Book contents
- The Cambridge Handbook of Computational Cognitive Sciences
- Cambridge Handbooks in Psychology
- The Cambridge Handbook of Computational Cognitive Sciences
- Copyright page
- Contents
- Preface
- Contributors
- Part I Introduction
- 1 An Overview of Computational Cognitive Sciences
- Part II Cognitive Modeling Paradigms
- Part III Computational Modeling of Basic Cognitive Functionalities
- Part IV Computational Modeling in Various Cognitive Fields
- Part V General Discussion
- Index
- References
1 - An Overview of Computational Cognitive Sciences
from Part I - Introduction
Published online by Cambridge University Press: 21 April 2023
Book contents
- The Cambridge Handbook of Computational Cognitive Sciences
- Cambridge Handbooks in Psychology
- The Cambridge Handbook of Computational Cognitive Sciences
- Copyright page
- Contents
- Preface
- Contributors
- Part I Introduction
- 1 An Overview of Computational Cognitive Sciences
- Part II Cognitive Modeling Paradigms
- Part III Computational Modeling of Basic Cognitive Functionalities
- Part IV Computational Modeling in Various Cognitive Fields
- Part V General Discussion
- Index
- References
Summary
This chapter provides an introduction and an overview of computational cognitive sciences. Computational cognitive sciences explore the essence of cognition and various cognitive functionalities through developing mechanistic, process-based understanding by specifying corresponding computational models. These models impute computational processes onto cognitive functions and thereby produce runnable programs. Detailed simulations and other operations can then be conducted. Understanding the human mind strictly from observations of, and experiments with, human behavior is ultimately untenable. Computational modeling is therefore both useful and necessary. Computational cognitive models are theoretically important because they represent detailed cognitive theories in a unique, indispensable way. Computational cognitive modeling has thus far deepened the understanding of the processes and the mechanisms of the mind in a variety of ways.
- Type
- Chapter
- Information
- Publisher: Cambridge University PressPrint publication year: 2023
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, 587–640.CrossRefGoogle ScholarPubMed
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, 535–564.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
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. 469–478). 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.CrossRefGoogle 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, 21–23.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.Google Scholar
Helie, S., & Sun, R. (2014). Autonomous learning in psychologically oriented cognitive architectures: a survey. New Ideas in Psychology, 34, 37–55.Google 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, 30–43.Google Scholar
Kotseruba, I., & Tsotsos, J. K. (2020). 40 years of cognitive architectures: core cognitive abilities and practical applications. Artificial Intelligence Review, 53, 17–94.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, 1–26.CrossRefGoogle ScholarPubMed
McClelland, J. L. (2009). The place of modeling in cognitive science. Topics in Cognitive Science, 1(1), 11–38.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, 914–917.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), 3–65.CrossRefGoogle ScholarPubMed
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. 95–128). Cambridge, MA: MIT Press.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, 113–126.CrossRefGoogle Scholar
Nisbett, R., Peng, K., Choi, I., & Norenzayan, A. (2001). Culture and systems of thought: holistic versus analytic cognition. Psychological Review, 108(2), 291–310.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. 584–589). 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, 219–235.Google Scholar
Reed, S. K. (2019). Building bridges between AI and cognitive psychology. AI Magazine, 40, 17–28.Google 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), 358–367.CrossRefGoogle 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. 477–504). 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), 145–174.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), 5–28.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), 341–373.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), 159–193.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), 124–140.CrossRefGoogle 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), 613–637.Google Scholar
Sun, R., & Ling, C. (1998). Computational cognitive modeling, the source of power and other related issues. AI Magazine, 19(2), 113–120.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.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
Vicente, K., & Wang, J. (1998). An ecological theory of expertise effects in memory recall. Psychological Review, 105(1), 33–57.Google Scholar