. S. (1987). SOAR: an architecture for general intelligence. Artificial Intelligence, 33, 1–64.Google Scholar
Laird, J. E., Lebiere, C., & Rosenbloom, P. S. (2017). A standard model of the mind: toward a common computational framework across artificial intelligence, cognitive science, neuroscience, and robotics. AI Magazine, 38(4), 13–26.CrossRefGoogle Scholar
Lewis, R. L. (1996) Interference in short-term memory: the magical number two (or three) in sentence processing. Journal of Psycholinguistic Research, 25, 93–115.CrossRefGoogle ScholarPubMed
Lovett, M. C., Reder, L. M., & Lebiere, C. (1999). Modeling working memory in a unified architecture: an ACT-R perspective. In Miyake, A. & Shah, P. (Eds.), Models of Working Memory (pp. 135–182). Cambridge: Cambridge University Press.Google Scholar
Marinier, R. P., & Laird, J. E. (2004). Toward a comprehensive computational model of emotions and feelings. Proceedings of the Sixth International Conference on Cognitive Modeling (pp. 172–177). Mahwah, NJ: Erlbaum.Google Scholar
McClelland, J. L., McNaughton, B. L., & O’Reilly, R. C. (1995). Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102, 419–457.CrossRefGoogle ScholarPubMed
McCloskey, M., & Cohen, N. J. (1989). Catastrophic interference in connectionist networks: the sequential learning problem. In Bower, G. H. (Ed.), The Psychology of Learning and Motivation (vol. 24, pp. 109–164). San Diego, CA: Academic Press.Google Scholar
Meyer, D. E., & Kieras, D. E. (1997). A computational theory of executive cognitive processes and multiple-task performance. Part 1. Basic mechanisms Psychological Review, 104, 2–65.Google ScholarPubMed
Miller, G. A. (1956). The magic number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review, 63, 81–97.Google Scholar
Nason, S., & Laird, J. E. (2004). Soar-RL: integrating reinforcement learning with Soar. Proceedings of the Sixth International Conference on Cognitive Modeling (pp. 208–213). Mahwah, NJ: Erlbaum.Google Scholar
Newell, A. (1990). Unified Theories of Cognition. Cambridge, MA: Harvard University Press.Google Scholar
Newell, A., & Simon, H. A. (1963). GPS, a program that simulates human thought. In Feigenbaum, E. A. & Feldman, J. (Eds.), Computers and Thought. New York, NY: McGraw-Hill.Google Scholar
O’Reilly, R. C., & Munakata, Y. (2000). Computational Explorations in Cognitive Neuroscience. Cambridge, MA: MIT Press.Google Scholar
Pashler, H. (1994). Dual-task interference in simple tasks: data and theory. Psychological Bulletin, 116, 220–244.Google Scholar
Penrose, R. (1989). The Emperor’s New Mind. Oxford: Oxford University Press.Google Scholar
Popper, K. R. (1962). Conjectures and Refutations: The Growth of Scientific Knowledge. New York, NY: Basic Books.Google Scholar
Roberts, S., & Pashler, H. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107, 358–367.Google Scholar
Salvucci, D. D., & Macuga, K. L. (2002). Predicting the effects of cellular-phone dialing on driver performance. Cognitive Systems Research, 3, 95–102.Google Scholar
Salvucci, D. D., & Taatgen, N. A. (2008). Threaded cognition: an integrated theory of concurrent multitasking. Psychological Review, 114(1), 101–130.Google Scholar
Schumacher, E. H., Seymour, T. L., Glass, J. M., et al. (2001). Virtually perfect time sharing in dual-task performance: uncorking the central cognitive bottleneck. Psychological Science, 12(2), 101–108.Google Scholar
Singley, M. K., & Anderson, J. R. (1985). The transfer of text-editing skill. International Journal of Man-Machine Studies, 22(4), 403–423.Google Scholar
Smolensky, P. (1990). Tensor product variable binding and the representation of symbolic structures in connectionist networks. Artificial Intelligence, 46, 159–216.CrossRefGoogle Scholar
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), 541.Google Scholar
Sun, R. (2016). Anatomy of the Mind: Exploring Psychological Mechanisms and Processes with the Clarion Cognitive Architecture. New York, NY: Oxford University Press.CrossRefGoogle Scholar
Sun, R., Merrill, E., & Peterson, T. (2001). From implicit skills to explicit knowledge: a bottom-up model of skill learning. Cognitive Science, 25(2), 203–244.Google Scholar
Sun, R., Slusarz, P., & Terry, C. (2005). The interaction of the explicit and the implicit in skill learning: a dual-process approach. Psychological Review, 112(1), 159–192.CrossRefGoogle ScholarPubMed
Sun, R., & Zhang, X. (2004). Top-down versus bottom-up learning in cognitive skill acquisition. Cognitive Systems Research, 5(1), 63–89.Google Scholar
Taatgen, N. A. (2005). Modeling parallelization and speed improvement in skill acquisition: from dual tasks to complex dynamic skills. Cognitive Science, 29, 421–455.Google Scholar
Taatgen, N. A. (2007). The minimal control principle. In: Gray, W. (Ed.), Integrated Models of Cognitive Systems. Oxford: Oxford University Press.Google Scholar
Taatgen, N. A. (2013). The nature and transfer of cognitive skills. Psychological Review, 120(3), 439–471.Google Scholar
Taatgen, N. A. (2018). The representation of task knowledge at multiple levels of abstraction. In Gluck, K. A. & Laird, J. E. (Eds.), Interactive Task Learning: Humans, Robots, and Agents Acquiring New Tasks Through Natural Interactions (pp. 75–90), Cambridge, MA: MIT Press.Google Scholar
Taatgen, N. A. (2019). A spiking neural architecture that learns tasks. In Stewart, T. (Ed.), Proceedings of the 17th International Conference on Cognitive Modeling.Google Scholar
Taatgen, N. A., & Anderson, J. R. (2002). Why do children learn to say “broke”? A model of learning the past tense without feedback. Cognition, 86(2), 123–155.Google Scholar
Taatgen, N. A., Huss, D., Dickison, D., & Anderson, J. R. (2008). The acquisition of robust and flexible cognitive skills. Journal of Experimental Psychology: General, 137(3), 548.Google Scholar
Taatgen, N. A., & Lee, F. J. (2003). Production compilation: a simple mechanism to model complex skill acquisition. Human Factors, 45(1), 61–76.Google Scholar
Taatgen, N. A., van Rijn, D. H., & Anderson, J. R. (2007). An integrated theory of prospective time interval estimation: the role of cognition, attention and learning. Psychological Review, 114(3), 577–598.CrossRefGoogle ScholarPubMed
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.Google Scholar
Tambe, M., Newell, A., & Rosenbloom, P. S. (1990). The problem of expensive chunks and its solution by restricting expressiveness. Machine Learning, 5, 299–348.Google Scholar
Turing, A. (1936). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, 2nd series, 42, 230–265.Google Scholar
Turing, A. (1950). Computing machinery and intelligence. Mind, 59, 433–460.Google Scholar
Young, R. M., & Lewis, R. L. (1999). The Soar cognitive architecture and human working memory. In Miyake, A. & Shah, P. (Eds.) Models of Working Memory (pp. 224–256). Cambridge: Cambridge University Press.Google Scholar

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