Ecological rationality or nested sets? Individual differences in cognitive processing predict Bayesian reasoning

Psychonomic Bulletin and Review

Volumn 21, Issue 1, 2014, Pages 198-204

  Sirota, Miroslav ^a   Juanchich, Marie ^b   Hagmayer, York ^a  

^a KING'S COLLEGE LONDON   (United Kingdom)

^b KINGSTON UNIVERSITY   (United Kingdom)

Author keywords

Bayesian reasoning; Ecological rationality account; Individual differences; Natural frequencies; Nested sets account

Indexed keywords

ADOLESCENT; ADULT; ARTICLE; BAYES THEOREM; COGNITION; FEMALE; HUMAN; INDIVIDUALITY; MALE; PHYSIOLOGY; PREDICTIVE VALUE; THINKING; YOUNG ADULT;

ADOLESCENT; ADULT; BAYES THEOREM; COGNITION; FEMALE; HUMANS; INDIVIDUALITY; MALE; PREDICTIVE VALUE OF TESTS; THINKING; YOUNG ADULT;

EID: 84893050286     PISSN: 10699384     EISSN: 15315320     Source Type: Journal    
DOI: 10.3758/s13423-013-0464-6     Document Type: Article

References (38)

1. Barbey, A. K., & Sloman, S. A. (2007a). Base-rate respect: From ecological rationality to dual processes. Behavioral and Brain Sciences, 30, 241-254.
2. Barbey, A. K., & Sloman, S. A. (2007b). Base-rate respect: From statistical formats to cognitive structures. Author's response to commentaries. Behavioral and Brain Sciences, 30, 287-292.
3. Baron, J. (2005). Rationality and Intelligence. Cambridge, UK: Cambridge University Press.
4. Barrett, H. C., & Kurzban, R. (2006). Modularity in cognition: Framing the debate. Psychological Review, 113, 628-647.
5. Brase, G. L. (2008). Frequency interpretation of ambiguous statistical information facilitates Bayesian reasoning. Psychonomic Bulletin and Review, 15, 284-289.
6. Brase, G. L. (2007). The (In)flexibility of evolved frequency representations for statistical reasoning: Cognitive styles and brief prompts do not influence Bayesian inference. Acta Psychologica Sinica, 39, 398-405.
7. Brase, G. L., & Barbey, A. K. (2006). Mental representations of statistical information. In A. Columbus (Ed.), Advances in psychology research, vol. 41 (pp. 91-113). New York: Nova Science Publishers, Inc.
8. Brase, G., Cosmides, L., & Tooby, J. (1998). Individuation, counting, and statistical inference: The role of frequency and whole object representations in judgment under uncertainty. Journal of Experimental Psychology: General, 127, 3-21.
9. Coltheart, M. (1999). Modularity and cognition. Trends in Cognitive Sciences, 3, 115-120.
10. Cosmides, L., & Tooby, J. (1996). Are humans good intuitive statisticians after all? Rethinking some conclusions from the literature on judgment under uncertainty. Cognition, 58, 1-73.
11. Cosmides, L., & Tooby, J. (2008). Can a general deontic logic capture the facts of human moral reasoning? How the mind interprets social exchange rules and detects cheaters. In W. Sinnott-Armstrong (Ed.), Moral psychology (pp. 53-119). Cambridge: MIT Press.
12. De Neys, W. (2006). Dual processing in reasoning: Two systems but one reasoner. Psychological Science, 17, 428-433.
13. De Neys, W. (2007). Nested sets and base rate neglect: Two types of reasoning? Behavioral and Brain Sciences, 30, 260-261.
14. Eddy, D. M. (1982). Probabilistic reasoning in clinical medicine: Problems and opportunities. In D. Kahneman, P. Slovic, & A. Tversky (Eds.), Judgment under uncertainty: Heuristics and biases (pp. 249-67). Cambridge: Cambridge University Press.
15. Frederick, S. (2005). Cognitive reflection and decision making. Journal of Economic Perspectives, 19, 25-42.
16. Gigerenzer, G., & Hoffrage, U. (1995). How to improve Bayesian reasoning without instruction: Frequency formats. Psychological Review, 102, 684-704.
17. Gigerenzer, G., & Hoffrage, U. (1999). Overcoming difficulties in Bayesian reasoning: A reply to Lewis and Keren (1999) and Mellers and McGraw (1999). Psychological Review, 106, 425-430.
18. Girotto, V., & Gonzalez, M. (2001). Solving probabilistic and statistical problems: A matter of information structure and question form. Cognition, 78, 247-276.
19. Kintsch, W., & Greeno, J. G. (1985). Understanding and solving word arithmetic problems. Psychological Review, 92, 109-129.
20. Kintsch, W. (1988). The use of knowledge in discourse processing: A construction-integration model. Psychological Review, 95, 163-182.
21. Lesage, E., Navarrete, G., & De Neys, W. (2013). Evolutionary modules and Bayesian facilitation: The role of general cognitive resources. Thinking & Reasoning, 19, 27-53.
22. Lewis, C., & Keren, G. (1999). On the difficulties underlying Bayesian reasoning: Comment on Gigerenzer and Hoffrage. Psychological Review, 106, 411-416.
23. Liberali, J. M., Reyna, V. F., Furlan, S., Stein, L. M., & Pardo, S. T. (2012). Individual differences in numeracy and cognitive reflection, with implications for biases and fallacies in probability judgment. Journal of Behavioral Decision Making, 25, 361-381.
24. Macpherson, R., & Stanovich, K. E. (2007). Cognitive ability, thinking dispositions, and instructional set as predictors of critical thinking. Learning and Individual Differences, 17, 115-127.
25. Pashler, H., & Wagenmakers, E.-J. (2012). Editors' introduction to the special section on replicability in psychological science: A crisis of confidence? Perspectives on Psychological Science, 7, 528-530.
26. Pacini, R., & Epstein, S. (1999). The relation of rational and experiential information processing styles to personality, basic beliefs, and the ratio-bias phenomenon. Journal of Personality and Social Psychology, 76, 972-987.
27. Ragnubar, K. P., Barnes, M. A., & Hecht, S. A. (2010). Working memory and mathematics: A review of developmental, individual difference, and cognitive approaches. Learning and Individual Differences, 20, 110-122.
28. Raven, J. C., Court, J. H., & Raven, J. (1977). Manual for advanced progressive matrices (Sets I & IT). London: H. K. Lewis & Co.
29. Raven, J. C., Court, J. H., & Raven, J. (1991). Ravenove progresívne matice pre pokročilých. Bratislava: Psychodiagnostika.
30. Siegrist, M., & Keller, C. (2011). Natural frequencies and Bayesian reasoning: The impact of formal education and problem context. Journal of Risk Research, 14, 1039-1055.
31. Sirota, M., & Juanchich, M. (2011). Role of numeracy and cognitive reflection in Bayesian inference with natural frequencies. Studia Psychologica, 53, 151-161.
32. Sloman, S. A., Over, D., Slovak, L., & Stibel, J. M. (2003). Frequency illusions and other fallacies. Organizational Behavior & Human Decision Processes, 91, 296-309.
33. Stanovich, K. E. (1999). Who is rational? Studies in individual differences in reasoning. New Jersey: Lawrence Erlbaum Associates.
34. Stanovich, K. E., & West, R. F. (1998). Individual differences in rational thought. Journal of Experimental Psychology - General, 127, 161-188.
35. Toplak, M. E., West, R. F., & Stanovich, K. E. (2011). The Cognitive Reflection Test as a predictor of performance on heuristics and biases tasks. Memory & Cognition, 39, 1275-1289.
36. Tversky, A., & Kahneman, D. (1983). Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment. Psychological Review, 90, 293-315.
37. Yamagishi, K. (2003). Facilitating normative judgments of conditional probability: Frequency or nested sets? Experimental Psychology, 50, 97-106.
38. Zhu, L., & Gigerenzer, G. (2006). Children can solve Bayesian problems: The role of representation in mental computation. Cognition, 98, 287-308.