Antecedent probability and the efficiency of psychometric signs, patterns, or cutting scores.

Meehl, Paul E.; Rosen, Albert

doi:10.1037/h0048070

Cited by 1,202 publications

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“…In causal inference, there is some preliminary evidence for this phenomenon. For example, in medical reasoning, clinicians often exhibit the fallacy when judging the probability of a disease (the cause) based on a set of symptoms (the effects) (Meehl & Rosen, 1955;Hammerton, 1973;Liu, 1975;Eddy, 1982). Despite the evidence for the inverse fallacy in these studies, Krynski and Tenenbaum (2007) suggested that the phenomenon is limited, only occurring when both probabilities have roughly the same value.…”

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confidence: 99%

A quantum probability framework for human probabilistic inference.

Trueblood

Yearsley

Pothos

2017

Journal of Experimental Psychology: General

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThere is considerable variety in human inference (e.g., a doctor inferring the presence of a disease, a juror inferring the guilt of a defendant, or someone inferring future weight loss based on diet and exercise). As such, people display a wide range of behaviors when making inference judgments.Sometimes, people's judgments appear Bayesian (i.e., normative), but in other cases, judgments deviate from the normative prescription of classical probability theory. How can we combine bothBayesian and non-Bayesian influences in a principled way? We propose a unified explanation of human inference using quantum probability theory. In our approach, we postulate a hierarchy of mental representations, from 'fully' quantum to 'fully' classical, which could be adopted in different situations. In our hierarchy of models, moving from the lowest level to the highest involves changing assumptions about compatibility (i.e., how joint events are represented). Using results from three experiments, we show that our modeling approach explains five key phenomena in human inference including order effects, reciprocity (i.e., the inverse fallacy), memorylessness, violations of the Markov condition, and anti-discounting. As far as we are aware, no existing theory or model can explain all five phenomena. We also explore transitions in our hierarchy, examining how representations change from more quantum to more classical. We show that classical representations provide a better account of data as individuals gain familiarity with a task.We also show that representations vary between individuals, in a way that relates to a simple measure of cognitive style, the Cognitive Reflection Test.Keywords: Human judgment, quantum probability theory, Bayes' rule, order effects, Markov condition A Quantum Framework for Probabilistic Inference 3 A Quantum Probability Framework for Human Probabilistic InferenceEveryday we face situations where we must make inferences about the world around us. For example, a doctor must determine the likelihood that a patient has a disease based on a set of symptoms. A juror must decide the probability that a defendant is guilty after hearing the cases made by the prosecution and defense. Or, maybe you want to judge the likelihood that you will weigh less next month if you start exercising more regularly and you improve your diet. In general, the inference problem involves judging the likelihood of some hypothesis (e.g., presence of a disease, guilt of a defendant, future weight loss) based on a series of evidence (e.g., medicalsymptoms, prosecution and defense cases, changes in your diet and exercise).Bayesian inference is widely accepted as the normative approach to inference. However, decades of research in human judgment and decision-making have suggested that people's judgments often violate the rules of Bayesian inference and classical probability theory (Tversky...

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confidence: 99%

A quantum probability framework for human probabilistic inference.

Trueblood

Yearsley

Pothos

2017

Journal of Experimental Psychology: General

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“…As such, the range-frequency model might not be directly applicable. However, some features of the model, such as the tendency to use categories with equal frequencies, might explain the general tendency to ignore base rates (Balla, Elstein, & Gates, 1983;Casscells, Schoenberger, & Grayboys, 1978;Kahneman & Tversky, 1973;Meehl & Rosen, 1955). Furthermore, insofar as these three more specific axes tend to tap more global evaluative dimensions, similar contextual effects should be expected, and range-frequency theory would be directly applicable.…”

Section: Discussionmentioning

confidence: 99%

Reducing the dependence of clinical judgment on the immediate context: Effects of number of categories and type of anchors.

Wedell¹,

Parducci²,

Lane³

1990

Journal of Personality and Social Psychology

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Two experiments explored methods for standardizing ratings of the psychopathology of clinical case histories. In both experiments, the same case histories were rated as more pathological when mostly mild rather than severe cases were presented as the immediate context. Psychometric analyses demonstrated that this type of contextual effect is a potentially important source of unreliability in clinical judgment. In Experiment 1, increasing the number of points in the rating scale from 3 to either 7 or 100 significantly reduced the effects of the immediate context. Ratings were parsimoniously modeled by Parducci's (1983) range-frequency theory. In Experiment 2, providing verbal anchors in the form of either detailed DSM descriptions for each rating category or sample case histories for the two end-categories increased the reliability of the ratings by reducing the effects of the immediate contexts; however, these reductions occurred only when the ranges of the immediate contexts had been severely restricted. According to the range-frequency analysis, verbal anchors served to equate the endpoints of the subjective range for the different contextual conditions. Comparison with previous research suggests that the anchors also reduced the effects of the sequential position in which clinical cases appear. We therefore recommend that studies of the reliability of behavioral assessment techniques take into account the effects of differences in context.Although reliability does not imply validity, it is a necessary prerequisite. Unfortunately, clinical assessment in psychology is notoriously unreliable. Eysenck, Wakefield, and Friedman (1983) have documented the lack of interrater reliability. Goldberg (1968Goldberg ( , 1970 concluded that clinical judgments tend to be unreliable and only minimally related to the degree of experience of the person making the assessment. Arnoff (1954) reported an actual decrease in reliability with greater clinical experience.One generally accepted method for improving clinical prediction has been the use of statistical models. In particular, there is overwhelming evidence for the superiority of simple linear models over clinicians in combining information to form a diagnosis (Dawes, 1979;Dawes&Corrigan, 1974;Einhorn, 1972;Goldberg, 1968Goldberg, , 1970Meehl, 1954). But as Sawyer (1966) has pointed out, combining information is only the second half of the problem, the first half being the problem of measurement. Although there are a large number of objective measurement devices currently in use in clinical psychology, it is doubtful that the clinician will be supplanted in the near future as a primary source of information used in prediction. Therefore, development of greater accuracy of clinical judgments is of fundamenPreparation of this article was carried out while Douglas H.

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“…The classic paper analyzing these difficulties, by Meehl and Rosen, delineated two types of error: false negatives and false positives (Meehl and Rosen, 1955). Correctly discerning the condition in question, in those who actually have the condition, is called 'sensitivity'.…”

Section: Predicting Rare Events Validating Surrogate Markers Of Rare mentioning

confidence: 99%

The Flawed Basis for FDA Post-Marketing Safety Decisions: The Example of Anti-Depressants and Children

Klein

2005

Neuropsychopharmacol

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The FDA (February 3, 2005) issued a black box warning that all antidepressants increase the risk of suicidal thinking and behavior in children and adolescents that must be cited in all advertising as well as included in the package insert. Following this, there was a sharp decrease in antidepressant prescriptions for children with uncertain public health impact. The current black box does not claim that these medications increase the risk of completed suicides, although this is the clear implication of the term 'suicidality'. The interpretation by the press is unequivocal that lethal outcomes prompted this action. This review concludes the following: (1) Since no suicide occurred in clinical trials of approximately 4400 children, the analyses relied upon 'suicidality' as a surrogate. (2) The classification of adverse events by the Columbia group necessarily relied on inferences, because the available evidence was not prospectively collected for this purpose. (3) The data analysis relied on a composite variable labeled 'suicidality', an unvalidated, inappropriate surrogate. Specific criticisms of analytic procedures and inferences are presented. The failure of the FDA's post-marketing surveillance system is reviewed. The data necessary for objective evaluations of possible post-marketing harm cannot be gathered by the current process. Proper prospective post-marketing surveillance by linked computerized medical records is a crucial issue that deserves major public and political attention and prompt action.

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Antecedent probability and the efficiency of psychometric signs, patterns, or cutting scores.

Cited by 1,202 publications

References 14 publications

A quantum probability framework for human probabilistic inference.

A quantum probability framework for human probabilistic inference.

Reducing the dependence of clinical judgment on the immediate context: Effects of number of categories and type of anchors.

The Flawed Basis for FDA Post-Marketing Safety Decisions: The Example of Anti-Depressants and Children

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