“I'm <span class='italic'>m/n</span> Confident That I'm Correct”: Confidence in Foresight and Hindsight as a Sampling Probability

doi:10.1017/CBO9780511614576.017

17 - “I'm m/n Confident That I'm Correct”: Confidence in Foresight and Hindsight as a Sampling Probability

Published online by Cambridge University Press: 02 February 2010

Anders Winman and

Peter Juslin

Edited by

Klaus Fiedler and

Peter Juslin

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Anders Winman: Affiliation:
Uppsala University, Sweden
Peter Juslin: Affiliation:
Professor of Psychology, Uppsala University in Sweden
Klaus Fiedler: Affiliation:
Ruprecht-Karls-Universität Heidelberg, Germany
Peter Juslin: Affiliation:
Umeå Universitet, Sweden

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Summary

Occasionally the development of a scientific discipline recapitulates the trajectory of individual development from novice to expert and, particularly, that the early stages of a science coincides with the current naïve or novice understanding of phenomena. For example, people's lay theories of physical phenomena recapitulate many of the conceptions that characterized Aristotelian physics (McCloskey, 1983). Likewise, the animistic metaphysics of prescientific “primitive cultures,” attributing spirits and subjectlike qualities to (what are today considered) inanimate objects like stones and trees, is recapitulated in the young child's understanding of the environment (Piaget, 1951).

The early development of probability theory was largely linked to the classical (m/n) definition of probability: The probability of an event equals the ratio between the number m of favorable outcomes relative to the number n of equally possible outcomes (Hacking, 1975). Although frequency and degree of belief were separate aspects, for a long time they were considered two associated and relatively unproblematic sides of the same basic probability concept. Since then classical probability has been replaced by the two competing schools of frequentists and subjectivists (Hacking, 1975; Oaks, 1986). Whereas relative frequencies naturally preserve the extensional properties that originally constrained the development of probability theory, such as subset–set inclusions and the property of being a real number in the interval [0, 1], the interpretation as a subjective degree of belief is premised on the assumption (or hope) that the judge's beliefs conform to these extensional rules (Savage, 1954).

Type: Chapter
Information: Information Sampling and Adaptive Cognition , pp. 409 - 439

DOI: https://doi.org/10.1017/CBO9780511614576.017 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2005

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References

Baranski, J. V., & Petrusic, W. M. (1994). The calibration and resolution of confidence in perceptual judgments. Perception and Psychophysics, 55, 412–428CrossRef Google Scholar PubMed

Björkman, M. (1994). Internal cue theory: Calibration and resolution of confidence in general knowledge. Organizational Behavior and Human Decision Processes, 57, 386–405CrossRef Google Scholar

Björkman, M., Juslin, P., & Winman, A. (1993). Realism of confidence in sensory discrimination: The underconfidence phenomenon. Perception & Psychophysics, 54, 75–81CrossRef Google Scholar PubMed

Brunswik, E. (1952). The conceptual framework of psychology. Chicago: University of Chicago PressGoogle Scholar

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

Campbell, J. D., & Tesser, A. (1983). Motivational interpretations of hindsight bias: An individual difference analysis. Journal of Personality, 51, 605–620CrossRef Google Scholar

Christensen-Szalanski, J. J. J., & Bushyhead, J. B. (1981). Physicians' use of probabilistic information in a real clinical setting. Journal of Experimental Psychology: Human and Performance, 7, 928–935Google Scholar

Christensen-Szalanski, J. J. J., & Willham, C. F. (1991). The hindsight bias: A meta-analysis. Organizational Behavior and Human Decision Processes, 48, 147–168CrossRef Google Scholar

Doherty, M. E., Brake, G., & Kleiter, G. D. (2001). The contribution of representative design to calibration research. In Hammond, K. R. and Stewart, T. R., (Eds.), The essential Brunswik. New York: Oxford University PressGoogle Scholar

Fischhoff, B. (1975). Hindsight ≠ Foresight: The effect of outcome knowledge on judgment under uncertainty. Journal of Experimental Psychology: Human Perception and Performance, 1, 288–299Google Scholar

Fischhoff, B. (1977). Perceived informativeness of facts. Journal of Experimental Psychology: Human Perception and Performance, 3, 349–358Google Scholar

Fischhoff, B. (1982a). For those condemned to study the past: Reflections on historical judgment. Reprinted in Kahneman, D., Slovic, P., & Tversky, A. (Eds.), Judgment under uncertainty: Heuristics and biases. New York: Cambridge University PressCrossRef Google Scholar

Fischhoff, B. (1982b). Debiasing. In Kahneman, D., Slovic, P., & Tversky, A. (Eds.), Judgment under uncertainty: Heuristics and biases (pp. 422–444). New York: Cambridge University PressCrossRef Google Scholar

Gigerenzer, G. (1991). How to make cognitive illusions disappear: Beyond “heuristics and biases.”European Review of Social Psychology, 2, 83–115CrossRef Google Scholar

Gigerenzer, G., Hoffrage, U., & Kleinbölting, H. (1991). Probabilistic mental models: A Brunswikian theory of confidence. Psychological Review, 98, 506–528CrossRef Google Scholar

Gilovich, T., Griffin, D., & Kahneman, D. (Eds.) (2002). Heuristics and biases: The psychology of intuitive judgment. New York: Cambridge University PressCrossRef Google Scholar

Griffin, D., & Tversky, A. (1992). The weighing of evidence and the determinants of confidence. Cognitive Psychology, 24, 411–435CrossRef Google Scholar

Hacking, I. (1975). The emergence of probability. Cambridge, UK: Cambridge University PressGoogle Scholar

Hammond, K. R., & Stewart, T. R. (Eds.) (2001). The essential Brunswik: Beginnings, explications, applications. New York: Oxford University PressGoogle Scholar

Hasher, L., & Zacks, R. T. (1984). The automatic processing of fundamental information: The case of frequency of occurrence. American Psychologist, 39, 1372–1388CrossRef Google Scholar PubMed

Hasher, L., & Zacks, R. T. (1979). Automatic and effortful processes in memory. Journal of Experimental Psychology: General, 108, 356–388CrossRef Google Scholar

Hawkins, S. A., & Hastie, R. (1990). Hindsight: Biased judgments of past events after the outcomes are known. Psychological Bulletin, 107, 311–327CrossRef Google Scholar

Hoch, S. J., & Loewenstein, G. F. (1989). Outcome feedback: Hindsight and information. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15, 605–619Google Scholar

Hoffrage, U., & Pohl, R. F. (2003). Research on hindsight bias: A rich past, a productive present, and a challenging future. Memory, 11, 357–377CrossRef Google Scholar

Hoffrage, U., Hertwig, R., & Gigerenzer, G. (2000). Hindsight bias: A by-product of knowledge updating?Journal of Experimental Psychology: Learning, Memory and Cognition, 26, 566–581Google Scholar PubMed

Hurly, T. A. (1992). Energetic reserves of marsh tits (Parus palustris): food and fat storage in response to variable food supply. Behavioral Ecology, 3, 181–188CrossRef Google Scholar

Juslin, P. (1994). The overconfidence phenomenon as a consequence of informal experimenter-guided selection of almanac items. Organizational Behavior and Human Decision Processes, 57, 226–246CrossRef Google Scholar

Juslin, P., & Olsson, H. (1997). Thurstonian and Brunswikian origins of uncertainty in judgment: A sensory sampling model of confidence in sensory discrimination, Psychological Review, 104, 344–366CrossRef Google Scholar PubMed

Juslin, P., Olsson, H., & Winman, A. (1998). The calibration issue: Theoretical comments on Suantak, Bolger, and Ferrell (1996). Organizational Behavior and Human Decision Processes, 73, 3–26CrossRef Google Scholar

Juslin, P., Wennerholm, P., & Olsson, H. (1999). Format dependence in subjective probability calibration. Journal of Experimental Psychology: Learning, Memory and Cognition, 25, 1038–1052Google Scholar

Juslin, P., Winman, A., & Persson, T. (1995). Can overconfidence be used as an indicator of reconstructive rather than retrieval processes?Cognition, 54, 99–130CrossRef Google Scholar PubMed

Juslin, P., Winman, A., & Olsson, H. (2000). Naive empiricism and dogmatism in confidence research: A critical examination of the hard-easy effect. Psychological Review, 107, 384–396CrossRef Google Scholar PubMed

Juslin, P., Winman, A., & Olsson, H. (2003). Calibration, additivity, and source independence of probability judgments in general knowledge and sensory discrimination tasks. Organizational Behavior and Human Decision Processes, 92, 34–51CrossRef Google Scholar

Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment under uncertainty: Heuristics and biases. Cambridge, UK: Cambridge University PressCrossRef Google Scholar

Kahneman, D., & Tversky, A. (1982). Variants of uncertainty. Cognition, 11, 143–157CrossRef Google Scholar

Klayman, J., Soll, J. B., González-Vallejo, C., & Barlas, S. (1999). Overconfidence? It depends on how, what, and whom you ask. Organizational Behavior and Human Decision Processes, 79, 216–247CrossRef Google Scholar

Kleitman, S., & Stankov, L. (2001). Ecological and person-driven aspects of metacognitive processes in test-taking. Applied Cognitive Psychology, 15, 321–341CrossRef Google Scholar

Koriat, A., Lichtenstein, S., & Fischhoff, B. (1980). Reasons for confidence. Journal of Experimental Psychology: Human Learning and Memory, 6, 107–118Google Scholar

Lichtenstein, S., & Fischhoff, B. (1977). Do those who know more also know more about how much they know?Organizational Behavior and Human Performance, 20, 159–183CrossRef Google Scholar

Lichtenstein, S., Slovic, P., Fischhoff, B., Layman, M., & Combs, B. (1978). Judged frequency of lethal events. Journal of Experimental Psychology: Human Learning and Memory, 4, 551–578Google Scholar

Manis, M., Dovenalina, I., Avis, N. E., & Cardoze, S. (1980). Base rates can affect individual predictions. Journal of Personality and Social Psychology, 38, 287–298CrossRef Google Scholar

McCloskey, M. (1983). Naive theories of motion. In: Gentner, D. & Stevens, A. L. (Eds.), Mental models (pp. 299–324). London: Lawrence ErlbaumGoogle Scholar

Medin, D. L., & Edelson, S. M. (1988). Problem structure and the use of base-rate information form experience. Journal of Experimental Psychology: General, 117, 68–85CrossRef Google Scholar

Oaks, M. (1986). Statistical inference: A commentary for the social and behavioral sciences. New York: WileyGoogle Scholar

Piaget, J. (1951). The child's conception of physical causality. New York: The Humanities PressGoogle Scholar

Peirce, C. S., & Jastrow, J. (1884). On small differences of sensation. Memoirs National Academy of Sciences, 3, 73–83Google Scholar

Ronis, D. L., & Yates, J. F. (1987). Components of probability judgment accuracy: Individual consistency and effects of subject matter and assessment method. Organizational Behavior and Human Decision Processes, 40, 193–218CrossRef Google Scholar

Savage, L. J. (1954). The foundations of statistics. New York: DoverGoogle Scholar

Sedlmeier, P., & Betsch, T. (2002). Etc. Frequency processing and cognition. New York: Oxford University PressCrossRef Google Scholar

Soll, J. B. (1996). Determinants of overconfidence and miscalibration: The roles of random error and ecological structure. Organizational Behavior and Human Decision Processes, 65, 117–137CrossRef Google Scholar

Suantak, L., Bolger, F., & Ferrell, W. R. (1996). The “hard-easy effect” in subjective probability calibration. Organizational Behavior and Human Decision Processes, 67, 201–221CrossRef Google Scholar

Taylor, S. E., & Brown, J. D. (1988). Illusion of well-being: A social psychological perspective on mental health. Psychological Bulletin, 103, 193–210CrossRef Google Scholar PubMed

Tversky, A., & Kahneman, D. (1983). Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment. Psychological Review, 90, 293–315CrossRef Google Scholar

Vickers, D., & Pietsch, A. (2001). Decision making and memory: A critique of Juslin and Olsson's (1997) sampling model of sensory discrimination. Psychological Review, 108, 789–804CrossRef Google Scholar PubMed

Walster, E. (1967). Second guessing important events. Human Relations, 20, 239–249CrossRef Google Scholar

Winman, A. (1997a). The importance of item selection in “knew-it-all-along” studies of general knowledge. Scandinavian Journal of Psychology, 38, 63–72CrossRef Google Scholar

Winman, A. (1997b). Knowing if you would have known: A model of the hindsight bias. Comprehensive summaries from the faculty of Social Sciences, 69. Uppsala University Library, Uppsala, SwedenGoogle Scholar

Winman, A. (1999). Cognitive processes behind the “knew-it-all-along effect.”Scandinavian Journal of Psychology, 40, 135–145CrossRef Google Scholar

Winman, A., & Juslin, P. (2003). Learning of subjective probabilities. Manuscript in preparation

Winman, A., Juslin, P., & Björkman, M. (1998). The confidence-hindsight mirror effect in judgment: An accuracy-assessment model for the knew-it-all-along phenomenon. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24, 415–431Google Scholar