Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-28T04:28:07.993Z Has data issue: false hasContentIssue false

Can quantum probability provide a new direction for cognitive modeling?

Published online by Cambridge University Press:  14 May 2013

Emmanuel M. Pothos
Affiliation:
Department of Psychology, City University London, London EC1V 0HB, United Kingdom. [email protected]://www.staff.city.ac.uk/~sbbh932/
Jerome R. Busemeyer
Affiliation:
Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405. [email protected]://mypage.iu.edu/~jbusemey/home.html

Abstract

Classical (Bayesian) probability (CP) theory has led to an influential research tradition for modeling cognitive processes. Cognitive scientists have been trained to work with CP principles for so long that it is hard even to imagine alternative ways to formalize probabilities. However, in physics, quantum probability (QP) theory has been the dominant probabilistic approach for nearly 100 years. Could QP theory provide us with any advantages in cognitive modeling as well? Note first that both CP and QP theory share the fundamental assumption that it is possible to model cognition on the basis of formal, probabilistic principles. But why consider a QP approach? The answers are that (1) there are many well-established empirical findings (e.g., from the influential Tversky, Kahneman research tradition) that are hard to reconcile with CP principles; and (2) these same findings have natural and straightforward explanations with quantum principles. In QP theory, probabilistic assessment is often strongly context- and order-dependent, individual states can be superposition states (that are impossible to associate with specific values), and composite systems can be entangled (they cannot be decomposed into their subsystems). All these characteristics appear perplexing from a classical perspective. However, our thesis is that they provide a more accurate and powerful account of certain cognitive processes. We first introduce QP theory and illustrate its application with psychological examples. We then review empirical findings that motivate the use of quantum theory in cognitive theory, but also discuss ways in which QP and CP theories converge. Finally, we consider the implications of a QP theory approach to cognition for human rationality.

Type
Target Article
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aerts, D. (2009) Quantum structure in cognition. Journal of Mathematical Psychology 53:314–48.Google Scholar
Aerts, D. & Aerts, S. (1995) Applications of quantum statistics in psychological studies of decision processes. Foundations of Science 1:8597.Google Scholar
Aerts, D. & Gabora, L. (2005b) A theory of concepts and their combinations II: A Hilbert space representation. Kybernetes 34:192221.CrossRefGoogle Scholar
Aerts, D. & Sozzo, S. (2011b) Quantum structure in cognition: Why and how concepts are entangled. In: Proceedings of the Quantum Interaction Conference, pp. 118–29. Springer.Google Scholar
Anderson, J. R. (1990) The adaptive character of thought. Erlbaum.Google Scholar
Anderson, J. R. (1991) The adaptive nature of human categorization. Psychological Review 98:409–29.Google Scholar
Anderson, N. (1971) Integration theory and attitude change. Psychological Review 78:171206.CrossRefGoogle Scholar
Ashby, F. G. & Perrin, N. A. (1988) Towards a unified theory of similarity and recognition. Psychological Review 95:124–50.Google Scholar
Atmanspacher, H. (2004) Quantum theory and consciousness: An overview with selected examples. Discrete Dynamics 8:5173.Google Scholar
Atmanspacher, H. & Filk, T. (2010) A proposed test of temporal nonlocality in bistable perception. Journal of Mathematical Psychology 54:314–21.CrossRefGoogle Scholar
Atmanspacher, H., Filk, T. & Romer, H. (2004) Quantum zero features of bistable perception. Biological Cybernetics 90:3340.Google Scholar
Atmanspacher, H. & Römer, H. (2012) Order effects in sequential measurements of non-commuting psychological observables. Journal of Mathematical Psychology 56:274–80.Google Scholar
Atmanspacher, H., Römer, H. & Walach, H. (2002) Weak quantum theory: Complementarity and entanglement in physics and beyond. Foundations of Physics 32:379406.Google Scholar
Baaquie, B. E. (2004) Quantum finance: Path integrals and Hamiltonians for options and interest rates. Cambridge University Press.Google Scholar
Bar-Hillel, M. & Neter, E. (1993) How alike is it versus how likely is it: A disjunction fallacy in probability judgments. Journal of Personality and Social Psychology 65:1119–31.Google Scholar
Barkan, R. & Busemeyer, J. R. (2003) Modeling dynamic inconsistency with a changing reference point. Journal of Behavioral Decision Making 16:235–55.Google Scholar
Bergus, G. R., Chapman, G. B., Levy, B. T., Ely, J. W. & Oppliger, R. A. (1998) Clinical diagnosis and order information. Medical Decision Making 18:412–17.CrossRefGoogle Scholar
Birnbaum, M. H. (2008) New paradoxes of risky decision making. Psychological Review 115:463501.CrossRefGoogle ScholarPubMed
Blutner, R. (2009) Concepts and bounded rationality: An application of Niestegge's approach to conditional quantum probabilities. In: Foundations of probability and physics-5, ed. Acardi, L. E. A., Adenier, G., Fuchs, C., Jaeger, G., Khrennikov, A. Y., Larsson, J.-Å. & Stenholm, S., pp. 302–10. American Institute of Physics Conference Proceedings.Google Scholar
Bordley, R. F. (1998) Quantum mechanical and human violations of compound probability principles: Toward a generalized Heisenberg uncertainty principle. Operations Research 46:923–26.CrossRefGoogle Scholar
Brainerd, C. J. & Reyna, V. F. (2008) Episodic over-distribution: A signature effect of familiarity without recognition. Journal of Memory & Language 58:765–86.Google Scholar
Brainerd, C. J., Reyna, V. F. & Ceci, S. J. (2008) Developmental reversals in false memory: A review of data and theory. Psychological Bulletin 134:343–82.CrossRefGoogle ScholarPubMed
Brainerd, C. J., Reyna, V. F. & Mojardin, A. H. (1999) Conjoint recognition. Psychological Review 106:160–79.Google Scholar
Bruza, P. D., Kitto, K., Nelson, D. & McEvoy, C. L. (2009) Is there something quantum-like about the human mental lexicon? Journal of Mathematical Psychology 53:362–77.Google Scholar
Busemeyer, J. R. & Bruza, P. D. (2012) Quantum models of cognition and decision. Cambridge University Press.Google Scholar
Busemeyer, J. R., Matthew, M. & Wang, Z. A. (2006a) Quantum game theory explanation of disjunction effects. In: Proceedings of the 28th Annual Conference of the Cognitive Science Society, ed. Sun, R. & Miyake, N., pp. 131–35. Erlbaum.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):193218.Google Scholar
Busemeyer, J. R., Wang, J. & Shiffrin, R. M. (2012) Bayesian model comparison of quantum versus traditional models of decision making for explaining violations of the dynamic consistency principle. Paper presented at Foundations and Applications of Utility, Risk and Decision Theory, Atlanta, Georgia.Google Scholar
Busemeyer, J. R., Wang, Z. & Lambert-Mogiliansky, A. (2009) Comparison of Markov and quantum models of decision making. Journal of Mathematical Psychology 53:423–33.Google Scholar
Busemeyer, J. R., Wang, Z. & Townsend, J. T. (2006) Quantum dynamics of human decision-making. Journal of Mathematical Psychology 50:220–41.Google Scholar
Carlson, B. W. & Yates, J. F. (1989) Disjunction errors in qualitative likelihood judgment. Organizational Behavior and Human Decision Processes 44:368–79.Google Scholar
Conte, E., Khrennikov, A. Y., Todarello, O., Federici, A., Mendolicchio, L. & Zbilut, J. P. (2009) Mental states follow quantum mechanics during perception and cognition of ambiguous figures. Open Systems and Information Dynamics 16:117.Google Scholar
Croson, R. (1999) The disjunction effect and reason-based choice in games. Organizational Behavior and Human Decision Processes 80:118–33.CrossRefGoogle ScholarPubMed
de Barros, J. A. & Suppes, P. (2009) Quantum mechanics, interference, and the brain. Journal of Mathematical Psychology 53:306–13.Google Scholar
de Finetti, B., Machi, A. & Smith, A. (1993) Theory of probability: A critical introductory treatment. Wiley.Google Scholar
Feldman, J. M. & Lynch, J. G. (1988) Self-generated validity and other effects of measurement on belief, attitude, intention, and behavior. Journal of Applied Psychology 73:421–35.Google Scholar
Festinger, L. (1957) A theory of cognitive dissonance. Stanford University Press.Google Scholar
Fine, A. (1982) Joint distributions, quantum correlations, and commuting observables. Journal of Mathematical Physics 23:1306–10.Google Scholar
Fodor, J. A. (1983) The modularity of mind. The MIT Press.CrossRefGoogle Scholar
Gavanski, I. & Roskos-Ewoldsen, D. R. (1991) Representativeness and conjoint probability. Journal of Personality and Social Psychology 61:181–94.Google Scholar
Gigerenzer, G. & Todd, P. M. (1999) Simple heuristics that make us smart. Oxford University Press.Google Scholar
Goldstone, R. L. (1994) Similarity, interactive activation, and mapping. Journal of Experimental Psychology: Learning, Memory and Cognition 20:328.Google Scholar
Griffiths, R. B. (2003) Consistent quantum theory. Cambridge University Press.Google Scholar
Griffiths, T. L., Chater, N., Kemp, C., Perfors, A. & Tenenbaum, J. B. (2010) Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences 14:357–64.CrossRefGoogle ScholarPubMed
Grover, L. K. (1997) Quantum mechanics helps in searching for a needle in a haystack. Physical Review Letters 79:325–28.Google Scholar
Hahn, U., Chater, N. & Richardson, L. B. (2003) Similarity as transformation. Cognition 87:132.Google Scholar
Hammeroff, S. R. (1998) Quantum computation in brain microtubules? The Penrose-Hammeroff “orch-or” model of consciousness. Philosophical Transactions of the Royal Society A 356:1869–96.Google Scholar
Hameroff, S. R. (2007) The brain is both neurocomputer and quantum computer. Cognitive Science 31:1035–45.Google Scholar
Hampton, J. A. (1988a) Disjunction of natural concepts. Memory & Cognition 16:579–91.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 14:1232.Google Scholar
Hogarth, R. M. & Einhorn, H. J. (1992) Order effects in belief updating: The belief-adjustment model. Cognitive Psychology 24:155.CrossRefGoogle Scholar
Hughes, R. I. G. (1989) The structure and interpretation of quantum mechanics. Harvard University Press.CrossRefGoogle Scholar
Isham, C. J. (1989) Lectures on quantum theory. World Scientific.Google Scholar
Jibu, M. & Yasue, K. (1995) Quantum brain dynamics and consciousness. Benjamins.Google Scholar
Johnson, E. J., Haubl, G. & Keinan, A. (2007) Aspects of endowment: A query theory of value construction. Journal of Experimental Psychology: Learning, Memory and Cognition 33(3):461–73.Google Scholar
Jones, M. & Love, B. C. (2011) Bayesian fundamentalism or enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition. Behavioral and Brain Sciences 34:169231.Google Scholar
Kahneman, D., Slovic, P. & Tversky, A. (1982) Judgment under uncertainty: Heuristics and biases. Cambridge University Press.Google Scholar
Kahneman, D. & Tversky, A. (1979) Prospect theory: An analysis of decision under risk. Econometrica 47:263–91.Google Scholar
Khrennikov, A. Y. (2010) Ubiquitous quantum structure: From psychology to finance. Springer.Google Scholar
Kolmogorov, A. N. (1933/1950) Foundations of the theory of probability. Chelsea Publishing Co.Google Scholar
Krueger, J. I., DiDonato, T. E. & Freestone, D. (2012) Social projection can solve social dilemmas. Psychological Inquiry 23:127.CrossRefGoogle Scholar
Krumhansl, C. L. (1978) Concerning the applicability of geometric models to similarity data: The interrelationship between similarity and spatial density. Psychological Review 85:445–63.Google Scholar
Lambert-Mogiliansky, A., Zamir, S. & Zwirn, H. (2009) Type indeterminacy: A model of the KT(Kahneman–Tversky)-man. Journal of Mathematical Psychology 53(5):349–61.Google Scholar
Li, S. & Taplin, J. (2002) Examining whether there is a disjunction effect in prisoner's dilemma games. Chinese Journal of Psychology 44:2546.Google Scholar
Litt, A., Eliasmith, C., Kroon, F. W., Weinstein, S. & Thagard, P. (2006) Is the brain a quantum computer? Cognitive Science 30:593603.CrossRefGoogle Scholar
Markman, A. B. & Gentner, D. (1993) Splitting the differences: A structural alignment view of similarity. Journal of Memory and Language 32:517–35.Google Scholar
Marr, D. (1982) Vision: A computational investigation into the human representation and processing of visual information. W. H. Freeman.Google Scholar
McKenzie, C. R. M., Lee, S. M. & Chen, K. K. (2002) When negative evidence increases confidence: Change in belief after hearing two sides of a dispute. Journal of Behavioral Decision Making 15:118.Google Scholar
Moore, D. W. (2002) Measuring new types of question-order effects. Public Opinion Quarterly 66:8091.Google Scholar
Nielsen, M. A. & Chuang, I. L. (2000) Quantum computation and quantum information. Cambridge University Press.Google Scholar
Nosofsky, R. M. (1984) Choice, similarity, and the context theory of classification. Journal of Experimental Psychology: Learning, Memory & Cognition 10:104–14.Google Scholar
Oaksford, M. & Chater, N. (2007) Bayesian rationality: The probabilistic approach to human reasoning. Oxford University Press.Google Scholar
Oaksford, M. & Chater, N. (2009) Pre'cis of Bayesian rationality: The probabilistic approach to human reasoning. Behavioral and Brain Sciences 32:69120.Google Scholar
Penrose, R. (1989) The emperor's new mind. Oxford University Press.Google Scholar
Perfors, A., Tenenbaum, J. B., Griffiths, T. L. & Xu, F. (2011) A tutorial introduction to Bayesian models of cognitive development. Cognition 120:302–21.Google Scholar
Pothos, E. M. & Busemeyer, J. R. (2009) A quantum probability explanation for violations of “rational” decision theory. Proceedings of the Royal Society B 276:2171–78.Google Scholar
Pothos, E. M. & Busemeyer, J. R. (2011) A quantum probability explanation for violations of symmetry in similarity judgments. In: Proceedings of the 32nd Annual Conference of the Cognitive Science Society, pp. 2848–54. LEA.Google Scholar
Redei, M. & Summers, S. J. (2007) Quantum probability theory. Studies in the History and Philosophy of Modern Physics 38:390417.CrossRefGoogle Scholar
Reyna, V. F. (2008) A theory of medical decision making and health: Fuzzy trace theory. Medical Decision Making 28:850–65.Google Scholar
Reyna, V. F. & Brainerd, C. J. (1995) Fuzzy-trace theory: An interim synthesis. Learning and Individual Differences 7:175.Google Scholar
Ricciardi, L. M. & Umezawa, H. (1967) Brain and physics of many bodied problems. Kybernetik 4:4448.Google Scholar
Sanborn, A. N., Griffiths, T. L. & Navarro, D. J. (2010) Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review 117:1144–67.Google Scholar
Savage, L. (1954) The foundations of statistics. Wiley.Google Scholar
Schuman, H. & Presser, S. (1981) Questions and answers in attitude surveys: Experiments on question form, wording, and content. Academic Press.Google Scholar
Schwarz, N. (2007) Attitude construction: Evaluation in context. Social Cognition 25:638–56.Google Scholar
Shafer, G. & Tversky, A. (1985) Languages and designs for probability judgment. Cognitive Science 9:309–39.Google Scholar
Shafir, E. & Tversky, A. (1992) Thinking through uncertainty: nonconsequential reasoning and choice. Cognitive Psychology 24:449–74.Google Scholar
Shanteau, J. C. (1970) An additive model for sequential decision making. Journal of Experimental Psychology 85:181191.Google Scholar
Sher, S. & McKenzie, C. R. M. (2008) Framing effects and rationality. In: The probabilistic mind: Prospects for Bayesian cognitive science, ed. Chater, N. & Oaksford, M., pp. 7996. Oxford University Press.Google Scholar
Sides, A., Osherson, D., Bonini, N. & Viale, R. (2002) On the reality of the conjunction fallacy. Memory and Cognition 30:191–98.CrossRefGoogle ScholarPubMed
Simon, H. A. (1955) A behavioral model of rational choice. The Quarterly Journal of Economics 69:99118.CrossRefGoogle Scholar
Sloman, S. A. (1993) Feature-based induction. Cognitive Psychology 25:231–80.Google Scholar
Smolensky, P. (1990) Tensor product variable binding and the representation of symbolic structures in connectionist networks. Artificial Intelligence 46:159216.Google Scholar
Stolarz-Fantino, S., Fantino, E., Zizzo, D. J. & Wen, J. (2003) The conjunction effect: New evidence for robustness. American Journal of Psychology 116(1):1534.CrossRefGoogle ScholarPubMed
Tenenbaum, J. B. & Griffiths, T. L. (2001) The rational basis of representativeness. In: Proceedings of the 23rd Annual Conference of the Cognitive Science Society, pp. 1036–41.Google Scholar
Tenenbaum, J. B., Kemp, C., Griffiths, T. L. & Goodman, N. (2011) How to grow a mind: Statistics, structure, and abstraction. Science 331:1279–85.Google Scholar
Tentori, K. & Crupi, V. (2012) On the conjunction fallacy and the meaning of and, yet again: A reply to Hertwig, Benz, and Krauss (2008). Cognition 122:123–34.Google Scholar
Tourangeau, R., Rips, L. J. & Rasinski, K. A. (2000) The psychology of survey response. Cambridge University Press.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 8:83105.Google Scholar
Trueblood, J. S. & Busemeyer, J. R. (2011) A comparison of the belief-adjustment model and the quantum inference model as explanations of order effects in human inference. Cognitive Science 35(8):1518–52.CrossRefGoogle Scholar
Tversky, A. (1977) Features of similarity. Psychological Review 84(4):327–52.Google Scholar
Tversky, A. & Kahneman, D. (1973) Availability: A heuristic for judging frequency and probability. Cognitive Psychology 5:207–32.Google Scholar
Tversky, A. & Kahneman, D. (1974) Judgment under uncertainty: Heuristics and biases. Science 185:1124–31.Google Scholar
Tversky, A. & Kahneman, D. (1983) Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment. Psychological Review 90(4): 293315.Google Scholar
Tversky, A. & Koehler, D. J. (1994) Support theory: A nonextensional representation of subjective probability. Psychological Review 101:547–67.Google Scholar
Tversky, A. & Shafir, E. (1992) The disjunction effect in choice under uncertainty. Psychological Science 3:305309.Google Scholar
Vitiello, G. (1995) Dissipation and memory capacity in the quantum brain model. International Journal of Modern Physics B9:973–89.Google Scholar
Wakker, P. P. (2010) Prospect theory for risk and ambiguity. Cambridge University Press.Google Scholar
Walker, L., Thibaut, J. & Andreoli, V. (1972) Order of presentation at trial. Yale Law Journal 82:216–26.Google Scholar
Wang, Z. & Busemeyer, J. R. (in press) A quantum question order model supported by empirical tests of an a priori and precise prediction. Topics in Cognitive Science.Google Scholar
Wang, Z. J., Busemeyer, J. R., Atmanspacher, H. & Pothos, E. M. (in press) The potential for using quantum theory to build models of cognition. Topics in Cognitive Science.Google Scholar
Wason, P. C. (1960) On the failure to eliminate hypotheses in a conceptual task. Quarterly Journal of Experimental Psychology 12:129–40.Google Scholar
Wedell, D. H. & Moro, R. (2008) Testing boundary conditions for the conjunction fallacy: Effects of response mode, conceptual focus, and problem type. Cognition 107:105–36.Google Scholar
Wills, A. J. & Pothos, E. M. (2012) On the adequacy of current empirical evaluations of formal models of categorization. Psychological Bulletin 138:102–25.Google Scholar
Yukalov, V. & Sornette, D. (2010) Decision theory with prospect interference and entanglement. Theory and Decision 70:283328.Google Scholar