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Part IV - Truncation and Stopping Rules

Published online by Cambridge University Press:  01 June 2023

Edited by
Klaus Fiedler ,
Peter Juslin  and
Jerker Denrell
Klaus Fiedler
Affiliation:
Universität Heidelberg
Peter Juslin
Affiliation:
Uppsala Universitet, Sweden
Jerker Denrell
Affiliation:
University of Warwick
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Sampling in Judgment and Decision Making , pp. 287 - 356
Publisher: Cambridge University Press
Print publication year: 2023

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References

Anderson, J. R., Bothell, D., & Byrne, M. D. et al. (2004). An integrated theory of the mind. Psychological Review, 111(4), 1036.CrossRefGoogle ScholarPubMed
Baumann, C., Singmann, H., Gershman, S. J., & von Helversen, B. (2020). A linear threshold model for optimal stopping behavior. Proceedings of the National Academy of Sciences, 117(23), 12750–12755. https://doi.org/10.1073/pnas.2002312117Google Scholar
Busemeyer, J. R., & Townsend, J. T. (1993). Decision field theory: A dynamic cognitive approach to decision making. Psychological Review, 100, 432–459. https://doi.org/10.1037/0033-295x.100.3.432Google Scholar
Dutt, V., & Gonzalez, C. (2015). Accounting for outcome and process measures in dynamic decision-making tasks through model calibration. Pittsburgh: Carnegie Mellon University. https://doi.org/10.11588/jddm.2015.1.17663Google Scholar
Erev, I., Ert, E., & Roth, A. E. et al. (2010). A choice prediction competition for choices from experience and from description. Journal of Behavioral Decision Making, 23, 15–47. https://doi:10.1002/bdm.683Google Scholar
Gonzalez, C. (2013). The boundaries of instance-based learning theory for explaining decisions from experience. In Chandrasekhar Pammi, V. S. & Srinivasan, N. (Eds.), Progress in brain research (Vol. 202, pp. 73–98). Amsterdam: Elsevier. https://doi.org/10.1016/b978-0-444-62604-2.00005-8Google Scholar
Gonzalez, C., Ben‐Asher, N., Martin, J. M., & Dutt, V. (2015). A cognitive model of dynamic cooperation with varied interdependency information. Cognitive Science, 39(3), 457–495. https://doi.org/10.1111/cogs.12170Google Scholar
Gonzalez, C., & Dutt, V. (2011). Instance-based learning: Integrating sampling and repeated decisions from experience. Psychological Review, 118(4), 523–551. https://doi:10.1037/a0024558Google Scholar
Gonzalez, C., & Dutt, V. (2016). Exploration and exploitation during information search and consequential choice Journal of Dynamic Decision Making, 2(2),1–8.Google Scholar
Gonzalez, C., Lerch, J. F., & Lebiere, C. (2003). Instance-based learning in dynamic decision making. Cognitive Science, 27(4), 591–635. https://doi:10.1016/S0364-0213(03)00031-4Google Scholar
Gonzalez, C., & Mehlhorn, K. (2016). Framing from experience: Cognitive processes and predictions of risky choice. Cognitive Science, 40(5), 1163–1191. https://doi.org/10.1111/cogs.12268Google Scholar
Guan, M., Stokes, R., Vandekerckhove, J., & Lee, M. D. (2020). A cognitive modeling analysis of risk in sequential choice tasks. Judgment and Decision Making, 15(5), 823–850. https://doi.org/10.31234/osf.io/evzp9Google Scholar
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. https://doi.org/10.1002/bdm.598Google Scholar
Hertwig, R. (2015). Decisions from experience. The Wiley Blackwell handbook of judgment and decision making, 2, 239–267. https://doi.org/10.1002/9781118468333.ch8Google Scholar
Hertwig, R., Barron, G., Weber, E. U., & Erev, I. (2004). Decisions from experience and the effect of rare events in risky choice. Psychological Science, 15, 534–539. https://doi:10.1111/j.0956-7976.2004.00715.xGoogle Scholar
Hertwig, R., & Erev, I. (2009). The description–experience gap in risky choice. Trends in Cognitive Sciences, 13(12), 517–523. https://doi:10.1016/j.tics.2009.09.004Google Scholar
Hertwig, R., & Pleskac, T. J. (2010). Decisions from experience: Why small samples? Cognition, 115, 225–237. https://doi.org/10.1016/j.cognition.2009.12.009Google Scholar
Lebiere, C. (1999). A blending process for aggregate retrievals. In Proceedings of the 6th ACT-R Workshop. Fairfax, VA: George Mason University,Google Scholar
Lejarraga, T., Dutt, V., & Gonzalez, C. (2012). Instance‐based learning: A general model of repeated binary choice. Journal of Behavioral Decision Making, 25(2), 143–153. https://doi.org/10.1037/e722992011-088Google Scholar
Lejarraga, T., & Gonzalez, C. (2011). Effects of feedback and complexity on repeated decisions from description. Organizational Behavior and Human Decision Processes, 116(2), 286–295. https://doi.org/10.1016/j.obhdp.2011.05.001Google Scholar
Lejarraga, T., Hertwig, R., & Gonzalez, C. (2012). How choice ecology influences search in decisions from experience. Cognition, 124(3), 334–342. https://doi.org/10.1016/j.cognition.2012.06.002Google Scholar
Lejarraga, T., Lejarraga, J., & Gonzalez, C. (2014). Decisions from experience: How groups and individuals adapt to change. Memory & Cognition, 42(8), 1384–1397. https://doi.org/10.3758/s13421-014-0445-7Google Scholar
March, J. G., & Simon, H. A. (1958). Organizations. New York: Wiley,Google Scholar
Markant, D., Pleskac, T. J., Diederich, A., Pachur, T., & Hertwig, R. (2015). Modeling choice and search in decisions from experience: A sequential sampling approach. In 37th annual meeting of the Cognitive Science Society (pp. 1512–1517). Cognitive Science Society.Google Scholar
Simon, H. (1957). A behavioral model of rational choice. Models of man, social and rational: Mathematical essays on rational human behavior in a social setting (pp. 241–269). New York: Wiley.Google Scholar
Srivastava, N., Müller-Trede, J., Schrater, P. R., & Vul, E. (2016). Modeling sampling duration in decisions from experience. In Proceedings of the 38th Annual Meeting of the Cognitive Science Society.Google Scholar
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323.CrossRefGoogle Scholar
Wulff, D. U., Mergenthaler-Canseco, M., & Hertwig, R. (2018). A meta-analytic review of two modes of learning and the description–experience gap. Psychological bulletin, 144(2), 140. https://doi.org/10.1037/bul0000115Google Scholar

References

Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological Bulletin, 82(2), 261–277. https://doi.org/10.1037/h0076477Google Scholar
Alves, H., Unkelbach, C., Burghardt, J., Koch, A. S., Krüger, T., & Becker, V. D. (2015). A density explanation of valence asymmetries in recognition memory. Memory & Cognition, 43(6), 896–909. https://doi.org/10.3758/s13421-015-0515-5CrossRefGoogle ScholarPubMed
Asch, S. E. (1946). Forming impressions of personality. Journal of Abnormal and Social Psychology, 41, 258–290. http://dx.doi.org/10.1037/h0055756Google Scholar
Busemeyer, J. R., & Townsend, J. T. (1993). Decision field theory: A dynamic-cognitive approach to decision making in an uncertain environment. Psychological Review, 100(3), 432–459. doi:10.1037//0033-295X.100.3.432Google Scholar
Coenen, A., & Gureckis, T. M. (2016). The distorting effect of deciding to stop sampling. Proceedings of the 38th Annual Conference of the Cognitive Science Society.Google Scholar
De Finetti, B. (1937). La prévision: Ses lois logiques, ses sources subjectives [Foresight: Its logical laws, its subjective sources]. Annales de l’Institut Henri Poincaré, 17, 1–68.Google Scholar
Denrell, J. (2005). Why most people disapprove of me: Experience sampling in impression formation. Psychological Review, 112(4), 951–978. https://doi.org/10.1037/0033-295X.112.4.951CrossRefGoogle ScholarPubMed
Desender, K., Boldt, A., & Yeung, N. (2018). Subjective confidence predicts information seeking in decision making. Psychological Science, 29(5), 761–778. https://doi.org/10.1177/0956797617744771Google Scholar
Dieckmann, A., & Rieskamp, J. (2007). The influence of information redundancy on probabilistic inferences. Memory & Cognition, 35(7), 1801–1813. https://doi.org/10.3758/BF03193511CrossRefGoogle ScholarPubMed
Dougherty, M. R. P. (2001). Integration of the ecological and error models of overconfidence using a multiple-trace memory model. Journal of Experimental Psychology: General, 130(4), 579–599. https://doi.org/10.1037/0096-3445.130.4.579Google Scholar
Edwards, W. (1965). Optimal strategies for seeking information: Models for statistics, choice reaction times, and human information processing. Journal of Mathematical Psychology, 2(2), 312–329. https://doi.org/10.1016/0022-2496(65)90007-6CrossRefGoogle Scholar
Erev, I., Wallsten, T. S., & Budescu, D. V. (1994). Simultaneous over- and underconfidence: The role of error in judgment processes. Psychological Review, 101(3), 519–527. https://doi.org/10.1037/0033-295X.101.3.519Google Scholar
Fiedler, K. (2000). Beware of samples! A cognitive-ecological sampling approach to judgment biases. Psychological Review, 107(4), 659–676. https://doi.org/10.1037/0033-295X.107.4.659Google Scholar
Fiedler, K., & Wänke, M. (2009). The cognitive-ecological approach to rationality in social psychology. Social Cognition, 27(5), 699–732. https://doi.org/10.1521/soco.2009.27.5.699Google Scholar
Frey, R., Hertwig, R., & Rieskamp, J. (2014). Fear shapes information acquisition in decisions from experience. Cognition, 132(1), 90–99. https://doi.org/10.1016/j.cognition.2014.03.009Google Scholar
Galton, F. (1894). Natural inheritance. Macmillan.Google Scholar
Gino, F., Brooks, A. W., & Schweitzer, M. E. (2012). Anxiety, advice, and the ability to discern: Feeling anxious motivates individuals to seek and use advice. Journal of Personality and Social Psychology, 102(3), 497–512. https://doi.org/10.1037/a0026413Google Scholar
Hadar, L., & Fox, C. R. (2009). Information asymmetry in decision from description versus decision from experience. Judgment and Decision Making, 4, 317–325.Google Scholar
Hertwig, R., & Pleskac, T. J. (2010). Decisions from experience: Why small samples? Cognition, 115, 225–237. http://dx.doi.org/10.1016/j.cognition.2009.12.009Google Scholar
Juslin, P., & Olsson, H. (1997). Thurstonian and Brunswikian origins of uncertainty in judgment: A sampling model of confidence in sensory discrimination. Psychological Review, 104(2), 344–366. https://doi.org/10.1037/0033-295X.104.2.344CrossRefGoogle ScholarPubMed
Juslin, P., Olsson, H., & Björkman, M. (1997). Brunswikian and Thurstonian origins of bias in probability assessment: On the interpretation of stochastic components of judgment. Journal of Behavioral Decision Making, 10(3), 189–209. https://doi.org/10.1002/(SICI)1099-0771(199709)10:3<189::AID-BDM258>3.0.CO;2-4Google Scholar
Koch, A., Alves, H., Krüger, T., & Unkelbach, C. (2016). A general valence asymmetry in similarity: Good is more alike than bad. Journal of Experimental Psychology: Learning, Memory, and Cognition, 42(8), 1171–1192. https://doi.org/10.1037/xlm0000243Google Scholar
Kutzner, F. L., & Fiedler, K. (2015). No correlation, no evidence for attention shift in category learning: Different mechanisms behind illusory correlations and the inverse base-rate effect. Journal of Experimental Psychology: General, 144(1), 58–75. https://doi.org/10.1037/a0038462Google Scholar
Lejarraga, T., Hertwig, R., & Gonzalez, C. (2012). How choice ecology influences search in decisions from experience. Cognition, 124(3), 334–342. https://doi.org/10.1016/j.cognition.2012.06.002Google Scholar
Levy, L. H. (1967). The effects of variance on personality impression formation. Journal of Personality, 35(2), 179–193. https://doi.org/10.1111/j.1467-6494.1967.tb01423.xGoogle Scholar
Parducci, A. (1965). Category judgment: A range-frequency model. Psychological Review, 72(6), 407–418. https://doi.org/10.1037/h0022602Google Scholar
Peterson, W. W. T. G., Birdsall, T., & Fox, W. (1954). The theory of signal detectability. Transactions of the IRE Professional Group on Information Theory, 4(4), 171–212.Google Scholar
Pleskac, T. J., & Busemeyer, J. R. (2010). Two-stage dynamic signal detection: A theory of choice, decision time, and confidence. Psychological Review, 117(3), 864–901. doi:10.1037/A0019737Google Scholar
Prager, J., & Fiedler, K. (2021a). Forming impressions from self-truncated samples of traits: Interplay of Thurstonian and Brunswikian sampling effects. Journal of Personality and Social Psychology, 121(3), 474–497. https://doi.org/10.1037/pspa0000274.suppGoogle Scholar
Prager, J., & Fiedler, K. (2021b). Small-group homogeneity: A crucial ingredient to inter-group sampling and impression formation. Unpublished manuscript, Heidelberg University.Google Scholar
Prager, J., Krueger, J. I., & Fiedler, K. (2018). Towards a deeper understanding of impression formation: New insights gained from a cognitive-ecological perspective. Journal of Personality and Social Psychology, 115(3), 379–397. https://doi.org/10.1037/pspa0000123Google Scholar
Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59–108. https://doi.org/10.1037/0033-295X.85.2.59Google Scholar
Reeder, G. D., & Brewer, M. B. (1979). A schematic model of dispositional attribution in interpersonal perception. Psychological Review, 86(1), 61–79. https://doi.org/10.1037/0033-295X.86.1.61Google Scholar
Rothbart, M., & Park, B. (1986). On the confirmability and disconfirmability of trait concepts. Journal of Personality and Social Psychology, 50(1), 131–142. https://doi.org/10.1037/0022-3514.50.1.131Google Scholar
Schwarz, N., & Clore, G. L. (1983). Mood, misattribution, and judgments of well-being: Informative and directive functions of affective states. Journal of Personality and Social Psychology, 45(3), 513–523. https://doi.org/10.1037/0022-3514.45.3.513Google Scholar
Skowronski, J. J., & Carlston, D. E. (1987). Social judgment and social memory: The role of cue diagnosticity in negativity, positivity, and extremity biases. Journal of Personality and Social Psychology, 52(4), 689–699. https://doi.org/10.1037/0022-3514.52.4.689Google Scholar
Skowronski, J. J., & Carlston, D. E. (1989). Negativity and extremity biases in impression formation: A review of explanations. Psychological Bulletin, 105(1), 131–142. https://doi.org/10.1037/0033-2909.105.1.131Google Scholar
Soll, J. B. (1999). Intuitive theories of information: Beliefs about the value of redundancy. Cognitive Psychology, 38(2), 317–346. https://doi.org/10.1006/cogp.1998.0699Google Scholar
Stewart, N., Chater, N., & Brown, G. D. A. (2006). Decision by sampling. Cognitive Psychology, 53(1), 1–26. https://doi.org/10.1016/j.cogpsych.2005.10.003Google Scholar
Tanner, W. P., Jr., & Swets, J. A. (1954). A decision-making theory of visual detection. Psychological Review, 61(6), 401–409. https://doi.org/10.1037/h0058700Google Scholar
Thurstone, L. L. (1927a). A law of comparative judgment. Psychological Review, 34(4), 273–286. https://doi.org/10.1037/h0070288Google Scholar
Thurstone, L. L. (1927b). Psychophysical analysis. American Journal of Psychology, 38, 368–389. https://doi.org/10.2307/1415006Google Scholar
Trope, Y., & Bassok, M. (1982). Confirmatory and diagnosing strategies in social information gathering. Journal of Personality and Social Psychology, 43(1), 22–34. https://doi.org/10.1037/0022-3514.43.1.22Google Scholar
Unkelbach, C., Fiedler, K., Bayer, M., Stegmüller, M., & Danner, D. (2008). Why positive information is processed faster: The density hypothesis. Journal of Personality and Social Psychology, 95(1), 36–49. https://doi.org/10.1037/0022-3514.95.1.36Google Scholar
Wulff, D. U., Hills, T. T., & Hertwig, R. (2015). How short- and long-run aspirations impact search and choice in decisions from experience. Cognition, 144, 29–37. https://doi.org/10.1016/j.cognition.2015.07.006Google Scholar

References

Balci, F., Simen, P., & Niyogi, R., et al. (2011). Acquisition of decision making criteria: Reward rate ultimately beats accuracy. Attention, Perception, and Psychophysics, 73(2), 640–657. https://doi.org/10.3758/s13414-010-0049-7Google Scholar
Connolly, T., & Serre, P. (1984). Information search in judgment tasks: The effects of unequal cue validity and cost. Organizational Behavior and Human Performance, 34(3), 387–401. https://doi.org/10.1016/0030-5073(84)90045-XGoogle Scholar
Denrell, J. & Le Mens, G. (2023) The hot stove effect. In Fiedler, Klaus, Juslin, Peter, & Denrell, Jerker (Eds.), Sampling in judgment and decision making (pp. 90–111). Cambridge: Cambridge University Press.Google Scholar
Denrell, J., & March, J. G. (2001). Adaptation as information restriction: The hot stove effect. Organization Science, 12(5), 523–538. https://doi.org/10.1287/orsc.12.5.523.10092Google Scholar
Dhami, M. K., Hertwig, R., & Hoffrage, U. (2004). The role of representative design in an ecological approach to cognition. Psychological Bulletin, 130(6), 959–988. https://doi.org/10.1037/0033-2909.130.6.959Google Scholar
Edwards, W. (1965). Optimal strategies for seeking information: Models for statistics, choice reaction-times, and human information-processing. Journal of Mathematical Psychology, 2(2), 312. https://doi.org/10.1016/0022-2496(65)90007-6Google Scholar
Evans, N. J., Bennett, A. J., & Brown, S. D. (2019). Optimal or not: Depends on the task. Psychonomic Bulletin and Review, 26(3), 1027–1034. https://doi.org/10.3758/s13423-018-1536-4Google Scholar
Evans, N. J., & Brown, S. D. (2017). People adopt optimal policies in simple decision-making, after practice and guidance. Psychonomic Bulletin and Review, 24(2), 597–606. https://doi.org/10.3758/s13423-016-1135-1Google Scholar
Fiedler, K., McCaughey, L., Prager, J., Eichberger, J., & Schnell, K. (2021). Speed–accuracy trade-offs in sample-based decisions. Journal of Experimental Psychology: General, 150(6), 1203–1224, https://doi.org/10.1037/xge0000986Google Scholar
Fiedler, K., Prager, J., & McCaughey, L (in press). Metacognitive Myopia: A Major Obstacle on the Way to Rationality. Current Directions in Psychological Science, 311–333.Google Scholar
Fischhoff, B., Slovic, P., & Lichtenstein, S. (1979). Subjective sensitivity analysis. Organizational Behavior and Human Performance, 23(3), 339–359. https://doi.org/10.1016/0030-5073(79)90002-3Google Scholar
Fried, L. S., & Peterson, C. R. (1969). Information seeking: Optional versus fixed stopping. Journal of Experimental Psychology, 80(3), 525–529. https://doi.org/10.1037/h0027484Google Scholar
Gigerenzer, G. (2004). Striking a blow for sanity in theories of rationality. In Augier, M. & March, J. G. (Eds.), Models of a man: Essays in memory of Herbert A. Simon (pp. 389–409). Cambridge, MA: MITGoogle Scholar
Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103, 650–669.Google Scholar
Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecological rationality: The recognition heuristic. Psychological Review, 109(1), 75.CrossRefGoogle ScholarPubMed
Harris, C. A., & Custers, R. (2023). Biased preferences through exploitation. In Fiedler, Klaus, Juslin, Peter, & Denrell, Jerker (Eds.), Sampling in judgment and decision making (pp. 00–00). Cambridge: Cambridge University Press.Google Scholar
Hausfeld, J., & Resnjanskij, S. (2018) Risky decisions and the opportunity costs of time. Ifo Working Paper No. 269. Munich: Ifo Institute.Google Scholar
Hershman, R. L., & Levine, J. R. (1970). Deviations from optimum information-purchase strategies in human decision-making. Organizational Behavior and Human Performance, 5(4), 313–329. https://doi.org/10.1016/0030-5073(70)90023-1Google Scholar
Hsee, C. K., & Zhang, J. (2010). General evaluability theory. Perspectives on Psychological Science, 5(4), 343–355. https://doi.org/10.1177/1745691610374586Google Scholar
Jarvstad, A., Rushton, S. K., Warren, P. A., & Hahn, U. (2012). Knowing when to move on: Cognitive and perceptual decisions in time. Psychological Science, 23(6), 589–597. https://doi.org/10.1177/0956797611426579Google Scholar
Larrick, R. P., & Soll, J. B. (2008). The MPG illusion. Science, 320, 1593–1594. http://dx.doi.org/10.1126/science.1154983Google Scholar
McCaughey, L., Prager, J., & Fiedler, K (2022). Rivals reloaded: Adapting tosample-based speed–accuracy trade-offs through competitive pressure. Manuscript submitted for publication.Google Scholar
McCaughey, L., Prager, J., & Fiedler, K. (2022). Adapting to information search costs in sample-based decisions. Manuscript in preparation.Google Scholar
Madan, C. R., Spetch, M. L., & Ludvig, E. A. (2015). Rapid makes risky: Time pressure increases risk seeking in decisions from experience. Journal of Cognitive Psychology, 5911(June), 1–8. https://doi.org/10.1080/20445911.2015.1055274Google Scholar
Navarro, D. J., Newell, B. R., & Schulze, C. (2016). Learning and choosing in an uncertain world: An investigation of the explore–exploit dilemma in static and dynamic environments. Cognitive Psychology, 85, 43–77. https://doi.org/10.1016/j.cogpsych.2016.01.001Google Scholar
Payne, J. W., Bettman, J. R., & Luce, M. F. (1996). When time is money: Decision behavior under opportunity–cost time pressure. Organizational Behavior and Human Decision Processes, 66(2), 131–152. https://doi.org/10.1006/obhd.1996.0044Google Scholar
Phillips, N. D., Hertwig, R., Kareev, Y., & Avrahami, J. (2014). Rivals in the dark: How competition influences search in decisions under uncertainty. Cognition, 133(1), 104–119. https://doi.org/10.1016/j.cognition.2014.06.006Google Scholar
Pitz, G. F. (1968). Information seeking when available information is limited. Journal of Experimental Psychology, 76(1), 25–34. https://doi.org/10.1037/h0025302Google Scholar
Pitz, G. F., Reinhold, H., & Scott Geller, E. (1969). Strategies of information seeking in deferred decision making. Organizational Behavior and Human Performance, 4(1), 1–19. https://doi.org/10.1016/0030-5073(69)90028-2Google Scholar
Rieskamp, J., & Hoffrage, U. (2008). Inferences under time pressure: How opportunity costs affect strategy selection. Acta Psychologica, 127(2), 258–276. https://doi.org/10.1016/j.actpsy.2007.05.004Google Scholar
Sedlmeier, P., Hertwig, R., & Gigerenzer, G. (1998). Are judgment of the positional frequencies of letters systematically biased due to availability? Journal of Experimental Psychology: Learning Memory, and Cognition, 24(3), 754–770. https://doi.org/10.1037/0278-7393.24.3.754Google Scholar
Slovic, P., & Lichtenstein, S. (1971). Comparison of Bayesian and regression approaches to the study of information processing in judgment. Organizational Behavior and Human Performance, 6(6), 649–744. https://doi.org/10.1016/0030-5073(71)90033-XGoogle Scholar
Snapper, K. J., & Peterson, C. R. (1971). Information seeking and data diagnosticity. Journal of Experimental Psychology, 87(3), 429–433. https://doi.org/10.1037/h0030557Google Scholar
Svenson, O., & Eriksson, G. (2017). Mental models of driving and speed: Biases, choices and reality. Transport Reviews, 37, 653–666. http://dx.doi.org/10.1080/01441647.2017.1289278Google Scholar
Todd, P. M., & Gigerenzer, G. (2007). Environments that make us smart: Ecological rationality. Current Directions in Psychological Science, 16(3), 167–171. https://doi.org/10.1111/j.1467-8721.2007.00497.xGoogle Scholar

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