PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Czasopismo
2020 | nr 33 | 25--53
Tytuł artykułu

Zdolności numeryczne jako kluczowe zdolności poznawcze w procesie podejmowania decyzji

Warianty tytułu
Języki publikacji
PL
Abstrakty
Celem artykułu jest dokonanie przeglądu modeli teoretycznych oraz badań empirycznych nad rolą zdolności numerycznych (tj. zdolności umysłowych w przetwarzaniu informacji numerycznych) w podejmowaniu decyzji w warunkach ryzyka i niepewności. Badania prowadzone w ostatniej dekadzie wskazują, że zdolności numeryczne są jednym z najważniejszych predyktorów podejmowania dobrych decyzji, którego przewidywania są niezależne od innych konstruktów psychologicznych oraz zdolności umysłowych (takich jak inteligencja płynna czy refleksyjność poznawcza). Kluczowa rola zdolności numerycznych jest opisywana w co najmniej trzech modelach teoretycznych: teorii śladu rozmytego, teorii umiejętnego podejmowania decyzji oraz koncepcji wielorakich zdolności numerycznych. Wyniki licznych badań empirycznych wskazują na to, że u podłoża podejmowania lepszych decyzji przez osoby z wysokim poziomem zdolności numerycznych leżą mechanizmy psychologiczne natury poznawczej, motywacyjnej i afektywnej. Odkrycia dotyczące funkcjonowania osób z wysokim i niskim poziomem zdolności numerycznych posłużyły do opracowania zarówno doraźnych (np. pomoce wizualne lub komunikowanie ryzyka w formacie doświadczeniowym), jak i długofalowych (np. treningi poznawcze) metod wspierania procesu podejmowania decyzji. Dzięki tym pomocom decyzyjnym opracowano skuteczne sposoby wspierania osób z niskim poziomem zdolności numerycznych w trafnej ocenie i rozumieniu ryzyka oraz podejmowaniu dobrych decyzji. (abstrakt oryginalny)
EN
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. (original abstract)
Czasopismo
Rocznik
Numer
Strony
25--53
Opis fizyczny
Twórcy
  • SWPS Uniwersytet Humanistycznospołeczny
autor
  • SWPS Uniwersytet Humanistycznospołeczny
  • SWPS Uniwersytet Humanistycznospołeczny
Bibliografia
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Baron, J. (2008). Thinking and deciding (4th ed.). Cambridge, UK: Cambridge University Press.
  • 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.
  • 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.
  • 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.
  • Campbell, J.I.D. (Ed.). (2005). Handbook of Mathematical Cognition. New York, NY: Taylor & Francis Group.
  • Carroll, J.B. (1993). Human cognitive abilities. Cambridge: Cambridge University Press.
  • 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.
  • 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.
  • 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.
  • 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.
  • Dehaene, S. (1997). The number sense: how the mind creates mathematics. Oxford, England: Oxford University Press.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-292.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Nęcka, E. (2018). Trening poznawczy [The cognitive training]. Warszawa: PWN.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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/.
  • Payne, J. W., Bettman, J.R., & Johnson, E.J. (1993). The Adaptive Decision Maker. Cambridge: Cambridge University Press.
  • Peters, E. (2017). Educating good decisions. Behavioural Public Policy, 1(02), 162-176. https://doi.org/10.1017/bpp.2016.15.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Reber, A.S. (1993). Implicit Learning and Tacit Knowledge. New York: Oxford University Press. 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Strelau, J. (2014). Różnice indywidualne. Historia-determinanty-zastosowanie. Scholar.
  • 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.
  • 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.
  • 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.
  • 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.
  • Tversky, A., & Kahneman, D. (1981). The Framing of Decisions and the Psychology of Choice. Science, 211(4481), 453-458.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171599723

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.