Educating Intuition: Reducing Risky Decisions Using Fuzzy-Trace Theory

Current Directions in Psychological Science

Volumn 24, Issue 5, 2015, Pages 392-398

  Reyna, Valerie F ^a,b,c   Weldon, Rebecca B ^a   McCormick, Michael ^a  

^a Department of Human Development ^*  (United States)

^b Human Neuroscience Institute   (United States)

^c Cornell University ^*  (United States)

Author keywords

adolescence; behavior change; decision making; fuzzy trace theory; health

Indexed keywords

EID: 84944066071     PISSN: 09637214     EISSN: 14678721     Source Type: Journal    
DOI: 10.1177/0963721415588081     Document Type: Article

References (43)

1. I.N.DefoeJ.J.S.DubasB.FignerM.A.G.van Aken (2014). A meta-analysis on age differences in risky decision making: Adolescents versus children and adults. Psychological Bulletin, 141, 48–84. doi:http://dx.doi.org/10.1037/a0038088. The first comprehensive evaluation of evidence for age differences in risk taking, comparing predictions of neurodevelopmental imbalance theory and fuzzy-trace theory.
2. V.F.ReynaV.Zayas (2014). The neuroscience of risky decision making. Washington, DC: American Psychological Association. Leading researchers review the field and explain the underpinnings of risky decision making in the brain.
3. A.B.SuleimanC.D.Brindis (2014). Adolescent school-based sex education: Using developmental neuroscience to guide new directions for policy and practice. Sexuality Research and Social Policy, 11, 137–152. doi:10.1007/s13178-014-0147-8. A masterful review of sexual education and policy in light of conceptual and empirical advances in understanding adolescent brain development.
4. J.S.Anderson (2014). Cortical underconnectivity hypothesis in autism: Evidence from functional connectivity MRI. In V.B.PatelV.R.PreedyC.R.Martin (Eds.), Comprehensive guide to autism (pp. 1457–1471). New York, NY: Springer.
5. C.J.BrainerdV.F.Reyna (2005). The science of false memory. New York, NY: Oxford University Press.
6. C.J.BrainerdV.F.ReynaC.F.A.GomesA.E.KenneyC.J.GrossE.S.TaubR.N.Spreng (2014). Dual-retrieval models and neurocognitive impairment. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40, 41–65.
7. C.J.BrainerdV.F.ReynaM.L.Howe (2009). Trichotomous processes in early memory development, aging, and neurocognitive impairment: A unified theory. Psychological Review, 116, 783–832. doi:10.1037/a0016963
8. D.A.BroniatowskiE.Y.KleinV.F.Reyna (2015). Germs are germs, and why not take a risk?: Patients’ expectations for prescribing antibiotics in an inner city emergency department. Medical Decision Making, 35, 60–67. doi:10.1177/0272989X14553472
9. B.J.CaseyK.Caudle (2013). The teenage brain: Self control. Current Directions in Psychological Science, 22, 82–87. doi:10.1177/0963721413480170
10. B.De MartinoN.A.HarrisonS.KnafoG.BirdR.J.Dolan (2008). Explaining enhanced logical consistency during decision making in autism. The Journal of Neuroscience, 28, 10746–10750. doi:10.1523/JNEUROSCI.2895-08.2008
11. N.A.DennisC.R.BowmanS.N.Vandekar (2012). True and phantom recollection: An fMRI investigation of similar and distinct neural correlates and connectivity. NeuroImage, 59, 2982–2993. doi:10.1016/i.neuroimage.2011.09.079
12. B.FignerE.U.Weber (2011). Who takes risks when and why?: Determinants of risk taking. Current Directions in Psychological Science, 20, 211–216. doi:10.1177/0963721411415790
13. L.FraenkelE.PetersP.CharpentierB.OlsenL.ErranteR.T.SchoenV.Reyna (2012). Decision tool to improve the quality of care in rheumatoid arthritis. Arthritis Care & Research, 64, 977–985. doi:10.1002/acr.21657
14. U.Frith (2012). Why we need cognitive explanations of autism. The Quarterly Journal of Experimental Psychology, 65, 2073–2092. doi:10.1080/17470218.2012.697178
15. K.FujitaH.A.Han (2009). Moving beyond deliberative control of impulses: The effects of construal levels on evaluative associations in self-control. Psychological Science, 20, 799–804.
16. J.FukukuraM.J.FergusonK.Fujita (2013). Psychological distance can improve decision making under information overload via gist memory. Journal of Experimental Psychology: General, 142, 658–665. doi:10.1037/a0030730
17. M.GerrardF.X.GibbonsA.HoulihanM.L.StockE.A.Pomery (2008). A dual-process approach to health risk decision making. Developmental Review, 28, 29–61.
18. R.M.HogarthE.Soyer (2015). Improving judgments and decisions by experiencing simulated outcomes. In E.A.WilhelmsV.F.Reyna (Eds.), Neuroeconomics, judgment, and decision making (pp. 254–274). New York, NY: Psychology Press.
19. M.A.JustT.A.KellerR.K.Kana (2013). A theory of autism based on frontal-posterior underconnectivity. In M.A.JustK.A.Pelphrey (Eds.), Development and brain systems in autism (pp. 35–63). New York, NY: Psychology Press.
20. D.KirbyK.CoyleF.AltonL.RolleriL.Robin (2011). Reducing adolescent sexual risk: A theoretical guide for developing and adapting curriculum-based programs. Scotts Valley, CA: ETR Associates.
21. A.KühbergerC.Tanner (2010). Risky choice framing: Task versions and a comparison of prospect theory and fuzzy-trace theory. Journal of Behavioral Decision Making, 23, 314–329. doi:10.1002/bdm.656
22. I.P.LevinE.A.BossardG.J.GaethH.Yan (2014). The combined role of task, child’s age and individual differences in understanding decision processes. Judgment and Decision Making, 9, 274–286.
23. I.P.LevinJ.A.WellerL.A.HarshmanA.A.Pederson (2007). Age-related differences in adaptive decision making: Sensitivity to expected value in risky choice. Judgment and Decision Making, 2, 225–233.
24. H.L.MillerT.N.OdegardG.Allen (2014). Evaluating information processing in Autism Spectrum Disorder: The case for Fuzzy Trace Theory. Developmental Review, 34, 44–76.
25. B.A.MillsV.F.ReynaS.M.Estrada (2008). Explaining contradictory relations between risk perception and risk taking. Psychological Science, 19, 429–433. doi:10.1111/j.1467-9280.2008.02104.x.
26. V.F.Reyna (2008). A theory of medical decision making and health: Fuzzy-trace theory. Medical Decision Making, 28, 850–865. doi:10.1177/0272989X08327066
27. V.F.Reyna (2012). A new intuitionism: Meaning, memory, and development in Fuzzy-Trace Theory. Judgment and Decision Making, 7, 332–359.
28. V.F.ReynaC.J.Brainerd (2011). Dual processes in decision making and developmental neuroscience: A fuzzy-trace model. Developmental Review, 31, 180–206. doi:10.1016/j.dr.2011.07.004
29. V.F.ReynaC.F.ChickJ.C.CorbinA.N.Hsia (2014). Developmental reversals in risky decision-making: Intelligence agents show larger decision biases than college students. Psychological Science, 25, 76–84. doi:10.1177/0956797613497022
30. V.F.ReynaK.CroomL.Staiano-CoicoM.L.LesserD.LewisJ.FrankT.Marchell (2013). Endorsement of a personal responsibility to adhere to the minimum drinking age law predicts consumption, risky behaviors, and alcohol-related harms. Psychology, Public Policy, and Law, 19, 380–394. doi:10.1037/a0032538
31. V.F.ReynaS.M.EstradaJ.A.DeMarinisR.M.MyersJ.M.StaniszB.A.Mills (2011). Neurobiological and memory models of risky decision making in adolescents versus young adults. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37, 1125–1142. doi:10.1037/a0023943
32. V.F.ReynaF.Farley (2006). Risk and rationality in adolescent decision making: Implications for theory, practice, and public policy. Psychological Science in the Public Interest, 7, 1–44. doi:10.1111/j.1529-1006.2006.00026.x
33. V.F.ReynaS.A.Huettel (2014). Reward, representation, and impulsivity: A theoretical framework for the neuroscience of risky decision making. In V.F.ReynaV.Zayas (Eds.), The neuroscience of risky decision making (pp. 11–42). Washington, DC: American Psychological Association.
34. V.F.ReynaF.J.Lloyd (2006). Physician decision-making and cardiac risk: Effects of knowledge, risk perception, risk tolerance, and fuzzy processing. Journal of Experimental Psychology: Applied, 12, 179–195. doi:10.1037/1076-898X.12.3.179
35. V.F.ReynaB.A.Mills (2007). Interference processes in fuzzy-trace theory: Aging, Alzheimer’s disease, and development. In D.S.GorfeinC.M.MacLeod (Eds.), Inhibition in cognition (pp. 185–210). Washington, DC: American Psychological Association.
36. V.F.ReynaB.A.Mills (2014). Theoretically motivated interventions for reducing sexual risk taking in adolescence: A randomized controlled experiment using fuzzy-trace theory. Journal of Experimental Psychology: General, 143, 1627–1648. doi:10.1037/a0036717
37. J.D.RoitmanE.M.BrannonM.L.Platt (2012). Representation of numerosity in posterior parietal cortex. Frontiers in Integrative Neuroscience, 6, Article 25. Retrieved from http://journal.frontiersin.org/article/10.3389/fnint.2012.00025/full
38. D.RomerM.Hennessy (2007). A biosocial-affect model of adolescent sensation seeking: The role of affect evaluation and peer-group influence in adolescent drug use. Prevention Science, 8, 89–101.
39. G.R.Samanez-LarkinB.Knutson (2014). Reward processing and risky decision making in the aging brain. In V.F.ReynaV.Zayas (Eds.), The neuroscience of risky decision making (pp. 123–142). Washington, DC: American Psychological Association.
40. L.Steinberg (2008). A social neuroscience perspective on adolescent risk-taking. Developmental Review, 28, 78–106.
41. A.TymulaL.A.R.BelmakerA.K.RoyL.RudermanK.MansonP.W.GlimcherI.Levy (2012). Adolescents’ risk-taking behavior is driven by tolerance to ambiguity. Proceedings of the National Academy of Sciences, USA, 109, 17135–17140.
42. V.VenkatramanJ.W.PayneJ.R.BettmanM.F.LuceS.A.Huettel (2009). Separate neural mechanisms underlie choices and strategic preferences in risky decision making. Neuron, 62, 593–602. doi:10.1016/j.neuron.2009.04.007
43. C.R.WolfeV.F.ReynaC.L.WidmerE.M.CedillosC.R.FisherP.G.Brust-RenckA.M.Weil (2015). Efficacy of a Web-based intelligent tutoring system for communicating genetic risk of breast cancer: A fuzzy-trace theory approach. Medical Decision Making, 35, 46–59. doi:10.1177/0272989X14535983