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Perspectives on Correctness in Probabilistic Inference from Psychology

Published online by Cambridge University Press:  23 December 2019

Emmanuel M. Pothos Open the ORCID record for Emmanuel M. Pothos [Opens in a new window] ,
Irina Basieva ,
Andrei Khrennikov  and
James M. Yearsley
Emmanuel M. Pothos*
Affiliation:
City, University of London (UK)
Irina Basieva
Affiliation:
City, University of London (UK)
Andrei Khrennikov
Affiliation:
Linnéuniversitetet (Sweden)
James M. Yearsley
Affiliation:
City, University of London (UK)
*
*Correspondence concerning this article should be addressed to Emmanuel M. Pothos. City, University of London. Department of Psychology. EC1V 0HB London (UK). E-mail: emmanuel.pothos.1@city.ac.uk
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Abstract

Research into decision making has enabled us to appreciate that the notion of correctness is multifaceted. Different normative framework for correctness can lead to different insights about correct behavior. We illustrate the shifts for correctness insights with two tasks, the Wason selection task and the conjunction fallacy task; these tasks have had key roles in the development of logical reasoning and decision making research respectively. The Wason selection task arguably has played an important part in the transition from understanding correctness using classical logic to classical probability theory (and information theory). The conjunction fallacy has enabled a similar shift from baseline classical probability theory to quantum probability. The focus of this overview is the latter, as it represents a novel way for understanding probabilistic inference in psychology. We conclude with some of the current challenges concerning the application of quantum probability theory in psychology in general and specifically for the problem of understanding correctness in decision making.

Keywords

conjunction fallacydecision makingquantum theory
Type
Research Article
Information
The Spanish Journal of Psychology , Volume 22 , 2019 , E55
Copyright
Copyright © Universidad Complutense de Madrid and Colegio Oficial de Psicólogos de Madrid 2019 

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Footnotes

This paper grew out of an invited talk given at the VII Advanced International Seminar – Mathematical Models of Decision Making Processes: State of the Art and Challenges held at the School of Psychology, Universidad Complutense de Madrid (Spain) in October 2018 (http://eventos.ucm.es/go/DecisionMakingModels). Emmanuel M. Pothos was supported by Leverhulme Trust grant RPG–2015–311 and ONRG grant N62909–19–1–2000; Irina Basieva was supported by grant H2020–MSCA–IF–2015 696331.

How to cite this article:

Pothos, E. M., Basieva, I., Khrennikov, A., & Yearsley J. M. (2019). Perspectives on correctness in probabilistic inference from psychology. The Spanish Journal of Psychology, 22. e55. Doi:10.1017/sjp.2019.48

References

Anderson, J. R. (1990). The adaptive character of thought. Hillsdale, NJ: Erlbaum.Google Scholar
Anderson, J. R. (1991a). Is human cognition adaptive? Behavioral and Brain Sciences, 14, 471–485. https://doi.org/10.1017/S0140525X00070801CrossRefGoogle Scholar
Anderson, J. R. (1991b). The adaptive nature of human categorization. Psychological Review, 98, 409–429. https://doi.org/10.1037/0033-295X.98.3.409CrossRefGoogle Scholar
Asano, M. Basieva, I., Khrennikov, A., Ohya, M., & Tanaka, Y. (2012). Quantum-like generalization of the Bayesian updating scheme for objective and subjective mental uncertainties. Journal of Mathematical Psychology, 56, 166–175.CrossRefGoogle Scholar
Asano, A., Basieva, I., Khrennikov, A., Ohya, M., & Yamato, I. (2013). Non-Kolmogorovian approach to the context-dependent systems breaking the classical probability law. Foundations of Physics, 43, 895–911. https://doi.org/10.1007/s10701-013-9725-5CrossRefGoogle Scholar
Asano, M., Basieva, I., Pothos, E. M., & Khrennikov, A. (2018). State entropy and differentiation phenomenon. Entropy, 20, 394–408. https://doi.org/10.3390/e20060394CrossRefGoogle Scholar
Bergus, G. R., Chapman, G. B., Levy, B. T., Ely, J. W., & Oppliger, R. A. (1998). Clinical diagnosis and order of information. Medical Decision Making, 18, 412–417. https://doi.org/10.1177/0272989X9801800409CrossRefGoogle Scholar
Braine, M. D. S., O’Brien, D. P., Noveck, I. A., Samuels, M. C., Lea, R. B., Fisch, S. M., & Yang, Y. (1995). Predicting intermediate and multiple conclusions in propositional logic inference problems: Further evidence for a mental logic. Journal of Experimental Psychology: General, 124(3), 263–292. https://doi.org/10.1037/0096-3445.124.3.263CrossRefGoogle Scholar
Bruza, P. D., Wang, Z., & Busemeyer, J. R. (2015). Quantum cognition: A new theoretical approach to psychology. Trends in Cognitive Sciences, 19, 383–393. https://doi.org/10.1016/j.tics.2015.05.001CrossRefGoogle ScholarPubMed
Busemeyer, J. R., & Bruza, P. (2011). Quantum models of cognition and decision making. Cambridge, UK: Cambridge University Press.Google Scholar
Busemeyer, J. R., Pothos, E., Franco, R., & Trueblood, J. S. (2011). A quantum theoretical explanation for probability judgment errors. Psychological Review, 118, 193–218. https://doi.org/10.1037/a0022542CrossRefGoogle ScholarPubMed
Cervantes, V. H., & Dzhafarov, E. N. (2018). Snow queen is evil and beautiful: Experimental evidence for probabilistic contextuality in human choices. Decision, 5, 193–204. https://doi.org/10.1037/dec0000095CrossRefGoogle Scholar
Chater, N., & Oaksford, M. (1993). Logicism, mental models and everyday reasoning. Mind & Language, 8, 72–89. https://doi.org/10.1111/j.1468-0017.1993.tb00271.xCrossRefGoogle Scholar
Cheng, P. W., & Holyoak, K. J. (1985). Pragmatic reasoning schemas. Cognitive Psychology, 17, 391–416. https://doi.org/10.1016/0010-0285(85)90014-3CrossRefGoogle ScholarPubMed
de Finetti, B., Machi, A., & Smith, A. (1993). Theory of Probability: A critical introductory treatment. New York, NY: Wiley.Google Scholar
Dulany, D. E., & Hilton, D. J. (1991). Conversational implicature, conscious representation, and the conjunction fallacy. Social Cognition, 9, 85–110. https://doi.org/10.1521/soco.1991.9.1.85CrossRefGoogle Scholar
Dzhafarov, E. N., Kujala, J. V., Cervantes, V. H., Zhang, R., & Jones, M. (2016). On contextuality in behavioral data. Philosophical Transactions of the Royal Society A, 374, 20150234. https://doi.org/10.1098/rsta.2015.0234CrossRefGoogle Scholar
Elqayam, S., & Evans, J. S. B. T. (2013). Rationality in the new paradigm: Strict versus soft bayesian approaches. Thinking and Reasoning, 19, 453–470.CrossRefGoogle Scholar
Evans, J. S. B. T. (1991). Theories of human reasoning: The fragmented state of the art. Theory & Psychology, 1, 83–105. https://doi.org/10.1177/0959354391011006CrossRefGoogle Scholar
Evans, J. S. B. T., Newstead, S. E., & Byrne, R. J. M. (1991). Human reasoning: The psychology of deduction. Hove, UK: Lawrence Erlbaum Associates.Google Scholar
Evans, J. S. B. T., Handley, S. J., Neilens, H., & Over, D. E. (2007). Thinking about conditionals: A study of individual differences. Memory and Cognition, 35(7), 1772–1784. https://doi.org/10.3758/BF03193509CrossRefGoogle ScholarPubMed
Feldman, J. (2000). Minimization of Boolean complexity in human concept learning. Nature, 407, 630–633. https://doi.org/10.1038/35036586CrossRefGoogle ScholarPubMed
Gamez-Djokic, M., & Molden, D. (2016). Beyond affective influences on deontological moral judgment: The role of motivations for prevention in the moral condemnation of harm. Personality and Social Psychology Bulletin, 42(11), 1522–1537. https://doi.org/10.1177/0146167216665094CrossRefGoogle ScholarPubMed
Garner, W. R. (1974). The processing of information and structure. New York, NY: Lea.Google Scholar
Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103(4), 650–669. https://doi.org/10.1037/0033-295X.103.4.650CrossRefGoogle ScholarPubMed
Gilboa, I. (2000). Theory of decision under uncertainty. Cambridge, UK: Cambridge University Press:Google Scholar
Goodman, N. D., Tenenbaum, J. B., & Gerstenberg, T. (2015). Concepts in probabilistic language of thought. In Margolis, E. & Laurence, S. (Eds.), New directions in the study of concepts (pp. 623–653). Cambridge, MA: MIT Press.Google Scholar
Grice, H. P. (1975). Logic and conversation. In Cole, P. & Morgan, J. L. (Eds.), Syntax and semantics 3: Speech acts (pp. 41–58). San Diego, CA: Academic Press.Google Scholar
Griffiths, T. L., Lieder, F., & Goodman, N. D. (2015). Rational use of cognitive resources: Levels of analysis between the computational and the algorithmic. TopiCS, Topics in Cognitive Science, 7, 217–229. https://doi.org/10.1111/tops.12142CrossRefGoogle ScholarPubMed
Haven, E., & Khrennikov, A. (2013). Quantum Social Science. Cambridge, UK: Cambridge University PressCrossRefGoogle Scholar
Howson, C. & Urbach, P. (1993). Scientific reasoning: The Bayesian approach. Chicago, IL: Open Court.Google Scholar
Kahane, G., & Shackel, N. (2010). Methodological issues in the neuroscience of moral judgement. Mind & Language, 25, 561–582. https://doi.org/10.1111/j.1468-0017.2010.01401.xCrossRefGoogle ScholarPubMed
Kahneman, D. (2001). Thinking fast and slow. London, UK: Penguin.Google Scholar
Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment under uncertainty: Heuristics and biases. New York, NY: Cambridge University Press.CrossRefGoogle Scholar
Kolmogorov, A. N. (1933). Foundations of the theory of probability. New York, NY: Chelsea Publishing Co.Google Scholar
Khrennikov, A. Y. (2010). Ubiquitous quantum structure: From psychology to finance. Heidelberg, Germany: Springer.Google Scholar
Khrennikov, A., & Basieva, I. (2014). Possibility to agree on disagree from quantum information and decision making. Journal of Mathematical Psychology, 62–63, 1–15. https://doi.org/10.1016/j.jmp.2014.09.003CrossRefGoogle Scholar
Khrennikov, A., Basieva, I., Pothos, E. M., & Yamato, I. (2018). Quantum probability in decision making from quantum information representation of neuronal states. Scientific Reports, 8, Article 16225. https://doi.org/10.1038/s41598-018-34531-3CrossRefGoogle ScholarPubMed
Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350, 1332–1338. https://doi.org/10.1126/science.aab3050CrossRefGoogle ScholarPubMed
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, 1–18. https://doi.org/10.1002/bdm.400CrossRefGoogle Scholar
Miller, G. A. (1958). Free recall of redundant strings of letters. Journal of Experimental Psychology, 56, 485–491. https://doi.org/10.1037/h0044933CrossRefGoogle ScholarPubMed
Moro, R. (2009). On the nature of the conjunction fallacy. Synthese, 171, 1–24. https://doi.org/10.1007/s11229-008-9377-8CrossRefGoogle Scholar
Oaksford, M., & Chater, N. (1994). A rational analysis of the selection task as optimal data selection. Psychological Review, 101, 608–631. https://doi.org/10.1037/0033-295X.101.4.608CrossRefGoogle Scholar
Oaksford, M., & Chater, N. (2009). Précis of Bayesian rationality: The probabilistic approach to human reasoning. Behavioral and Brain Sciences, 32, 69–84.CrossRefGoogle ScholarPubMed
Perfors, A., Tenenbaum, J. B., Griffiths, T. L., & Xu, F. (2011). A tutorial introduction to Bayesian models of cognitive development. Cognition, 120, 302–321.CrossRefGoogle ScholarPubMed
Pothos, E. M., & Busemeyer, J. R. (2013). Can quantum probability provide a new direction for cognitive modeling? Behavioral & Brain Sciences, 36, 255–274. https://doi.org/10.1017/S0140525X12001525CrossRefGoogle ScholarPubMed
Pothos, E. M., Busemeyer, J. R., & Trueblood, J. S. (2013). A quantum geometric model of similarity. Psychological Review, 120, 679–696. https://doi.org/10.1037/a0033142CrossRefGoogle ScholarPubMed
Pothos, E. M., Busemeyer, J. R., Shiffrin, R. M., & Yearsley, J. M. (2017). The rational status of quantum cognition. Journal of Experimental Psychology: General, 146, 968–987. https://doi.org/10.1037/xge0000312CrossRefGoogle ScholarPubMed
Shafir, E., & Tversky, A. (1992). Thinking through uncertainty: Nonconsequential reasoning and choice. Cognitive Psychology, 24, 449–474. https://doi.org/10.1016/0010-0285(92)90015-TCrossRefGoogle Scholar
Sloman, S. A. (1996). The empirical case for two systems of reasoning. Psychological Bulletin, 119, 3–22. https://doi.org/10.1037/0033-2909.119.1.3CrossRefGoogle Scholar
Sorkin, R. D. (1994). Quantum Mechanics as Quantum Measure Theory. Modern Physics Letters A, 9, 3119–3127. https://doi.org/10.1142/S021773239400294XCrossRefGoogle Scholar
Tenenbaum, J. B, Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011). How to grow a mind: Statistics, structure, and abstraction. Science, 331, 1279–1285. https://doi.org/10.1126/science.1192788CrossRefGoogle Scholar
Tentori, K., Bonini, N., & Osherson, D. (2004). The conjunction fallacy: A misunderstanding about conjunction? Cognitive Science, 28, 467–477. https://doi.org/10.1207/s15516709cog2803_8CrossRefGoogle Scholar
Trueblood, J. S., & Busemeyer, J. R. (2011). A quantum probability account of order effects in inference. Cognitive Science, 35, 1518–1552. https://doi.org/10.1111/j.1551-6709.2011.01197.xCrossRefGoogle ScholarPubMed
Tversky, A., & Kahneman, D. (1983). Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment. Psychological Review, 90, 293–315. https://doi.org/10.1037/0033-295X.90.4.293CrossRefGoogle Scholar
Valdesolo, P., & DeSteno, D. (2006). Manipulations of emotional context shape moral judgment. Psychological Science, 17, 476–477. https://doi.org/10.1111/j.1467-9280.2006.01731.xCrossRefGoogle ScholarPubMed
Wang, Z., Busemeyer, J. R., Atmanspacher, H., & Pothos, E. M. (2013). The potential of using quantum theory to build models of cognition. TopiCS, Topics in Cognitive Science, 5, 672–688. https://doi.org/10.1111/tops.12043Google ScholarPubMed
Wason, P. C. (1968). Reasoning about a rule. Quarterly Journal of Experimental Psychology, 20, 273–281. https://doi.org/10.1080/14640746808400161CrossRefGoogle ScholarPubMed
Wason, P. C., & Johnson-Laird, P. N. (1972). The psychology of reasoning: Structure and content. Cambridge, MA: Harvard University Press.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, 105–136. https://doi.org/10.1016/j.cognition.2007.08.003CrossRefGoogle ScholarPubMed
White, L. C., Pothos, E. M., & Busemeyer, J. R. (2014). Sometimes it does hurt to ask: The constructive role of articulating impressions. Cognition, 133, 48–64. https://doi.org/10.1016/j.cognition.2014.05.015CrossRefGoogle ScholarPubMed
Yearsley, J. M., & Busemeyer, J. R (2016). Quantum cognition and decision theories: A tutorial. Journal of Mathematical Psychology, 74, 99–116. https://doi.org/10.1016/j.jmp.2015.11.005CrossRefGoogle Scholar