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Decyzje

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Year 8/2020 
Issue 33

Zdolności numeryczne jako kluczowe zdolności poznawcze w procesie podejmowania decyzji

Agata Sobków
SWPS Uniwersytet Humanistycznospołeczny

Jakub Figol
SWPS Uniwersytet Humanistycznospołeczny

Jakub Traczyk
SWPS Uniwersytet Humanistycznospołeczny

8/2020 (33) Decyzje

DOI 10.7206/DEC.1733-0092.139

Abstract

The goal of the present paper is to review recent theoretical models and empirical studies on the role of numeracy (i.e., cognitive ability in processing numerical information) in decision making under risk and uncertainty. The research conducted in the last decade points that numeracy is the most robust predictor of making good decisions, which predictions are independent of other psychological constructs or cognitive abilities (such as fluid Intelligence or cognitive reflection). The pivotal role of numeracy has been described in at least three theoretical models: Fuzzy-Trace Theory, Skilled Decision Theory, and Multiple Numeric Competencies model. Furthermore, the results of numerous research indicate that better decisions made by people with high numeracy are underpinned by various psychological mechanisms of the cognitive, motivational, and affective nature. Findings related to the performance of people with high and low numeracy served to develop both immediate (e.g., visual aids or an experience-based format of risk communication) and long-term (e.g.,cognitive training) methods of improving the decision-making process. Based on these decision aids, we can effectively support people with low numeracy in an accurate risk assessment, risk comprehension, and making better decisions.

References

  1. Allais, M. (1953). L’ Extension des Theories de l’Equilibre Economique General et du Rendement Social au Cas du Risque. Econometrica, 21(2), 269–290. Retrieved from https://www.jstor.org/ stable/1905539 [Google Scholar]
  2. Allan, J.N. (2018). Numeracy vs. Intelligence: A model of the relationship between cognitive abilities and decision making. University of Oklahoma. Retrieved from https://shareok.org/handle/ 11244/299906 [Google Scholar]
  3. Armstrong, B., & Spaniol, J. (2017). Experienced Probabilities Increase Understanding of Diagnostic Test Results in Younger and Older Adults. Medical Decision Making, 37(6), 670–679. https:// doi.org/10.1177/0272989X17691954 [Google Scholar]
  4. Ashby, N.J.S. (2017). Numeracy predicts preference consistency: Deliberative search heuristics increase choice consistency for choices from description and experience. Judgment and Decision Making, 12(2), 128–139. [Google Scholar]
  5. Au, J., Sheehan, E., Tsai, N., Duncan, G.J., Buschkuehl, M., & Jaeggi, S.M. (2015). Improving fluid intelligence with training on working memory: a meta-analysis. Psychonomic Bulletin and Review, 22(2), 366–377. https://doi.org/10.3758/s13423-014-0699-x [Google Scholar]
  6. Baron, J. (2008). Thinking and deciding (4th ed.). Cambridge, UK: Cambridge University Press. [Google Scholar]
  7. Brandstätter, E., Gigerenzer, G., & Hertwig, R. (2006). The priority heuristic: making choices without trade-offs. Psychological Review, 113(2), 409–432. https://doi.org/10.1037/0033-295X.113.2.409 [Google Scholar]
  8. Broniatowski, D.A., & Reyna, V. F. (2018). A formal model of fuzzy-trace theory: Variations on framing effects and the Allais Paradox. Decision, 5(4), 205–252. https://doi.org/10.1037/dec0000083 [Google Scholar]
  9. Busemeyer, J., & Townsend, J. (1993). Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment. Psychological Review, 100(3), 432–459. [Google Scholar]
  10. Campbell, J.I.D. (Ed.). (2005). Handbook of Mathematical Cognition. New York, NY: Taylor & Francis Group. [Google Scholar]
  11. Carroll, J.B. (1993). Human cognitive abilities. Cambridge: Cambridge University Press. [Google Scholar]
  12. Catena, A., Maldonado, A., & Cándido, A. (1998). The effect of frequency of judgement and the type of trials on covariation learning. Journal of Experimental Psychology: Human Perception and Performance, 24(2), 481–495. https://doi.org/10.1037/0096-1523.24.2.481 [Google Scholar]
  13. Cokely, E.T., Feltz, A., Ghazal, S., Allan, J.N., Petrova, D.G., & Garcia-Retamero, R. (2018). Decision Making Skill: From Intelligence to Numeracy and Expertise. In K.A. Ericsson, R.R. Hoffman, A. Kozbelt, & A.M. Williams (Eds.), Cambridge Handbook of Expertise and Expert Performance (2nd ed., pp. 476–505). New York, NY: Cambridge University Press. [Google Scholar]
  14. Cokely, E.T., Galesic, M., Schult, E., & Garcia-Retamero, R. (2012). Measuring Risk Literacy: The Berlin Numeracy Test. Judgment and Decision Making, 7(1), 25–47. [Google Scholar]
  15. Cokely, E.T., & Kelley, C.M. (2009). Cognitive abilities and superior decision making under risk : A protocol analysis and process model evaluation. Judgment and Decision Making, 4(1), 20–33. [Google Scholar]
  16. Dehaene, S. (1997). The number sense: how the mind creates mathematics. Oxford, England: Oxford University Press. [Google Scholar]
  17. Dehaene, S. (2003). The neural basis of the Weber-Fechner law: a logarithmic mental number line. Trends in Cognitive Sciences, 7(4), 145–147. https://doi.org/10.1016/S1364-6613(03)00055-X [Google Scholar]
  18. Dolan, J.G., Cherkasky, O.A., Li, Q., Chin, N., & Veazie, P.J. (2016). Should Health Numeracy Be Assessed Objectively or Subjectively? Medical Decision Making, 36(7), 868–875. https://doi.org/ 10.1177/0272989X15584332 [Google Scholar]
  19. Estrada-Mejia, C., de Vries, M., & Zeelenberg, M. (2016). Numeracy and wealth. Journal of Economic Psychology, 54(1), 53–63. https://doi.org/10.1016/j.joep.2016.02.011 [Google Scholar]
  20. Estrada-Mejia, C., Peters, E., Dieckmann, N.F., Zeelenberg, M., De Vries, M., & Baker, D. P. (2020). Schooling, numeracy, and wealth accumulation: A study involving an agrarian population. Journal of Consumer Affairs. https://doi.org/10.1111/joca.12294 [Google Scholar]
  21. Fagerlin, A., Zikmund-Fisher, B.J., Ubel, P.A., Jankovic, A., Derry, H.A., & Smith, D.M. (2007). Measuring numeracy without a math test: Development of the subjective numeracy scale. Medical Decision Making, 27(5), 672–680. https://doi.org/10.1177/0272989X07304449 [Google Scholar]
  22. Galesic, M., & Garcia-Retamero, R. (2011). Graph Literacy A Cross-Cultural Comparison. Medical Decision Making, 31(3), 444–457. https://doi.org/10.1177/0272989X10373805 [Google Scholar]
  23. Garcia-Retamero, R., Andrade, A., Sharit, J., & Ruiz, J.G. (2015). Is patients’ numeracy related to physical and mental health? Medical Decision Making, 35(4), 501–511. https://doi.org/ 10.1177/0272989X15578126 [Google Scholar]
  24. Garcia-Retamero, R., & Cokely, E.T. (2013). Communicating Health Risks With Visual Aids. Current Directions in Psychological Science, 22(5), 392–399. https://doi.org/10.1177/0963721413491570 [Google Scholar]
  25. Garcia-Retamero, R., & Cokely, E.T. (2017). Designing Visual Aids That Promote Risk Literacy: A Systematic Review of Health Research and Evidence-Based Design Heuristics. Human Factors: The Journal of the Human Factors and Ergonomics Society, 59(4), 582–627. https://doi. org/10.1177/0018720817690634 [Google Scholar]
  26. Garcia-Retamero, R., Cokely, E.T., & Hoffrage, U. (2015). Visual aids improve diagnostic inferences and metacognitive judgment calibration. Frontiers in Psychology, 6(932), 1–12. https://doi. org/10.3389/fpsyg.2015.00932 [Google Scholar]
  27. Garcia-Retamero, R., & Galesic, M. (2010). Who proficts from visual aids: Overcoming challenges in people’s understanding of risks. Social Science and Medicine, 70(7), 1019–1025. https://doi. org/10.1016/j.socscimed.2009.11.031 [Google Scholar]
  28. Garcia-Retamero, R., Sobkow, A., Petrova, D. G., Garrido, D., & Traczyk, J. (2019). Numeracy and Risk Literacy: What Have We Learned so Far? Spanish Journal of Psychology, e10, 1–11. https:// doi.org/10.1017/sjp.2019.16 [Google Scholar]
  29. Ghazal, S., Cokely, E.T., & Garcia-Retamero, R. (2014). Predicting biases in very highly educated samples: Numeracy and metacognition. Judgment and Decision Making, 9(1), 15–34. [Google Scholar]
  30. Hasher, L., & Zacks, R.T. (1984). Automatic processing of fundamental information: The case of frequency of occurrence. American Psychologist, 39(12), 1372–1388. https://doi.org/10.1037/0003- -066X.39.12.1372 [Google Scholar]
  31. Hogarth, R.M. (2015). What’s a “Good” Decision? Issues in Assessing Procedural and Ecological Quality. In G. Keren & G. Wu (Eds.), The Wiley Blackwell Handbook of Judgement and Decision Making (pp. 952–972). John Wiley & Sons, Ltd. [Google Scholar]
  32. Izard, V., & Dehaene, S. (2008). Calibrating the mental number line. Cognition, 106(3), 1221–1247. https://doi.org/10.1016/j.cognition.2007.06.004 [Google Scholar]
  33. Jaeggi, S.M., Buschkuehl, M., Jonides, J., & Perrig, W.J. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences of the United States of America, 105(19), 6829–6833. https://doi.org/10.1073/pnas.0801268105 [Google Scholar]
  34. Jaeggi, S.M., Studer-Luethi, B., Buschkuehl, M., Su, Y.F., Jonides, J., & Perrig, W.J. (2010). The relationship between n-back performance and matrix reasoning - implications for training and transfer. Intelligence, 38(6), 625–635. https://doi.org/10.1016/j.intell.2010.09.001 [Google Scholar]
  35. Jasper, J.D., Bhattacharya, C., & Corser, R. (2017). Numeracy Predicts More Effortful and Elaborative Search Strategies in a Complex Risky Choice Context: A Process-Tracing Approach. Journal of Behavioral Decision Making, 30(2), 224–235. https://doi.org/10.1002/bdm.1934 [Google Scholar]
  36. Jasper, J.D., Bhattacharya, C., Levin, I.P., Jones, L., & Bossard, E. (2013). Numeracy as a Predictor of Adaptive Risky Decision Making. Journal of Behavioral Decision Making, 26(2), 164–173. https://doi.org/10.1002/bdm.1748 [Google Scholar]
  37. Kable, J.W., Caulfield, M.K., Falcone, M., McConnell, M., Bernardo, L., Parthasarathi, T., … Lerman, C. (2017). No Effect of Commercial Cognitive Training on Brain Activity, Choice Behavior, or Cognitive Performance. The Journal of Neuroscience, 37(31), 7390–7402. https://doi. org/10.1523/JNEUROSCI.2832-16.2017 [Google Scholar]
  38. Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263–292. [Google Scholar]
  39. Kucian, K., Grond, U., Rotzer, S., Henzi, B., Schönmann, C., Plangger, F., … von Aster, M. (2011). Mental number line training in children with developmental dyscalculia. NeuroImage, 57(3), 782–795. https://doi.org/10.1016/j.neuroimage.2011.01.070 [Google Scholar]
  40. Låg, T., Bauger, L., Lindberg, M., & Friborg, O. (2014). The Role of Numeracy and Intelligence in Health-Risk Estimation and Medical Data Interpretation. Journal of Behavioral Decision Making, 27(2), 95–108. https://doi.org/10.1002/bdm.1788 [Google Scholar]
  41. Leibovich, T., Katzin, N., Harel, M., & Henik, A. (2017). From “sense of number” to “sense of magnitude”: The role of continuous magnitudes in numerical cognition. Behavioral and Brain Sciences, 40, e164. https://doi.org/10.1017/S0140525X16000960 [Google Scholar]
  42. 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(4), 361–381. https://doi.org/10.1002/ bdm.752 [Google Scholar]
  43. Lipkus, I.M., Samsa, G., & Rimer, B.K. (2001). General performance on a numeracy scale among highly educated samples. Medical Decision Making, 21(1), 37–44. https://doi.org/ 10.1177/0272989X0102100105 [Google Scholar]
  44. Loomes, G., & Sugden, R. (1982). Regret Theory: An Alternative Theory of Rational Choice Under Uncertainty. The Economic Journal, 92(368), 805–824. https://doi.org/10.2307/2232669 [Google Scholar]
  45. Lopes, L.L. (1987). Between hope and fear: The psychology of risk. Advances in Experimental Social Psychology, 20, 255–295. https://doi.org/10.1016/S0065-2601(08)60416-5 [Google Scholar]
  46. Melby-Lervåg, M., & Hulme, C. (2013). Is working memory training effective? A meta-analytic review. Developmental Psychology, 49(2), 270–291. https://doi.org/10.1037/a0028228 [Google Scholar]
  47. Miron-Shatz, T., Hanoch, Y., Doniger, G.M., Omer, Z.B., & Ozanne, E.M. (2014). Subjective but not objective numeracy influences willingness to pay for BRCA1 / 2 genetic testing. Judgment and Decision Making, 9(2), 152–158. [Google Scholar]
  48. Nęcka, E. (2018). Trening poznawczy [The cognitive training]. Warszawa: PWN. [Google Scholar]
  49. Okan, Y., Galesic, M., & Garcia-Retamero, R. (2016). How People with Low and High Graph Literacy Process Health Graphs: Evidence from Eye-tracking. Journal of Behavioral Decision Making, 29(2–3), 271–294. https://doi.org/10.1002/bdm.1891 [Google Scholar]
  50. Okan, Y., Garcia-Retamero, R., Cokely, E.T., & Maldonado, A. (2012). Individual Differences in Graph Literacy: Overcoming Denominator Neglect in Risk Comprehension. Journal of Behavioral Decision Making, 25(4), 390–401. https://doi.org/10.1002/bdm.751 [Google Scholar]
  51. Okan, Y., Stone, E.R., & Bruin, B. de B. (2018). Designing Graphs that Promote Both Risk Understanding and Behavior Change. Risk Analysis, 38(5), 929–946. https://doi.org/10.1111/risa.12895 [Google Scholar]
  52. Park, I., & Cho, S. (2018). The influence of number line estimation precision and numeracy on risky financial decision making. International Journal of Psychology. https://doi.org/10.1002/ ijop.12475 [Google Scholar]
  53. Payne, J. W., Bettman, J.R.J.R., & Johnson, E.J.E. (1988). Adaptive strategy selection in decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14(3), 534. Retrieved from http://psycnet.apa.org/journals/xlm/14/3/534/ [Google Scholar]
  54. Payne, J. W., Bettman, J.R., & Johnson, E.J. (1993). The Adaptive Decision Maker. Cambridge: Cambridge University Press. [Google Scholar]
  55. Peters, E. (2017). Educating good decisions. Behavioural Public Policy, 1(02), 162–176. https://doi. org/10.1017/bpp.2016.15 [Google Scholar]
  56. Peters, E., & Bjälkebring, P. (2015). Multiple numeric competencies: When a number is not just a number. Journal of Personality and Social Psychology, 108(5), 802–822. https://doi.org/10.1037/ pspp0000019 [Google Scholar]
  57. Peters, E., Fennema, M.G., & Tiede, K.E. (2019). The loss-bet paradox: Actuaries, accountants, and other numerate people rate numerically inferior gambles as superior. Journal of Behavioral Decision Making, 32(1), 15–29. https://doi.org/10.1002/bdm.2085 [Google Scholar]
  58. Peters, E., & Levin, I.P. (2008). Dissecting the risky-choice framing effect: Numeracy as an individual- difference factor in weighting risky and riskless options. Judgment and Decision Making, 3(6), 435–448. [Google Scholar]
  59. Peters, E., Shoots-Reinhard, B., Tompkins, M.K., Schley, D., Meilleur, L., Sinayev, A., … Crocker, J. (2017). Improving numeracy through values affirmation enhances decision and STEM outcomes. PLOS ONE, 12(7), e0180674. https://doi.org/10.1371/journal.pone.0180674 [Google Scholar]
  60. Peters, E., Tompkins, M.K., Knoll, M.A.Z., Ardoin, S.P., Shoots-Reinhard, B., & Meara, A. S. (2019). Despite high objective numeracy, lower numeric confidence relates to worse financial and medical outcomes. Proceedings of the National Academy of Sciences, 116(39), 19386–19391. https:// doi.org/10.1073/pnas.1903126116 [Google Scholar]
  61. Peters, E., Västfjäll, D., Slovic, P., Mertz, C.K., Mazzocco, K., & Dickert, S. (2006). Numeracy and decision making. Psychological Science, 17(5), 407–413. https://doi.org/10.1111/j.1467- -9280.2006.01720.x [Google Scholar]
  62. Petrova, D.G., Garcia-Retamero, R., Catena, A., Cokely, E., Heredia Carrasco, A., Arrebola Moreno, A., & Ramírez Hernández, J.A. (2017). Numeracy Predicts Risk of Pre-Hospital Decision Delay: a Retrospective Study of Acute Coronary Syndrome Survival. Annals of Behavioral Medicine, 51(2), 292–306. https://doi.org/10.1007/s12160-016-9853-1 [Google Scholar]
  63. Petrova, D.G., Garcia-Retamero, R., Catena, A., & van der Pligt, J. (2016). To screen or not to screen: What factors influence complex screening decisions? Journal of Experimental Psychology: Applied, 22(2), 247–260. https://doi.org/10.1037/xap0000086 [Google Scholar]
  64. Petrova, D.G., Kostopoulou, O., Delaney, B. C., Cokely, E.T., & Garcia-Retamero, R. (2018). Strengths and Gaps in Physicians’ Risk Communication: A Scenario Study of the Influence of Numeracy on Cancer Screening Communication. Medical Decision Making, 38(3), 355–365. https://doi. org/10.1177/0272989X17729359 [Google Scholar]
  65. Petrova, D.G., Traczyk, J., & Garcia-Retamero, R. (2019). What shapes the probability weighting function? Influence of affect, numeric competencies, and information formats. Journal of Behavioral Decision Making, 32(2), 124–139. https://doi.org/10.1002/bdm.2100 [Google Scholar]
  66. Petrova, D.G., van der Pligt, J., & Garcia-Retamero, R. (2014). Feeling the Numbers: On the Interplay Between Risk, Affect, and Numeracy. Journal of Behavioral Decision Making, 27(3), 191– 199. https://doi.org/10.1002/bdm.1803 [Google Scholar]
  67. Quiggin, J. (1982). A theory of anticipated utility. Journal of Economic Behavior & Organization, 3(4), 323–343. https://doi.org/10.1016/0167-2681(82)90008-7 [Google Scholar]
  68. Reber, A.S. (1993). Implicit Learning and Tacit Knowledge. New York: Oxford University Press. [Google Scholar]
  69. Reyna, V.F., & Brainerd, C.J. (1995). Fuzzy-trace theory: An interim synthesis. Learning and Individual Differences, 7(1), 1–75. https://doi.org/10.1016/1041-6080(95)90031-4 [Google Scholar]
  70. Reyna, V.F., & Brainerd, C.J. (2008). Numeracy, ratio bias, and denominator neglect in judgments of risk and probability. Learning and Individual Differences, 18(1), 89–107. https://doi.org/10.1016/j. lindif.2007.03.011 [Google Scholar]
  71. Reyna, V.F., & Brainerd, C.J. (2011). Dual Processes in Decision Making and Developmental Neuroscience: A Fuzzy-Trace Model. Developmental Review, 31(2–3), 180–206. https://doi.org/10.1016/j. dr.2011.07.004 [Google Scholar]
  72. Reyna, V., & Brust-Renck, P. (2014). A review of theories of numeracy: Psychological mechanisms and implications for medical decision making. In B. Anderson & J. Schulkin (Eds.), Numerical Reasoning in Judgments and Decision Making about Health (pp. 215–251). Cambridge: Cambridge University Press. doi:10.1017/CBO9781139644358.011 [Google Scholar]
  73. Reyna, V.F., & Brust-Renck, P.G. (2020). How representations of number and numeracy predict decision paradoxes: A fuzzy-trace theory approach. Journal of Behavioral Decision Making, (February), 1–23. https://doi.org/10.1002/bdm.2179 [Google Scholar]
  74. Reyna, V.F., Estrada, S.M., DeMarinis, J.A., Myers, R.M., Stanisz, J.M., & Mills, B.A. (2011). Neurobiological and memory models of risky decision making in adolescents versus young adults. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37(5), 1125–1142. https:// doi.org/10.1037/a0023943 [Google Scholar]
  75. Reyna, V.F., Nelson, W.L., Han, P.K., & Dieckmann, N.F. (2009). How numeracy influences risk comprehension and medical decision making. Psychological Bulletin, 135(6), 943–973. https:// doi.org/10.1037/a0017327 [Google Scholar]
  76. Reyna, V.F., Rahimi-Golkhandan, S., Garavito, D.M.N., & Helm, R.K. (2018). The fuzzy-trace process model. In W. De Neys (Ed.), Dual Process Theory 2.0 (pp. 82–99). New York, NY: Routledge. [Google Scholar]
  77. Reynvoet, B., & Sasanguie, D. (2016). The Symbol Grounding Problem Revisited: A Thorough Evaluation of the ANS Mapping Account and the Proposal of an Alternative Account Based on Symbol– Symbol Associations. Frontiers in Psychology, 07. https://doi.org/10.3389/fpsyg.2016.01581 [Google Scholar]
  78. Ritchie, S.J., & Tucker-Drob, E. M. (2018). How Much Does Education Improve Intelligence? A Meta- Analysis. Psychological Science, 29(8), 1358–1369. https://doi.org/10.1177/0956797618774253 [Google Scholar]
  79. Rottenstreich, Y., & Hsee, C.K. (2001). Money, kisses, and electric shocks: on the affective psychology of risk. Psychological Science, 12(3), 185–190. https://doi.org/10.1111/1467-9280.00334 [Google Scholar]
  80. Schley, D.R., & Peters, E. (2014). Assessing “Economic Value”: Symbolic-Number Mappings Predict Risky and Riskless Valuations. Psychological Science, 25(3), 753–761. https://doi. org/10.1177/0956797613515485 [Google Scholar]
  81. Schwartz, L.M., Woloshin, S., Black, W.C., & Welch, H.G. (1997). The role of numeracy in understanding the benefit of screening mammography. Annals of Internal Medicine, 127(11), 966–972. https://doi.org/10.7326/0003-4819-127-11-199712010-00003 [Google Scholar]
  82. Simon, H.A. (1990). Invariants of human behavior. Annual Review of Psychology, 41(1), 1–20. https://doi.org/10.1146/annurev.biochem.64.1.721 [Google Scholar]
  83. Simons, D.J., Boot, W. R., Charness, N., Gathercole, S.E., Chabris, C.F., Hambrick, D.Z., & Stine- -Morrow, E.A.L.L. (2016). Do “Brain-Training” Programs Work? Psychological Science in the Public Interest, 17(3), 103–186. https://doi.org/10.1177/1529100616661983 [Google Scholar]
  84. Sobkow, A., Fulawka, K., Tomczak, P., Zjawiony, P., & Traczyk, J. (2019). Does mental number line training work? The effects of cognitive training on real-life mathematics, numeracy, and decision making. Journal of Experimental Psychology: Applied, 25(3), 372–385. https://doi.org/10.1037/ xap0000207 [Google Scholar]
  85. Sobkow, A., Garrido, D., & Garcia-Retamero, R. (2020). Cognitive Abilities and Financial Decision Making. In T. Zaleskiewicz & J. Traczyk (Eds.), Psychological Perspectives on Financial Decision Making, (pp. 71–87). New York: Springer. [Google Scholar]
  86. Sobkow, A., Olszewska, A., & Traczyk, J. (2020). Multiple numeric competencies predict decision outcomes beyond fluid intelligence and cognitive reflection. Intelligence, 80, 101452. https://doi. org/10.1016/j.intell.2020.101452 [Google Scholar]
  87. Sobkow, A., Traczyk, J., Kaufman, S. B., & Nosal, C. (2018). The structure of intuitive abilities and their relationships with intelligence and Openness to Experience. Intelligence, 67, 1–10. https:// doi.org/10.1016/j.intell.2017.12.001 [Google Scholar]
  88. Strelau, J. (2014). Różnice indywidualne. Historia-determinanty-zastosowanie. Scholar. [Google Scholar]
  89. Traczyk, J., & Fulawka, K. (2016). Numeracy moderates the influence of task-irrelevant affect on probability weighting. Cognition, 151, 37–41. https://doi.org/10.1016/j.cognition.2016.03.002 [Google Scholar]
  90. Traczyk, J., Lenda, D., Serek, J., Fulawka, K., Tomczak, P., Strizyk, K., … Sobkow, A. (2018). Does fear increase search effort in more numerate people? An experimental study investigating information acquisition in a decision from experience task. Frontiers in Psychology, 9, 1203. https:// doi.org/10.3389/FPSYG.2018.01203 [Google Scholar]
  91. Traczyk, J., Sobkow, A., Fulawka, K., Kus, J., Petrova, D.G., & Garcia-Retamero, R. (2018). Numerate decision makers don’t use more effortful strategies unless it pays: A process tracing investigation of skilled and adaptive strategy selection in risky decision making. Judgment and Decision Making, 13(4), 372–381. [Google Scholar]
  92. Traczyk, J., Sobkow, A., Matukiewicz, A., Petrova, D. G., & Garcia-Retamero, R. (2019). The experience- based format of probability improves probability estimates: The moderating role of individual differences in numeracy. International Journal of Psychology. https://doi.org/10.1002/ ijop.12566 [Google Scholar]
  93. Tversky, A., & Kahneman, D. (1981). The Framing of Decisions and the Psychology of Choice. Science, 211(4481), 453–458. [Google Scholar]
  94. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323. https://doi.org/10.1007/BF00122574 [Google Scholar]
  95. Tyszka, T., & Sawicki, P. (2011). Affective and cognitive factors influencing sensitivity to probabilistic information. Risk Analysis, 31(11), 1832–1845. https://doi.org/10.1111/j.1539-6924.2011.01644.x [Google Scholar]
  96. Vlek, C. (1984). What constitutes ‘a good decision’? Acta Psychologica, 56(1–3), 5–27. https://doi. org/10.1016/0001-6918(84)90004-0 [Google Scholar]
  97. Wegier, P., & Shaffer, V.A. (2017). Aiding risk information learning through simulated experience (ARISE): Using simulated outcomes to improve understanding of conditional probabilities in prenatal Down syndrome screening. Patient Education and Counseling, 100(10), 1882–1889. https://doi.org/10.1016/j.pec.2017.04.016 [Google Scholar]
  98. Weller, J.A., Dieckmann, N.F., Tusler, M., Mertz, C.K., Burns, W.J., & Peters, E. (2013). Development and Testing of an Abbreviated Numeracy Scale: A Rasch Analysis Approach. Journal of Behavioral Decision Making, 26(2), 198–212. https://doi.org/10.1002/bdm.1751 [Google Scholar]
  99. Woller-Carter, M.M., Okan, Y., Cokely, E.T., & Garcia-Retamero, R. (2012). Communicating and Distorting Risks with Graphs: An Eye-Tracking Study. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 56(1), 1723–1727. https://doi.org/10.1177/1071181312561345 [Google Scholar]
  100. Zacks, R.T., & Hasher, L. (2002). Frequency processing: A twenty-five year perspective. In P. Sedlmeier & T. Betsch (Eds.), ETC. Frequency processing and cognition, (pp. 21–36). New York: Oxford University Press. [Google Scholar]
  101. Zaleskiewicz, T., & Traczyk, J. (2020). Emotions and Financial Decision Making. In T. Zaleskiewicz & J. Traczyk (Eds.), Psychological Perspectives on Financial Decision Making, (pp 107–133). Springer. [Google Scholar]

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Zdolności numeryczne jako kluczowe zdolności poznawcze w procesie podejmowania decyzji. (2020). Zdolności numeryczne jako kluczowe zdolności poznawcze w procesie podejmowania decyzji. Decyzje, (33), 25-54. https://doi.org/10.7206/DEC.1733-0092.139 (Original work published 8/2020AD)

MLA style

“Zdolności Numeryczne Jako Kluczowe Zdolności Poznawcze W Procesie Podejmowania Decyzji”. 8/2020AD. Decyzje, no. 33, 2020, pp. 25-54.

Chicago style

“Zdolności Numeryczne Jako Kluczowe Zdolności Poznawcze W Procesie Podejmowania Decyzji”. Decyzje, Decyzje, no. 33 (2020): 25-54. doi:10.7206/DEC.1733-0092.139.