View Item 

Show simple item record

What if I am the one? Measuring individual differences in emotional sensitivity to probability and emotional reactivity to possibility
dc.contributor.authorLacey, Heather P.
dc.contributor.authorLacey, Steven C.
dc.contributor.authorScherer, Laura D.
dc.contributor.authorZikmund‐fisher, Brian J.
dc.date.accessioned2021-01-05T18:46:18Z
dc.date.availableWITHHELD_13_MONTHS
dc.date.available2021-01-05T18:46:18Z
dc.date.issued2021-01
dc.identifier.citationLacey, Heather P.; Lacey, Steven C.; Scherer, Laura D.; Zikmund‐fisher, Brian J. (2021). "What if I am the one? Measuring individual differences in emotional sensitivity to probability and emotional reactivity to possibility." Journal of Behavioral Decision Making 34(1): 3-19.
dc.identifier.issn0894-3257
dc.identifier.issn1099-0771
dc.identifier.urihttps://hdl.handle.net/2027.42/163862
dc.description.abstractCurrent theories of risk perception point to the powerful role of emotion and the neglect of probabilistic information in the face of risk, but these tendencies differ across individuals. We propose a method for measuring individuals’ emotional sensitivity to probability to assess how feelings about probabilities, rather than the probabilities themselves, influence decisions. Participants gave affective ratings (worry or excitement) to 14 risky events, each with a specified probability ranging from 1 in 10 to 1 in 10,000,000. For each participant, we regressed these emotional responses against item probabilities, estimating a slope (the degree to which emotional responses change with probability) and an intercept (the emotional reaction to an event with a fixed probability). These two parameters were treated as individual difference scores and included in models predicting reactions to several health risk scenarios. Both emotional sensitivity to probability (slope) and emotional reactivity to possibility (intercept) significantly predicted responses to these scenarios, above and beyond the predictive power of other well- established individual difference measures.
dc.publisherR Foundation for Statistical Computing
dc.publisherWiley Periodicals, Inc.
dc.subject.othersensitivity to probability
dc.subject.otherhealth decisions
dc.subject.otherindividual differences
dc.subject.otherrisk perception
dc.titleWhat if I am the one? Measuring individual differences in emotional sensitivity to probability and emotional reactivity to possibility
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelPsychology
dc.subject.hlbtoplevelSocial Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/163862/1/bdm2194.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/163862/2/bdm2194_am.pdf
dc.identifier.doi10.1002/bdm.2194
dc.identifier.sourceJournal of Behavioral Decision Making
dc.identifier.citedreferenceScherer, L. D., de Vries, M., Zikmund- Fisher, B. J., Witteman, H. O., & Fagerlin, A. ( 2015 ). Trust in deliberation: The consequences of deliberative decision strategies for medical decisions. Health Psychology, 34 ( 11 ), 1090 - 1099. https://doi.org/10.1037/hea0000203
dc.identifier.citedreferencePeters, E., Hibbard, J., Slovic, P., & Dieckmann, N. ( 2007 ). Numeracy skill and the communication, comprehension, and use of risk- benefit information. Health Affairs, 26 ( 3 ), 741 - 748. https://doi.org/10.1377/hlthaff.26.3.741
dc.identifier.citedreferencePeters, E., & Slovic, P. ( 2000 ). The springs of action: Affective and analytical information processing in choice. Personality and Social Psychology Bulletin, 26 ( 12 ), 1465 - 1475. https://doi.org/10.1177/01461672002612002
dc.identifier.citedreferencePetrova, D., Traczyk, J., & Garcia- Retamero, R. ( 2018 ). What shapes the probability weighting function? Influence of affect, numeric competencies, and information formats. Journal of Behavioral Decision Making, 32, 1 - 16. https://doi.org/10.1002/bdm.2100
dc.identifier.citedreferencePetrova, 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
dc.identifier.citedreferencePighin, S., Savadori, L., Barilli, E., Cremonesi, L., Ferrari, M., & Bonnefon, J.- F. ( 2011 ). The 1- in- x effect on the subjective assessment of medical probabilities. Medical Decision Making, 31 ( 5 ), 721 - 729. https://doi.org/10.1177/0272989X11403490
dc.identifier.citedreferencePolman, E., & Wu, K. ( 2019 ). Decision making for others involving risk: A review and meta- analysis. Journal of Economic Psychology, 77, 102184. https://doi.org/10.1016/j.joep.2019.06.007
dc.identifier.citedreferencePrelec, D. ( 1998 ). The probability weighting function. Econometrica, 66 ( 3 ), 497 - 527. https://doi.org/10.2307/2998573
dc.identifier.citedreferenceRottenstreich, 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
dc.identifier.citedreferenceSchwartz, B., Ward, A., Monterosso, J., Lyubomirsky, S., White, K., & Lehman, D. R. ( 2002 ). Maximizing versus satisficing: Happiness is a matter of choice. Journal of Personality and Social Psychology, 83 ( 5 ), 1178 - 1197. https://doi.org/10.1037/0022-3514.83.5.1178
dc.identifier.citedreferenceSchwartz, L. M. L., Woloshin, S. S., Black, W. C. W., & Welch, H. G. H. ( 1997 ). The role of numeracy in understanding the benefit of screening mammography. Annals of Internal Medicine, 127, 966 - 972. https://doi.org/10.7326/0003-4819-127-11-199712010-00003
dc.identifier.citedreferenceSlovic, P., & Peters, E. ( 2004 ). Risk perception and affect. Current Directions in Psychological Science, 15 ( 6 ), 322 - 325. https://doi.org/10.1111/j.1467-8721.2006.00461.x
dc.identifier.citedreferenceSteegen, S., Tuerlinckx, F., Gelman, A., & Vanpaemel, W. ( 2016 ). Increasing transparency through a multiverse analysis. Perspectives on Psychological Science, 11 ( 5 ), 702 - 712. https://doi.org/10.1177/1745691616658637
dc.identifier.citedreferenceSunstein, C. R., & Zeckhauser, R. ( 2011 ). Overreaction to fearsome risks. Environmental Resource Economics, 48 ( 3 ), 435 - 449. https://doi.org/10.1007/s10640-010-9449-3
dc.identifier.citedreferenceSuter, R. S., Pachur, T., & Hertwig, R. ( 2016 ). How affect shapes risky choice: Distorted probability weighting versus probability neglect. Journal of Behavioral Decision Making, 29, 437 - 449. https://doi.org/10.1002/bdm.1888
dc.identifier.citedreferenceThompson, M. M., Naccarato, M. E., Parker, K. C. H., & Moskowitz, G. ( 2001 ). The Personal Need for Structure (PNS) and Personal Fear of Invalidity (PFI) scales: Historical perspectives, present applications and future directions. In G. Moskowitz (Ed.), Cognitive social psychology: The Princeton symposium on the legacy and future of social cognition (pp. 19 - 39 ). Mahwah, NJ: Erlbaum.
dc.identifier.citedreferenceTraczyk, 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
dc.identifier.citedreferenceTrevena, L. J., Zikmund- Fisher, B. J., Edwards, A., Gaissmaier, W., Galesic, M., Han, P. K., - ¦ Woloshin, S. ( 2013 ). Presenting quantitiative information about decision outcomes: A risk communication primer for patient decision aid developers. BMC Medical Informatics and Decision Making, 13 ( Suppl 2 ), S7. https://doi.org/10.1186/1472-6947-13-S2-S7
dc.identifier.citedreferenceTversky, A., & Kahneman, D. ( 1992 ). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297 - 323. https://doi.org/10.1007/BF00122574
dc.identifier.citedreferencede Vries, M., Fagerlin, A., Witteman, H. O., & Scherer, L. D. ( 2013 ). Combining deliberation and intuition in patient decision support. Patient Education and Counseling, 91 ( 2 ), 154 - 160. https://doi.org/10.1016/j.pec.2012.11.016
dc.identifier.citedreferenceWeber, E. U., Blais, A. R., & Betz, N. E. ( 2002 ). A Domain- Specific Risk Attitude Scale: Measuring risk perceptions and risk behaviors. Journal of Behavioral Decision Making, 15 ( 4 ), 263 - 290. https://doi.org/10.1002/bdm.414
dc.identifier.citedreferenceWitteman, H., Julien, A., Ndjaboue, R., Exe, N., Kahn, V., Fagerlin, A., & Zikmund- Fisher, B. (In Press). What helps people make values- congruent medical decisions? Eleven strategies tested across six studies. Medical Decision Making.
dc.identifier.citedreferenceWu, G., & Gonzalez, R. ( 1996 ). Curvature of the probability weighting function. Management Science, 42, 1676 - 1690. https://doi.org/10.1287/mnsc.42.12.1676
dc.identifier.citedreferenceZikmund- Fisher, B. J. ( 2011 ). Time to retire the 1- in- x risk format. Medical Decision Making, 31 ( 5 ), 703 - 704. https://doi.org/10.1177/0272989X11418238
dc.identifier.citedreferenceAmsterlaw, J., Zikmund- Fisher, B. J., Fagerlin, A., & Ubel, P. A. ( 2006 ). Can avoidance of complications lead to biased healthcare decisions? Judgment and Decision Making, 1 ( 1 ), 64 - 75.
dc.identifier.citedreferenceBatteux, E., Ferguson, E., & Tunney, R. J. ( 2019 ). Do our risk preferences change when we make decisions for others? A meta- analysis of self- other differences in decisions involving risk. PLoS ONE, 14 ( 5 ), e0216566. https://doi.org/10.1371/journal.pone.0216566
dc.identifier.citedreferenceBechara, A., & Damasio, A. ( 2005 ). The somatic marker hypothesis: A neural theory of economic decision. Games and Economic Behavior, 52 ( 2 ), 336 - 372. https://doi.org/10.1016/j.geb.2004.06.010
dc.identifier.citedreferenceBlais, A. R., & Weber, E. U. ( 2006 ). A Domain- Specific Risk- Taking (DOSPERT) scale for adult populations. Judgment and Decision Making, 1, 33 - 47.
dc.identifier.citedreferenceBland, J. M., & Altman, D. G. ( 1986 ). Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, 327 ( 8476 ), 307 - 310. https://doi.org/10.1016/S0140-6736(86)90837-8
dc.identifier.citedreferenceBuhrmester, M., Kwang, T., & Gosling, S. D. ( 2011 ). Amazon’s Mechanical Turk: A new source of inexpensive, yet high- quality, data? Perspectives on Psychological Science, 6 ( 1 ), 3 - 5. https://doi.org/10.1177/1745691610393980
dc.identifier.citedreferenceCasler, K., Bickel, L., & Hackett, E. ( 2013 ). Separate but equal? A comparison of participants and data gathered via Amazon’s MTurk, social media, and face- to- face behavioral testing. Computers in Human Behavior, 29 ( 6 ), 2156 - 2160. https://doi.org/10.1016/j.chb.2013.05.009
dc.identifier.citedreferenceChang, E. C., Maydeu- Olivares, A., & D’Zurilla, T. J. ( 1997 ). Optimism and pessimism as partially independent constructs: Relationship to positive and negative affectivity and psychological well- being. Personality and Individual Differences, 23 ( 3 ), 433 - 440. https://doi.org/10.1016/S0191-8869(97)00040-8
dc.identifier.citedreferenceCokely, E. T., Galesic, M., Schulz, E., Ghazal, S., & Garcia- Retamero, R. ( 2012 ). Measuring risk literacy: The Berlin Numeracy Test. Judgment and Decision Making, 7, 25 - 47. https://doi.org/10.1037/t45862-000
dc.identifier.citedreferenceR Core Team. ( 2019 ). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/
dc.identifier.citedreferenceCuite, C. L., Weinstein, N. D., Emmons, K., & Colditz, G. ( 2008 ). A test of numeric formats for communicating risk probabilities. Medical Decision Making, 28 ( 3 ), 377 - 384. https://doi.org/10.1177/0272989X08315246
dc.identifier.citedreferenceDamasio, A. R. ( 1994 ). Descartes’ error: Emotion, reason, and the human brain. New York: G.P. Putnam.
dc.identifier.citedreferenceFagerlin, 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
dc.identifier.citedreferenceFerrer, R. A., & Klein, W. M. ( 2015 ). Risk perceptions and health behavior. Current Opinion in Psychology, 5, 85 - 89. https://doi.org/10.1016/j.copsyc.2015.03.012
dc.identifier.citedreferenceFigner, B., & Weber, E. U. ( 2011 ). Who takes risks when and why? Determinants of risk taking. Current Directions in Psychological Science, 20 ( 4 ), 211 - 216. https://doi.org/10.1177/0963721411415790
dc.identifier.citedreferenceGonzalez, R., & Wu, G. ( 1999 ). On the shape of the probability weighting function. Cognitive Psychology, 38 ( 1 ), 129 - 166. https://doi.org/10.1006/cogp.1998.0710
dc.identifier.citedreferenceGrimes, D. A., & Snively, G. R. ( 1999 ). Patients’ understanding of medical risks: Implications for genetic counseling. Obstetrics & Gynecology, 93 ( 6 ), 910 - 914. https://doi.org/10.1016/S0029-7844(98)00567-5
dc.identifier.citedreferenceHan, P. K., Reeve, B. B., Moser, R. P., & Klein, W. M. ( 2009 ). Aversion to ambiguity regarding medical tests and treatments: Measurement, prevalence, and relationship to sociodemographic factors. Journal of Health Communication, 14 ( 6 ), 556 - 572. https://doi.org/10.1080/10810730903089630
dc.identifier.citedreferenceHanoch, Y., Johnson, J. G., & Wilke, A. ( 2006 ). Domain specificity in experimental measures and participant recruitment. Psychological Science, 17 ( 4 ), 300 - 304. https://doi.org/10.1111/j.1467-9280.2006.01702.x
dc.identifier.citedreferenceHighhouse, S., Nye, C. D., Zhang, D. C., & Rada, T. B. ( 2017 ). Structure of the DOSPERT: Is there evidence for a general risk factor? Journal of Behavioral Decision Making, 30, 400 - 406. https://doi.org/10.1002/bdm.1953
dc.identifier.citedreferenceKahneman, D., & Tversky, A. ( 1979 ). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263 - 291. https://doi.org/10.2307/1914185
dc.identifier.citedreferenceKlein, W. M. P., & Ferrer, R. A. ( 2018 ). On being more amenable to threatening risk messages concerning close others (vis- à- vis the self). Personality and Social Psychology Bulletin, 44 ( 10 ), 1411 - 1423. https://doi.org/10.1177/0146167218769064
dc.identifier.citedreferenceLevy, A. G., & Baron, J. ( 2005 ). How bad is a 10% chance of losing a toe? Judgments of probabilistic conditions by doctors and laypeople. Memory & Cognition, 33 ( 8 ), 1399 - 1406. https://doi.org/10.3758/BF03193372
dc.identifier.citedreferenceLoewenstein, G., O’Donoghue, T., & Bhatia, S. ( 2015 ). Modelling the interplay between affect and deliberation. Decision, 2 ( 2 ), 55 - 81. https://doi.org/10.1037/dec0000029
dc.identifier.citedreferenceLoewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N. ( 2001 ). Risk as feelings. Psychological Bulletin, 127 ( 2 ), 267 - 286. https://doi.org/10.1037/0033-2909.127.2.26
dc.identifier.citedreferenceMukherjee, K. ( 2010 ). A dual system model of preferences under risk. Psychological Review, 117 ( 1 ), 243 - 255. https://doi.org/10.1037/a0017884
dc.identifier.citedreferenceMullinix, K. J., Leeper, T. J., Druckman, J. N., & Freese, J. ( 2015 ). The generalizability of survey experiments. Journal of Experimental Political Science, 2 ( 2 ), 109 - 138. https://doi.org/10.1017/XPS.2015.19
dc.identifier.citedreferenceNock, M. K., Wedig, M. M., Holmberg, E. B., & Hooley, J. M. ( 2008 ). The Emotion Reactivity Scale: Development, evaluation, and relation to self- injurious thoughts and behaviors. Behavior Therapy, 39, 107 - 116. https://doi.org/10.1016/j.beth.2007.05.005
dc.identifier.citedreferenceNorris, P., Pacini, R., & Epstein, S. ( 1998 ). The rational- experiential inventory, short form. Unpublished inventory. University of Massachusetts at Amherst.
dc.identifier.citedreferenceO’Keefe, L. M., Kearney, P. M., McCarthy, F. P., Khashan, A. S., Greene, R. A., North, R. A., - ¦ Kenny, L. C. ( 2015 ). Prevalence and predictors of alcohol use during pregnancy: Findings from international multicenter cohort studies. BMJ Open, 5, e006323. https://doi.org/10.1136/bmjopen-2014-006323
dc.identifier.citedreferencePachur, T., & Galesic, M. ( 2013 ). Strategy selection in risky choice: The impact of numeracy, affect, and cross- cultural differences. Journal of Behavioral Decision Making, 25 ( 3 ), 260 - 271. https://doi.org/10.1002/bdm.1757
dc.identifier.citedreferencePachur, T., Hertwig, R., & Wolkewitz, R. ( 2014 ). The affect gap in risky choice: Affect- rich outcomes attenuate attention to probability information. Decision, 1 ( 1 ), 64 - 78. https://doi.org/10.1037/dec0000006
dc.identifier.citedreferencePachur, T., Suter, R. S., & Hertwig, R. ( 2017 ). How the twain can meet: Prospect theory and models of heuristics in risky choice. Cognitive Psychology, 93, 44 - 73. https://doi.org/10.1016/j.cogpsych.2017.01.001
dc.identifier.citedreferencePaolacci, G., & Chandler, J. ( 2014 ). Inside the Turk: Understanding Mechanical Turk as a participant pool. Current Directions in Psychological Science, 23 ( 3 ), 184 - 188. https://doi.org/10.1177/0963721414531598
dc.identifier.citedreferencePeters, E. ( 2012 ). Beyond comprehension: The role of numeracy in judgments and decisions. Current Directions in Psychological Science, 21 ( 1 ), 31 - 35. https://doi.org/10.1177/0963721411429960
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
bdm2194.pdf
Size:
2.746MB
Format:
PDF
View/Open
Name:
bdm2194_am.pdf
Size:
562.3KB
Format:
PDF
View/Open

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.