How to train your Bayesian: A problem-representation transfer rather than a format-representation shift explains training effects

Quarterly Journal of Experimental Psychology

Volumn 68, Issue 1, 2015, Pages 1-9

  Sirota, Miroslav ^a   Kostovičová, Lenka ^b   Vallée Tourangeau, Frédéric ^c  

^a KING'S COLLEGE LONDON   (United Kingdom)

^b INSTITUTE OF CHEMISTRY   (Slovakia)

^c KINGSTON UNIVERSITY   (United Kingdom)

Author keywords

Bayes factor analysis; Bayesian reasoning; Problem solving; Representational training

Indexed keywords

ADOLESCENT; ADULT; BAYES THEOREM; CONCEPT FORMATION; CONTROLLED STUDY; FEMALE; FOLLOW UP; HUMAN; LEARNING; MALE; PHYSIOLOGY; RANDOMIZED CONTROLLED TRIAL; TEACHING; YOUNG ADULT;

ADOLESCENT; ADULT; BAYES THEOREM; CONCEPT FORMATION; FEMALE; FOLLOW-UP STUDIES; HUMANS; LEARNING; MALE; TEACHING; TRANSFER (PSYCHOLOGY); YOUNG ADULT;

EID: 84912052206     PISSN: 17470218     EISSN: 17470226     Source Type: Journal    
DOI: 10.1080/17470218.2014.972420     Document Type: Article

References (20)

1. Barbey, A. K., & Sloman, S. A. (2007). Base-rate respect: From ecological rationality to dual processes. Behavioral and Brain Sciences, 30, 241–297. doi:10.1017/S0140525X07001653
2. Brase, G. L. (2008). Frequency interpretation of ambiguous statistical information facilitates Bayesian reasoning. Psychonomic Bulletin & Review, 15, 284–289. doi:10.3758/PBR.15.2.284
3. 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. doi:10.1016/0010-0277(95)00664-8
4. Gentner, D., Loewenstein, J., & Thompson, L. (2003). Learning and transfer: A general role for analogical encoding. Journal of Educational Psychology, 95, 393–408. doi:10.1037/0022-0663.95.2.393
5. Gigerenzer, G., & Hoffrage, U. (1995). How to improve Bayesian reasoning without instruction: Frequency formats. Psychological Review, 102, 684–704. doi:10.1037/0033-295X.102.4.684
6. Girotto, V., & Gonzalez, M. (2001). Solving probabilistic and statistical problems: A matter of information structure and question form. Cognition, 78, 247–276. doi:10.1016/S0010-0277(00)00133-5
7. Girotto, V., & Gonzalez, M. (2002). Chances and frequencies in probabilistic reasoning: Rejoinder to Hoffrage, Gigerenzer, Krauss, and Martignon. Cognition, 84, 353–359. doi:10.1016/S0010-0277(02)00051-3
8. Hoffrage, U., & Gigerenzer, G. (2004). How to improve the diagnostic inferences of medical experts. In E. Kurz-Milke & G. Gigerenzer (Eds.), Experts in science and society (pp. 249–68). New York: Kluwer Academic/Plenum Publisher.
9. Kurzenhäuser, S., & Hoffrage, U. (2002). Teaching Bayesian reasoning: An evaluation of a classroom tutorial for medical students. Medical Teacher, 24, 516–521. doi:10.1080/0142159021000012540
10. Lesage, E., Navarrete, G., & De Neys, W. (2013). Evolutionary modules and Bayesian facilitation: The role of general cognitive resources. Thinking & Reasoning, 19, 27–53. doi:10.1080/13546783.2012.713177
11. Miles, A., & Konheim-Kalkstein, Y. L. (2008). The effects of visual representation on Bayesian decision making and Bayesian reasoning. Sentience – The University of Minnesota Undergraduate Journal of Psychology, 1, 1–6.
12. Novick, L. (1988). Analogical transfer, problem similarity and expertise. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 510–520. doi:10.1037/0278–7393.14.3.510
13. Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t-tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16, 225–237. doi:10.3758/PBR.16.2.225
14. Ruscio, J. (2003). Comparing Bayeś theorem to frequency-based approaches to teaching Bayesian reasoning. Teaching of Psychology, 30, 325–328.
15. Sedlmeier, P. (1999). Improving statistical reasoning: Theoretical models and practical implications. Mahwah, NJ: Erlbaum.
16. Sedlmeier, P., & Gigerenzer, G. (2001). Teaching Bayesian reasoning in less than two hours. Journal of Experimental Psychology: General, 130, 380–400. doi:10.1037/0096-3445.130.3.380
17. Sirota, M., Juanchich, M., & Hagmayer, Y. (2014). Ecological rationality or nested sets? Individual differences in cognitive processing predict Bayesian reasoning. Psychonomic Bulletin & Review, 21, 198–204. doi:10.3758/s13423-013-0464-6
18. Sirota, M., Kostovičová, L., & Juanchich, M. (2014). Effect of iconicity of visual displays on statistical reasoning: Evidence in favour of the null hypothesis. Psychonomic Bulletin & Review, 21, 961–968. doi:10.3758/s13423-013-0555-4
19. Sloman, S. A., Over, D., Slovak, L., & Stibel, J. M. (2003). Frequency illusions and other fallacies. Organizational Behavior and Human Decision Processes, 91, 296–309. doi:10.1016/S0749-5978(03)00021-9
20. Zhu, L., & Gigerenzer, G. (2006). Children can solve Bayesian problems: The role of representation in mental computation. Cognition, 98, 287–308. doi:10.1016/j.cognition.2004.12.003