Experience and the base-rate fallacy

Christensen-Szalanski, Jay; Beach, Lee Roy

doi:10.1016/0030-5073(82)90260-4

Cited by 222 publications

(65 citation statements)

References 13 publications

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“…(The probability matching observed in learning can be attributed to the uncertainty of the previously learned cue±outcome relationship, which was designed to be positive but not perfect.) Nevertheless, in trying to reconcile the ®ndings of Fantino (1995, 1996) with those of Christensen-Szalanski and Beach (1982), the answer may lie in the dierent response modes.…”

Section: Response Modementioning

confidence: 90%

“…Our prior ®ndings Fantino, 1995, 1996, Experiment 2) bear a special relationship to those of Christensen-Szalanski and Beach (1982), who exposed their subjects to positive and negative medical diagnostic test results, followed by actual medical outcomes. All these studies exposed subjects to base rates and test accuracy by means of direct experience with the relevant events.…”

Section: Response Modementioning

confidence: 96%

“…Other base-rate researchers have almost always used related but not identical stimuli, most often real or ®ctitious symptoms and diseases (Christensen-Szalanski and Beach, 1982;Estes et al, 1989;Gigerenzer and Horage, 1995;Gluck and Bower, 1988;Medin and Edelson, 1988;Shanks, 1990Shanks, , 1992. In some cases (e.g.…”

Section: Cue±outcome Relationshipmentioning

confidence: 98%

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What does and does not alleviate base-rate neglect under direct experience

Goodie

Fantino

1999

J. Behav. Decis. Making

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Section: Response Modementioning

confidence: 90%

Section: Response Modementioning

confidence: 96%

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What does and does not alleviate base-rate neglect under direct experience

Goodie

Fantino

1999

J. Behav. Decis. Making

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“…First, this study examined updating based on participants' own beliefs about the prior rather than experimentally provided numbers (Christensen-Szalanski & Beach, 1982;Evans et al, 2002). Second, this study tested experts; it is possible that having extensive experience and knowledge could lead to stronger use of that knowledge.…”

Section: Discussionmentioning

confidence: 99%

Physician Bayesian updating from personal beliefs about the base rate and likelihood ratio

Rottman

2016

Mem Cogn

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Whether humans can accurately make decisions in line with Bayes' rule has been one of the most important yet contentious topics in cognitive psychology. Though a number of paradigms have been used for studying Bayesian updating, rarely have subjects been allowed to use their own preexisting beliefs about the prior and the likelihood. A study is reported in which physicians judged the posttest probability of a diagnosis for a patient vignette after receiving a test result, and the physicians' posttest judgments were compared to the normative posttest calculated from their own beliefs in the sensitivity and false positive rate of the test (likelihood ratio) and prior probability of the diagnosis. On the one hand, the posttest judgments were strongly related to the physicians' beliefs about both the prior probability as well as the likelihood ratio, and the priors were used considerably more strongly than in previous research. On the other hand, both the prior and the likelihoods were still not used quite as much as they should have been, and there was evidence of other nonnormative aspects to the updating, such as updating independent of the likelihood beliefs. By focusing on how physicians use their own prior beliefs for Bayesian updating, this study provides insight into how well experts perform probabilistic inference in settings in which they rely upon their own prior beliefs rather than experimenter-provided cues. It suggests that there is reason to be optimistic about experts' abilities, but that there is still considerable need for improvement.

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“…Similarly, Cosmides and Tooby (1996) showed that natural frequencies improve Bayesian inferences in the Casscell et al 's (1978) problem as well. This hypothetical medical problem is numerically simpler (the hit rate is assumed to be 100%) than the problems in the Gigerenzer and Hoffrage (1995) study, and Cosmides and Tooby reported that 76% of the answers were Bayesian (see also Christensen-Szalanski & Beach, 1982).…”

Section: Natural Frequencies Help In Making Diagnostic Inferencesmentioning

confidence: 99%

How to Improve the Diagnostic Inferences of Medical Experts

Hoffrage

Gigerenzer

Experts in Science and Society

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Women are generally informed that mammography screening reduces the risk of dying from breast cancer by 25%. Does that mean that from 100 women who participate in screening, 25 lives will be saved? Although many people believe this to be the case, the conclusion is not justified. This figure means that from 1,000 women who participate in screening, 3 will die from breast cancer within 10 years, whereas from 1,000 women who do not participate, 4 will die. The difference between 4 and 3 is the 25% "relative risk reduction." Expressed as an "absolute risk reduction," however, this means that the benefit is 1 in 1,000, that is, 0.1%. Cancer organizations and health departments continue to inform women of the relative risk reduction, which gives a higher number-25% as compared to 0.1%-and makes the benefit of screening appear larger than if it were represented in absolute risks.The topic of this chapter is the representation of information on medical risks. As the case of mammography screening illustrates, the same information can be presented in various ways. The general point is that information always requires representation, and the choice between alternative representations can influence the patients' willingness to participate in screening, or more generally, patients' understanding of risks and choices of medical treatments. The ideal of "informed consent" can only be achieved if the patient knows about the pros and cons of a treatment, or the chances that a particular diagnosis is right or wrong. However, in order to communicate such uncertainties to the patients, the physician has to first understand statistical information and its implications. This requirement sharply contrasts with the fact that physicians are rarely trained in risk communication, and some still think that medicine can dispense with statistics and psychology. Such reluctance may also explain why previous research observed that a majority of physicians do not use relevant statistical information properly in diagnostic inference. Casscells, Schoenberger, and Grayboys (1978), for instance, asked 60 house officers, students, and physicians at the Harvard Medical School to estimate the probability of an unnamed disease given the following information:If a test to detect a disease whose prevalence is 1/1,000 has a false positive rate of 5 per cent, what is the chance that a person found to have a positive result actually has the disease, assuming that you know nothing about the person's symptoms or signs? (p. 999) The estimates varied wildly, from the most frequent estimate, 95% (27 out of 60), down to 2% (11 out of 60). The value of 2% is obtained by inserting the problem information into Bayes' rule (see below)-assuming that the sensitivity of the test, which is not specified in the problem, is approximately 100%. Casscells et al. (1978) concluded that "(...) in this group of students and physicians, formal decision analysis was almost entirely unknown and even common-sense reasoning about the interpretation of laboratory data was uncommon...

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Experience and the base-rate fallacy

Cited by 222 publications

References 13 publications

What does and does not alleviate base-rate neglect under direct experience

What does and does not alleviate base-rate neglect under direct experience

Physician Bayesian updating from personal beliefs about the base rate and likelihood ratio

How to Improve the Diagnostic Inferences of Medical Experts

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