The role of difficulty in dynamic risk mitigation decisions

  • Lisa Vangsness (Author)
    Kansas State University
    Lisa Vangsness holds two undergraduate degrees from the University of Iowa, and is pursuing a Ph.D. in Psychology from Kansas State University. Her research explores the many dimensions of effort and task difficulty.
  • Michael E. Young (Author)
    Kansas State University
    Dr. Michael Young has B.S. (University of Illinois) and M.S. (University of Minnesota) degrees in Computer Science and a Ph.D. in Psychology (University of Minnesota).  Dr. Young is the head of Psychological Sciences at Kansas State University. His primary research program involves the study of decision making in dynamic environments.

Identifiers (Article)

Abstract

Previous research suggests that individuals faced with risky choices seek ways to actively reduce their risks. The risk defusing operators (RDOs) that are identified through these searches can be used to prevent or compensate for (here, pre- and post-event RDOs, respectively) negative outcomes. Although several factors that affect RDO selection have been identified, they are limited to static decisions conducted during descriptive tasks. The factors that influence RDO selection in dynamically unfolding environments are unknown, and the relationship between task characteristics and RDO selection has yet to be mapped. We used a videogame environment to conduct two experiments to address these issues and found that experienced losses impacted risk mitigation strategy: when the task was difficult, participants experienced greater losses and were more likely to select preventive RDOs (Experiment 1). Additionally, risk mitigation behavior stabilized as participants gained experience with the task (Experiments 1 and 2) and could be shifted by making an RDO easier to use (Experiment 2). Exploratory analyses suggested that these risk mitigation choices were not driven by judgments of difficulty (JODs), even though participants’ JODs were accurate and aligned with task difficulty. This research suggests that while people seek preventive RDOs when tasks are difficult and risky, risk mitigation strategy is shaped by experienced losses; decision makers do not use JODs to anticipate future risks and inform risk mitigation decisions.

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References

Akaike, H. (1973). Maximum likelihood identification of Gaussian autoregressive moving average models. Biometrika, 60(2), 255–265. doi: 10.2307/2334537

Altmann, E. M., & Trafton, J. G. (2007). Timecourse of recovery from task interruption: Data and a model. Psychonomic Bulletin & Review, 14(6), 1079–1084. doi:10.3758/bf03193094

Bär, A. S., & Huber, O. (2008). Successful or unsuccessful search for risk defusing operators: Effects on decision behaviour. European Journal of Cognitive Psychology, 20(4), 807–827. doi:10.1080/09541440701686227

Böckenholt, U. (2004). Comparative judgments as an alternative to ratings: Identifying the scale origin. Psychological Methods, 9(4), 453–465. doi:10.1037/1082-989X.9.4.453

Brockmyer, J. H., Fox, C. M., Curtiss, K. A., McBroom, E., Burkhart, K. M., & Pidruzny, J. N. (2009). The development of the game engagement questionnaire: A measure of engagement in video game-playing. Journal of Experimental Social Psychology, 45(4), 624–634. doi:10.1016/j.jesp.2009.02.016

Brunswik, E. (1956). Perception and the representative design of psychological experiments. Berkeley, CA: University of California Press.

Camilleri, A. R., & Newell, B. R. (2013). The long and short of it: Closing the description-experience "gap" by taking the long-run view. Cognition, 126(1), 54–71. doi:10.1016/j.cognition.2012.09.001

Delta Dental (2014). 2014 Oral Health and Well-Being Survey. Retrieved from https://www.deltadental.com/DDPAOralHealthWellBeingSurveyBrochure2014.pdf.

Desender, K., Van Opstal, F., & Van den Bussche, E. (2017). Subjective experience of difficulty depends on multiple cues. Scientific Reports, 7, 1–14. doi:10.1038/srep44222

Desmond, P. A., & Hoyes, T. W. (1996). Workload variation, intrinsic risk and utility in a simulated air traffic control task: Evidence for compensatory effects. Safety Science, 22(1–3), 87–101. doi:10.1016/0925-7535(96)00008-2

Douglas, P., Leight, S., & David, J. (2005). When good intentions turn bad: Promoting natural hazard preparedness. Australian Journal of Emergency Management, 20(1), 25–30.

Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34(10), 906–911. doi:10.1037/0003-066x.34.10.906

Geath, G. J., & Shanteau, J. (1984). Reducing the influence of irrelevant information on experienced decision makers. Organizational Behavior & Human Performance, 33(2), 263–282. doi:10.1016/0030-5073(84)90024-2

Hau, R., Pleskac, T. J., & Hertwig, R. (2010). Decisions from experience and statistical probabilities: Why they trigger different choices than a priori probabilities. Journal of Behavioral Decision Making, 23(1), 48–68. doi:10.1002/bdm.665

Hau, R., Pleskac, T. J., Kiefer, J., & Hertwig, R. (2008). The description-experience gap in risky choice: The role of sample size and experienced probabilities. Journal of Behavioral Decision Making, 21(5), 493–518. doi:10.1002/bdm.598

Hertwig, R., & Erev, I., (2009). The description-experience gap in risky choice. Trends in Cognitive Science, 13(12), 517–523. doi:10.1016/j.tics.2009.09.004

Huber, O. (2012). Risky decisions: Active risk management. Current Directions in Psychological Science, 21(1), 26–30. doi:10.1177/0963721411422055

Huber, O., & Huber, O. W. (2003). Detectability of the negative event: Effect on the acceptance of pre- or postevent risk-defusing actions. Acta Psychologica, 113(1), 1–21. doi:10.1016/s0001-6918(02)00148-8

Huber, O., & Huber, O. W. (2008). Gambles vs. Quasirealistic scenarios: Expectations to find probability and riskdefusing information. Acta Psychologica, 127(2), 222–236. doi:10.1016/j.actpsy.2007.05.002

Huber, O., & Kunz, U. (2007). Time pressure in risky decisionmaking: Effect on risk defusing. Psychology Science, 49(4), 415–426.

Huber, O., & Macho, S. (2001). Probabilistic set-up and the search for probability information in quasi-naturalistic decision tasks. Risk Decision and Policy, 6(1), 1–16. doi:10.1017/s1357530901000230

Huber, O., Bär, A. S., & Huber, O. W. (2009). Justification pressure in risky decision making: Search for risk defusing operators. Acta Psychologica, 130(1), 17–24. doi:10.1016/j.actpsy.2008.09.009

Huber, O., Beutter, C., Montoya, J., & Huber, O. W. (2001). Riskdefusing behaviour: Towards an understanding of risky decision making. European Journal of Cognitive Psychology, 13(3), 409–426. doi:10.1080/09541440125915

Kahneman, D. (1973). Attention and effort. Englewood Cliffs, NJ: Prentice-Hall

Kanfer, R., & Ackerman, P. L. (1989). Motivation and cognitive abilities: An integrative/aptitude-treatment interaction approach to skill acquisition. Journal of Applied Psychology, 74(4), 657–690. doi: 10.1037//0021-9010.74.4.657

Knight, F. H. (1921). Risk, Uncertainty, and Profit. Boston: Houghton Mifflin.

Koehler, D. J., Brenner, L., & Griffin, D. (2002). The calibration of expert judgment: Heuristics and biases beyond the laboratory. In T. Gilovich, D. Griffin, & D. Kahneman (Eds.), Heuristics and biases: The psychology of intuitive judgment (pp. 686–715). Cambridge, UK: Cambridge University Press.

Koriat, A. (1997). Monitoring one’s own knowledge during study: A cue-utilization approach to judgments of learning. Journal of Experimental Psychology: General, 126(4), 349–370. doi:10.1037//0096-3445.126.4.349

Kurzban, R. (2016). The sense of effort. Current Opinion in Psychology, 7, 67–70. doi:10.1016/j.copsyc.2015.08.003

Kurzban, R., Duckworth, A., Kable, J. W., & Myers, J. (2013). An opportunity cost model of subjective effort and task performance. Behavioral and Brain Sciences, 36(6), 661–79. doi:10.1017/S0140525X12003196

Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation. American Psychologist, 57(9), 705–717. doi:10.1037//0003-066x.57.9.705

Lorist, M. M., Boksem, M. A., & Ridderinkhof, K. R. (2005). Impaired cognitive control and reduced cingulate activity during mental fatigue. Cognitive Brain Research, 24(2), 199–205. doi:10.1016/j.cogbrainres.2005.01.018

Lovett, M. C., & Anderson, J. R. (1996). History of success and current context in problem solving: Combined influences on operator selection. Cognitive Psychology, 31(2), 168–217. doi:10.1006/cogp.1996.0016

Lovett, M. C., & Schunn, C. D. (1999). Task representations, strategy variability, and base-rate neglect. Journal of Experimental Psychology: General, 128(2), 107–130. doi:10.1037/0096-3445.128.2.107

Mitchell, S. (2017). Devaluation of outcomes due to their cost: Extending discounting models beyond delay. In J. R. Stevens (Ed.), Nebraska Symposium on Motivation: Impulsivity (Vol. 64, pp. 145–161). Basel, Switzerland: Springer International Publishing.

Ozuru, Y., Kurby, C. A., & McNamara, D. S. (2012). The effect of metacomprehension judgment task on comprehension monitoring and metacognitive accuracy. Metacognition and Learning, 7(2), 113–131. doi:10.1007/s11409-012-9087-y

Pleskac, T. J., & Hertwig, R. (2014). Ecologically rational choice and the structure of the environment. Journal of Experimental Psychology General, 143(5), 2000–2019. doi:10.1037/xge0000013

Shanteau, J. (1992). Competence in experts: The role of task characteristics. Organizational Behavior and Human Decision Processes, 53(2), 252–266. doi:10.1016/0749-5978(92)90064-e

Sigurdsson, S. O., Taylor, M., A., & Wirth, O. (2013). Discounting the value of safety: Effects of perceived risk and effort. Journal of Safety Research, 46, 127–134. doi:10.1016/j.jsr.2013.04.006

Unity Game Engine (2016). [Computer Software]. (Version 5.4). San Francisco, CA: Unity.

Vallières, B. R., Hodgetts, H. M., Vachon, F., & Tremblay, S. (2016). Supporting dynamic change detection: Using the right tool for the task. Cognitive Research: Principles and Implications, 1(1), 32–52. doi:10.1186/s41235-016-0033-4

Vangsness, L. (2017). Perceptions of effort and risk assessment. (Unpublished master’s thesis). Kansas State University, Manhattan, KS.

Walton, M. E., Kennerley, S. W., Bannerman, D. M., Phillips, P. E., & Rushworth, M. F. (2006). Weighing up the benefits of work: Behavioral and neural analyses of effortrelated decision making. Neural Networks, 19(8), 1302–1314. doi:10.1016/j.neunet.2006.03.005

Zaleskiewicz, T., Piskorz, Z., & Borkowska, A. (2002). Fear or money? Decisions on insuring oneself against flood. Risk, Decision, and Policy, 7(3), 221–233. doi:10.1017/s1357530902000662
Published
2017-12-15
Language
en
Type, method or approach
original empirical work
Subjects
risk mitigation; metacognitive judgments of difficulty
Keywords
difficulty; risk mitigation; risk defusing operators; judgments of difficulty; dynamic environments