Sorting out the effects of witness sensitivity and response-criterion placement upon the inferential value of testimonial evidence

Schum, David A.

doi:10.1016/0030-5073(81)90045-3

Cited by 29 publications

(24 citation statements)

References 2 publications

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“…However, if Joe were the only engineer in a population of lawyers, the same witness would be unlikely to select this description of Joe. A recent paper by Schum (1981) gives a very thorough analysis of the evidential impact of testimony in Bayesian inference. Schum makes an important distinction between direct testimony concerning the hypothesis and testimony concerning a datum relevant to the hypothesis.…”

Section: Discussionmentioning

confidence: 99%

“…Similarly the distinction between the datum and the report of the datum should be maintained. The report may convey more information than the datum itself (Schum, 1981). For example, it may be that interest in mathematical puzzles is not highly diagnostic of being an engineer or lawyer, but the report of interest in mathematical puzzles may be highly diagnostic of a rare engineer in a population consisting mostly of lawyers.…”

Section: Discussionmentioning

confidence: 99%

“…Although it may be reasonable to assume for some sources of information that the ratio of hit rate to false alarm rate is independent of signal probability, this assumption would not be realistic for the human witness2 (Schum, 1981). A good deal of research has shown that a better approximation to the behavior of witnesses would be the curve shown in Figure 1 (Green & Swets, 1966).…”

Section: Signal Detection Theorymentioning

confidence: 99%

See 2 more Smart Citations

Base Rates in Bayesian Inference: Signal Detection Analysis of the Cab Problem

Birnbaum¹

1983

The American Journal of Psychology

265

109

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Signal Detection Theorymentioning

confidence: 99%

See 1 more Smart Citation

Base Rates in Bayesian Inference: Signal Detection Analysis of the Cab Problem

Birnbaum¹

1983

The American Journal of Psychology

265

109

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“…In other words, the explicit model can be reduced to the basic model, captured by Eq. 1, and, in this sense, the combination of both source and message content, explicitly considered, is in fact just another likelihood ratio (see also Schum, 1981 for a slightly different treatment but ultimately the same conclusion). Figure 2 and Equation 2 also allow us to characterize exactly what effect manipulations of source reliability will have on the overall likelihood of the evidence report given the hypothesis.…”

Section: Eq 2 P(erep|h) = P(erep|h Rel)*p(rel) + P(erep|h¬rel)*p(¬mentioning

confidence: 99%

Argument Content and Argument Source: An Exploration

Hahn¹,

Harris²,

Corner³

2009

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Argumentation is pervasive in everyday life. Understanding what makes a strong argument is therefore of both theoretical and practical interest. One factor that seems intuitively important to the strength of an argument is the reliability of the source providing it. Whilst traditional approaches to argument evaluation are silent on this issue, the Bayesian approach to argumentation (Hahn & Oaksford, 2007) is able to capture important aspects of source reliability. In particular, the Bayesian approach predicts that argument content and source reliability should interact to determine argument strength. In this paper, we outline the approach and then demonstrate the importance of source reliability in two empirical studies. These experiments show the multiplicative relationship between the content and the source of the argument predicted by the Bayesian framework.Résumé: L'argumentation est employée couramment dans la vie de tous les jours. Il est donc dans notre intérêt théorique et pratique de comprendre ce qui rend un argument puissant. Un facteur qui semble intuitivement important qui contribue à cette puissance est la fiabilité des sources d'information employées dans un argument. Quoique les approches traditionnelles sur l'évaluation d'un argument soient silencieuses sur ce sujet, l'approche bayesienne peut apporter quelques aspects importants sur l'évaluation de la fiabilité des sources: elle prédit que le contenu d'un argument et la fiabilité d'une source devraient agir un sur l'autre pour déterminer la puissance d'un argument. Dans cet article nous traçons les grandes lignes de cette approche et ensuite démontrons dans deux études empiriques l'importance d'évaluer la fiabilité d'une source d'information. Ces expériences dé-montrent une relation multiplicative entre le contenu et la source d'un argument, qui est prédite par l'approche bayesienne.

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“…In addition to developmental psychology, other research areas that have been interested in these concepts include the following: judgment and decision making (e.g., Birnbaum & Mellers, ; Birnbaum & Stegner, ), reasoning research (Stevenson & Over, ; Wolf, Rieger, & Knauff, ), and social psychological research into persuasion and attitude change (e.g., Brinol & Petty, ; Chaiken, ; Hovland, Janis, & Kelley, ; McGuire, ; O'Hara, Netemeyer, & Burton, ; Petty & Cacioppo, ; Pornpitakpan, —also incorporating direct applied research in advertising, for example, Braunsberger & Munch, ; Ohanian, ; Wiener & Mowen, ). Moreover, the concepts of trust and expertise are also of vital importance in the evaluation of legal testimony, and research has concerned both formalizations of how testimony should be viewed (e.g., Friedman, ; Hahn, Oaksford, & Harris, ; Lagnado, Fenton, & Neil, ; Schum, , ; Walton, ), and descriptive studies investigating the degree to which people are sensitive to different relevant aspects of a witness's testimony (e.g., Eaton & O'Callaghan, ; ForsterLee, Horowitz, Athaide‐Victor, & Brown, ; Harris & Hahn, ; Krauss & Sales, ; see Wells & Olson, , for a review). The importance of trust and expertise for humans, and hence its interest for researchers in psychology, predicts its importance for artificial intelligence systems, and hence its interest for computer scientists.…”

Section: Introductionmentioning

confidence: 99%

The Appeal to Expert Opinion: Quantitative Support for a Bayesian Network Approach

et al. 2015

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The appeal to expert opinion is an argument form that uses the verdict of an expert to support a position or hypothesis. A previous scheme-based treatment of the argument form is formalized within a Bayesian network that is able to capture the critical aspects of the argument form, including the central considerations of the expert's expertise and trustworthiness. We propose this as an appropriate normative framework for the argument form, enabling the development and testing of quantitative predictions as to how people evaluate this argument, suggesting that such an approach might be beneficial to argumentation research generally. We subsequently present two experiments as an example of the potential for future research in this vein, demonstrating that participants' quantitative ratings of the convincingness of a proposition that has been supported with an appeal to expert opinion were broadly consistent with the predictions of the Bayesian model.

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Sorting out the effects of witness sensitivity and response-criterion placement upon the inferential value of testimonial evidence

Cited by 29 publications

References 2 publications

Base Rates in Bayesian Inference: Signal Detection Analysis of the Cab Problem

Base Rates in Bayesian Inference: Signal Detection Analysis of the Cab Problem

Argument Content and Argument Source: An Exploration

The Appeal to Expert Opinion: Quantitative Support for a Bayesian Network Approach

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