Reconsidering “evidence” for fast-and-frugal heuristics

Abstract

In several recent reviews, authors have argued for the pervasive use of fast-and-frugal heuristics in human judgment. They have provided an overview of heuristics and have reiterated findings corroborating that such heuristics can be very valid strategies leading to high accuracy. They also have reviewed previous work that implies that simple heuristics are actually used by decision makers. Unfortunately, concerning the latter point, these reviews appear to be somewhat incomplete. More important, previous conclusions have been derived from investigations that bear some noteworthy methodological limitations. I demonstrate these by proposing a new heuristic and provide some novel critical findings. Also, I review some of the relevant literature often not—or only partially—considered. Overall, although some fast-and-frugal heuristics indeed seem to predict behavior at times, there is little to no evidence for others. More generally, the empirical evidence available does not warrant the conclusion that heuristics are pervasively used.

References

  1. Ayal, S., & Hochman, G. (2009). Ignorance or integration: The cognitive processes underlying choice behavior. Journal of Behavioral Decision Making, 22, 455–474.

    Article  Google Scholar 

  2. Birnbaum, M. H. (2008a). Evaluation of the priority heuristic as a descriptive model of risky decision making: Comment on Brandstätter, Gigerenzer, and Hertwig (2006). Psychological Review, 115, 253–260.

    PubMed  Article  Google Scholar 

  3. Birnbaum, M. H. (2008b). New tests of cumulative prospect theory and the priority heuristic: Probability-outcome tradeoff with branch splitting. Judgment & Decision Making, 3, 304–316.

    Google Scholar 

  4. Birnbaum, M. H., & LaCroix, A. R. (2008). Dimension integration: Testing models without trade-offs. Organizational Behavior & Human Decision Processes, 105, 122–133.

    Article  Google Scholar 

  5. Brandstätter, E., Gigerenzer, G., & Hertwig, R. (2006). Making choices without trade-offs: The priority heuristic. Psychological Review, 113, 409–432.

    PubMed  Article  Google Scholar 

  6. Bröder, A. (2000). Assessing the empirical validity of the “take-the-best” heuristic as a model of human probabilistic inference. Journal of Experimental Psychology: Learning, Memory, & Cognition, 26, 1332–1346.

    Article  Google Scholar 

  7. Bröder, A., & Eichler, A. (2006). The use of recognition information and additional cues in inferences from memory. Acta Psychologica, 121, 275–284.

    PubMed  Article  Google Scholar 

  8. Bröder, A., & Gaissmaier, W. (2007). Sequential processing of cues in memory-based multiattribute decisions. Psychonomic Bulletin & Review, 14, 895–900.

    Article  Google Scholar 

  9. Bröder, A., & Newell, B. R. (2008). Challenging some common beliefs: Empirical work within the adaptive toolbox metaphor. Judgment & Decision Making, 3, 205–214.

    Google Scholar 

  10. Busemeyer, J. R., & Johnson, J. G. (2004). Computational models of decision making. In D. J. Koehler & N. Harvey (Eds.), Blackwell handbook of judgment and decision making (pp. 133–154). Malden, MA: Blackwell.

    Google Scholar 

  11. Busemeyer, J. R., & Townsend, J. T. (1993). Decision field theory: A dynamic-cognitive approach to decision making in an uncertain environment. Psychological Review, 100, 432–459.

    PubMed  Article  Google Scholar 

  12. Dougherty, M. R., Franco-Watkins, A. M., & Thomas, R. (2008). Psychological plausibility of the theory of probabilistic mental models and the fast and frugal heuristics. Psychological Review, 115, 199–213.

    PubMed  Article  Google Scholar 

  13. Dougherty, M. R., Gettys, C. F., & Odgen, E. E. (1999). MINERVA-DM: A memory process model for judgments of likelihood. Psychological Review, 106, 180–209.

    Article  Google Scholar 

  14. Erdfelder, E., Auer, T.-S., Hilbig, B. E., Assfalg, A., Moshagen, M., & Nadarevic, L. (2009). Multinomial processing tree models: A review of the literature. Zeitschrift für Psychologie, 217, 108–124.

    Article  Google Scholar 

  15. Erdfelder, E., Küpper-Tetzel, C. E., & Mattern, S. (in press). Threshold models of recognition and the recognition heuristic. Judgment & Decision Making.

  16. Fiedler, K. (2010). How to study cognitive decision algorithms: The case of the priority heuristic. Judgment & Decision Making, 5, 21–32.

    Google Scholar 

  17. Frosch, C. A., McCloy, R., Beaman, C. P., & Goddard, K. (2010). Time to decide: Frugality vs. congruity in comparative judgment. Manuscript submitted for publication.

  18. Gigerenzer, G. (2008). Why heuristics work. Perspectives on Psychological Science, 3, 20–29.

    Article  Google Scholar 

  19. Gigerenzer, G., & Brighton, H. (2009). Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1, 107–143.

    Article  Google Scholar 

  20. Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103, 650–669.

    PubMed  Article  Google Scholar 

  21. Gigerenzer, G., Hoffrage, U., & Goldstein, D. G. (2008). Fast and frugal heuristics are plausible models of cognition: Reply to Dougherty, Franco-Watkins, and Thomas (2008). Psychological Review, 115, 230–237.

    PubMed  Article  Google Scholar 

  22. Glöckner, A. (2008). How evolution outwits bounded rationality: The efficient interaction of automatic and deliberate processes in decision making and implications for institutions. In C. Engel & W. Singer (Eds.), Better than conscious? Implications for performance and institutional analysis (pp. 259–284). Cambridge, MA: MIT Press.

    Google Scholar 

  23. Glöckner, A. (2009). Investigating intuitive and deliberate processes statistically: The multiple-measure maximum likelihood strategy classification method. Judgment & Decision Making, 4, 186–199.

    Google Scholar 

  24. Glöckner, A., & Betsch, T. (2008a). Do people make decisions under risk based on ignorance? An empirical test of the priority heuristic against cumulative prospect theory. Organizational Behavior & Human Decision Processes, 107, 75–95.

    Article  Google Scholar 

  25. Glöckner, A., & Betsch, T. (2008b). Modeling option and strategy choices with connectionist networks: Towards an integrative model of automatic and deliberate decision making. Judgment & Decision Making, 3, 215–228.

    Google Scholar 

  26. Glöckner, A., & Betsch, T. (2008c). Multiple-reason decision making based on automatic processing. Journal of Experimental Psychology: Learning, Memory, & Cognition, 34, 1055–1075.

    Article  Google Scholar 

  27. Glöckner, A., & Betsch, T. (2010). Accounting for critical evidence while being precise and avoiding the strategy selection problem in a parallel constraint satisfaction approach: A reply to Marewski (2010). Journal of Behavioral Decision Making, 23, 468–472.

    Article  Google Scholar 

  28. Glöckner, A., Betsch, T., & Schindler, N. (2010). Coherence shifts in probabilistic inference tasks. Journal of Behavioral Decision Making, 23, 439–462.

    Article  Google Scholar 

  29. Glöckner, A., & Bröder, A. (in press). Processing of recognition information and additional cues: A model-based analysis of choice, confidence, and response time. Judgment & Decision Making.

  30. Glöckner, A., & Herbold, A.-K. (in press). An eye-tracking study on information processing in risky decisions: Evidence for compensatory strategies based on automatic processes. Journal of Behavioral Decision Making.

  31. Glöckner, A., & Moritz, S. (2009). A fine-grained analysis of the jumping-to-conclusions bias in schizophrenia: Data-gathering, response confidence, and information integration. Judgment & Decision Making, 4, 587–600.

    Google Scholar 

  32. Glöckner, A., & Witteman, C. (2010). Beyond dual-process models: A categorization of processes underlying intuitive judgment and decision making. Thinking & Reasoning, 16, 1–25.

    Article  Google Scholar 

  33. Goldstein, D. G., & Gigerenzer, G. (1999). The recognition heuristic: How ignorance makes us smart. In G. Gigerenzer, P. M. Todd, & the ABC Research Group (Eds.), Simple heuristics that make us smart (pp. 37–58). New York: Oxford University Press.

    Google Scholar 

  34. Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecological rationality: The recognition heuristic. Psychological Review, 109, 75–90.

    PubMed  Article  Google Scholar 

  35. Goldstein, D. G., & Gigerenzer, G. (2009). Fast and frugal forecasting. International Journal of Forecasting, 25, 760–772.

    Article  Google Scholar 

  36. Hardman, D. (2009). Judgment and decision making. Malden, MA: Blackwell.

    Google Scholar 

  37. Hausmann, D., & Läge, D. (2008). Sequential evidence accumulation in decision making: The individual desired level of confidence can explain the extent of information acquisition. Judgment & Decision Making, 3, 229–243.

    Google Scholar 

  38. Hausmann, D., Läge, D., Pohl, R. F., & Bröder, A. (2007). Testing quickEst: No evidence for the quick-estimation heuristic. European Journal of Cognitive Psychology, 19, 446–456.

    Article  Google Scholar 

  39. Hertwig, R., Herzog, S. M., Schooler, L. J., & Reimer, T. (2008). Fluency heuristic: A model of how the mind exploits a by-product of information retrieval. Journal of Experimental Psychology: Learning, Memory, & Cognition, 34, 1191–1206.

    Article  Google Scholar 

  40. Hilbig, B. E. (2008a). Individual differences in fast-and-frugal decision making: Neuroticism and the recognition heuristic. Journal of Research in Personality, 42, 1641–1645.

    Article  Google Scholar 

  41. Hilbig, B. E. (2008b). One-reason decision making in risky choice? A closer look at the priority heuristic. Judgment & Decision Making, 3, 457–462.

    Google Scholar 

  42. Hilbig, B. E. (2010). Precise models deserve precise measures: A methodological dissection. Judgment & Decision Making, 5, 272–284.

    Google Scholar 

  43. Hilbig, B. E., Erdfelder, E., & Pohl, R. F. (2010). One-reason decision-making unveiled: A measurement model of the recognition heuristic. Journal of Experimental Psychology: Learning, Memory, & Cognition, 36, 123–134.

    Article  Google Scholar 

  44. Hilbig, B. E., & Pohl, R. F. (2008). Recognizing users of the recognition heuristic. Experimental Psychology, 55, 394–401.

    PubMed  Google Scholar 

  45. Hilbig, B. E., & Pohl, R. F. (2009). Ignoranceversus evidence-based decision making: A decision time analysis of the recognition heuristic. Journal of Experimental Psychology: Learning, Memory, & Cognition, 35, 1296–1305.

    Article  Google Scholar 

  46. Hilbig, B. E., Pohl, R. F., & Bröder, A. (2009). Criterion knowledge: A moderator of using the recognition heuristic? Journal of Behavioral Decision Making, 22, 510–522.

    Article  Google Scholar 

  47. Hilbig, B. E., Scholl, S. G., & Pohl, R. F. (2010). Think orblink—Is the recognition heuristic an “intuitive” strategy? Judgment & Decision Making, 5, 300–309.

    Google Scholar 

  48. Hogarth, R. M., & Karelaia, N. (2007). Heuristic and linear models of judgment: Matching rules and environments. Psychological Review, 114, 733–758.

    PubMed  Article  Google Scholar 

  49. Holyoak, K. J., & Simon, D. (1999). Bidirectional reasoning in decision making by constraint satisfaction. Journal of Experimental Psychology: General, 128, 3–31.

    Article  Google Scholar 

  50. Jekel, M., Nicklisch, A., & Glöckner, A. (2010). Implementation of the multiple-measure maximum likelihood strategy classification method in R: Addendum to Glöckner (2009) and practical guide for application. Judgment & Decision Making, 5, 54–63.

    Google Scholar 

  51. Johnson, E. J., Schulte-Mecklenbeck, M., & Willemsen, M. C. (2008). Process models deserve process data: Comment on Brandstätter, Gigerenzer, and Hertwig (2006). Psychological Review, 115, 263–272.

    PubMed  Article  Google Scholar 

  52. Juslin, P., & Olsson, H. (2004). Note on the rationality of rule-based versus exemplar-based processing in human judgment. Scandinavian Journal of Psychology, 45, 37–47.

    PubMed  Article  Google Scholar 

  53. Juslin, P., & Persson, M. (2002). PROBabilities from EXemplars (PROBEX): A “lazy” algorithm for probabilistic inference from generic knowledge. Cognitive Science, 26, 563–607.

    Article  Google Scholar 

  54. Lee, M. D., & Cummins, T. D. (2004). Evidence accumulation in decision making: Unifying the “take the best” and the “rational” models. Psychonomic Bulletin & Review, 11, 343–352.

    Article  Google Scholar 

  55. Marewski, J. N., Gaissmaier, W., & Gigerenzer, G. (2010). Good judgments do not require complex cognition. Cognitive Processing, 11, 103–121.

    PubMed  Article  Google Scholar 

  56. Marewski, J. N., Gaissmaier, W., Schooler, L. J., Goldstein, D. G., & Gigerenzer, G. (2009). Do voters use episodic knowledge to rely on recognition? In N. A. Taatgen & H. van Rijn (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 2232–2237). Austin, TX: Cognitive Science Society.

    Google Scholar 

  57. Marewski, J. N., Gaissmaier, W., Schooler, L. J., Goldstein, D. G., & Gigerenzer, G. (2010). From recognition to decisions: Extending and testing recognition-based models for multi-alternative inference. Psychonomic Bulletin & Review, 17, 287–309.

    Article  Google Scholar 

  58. McCloy, R., Beaman, C. P., Frosch, C. A., & Goddard, K. (2010). Fast and frugal framing effects? Journal of Experimental Psychology: Learning, Memory, & Cognition, 36, 1043–1052.

    Article  Google Scholar 

  59. Newell, B. R. (2005). Re-visions of rationality? Trends in Cognitive Sciences, 9, 11–15.

    PubMed  Article  Google Scholar 

  60. Newell, B. R., & Bröder, A. (2008). Cognitive processes, models and metaphors in decision research. Judgment & Decision Making, 3, 195–204.

    Google Scholar 

  61. Newell, B. R., Collins, P., & Lee, M. D. (2007). Adjusting the spanner: Testing an evidence accumulation model of decision making. In D. McNamara & G. Trafton (Eds.), Proceedings of the 29th Annual Conference of the Cognitive Science Society (pp. 533–538). Austin, TX: Cognitive Science Society.

    Google Scholar 

  62. Newell, B. R., & Fernandez, D. (2006). On the binary quality of recognition and the inconsequentially of further knowledge: Two critical tests of the recognition heuristic. Journal of Behavioral Decision Making, 19, 333–346.

    Article  Google Scholar 

  63. Newell, B. R., & Lee, M. D. (in press). The right tool forthe job? Comparing an evidence accumulation and a naive strategy selection model of decision making. Journal of Behavioral Decision Making.

  64. Newell, B. R., Rakow, T., Weston, N. J., & Shanks, D. R. (2004). Search strategies in decision making: The success of “success.” Journal of Behavioral Decision Making, 17, 117–137.

    Article  Google Scholar 

  65. Newell, B. R., & Shanks, D. R. (2003). Take the best or look at the rest? Factors influencing “one-reason” decision making. Journal of Experimental Psychology: Learning, Memory, & Cognition, 29, 53–65.

    Article  Google Scholar 

  66. Newell, B. R., & Shanks, D. R. (2004). On the role of recognition in decision making. Journal of Experimental Psychology: Learning, Memory, & Cognition, 30, 923–935.

    Article  Google Scholar 

  67. Newell, B. R., Weston, N. J., & Shanks, D. R. (2003). Empirical tests of a fast-and-frugal heuristic: Not everyone “takes-the-best.” Organizational Behavior & Human Decision Processes, 91, 82–96.

    Article  Google Scholar 

  68. Oppenheimer, D. M. (2003). Not so fast! (and not so frugal!): Rethinking the recognition heuristic. Cognition, 90, B1-B9.

    PubMed  Article  Google Scholar 

  69. Pachur, T., Bröder, A., & Marewski, J. (2008). The recognition heuristic in memory-based inference: Is recognition a non-compensatory cue? Journal of Behavioral Decision Making, 21, 183–210.

    Article  Google Scholar 

  70. Pachur, T., & Hertwig, R. (2006). On the psychology of the recognition heuristic: Retrieval primacy as a key determinant of its use. Journal of Experimental Psychology: Learning, Memory, & Cognition, 32, 983–1002.

    Article  Google Scholar 

  71. Pohl, R. F. (2006). Empirical tests of the recognition heuristic. Journal of Behavioral Decision Making, 19, 251–271.

    Article  Google Scholar 

  72. Reimer, T., & Katsikopoulos, K. V. (2004). The use of recognition in group decision-making. Cognitive Science, 28, 1009–1029.

    Article  Google Scholar 

  73. Richter, T., & Späth, P. (2006). Recognition is used as one cue among others in judgment and decision making. Journal of Experimental Psychology: Learning, Memory, & Cognition, 32, 150–162.

    Article  Google Scholar 

  74. Rieskamp, J. (2008). The probabilistic nature of preferential choice. Journal of Experimental Psychology: Learning, Memory, & Cognition, 34, 1446–1465.

    Article  Google Scholar 

  75. Schooler, L. J., & Hertwig, R. (2005). How forgetting aids heuristic inference. Psychological Review, 112, 610–628.

    PubMed  Article  Google Scholar 

  76. Schweickart, O., Brown, N. R., & Lee, P. J. (2009, November). On the role of recognition and magnitude-comparison in binary decision tasks. Paper presented at the Annual Meeting of the Society for Judgment and Decision Making, Boston.

  77. Snook, B., & Cullen, R. M. (2006). Recognizing National Hockey League greatness with an ignorance-based heuristic. Canadian Journal of Experimental Psychology, 60, 33–43.

    PubMed  Google Scholar 

  78. Wikipedia (n.d.). List of cities proper by population. Retrieved October 2009 from http://en.wikipedia.org/wiki/List_of_cities_proper_by_population.

  79. Zhao, J., & Oppenheimer, D. M. (2010). Beyond binary: Limitations of the binary choice paradigm for studying judgment heuristics. Manuscript submitted for publication.

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Hilbig, B.E. Reconsidering “evidence” for fast-and-frugal heuristics. Psychon Bull Rev 17, 923–930 (2010). https://doi.org/10.3758/PBR.17.6.923

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Keywords

  • Adherence Rate
  • Behavioral Decision
  • Cumulative Prospect Theory
  • Cognitive Science Society
  • Discrimination Rate