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7 - Quantum Models of Cognition

from Part II - Cognitive Modeling Paradigms

Published online by Cambridge University Press:  21 April 2023

By
Jerome R. Busemeyer  and
Emmanuel M. Pothos
Edited by
Ron Sun
Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
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Summary

Quantum cognition is a new field in cognitive science, which is characterized by the application of quantum probability theory, quantum dynamics, and quantum information processing to account for human behavior in cognitive tasks. This chapter provides an introduction to the basic principles and a review of applications of these principles to a wide range of cognitive tasks. The power of quantum cognition comes from using the same principles to coherently link together a wide range of phenomena that have never been previously connected together.

Type
Chapter
Information
The Cambridge Handbook of Computational Cognitive Sciences , pp. 242 - 274
Publisher: Cambridge University Press
Print publication year: 2023

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References

Aerts, D. (2009). Quantum structure in cognition. Journal of Mathematical Psychology, 53(5), 314–348.Google Scholar
Aerts, D., Gabora, L., & Sozzo, S. (2013). Concepts and their dynamics: a quantum-theoretic modeling of human thought. Topics in Cognitive Science, 5(4), 737–772.Google Scholar
Aerts, D., Sozzo, S., & Veloz, T. (2015). Quantum structure of negation and conjunction in human thought. Frontiers in Psychology, 6, 1447.Google Scholar
Asano, M., Basieva, I., Khrennikov, A., Ohya, M., & Tanaka, Y. (2017). A quantum-like model of selection behavior. Journal of Mathematical Psychology, 78, 2–12.Google Scholar
Asano, M., Ohya, M., Tanaka, Y., Basieva, I., & Khrennikov, A. (2011). Quantum-like model of brain’s functioning: decision making from decoherence. Journal of Theoretical Biology, 281(1), 56–64.Google Scholar
Ashtiani, M., & Azgomi, M. A. (2015). A survey of quantum-like approaches to decision making and cognition. Mathematical Social Sciences, 75, 49–80.Google Scholar
Atmanspacher, H., & Filk, T. (2013). The necker–zeno model for bistable perception. Topics in Cognitive Science, 5(4), 800–817.Google Scholar
Atmanspacher, H., Filk, T., & Romer, H. (2004). Quantum zero features of bistable perception. Biological Cybernetics, 90, 33–40.Google Scholar
Basieva, I., Pothos, E., Trueblood, J., Khrennikov, A., & Busemeyer, J. (2017). Quantum probability updating from zero priors (by-passing cromwells rule). Journal of Mathematical Psychology, 77, 58–69.Google Scholar
Birnbaum, M. (2008). New paradoxes of risky decision making. Psychological Review, 115, 463–501.Google Scholar
Boyer-Kassem, T., Duchêne, S., & Guerci, E. (2016). Testing quantum-like models of judgment for question order effect. Mathematical Social Sciences, 80, 33–46.Google Scholar
Brainerd, C. J., Wang, Z., & Reyna, V. (2013). Superposition of episodic memories: overdistribution and quantum models.Topics in Cognitive Science, 5(4), 773–799.Google Scholar
Broekaert, J. B., & Busemeyer, J. R. (2017). A hamiltonian driven quantum-like model for overdistribution in episodic memory recollection. Frontiers in Physics, 5, 23.Google Scholar
Broekaert, J. B., Busemeyer, J. R., & Pothos, E. M. (2020). The disjunction effect in two-stage simulated gambles. An experimental study and comparison of a heuristic logistic, Markov and quantum-like model. Cognitive Psychology, 117, 101–262.Google Scholar
Bruza, P. D., Kitto, K., Ramm, B. J., & Sitbon, L. (2015). A probabilistic framework for analysing the compositionality of conceptual combinations. Journal of Mathematical Psychology, 67, 26–38.Google Scholar
Bruza, P. D., Wang, Z., & Busemeyer, J. R. (2015). Quantum cognition: a new theoretical approach to psychology. Trends in Cognitive Sciences, 19(7), 383–393.Google Scholar
Busemeyer, J. R., & Bruza, P. D. (2012). Quantum Models of Cognition and Decision. Cambridge: Cambridge University Press.Google Scholar
Busemeyer, J. R., Kvam, P. D., & Pleskac, T. J. (2020). Comparison of Markov versus quantum dynamical models of human decision making. WIREs Cognitive Science, 11(4), e1576.Google Scholar
Busemeyer, J. R., Pothos, E. M., Franco, R., & Trueblood, J. S. (2011). A quantum theoretical explanation for probability judgment errors. Psychological Review, 118(2), 193–218.Google Scholar
Busemeyer, J. R., & Wang, Z. (2015). What is quantum cognition, and how is it applied to psychology? Current Directions in Psychological Science, 24(3), 163–169.Google Scholar
Busemeyer, J. R., & Wang, Z. (2017). Is there a problem with quantum models of psychological measurements? PLoS One, 12(11), e0187733.Google Scholar
Busemeyer, J. R., & Wang, Z. (2018). Hilbert space multidimensional theory. Psychological Review, 125(4), 572–591.Google Scholar
Busemeyer, J. R., Wang, Z., & Pothos, E. M. (2015). Quantum models of cognition and decision. In Busemeyer, J. R. (Ed.), Oxford Handbook of Computational and Mathematical Psychology. Oxford: Oxford University Press.Google Scholar
Busemeyer, J. R., Wang, Z., & Shiffrin, R. S. (2015). Bayesian model comparison favors quantum over standard decision theory account of dynamic inconsistency. Decision, 2, 1–12.Google Scholar
Busemeyer, J. R., Wang, Z., & Townsend, J. (2006). Quantum dynamics of human decision making. Journal of Mathematical Psychology, 50(3), 220–241.Google Scholar
Cervantes, V. H., & Dzhafarov, E. (2018). Snow queen is evil and beautiful: experimental evidence for probabilistic contextuality in human choices. Decision, 5, 193–204.Google Scholar
Costello, F., & Watts, P. (2018). Invariants in probabilistic reasoning. Cognitive Psychology, 100, 1–16.Google Scholar
Costello, F., Watts, P., & Fisher, C. (2017). Surprising rationality in probability judgment: assessing two competing models. Cognition, 170, 280–297.Google Scholar
Denolf, J., & Lambert-Mogiliansky, A. (2016). Bohr complementarity in memory retrieval. Journal of Mathematical Psychology, 73, 28–36.Google Scholar
Denolf, J., Martínez-Martínez, I., Josephy, H., & Barque-Duran, A. (2016). A quantum-like model for complementarity of preferences and beliefs in dilemma games. Journal of Mathematical Psychology, 78, 96–106.Google Scholar
Diederich, A., & Trueblood, J. S. (2018). A dynamic dual process model of risky decision making. Psychological Review, 125(2), 270–292.Google Scholar
Dirac, P. A. M. (1930/1958). The Principles of Quantum Mechanics. Oxford: Oxford University Press.Google Scholar
Dzhafarov, E. N., Zhang, R., & Kujala, J. (2016). Is there contextuality in behavioural and social systems? Philosophical Transactions of the Royal Society A, 374(2058), 20150099.Google Scholar
Fantino, E., Kulik, J., & Stolarz-Fantino, S. (1997). The conjunction fallacy: a test of averaging hypotheses. Psychonomic Bulletin and Review, 1, 96–101.Google Scholar
Favre, M., Wittwer, A., Heinimann, H. R., Yukalov, V. I., & Sornette, D. (2016). Quantum decision theory in simple risky choices. PLoS One, 11(12), e0168045.Google Scholar
Fuss, I. G., & Navarro, D. J. (2013). Open parallel cooperative and competitive decision processes: a potential provenance for quantum probability decision models. Topics in Cognitive Science, 5(4), 818–843.Google Scholar
Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: models of bounded rationality. Psychological Review, 103(4), 650–669.Google Scholar
Gleason, A. M. (1957). Measures on the closed subspaces of a Hilbert space. Journal of Mathematical Mechanics, 6, 885–893.Google Scholar
Hameroff, S. R. (2013). Quantum mechanical cognition requires quantum brain biology. Behavioral and Brain Sciences, 36(3), 287–288.Google Scholar
Hampton, J. A. (1988a). Disjunction of natural concepts. Memory and Cognition, 16, 579–591.Google Scholar
Hampton, J. A. (1988b). Overextension of conjunctive concepts: evidence for a unitary model for concept typicality and class inclusion. Journal of Experimental Psychology: Learning Memory and Cognition, 14, 12–32.Google Scholar
He, Z., & Jiang, W. (2018). An evidential Markov decision-making model. Information Sciences, 467, 357–372.Google Scholar
Hogarth, R., & Einhorn, H. J. (1992). Order effects in belief updating: the belief adjustment modeling. Cognitive Psychology, 24, 1–55.Google Scholar
Kellen, D., Singmann, H., & Batchelder, W. H. (2018). Classic-probability accounts of mirrored (quantum-like) order effects in human judgments. Decision, 5(4), 323–338.Google Scholar
Khrennikov, A. Y. (2010). Ubiquitous Quantum Structure: From Psychology to Finance. New York, NY: Springer.Google Scholar
Khrennikov, A. Y., Basieva, I., Dzhafarov, E. N., & Busemeyer, J. R. (2014). Quantum models for psychological measurements: an unsolved problem. PloS One, 9(10), e110909.Google Scholar
Khrennikov, A. Y., Basieva, I., Pothos, E. M., & Yamato, I. (2018). Quantum probability in decision making from quantum information representation of neuronal states. Scientific Reports, 8 (1), 1–8.Google Scholar
Kintsch, W. (2014). Similarity as a function of semantic distance and amount of knowledge. Psychological Review, 121(3), 559–561.Google Scholar
Kolmogorov, A. N. (1933/1950). Foundations of the Theory of Probability. New York, NY: Chelsea Publishing Co.Google Scholar
Kvam, P., Busemeyer, J. R., & Pleskac, T. (2021). Temporal oscillations in preference strength provide evidence for an open system model of constructed preference. Scientific Reports, 11, 8169.Google Scholar
Kvam, P. D., & Busemeyer, J. R. (2018). Quantum models of cognition and decision. In Batchelder, W. H., Colonius, H., Dzhafarov, E. N., & Myung, J. (Eds.), New Handbook of Mathematical Psychology, Vol. II. Cambridge: Cambridge University Press.Google Scholar
Kvam, P. D., & Pleskac, T. J. (2017). A quantum information architecture for cue-based heuristics. Decision, 4(4), 197–233.Google Scholar
Kvam, P. D., Pleskac, T. J., Yu, S., & Busemeyer, J. R. (2015). Interference effects of choice on confidence. Proceedings of the National Academy of Science, 112(34), 10645–10650.Google Scholar
La Mura, P. (2009). Projective expected utility. Journal of Mathematical Psychology, 53(5), 408–414.Google Scholar
Manousakis, E. (2009). Quantum formalism to describe binocular rivalry. Biosystems, 98(2), 57–66.Google Scholar
Martínez-Martínez, I. (2014). A connection between quantum decision theory and quantum games: the hamiltonian of strategic interaction. Journal of Mathematical Psychology, 58, 33–44.Google Scholar
Martínez-Martínez, I., & Sánchez-Burillo, E. (2016). Quantum stochastic walks on networks for decision-making. Scientific reports, 6, 23812. https://doi.org/10.1038/srep23812Google Scholar
Mistry, P. K., Pothos, E. M., Vandekerckhove, J., & Trueblood, J. S. (2018). A quantum probability account of individual differences in causal reasoning. Journal of Mathematical Psychology, 87, 76–97.Google Scholar
Moreira, C., & Wichert, A. (2016). Quantum-like Bayesian networks for modeling decision making. Frontiers in Psychology, 7, 11.Google Scholar
Nielsen, M. A., & Chuang, I. L. (2000). Quantum Computation and Quantum Information. Cambridge: Cambridge University Press.Google Scholar
Nilsson, H. (2008). Exploring the conjunction fallacy within a category learning framework. Journal of Behavioral Decision Making, 21, 471–490.Google Scholar
Peres, A. (1998). Quantum Theory: Concepts and Methods. Norwell, MA: Kluwer Academic.Google Scholar
Pothos, E. M., & Busemeyer, J. R. (2022). Quantum cognition. Annual Review of Psychology, 73, 749–778.Google Scholar
Pothos, E. M., & Busemeyer, J. R. (2009). A quantum probability model explanation for violations of ‘rational’ decision making. Proceedings of the Royal Society B, 276(1665), 2171–2178.Google Scholar
Pothos, E. M., & Busemeyer, J. R. (2013). Can quantum probability provide a new direction for cognitive modeling? Behavioral and Brain Sciences, 36, 255–274.Google Scholar
Pothos, E. M., Busemeyer, J. R., & Trueblood, J. S. (2013). A quantum geometric model of similarity. Psychological Review, 120(3), 679–696.Google Scholar
Pothos, E. M., & Trueblood, J. S. (2015). Structured representations in a quantum probability model of similarity. Journal of Mathematical Psychology, 64, 35–43.Google Scholar
Ratcliff, R., Smith, P. L., Brown, S. L., & McCoon, G. (2016). Diffusion decision model: current history and issues. Trends in Cognitive Science, 20, 260–281.Google Scholar
Rosner, A., Basieva, I., Barque-Duran, A., et al. (2022). Ambivalence in cognition. Cognitive Psychology, 134, 101464.Google Scholar
Sanborn, A. N., Griffiths, T. L., & Shiffrin, R. M. (2010). Uncovering mental representations with Markov chain monte carlo. Cognitive Psychology, 60(2), 63–106.Google Scholar
Savage, L. J. (1954). The Foundations of Statistics. Chichester: John Wiley & Sons.Google Scholar
Scheibehenne, B., Rieskamp, J., & Wagenmakers, E.-J. (2013). Testing adaptive toolbox models: a Bayesian hierarchical approach. Psychological Review, 120(1), 39.Google Scholar
Shafir, E., & Tversky, A. (1992). Thinking through uncertainty: nonconsequential reasoning and choice. Cognitive Psychology, 24, 449–474.Google Scholar
Sun, R. (2016). Anatomy of the Mind: Exploring Psychological Mechanisms and Processes with the Clarion Cognitive Architecture. Oxford: Oxford University Press.Google Scholar
Tesar, J. (2020). A quantum model of strategic decision-making explains the disjunction effect in the prisoner’s dilemma game. Decision, 7 (1), 43–54.Google Scholar
Townsend, J. T., Silva, K. M., Spencer-Smith, J., & Wenger, M. (2000). Exploring the relations between categorization and decision making with regard to realistic face stimuli. Pragmatics and Cognition, 8, 83–105.Google Scholar
Trueblood, J. S., & Busemeyer, J. R. (2010). A quantum probability account for order effects on inference. Cognitive Science, 35, 1518–1552.Google Scholar
Trueblood, J. S., & Hemmer, P. (2017). The generalized quantum episodic memory model. Cognitive Science, 41(8), 2089–2125.Google Scholar
Trueblood, J. S., Yearsley, J. M., & Pothos, E. M. (2017). A quantum probability framework for human probabilistic inference. Journal of Experimental Psychology: General, 146(9), 1307–1341.Google Scholar
Tversky, A. (1977). Features of similarity. Psychological Review, 84(4), 327.Google Scholar
Tversky, A., & Kahneman, D. (1983). Extensional versus intuitive reasoning: the conjunctive fallacy in probability judgment. Psychological Review, 90, 293–315.Google Scholar
Tversky, A., & Kahneman, D. (1990). Advances in prospect theory: cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297–323.Google Scholar
Tversky, A., & Shafir, E. (1992). The disjunction effect in choice under uncertainty. Psychological Science, 3, 305–309.Google Scholar
Von Neumann, J. (1932/1955). Mathematical Foundations of Quantum Theory. Princeton, NJ: Princeton University Press.Google Scholar
Wang, Z., & Busemeyer, J. (2016a). Comparing quantum versus Markov random walk models of judgements measured by rating scales. Philosophical Transactions of the Royal Society A, 374(2058), 20150098.Google Scholar
Wang, Z., & Busemeyer, J. R. (2016b). Interference effects of categorization on decision making. Cognition, 150, 133–149.Google Scholar
Wang, Z., Solloway, T., Shiffrin, R. M., & Busemeyer, J. R. (2014). Context effects produced by question orders reveal quantum nature of human judgments. Proceedings of the National Academy of Sciences, 111(26), 9431–9436.Google Scholar
White, L. C., Pothos, E., & Busemeyer, J. (2014). Sometimes it does hurt to ask: the constructive role of articulating impressions. Cognition, 1, 48–64.Google Scholar
Yearsley, J. M., & Busemeyer, J. R. (2016). Quantum cognition and decision theories. Journal of Mathematical Psychology, 74, 99–116.Google Scholar
Yearsley, J. M., & Pothos, E. M. (2016). Zeno’s paradox in decision-making. Proceedings of the Royal Society B: Biological Sciences, 283(1828), 20160291.Google Scholar
Yearsley, J. M., & Trueblood, J. (2018). A quantum theory account of order effects and conjunction fallacies in political judgments. Psychonomic Bulletin & Review, 25, 1517–1525.Google Scholar
Yukalov, V. I., & Sornette, D. (2011). Decision theory with prospect interference and entanglement. Theory and Decision, 70, 283–328.Google Scholar

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