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Part I - Introduction

Published online by Cambridge University Press:  21 April 2023

Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
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Publisher: Cambridge University Press
Print publication year: 2023

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References

Anderson, J. R. & Lebiere, C. (1998). The Atomic Components of Thought. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Anderson, J. R. & Lebiere, C. (2003). The Newell Test for a theory of cognition. Behavioral and Brain Sciences, 26, 587640.Google Scholar
Arbib, M. A. & Bonaiuto, J. (Eds.). (2016). From Neuron to Cognition via Computational Neuroscience. Cambridge, MA: MIT Press.Google Scholar
Bechtel, W. & Graham, G. (Eds.). (1998). A Companion to Cognitive Science. Cambridge: Blackwell.Google Scholar
Boden, M. (2006). Mind as Machine: A History of Cognitive Science. Oxford: Oxford University Press.Google Scholar
Boyer, P., & Ramble, C. (2001). Cognitive templates for religious concepts: cross-cultural evidence for recall of counter-intuitive representations. Cognitive Science, 25, 535564.Google Scholar
Brekhus, W., & Ignatow, G. (Eds.). (2019). The Oxford Handbook of Cognitive Sociology. New York, NY: Oxford University Press.Google Scholar
Bringsjord, S., & Govindarajulu, N. S. (2018). Artificial intelligence. In Stanford Encyclopedia of Philosophy. Retrieved from: https://plato.stanford.edu/entries/artificial-intelligence/ [last accessed August 9, 2022].Google Scholar
Busemeyer, J. R., Wang, Z., Townsend, J. T., & Eidels, A. (2015). The Oxford Handbook of Computational and Mathematical Psychology. New York, NY: Oxford University Press.CrossRefGoogle Scholar
Chipman, S. (Ed.). (2017). The Oxford Handbook of Cognitive Science. New York, NY: Oxford University Press.Google Scholar
Chomsky, N. (1968). Language and Mind. New York, NY: Harcourt, Brace, and World.Google Scholar
Coombs, C., Dawes, R., & Tversky, A. (1970). Mathematical Psychology. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
Craver, C. F., & Bechtel, W. (2006). Mechanism. In Sarkar, S. & Pfeifer, J. (Eds.), Philosophy of Science: An Encyclopedia (pp. 469478). New York, NY: Routledge.Google Scholar
Dayan, P. (2003). Levels of analysis in neural modeling. In Nadel, L. (Ed.), Encyclopedia of Cognitive Science. London: Macmillan.Google Scholar
Dong, T. (2021). A Geometric Approach to the Unification of Symbolic Structures and Neural Networks. Berlin: Springer.Google Scholar
Durkheim, W. (1895/1962). The Rules of the Sociological Method. Glencoe, IL: The Free Press.Google Scholar
Frankish, K. & Ramsey, W. (Eds.). (2014). The Cambridge Handbook of Artificial Intelligence. New York, NY: Cambridge University Press.Google Scholar
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Cambridge, MA: MIT Press.Google Scholar
Griffiths, T. L. (2015). Manifesto for a new (computational) cognitive revolution. Cognition, 135, 2123.Google Scholar
Grossberg, S. (1982). Studies of Mind and Brain: Neural Principles of Learning, Perception, Development, Cognition, and Motor Control. Norwell, MA: Kluwer Academic Publishers.CrossRefGoogle Scholar
Helie, S., & Sun, R. (2014). Autonomous learning in psychologically oriented cognitive architectures: a survey. New Ideas in Psychology, 34, 3755.CrossRefGoogle Scholar
Hintzman, D. (1990). Human learning and memory: connections and dissociations. In Annual Review of Psychology (pp. 109–139). Palo Alto, CA: Annual Reviews Inc.Google Scholar
Jonas, E., & Kording, K. P. (2017). Could a neuroscientist understand a microprocessor? PLoS Computational Biology, 13, e1005268. https://doi.org/10.1371/journal.pcbi.1005268Google Scholar
Klahr, D., Langley, P., & Neches, R. (Eds.). (1987). Production System Models of Learning and Development. Cambridge, MA: MIT Press.Google Scholar
Koedinger, K., Anderson, J. R., Hadley, W. H., & Mark, M. (1997). Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education, 8, 3043.Google Scholar
Kotseruba, I., & Tsotsos, J. K. (2020). 40 years of cognitive architectures: core cognitive abilities and practical applications. Artificial Intelligence Review, 53, 1794.CrossRefGoogle Scholar
Levine, D. S. (2000). Introduction to Neural and Cognitive Modeling (2nd ed.). Mahwah, NJ: Erlbaum.CrossRefGoogle Scholar
Lewandowsky, S., & Farrell, S. (2011). Computational Modeling in Cognition. Thousand Oaks, CA: SAGE.Google Scholar
Luce, R. D. (1995). Four tensions concerning mathematical modeling in psychology. Annual Review of Psychology, 46, 126.Google Scholar
Marr, D. (1982). Vision. Cambridge, MA: MIT Press.Google Scholar
McClelland, J. L. (2009). The place of modeling in cognitive science. Topics in Cognitive Science, 1(1), 1138.Google Scholar
McShane, M., Bringsjord, S., Hendler, J., Nirenburg, S., & Sun, R. (2019). A response to Núñez et al.’s (2019) “What Happened to Cognitive Science?”. Topics in Cognitive Science, 11, 914917.Google Scholar
Meyer, D., & Kieras, D. (1997). A computational theory of executive cognitive processes and human multiple-task performance: Part 1, basic mechanisms. Psychological Review, 104(1), 365.Google Scholar
Miller, G., Galanter, E., & Pribram, K. (1960). Plans and the Structure of Behavior. New York, NY: Holt, Rinehart, and Winston.CrossRefGoogle Scholar
Minsky, M. (1981). A framework for representing knowledge. In Haugeland, J. (Ed.), Mind Design (pp. 95128). Cambridge, MA: MIT Press.Google Scholar
Minsky, M. (1985). The Society of Mind. New York, NY: Simon and Schuster.Google Scholar
Newell, A. (1990). Unified Theories of Cognition. Cambridge, MA: Harvard University Press.Google Scholar
Newell, A., & Simon, H. (1976). Computer science as empirical inquiry: symbols and search. Communication of ACM, 19, 113126.CrossRefGoogle Scholar
Nisbett, R., Peng, K., Choi, I., & Norenzayan, A. (2001). Culture and systems of thought: holistic versus analytic cognition. Psychological Review, 108(2), 291310.Google Scholar
Ohlsson, S., & Jewett, J. (1997). Simulation models and the power law of learning. In Proceedings of the 19th Annual Conference of the Cognitive Science Society (pp. 584589). Mahwah, NJ: Erlbaum.Google Scholar
Pew, R. W., & Mavor, A. S. (Eds.). (1998). Modeling Human and Organizational Behavior: Application to Military Simulations. Washington, DC: National Academy Press.Google Scholar
Rasmussen, J. (1986). Information Processing and Human-Machine Interaction: An Approach to Cognitive Engineering. Amsterdam: North-Holland.Google Scholar
Reber, A. (1989). Implicit learning and tacit knowledge. Journal of Experimental Psychology: General, 118, 219235.Google Scholar
Reed, S. K. (2019). Building bridges between AI and cognitive psychology. AI Magazine, 40, 1728.CrossRefGoogle Scholar
Regier, T. (2003). Constraining computational models of cognition. In Nadel, L. (Ed.), Encyclopedia of Cognitive Science (pp. 611–615). London: Macmillan.Google Scholar
Ritter, F. E., Shadbolt, N., Elliman, D., Young, R., Gobet, F., & Baxter, G. (2003). Techniques for Modeling Human Performance in Synthetic Environments: A Supplementary Review. Dayton, OH: Human Systems Information Analysis Center, Wright-Patterson Air Force Base.Google Scholar
Roberts, S., & Pashler, H. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358367.Google Scholar
Rosenbloom, P., Laird, J., & Newell, A. (1993). The SOAR Papers: Research on Integrated Intelligence. Cambridge, MA: MIT Press.Google Scholar
Rumelhart, D., McClelland, J., & the PDP Research Group. (1986). Parallel Distributed Processing (vol. I). Cambridge, MA: MIT Press.Google Scholar
Russell, S. J., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall.Google Scholar
Schank, R., & Abelson, R. (1977). Scripts, Plans, Goals, and Understanding: An Inquiry into Human Knowledge Structures. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Shultz, T. R. (2013). Computational models in developmental psychology. In Zelazo, P. D. (Ed.), Oxford Handbook of Developmental Psychology, Vol. 1: Body and Mind (pp. 477504). New York, NY: Oxford University Press.Google Scholar
Sloman, A., & Chrisley, R. (2005). More things than are dreamt of in your biology: information processing in biologically inspired robots. Cognitive Systems Research, 6(2), 145174.Google Scholar
Smith, L. B., & Thelen, E. (Eds.). (1993). A Dynamic Systems Approach to Development: Applications. Cambridge, MA: MIT Press.Google Scholar
Sun, R. (2001). Cognitive science meets multi-agent systems: a prolegomenon. Philosophical Psychology, 14(1), 528.Google Scholar
Sun, R. (2002). Duality of the Mind: A Bottom-up Approach Toward Cognition. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Sun, R. (2004). Desiderata for cognitive architectures. Philosophical Psychology, 17(3), 341373.Google Scholar
Sun, R. (Ed.). (2006). Cognition and Multi-agent Interaction: From Cognitive Modeling to Social Simulation. New York, NY: Cambridge University Press.Google Scholar
Sun, R. (2007). The importance of cognitive architectures: an analysis based on Clarion. Journal of Experimental and Theoretical Artificial Intelligence, 19(2), 159193.Google Scholar
Sun, R. (Ed.). (2008). The Cambridge Handbook of Computational Psychology. New York, NY: Cambridge University Press.Google Scholar
Sun, R. (2009). Theoretical status of computational cognitive modeling. Cognitive Systems Research, 10(2), 124140.Google Scholar
Sun, R. (Ed.). (2012). Grounding Social Sciences in Cognitive Sciences. Cambridge, MA: MIT Press.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.Google Scholar
Sun, R. (2020). Cognitive modeling. In Atkinson, P., Delamont, S., Cernat, A., Sakshaug, J. W., & Williams, R. A. (Eds.), SAGE Research Methods Foundations. Thousand Oaks, CA: SAGE. https://doi.org/10.4135/9781526421036869642Google Scholar
Sun, R., & Bookman, L. (Eds.). (1994). Computational Architectures Integrating Neural and Symbolic Processes. Boston, MA: Kluwer Academic Publishers.Google Scholar
Sun, R., Coward, A., & Zenzen, M. (2005). On levels of cognitive modeling. Philosophical Psychology, 18(5), 613637.Google Scholar
Sun, R., & Ling, C. (1998). Computational cognitive modeling, the source of power and other related issues. AI Magazine, 19(2), 113120.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), 159192.Google Scholar
Thagard, P. (2019). Cognitive Science. In Zalta, E. N. (Ed.), The Stanford Encyclopedia of Philosophy (Spring 2019 Edition). Available from: https://plato.stanford.edu/archives/spr2019/entries/cognitive-science/ [last accessed August 9, 2022].Google Scholar
Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433460.CrossRefGoogle Scholar
Vernon, D. (2014). Artificial Cognitive Systems: A Primer. Cambridge, MA: MIT Press.Google Scholar
Vicente, K., & Wang, J. (1998). An ecological theory of expertise effects in memory recall. Psychological Review, 105(1), 3357.Google Scholar
Vygotsky, L. (1986). Mind in Society. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar

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  • Introduction
  • Edited by Ron Sun, Rensselaer Polytechnic Institute, New York
  • Book: The Cambridge Handbook of Computational Cognitive Sciences
  • Online publication: 21 April 2023
  • Chapter DOI: https://doi.org/10.1017/9781108755610.002
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  • Introduction
  • Edited by Ron Sun, Rensselaer Polytechnic Institute, New York
  • Book: The Cambridge Handbook of Computational Cognitive Sciences
  • Online publication: 21 April 2023
  • Chapter DOI: https://doi.org/10.1017/9781108755610.002
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Introduction
  • Edited by Ron Sun, Rensselaer Polytechnic Institute, New York
  • Book: The Cambridge Handbook of Computational Cognitive Sciences
  • Online publication: 21 April 2023
  • Chapter DOI: https://doi.org/10.1017/9781108755610.002
Available formats
×