Scientists' Understanding of Propositional Logic: An Experimental Investigation

Kern, Leslie; Mirels, Herbert L.; Hinshaw, Virgil

doi:10.1177/030631283013001007

Cited by 47 publications

(25 citation statements)

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“…One factor is expertise level or previous experience of the tester [cf., Kern, Mirels and Hinshaw, 1983]. For example, Rohlman, Mynatt, Mynatt and Leventhal [1992] found that advanced computer science majors were better than both advanced students from other majors and first-year computer science majors at performing various versions of Wason's classic four-card selection task.…”

Section: Positive Test Biasmentioning

confidence: 99%

Positive test bias in software testing among professionals: A review

Leventhal

Teasley

Rohlman

et al. 1993

Lecture Notes in Computer Science

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Abstract. Fundamental but virtually unexplored issues in human-computer interaction involve the roles of biases in software engineering tasks. In studies of naturalistic testing tasks, as well as ones which follow common laboratory models in this area, we have found ample evidence that testers have positive test bias. This bias is manifest as a tendency to execute about four times as many positive tests, designed to show that "the program works," as tests which challenge the program. In our prior work, we have found that the expertise of the subjects, the completeness of the software specifications, and the presence/absence of program errors may reduce positive test bias. Skilled computer scientists invent specifications to test in the absence of actual specifications, but still exhibit positive test bias.

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Section: Positive Test Biasmentioning

confidence: 99%

Positive test bias in software testing among professionals: A review

Leventhal

Teasley

Rohlman

et al. 1993

Lecture Notes in Computer Science

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“…We had hypothesized that large performance differences would emerge for the different educational levels; however, the 48% solution rate on the standard task for master's subjects was a surprise. Moreover, severalother researchers have found poor performance by college-level faculty on standard versions (Griggs & Ransdell, 1985;Kerns, Mirels, & Hinshaw, 1983;cf. Tweney & Yachanin, 1985).…”

Section: Replicanonsmentioning

confidence: 99%

Logical knowledge and cue redundancy in deductive reasoning

Hoch

Tschirgi

1985

Memory & Cognition

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This study clarified the basis of Hoch and Tschirgi's (1983) finding of good performance on abstract deductive reasoning problems based on Wason's (1966) four-card selection task. Different versions of the problem were developed through the addition of cues about antecedent-consequent relations redundant with the logical structure of the task. In all versions of the task, abstract symbols were used, and subjects were asked to test the truth or falsity of an implication rule. Twenty-five subjects from each of three education levels (high school, bachelor's, and master's) solved one of four versions of the task. Forty-eight percent of the master's subjects solved the original abstract version of the task compared to less than 10% of the high school and bachelor's subjects. When the problems contained redundant cues, performance improved dramatically, most notably in the bachelor's subjects. Reasoning performance seemed to be a function of both the general inferential abilities that subjects brought to the task and the redundant cues conveyed through problem content. Many of the master's subjects had adequate logical knowledge to solve the problems without the addition of redundant cues, though performance did increase when such cues were present. In contrast, high school subjects appeared to have so little understanding of the logical structure of conditionals that cue redundancy improved performance only slightly.In his 1952 monograph, "The Conceptual Framework of Psychology," Brunswik argued that human performance is determined by the perception and integration of multiple probabilistic cues. Typically, individual cues in the environment provide only partially valid information about the objects of perception. Performance, however, is a function not only of the individual cues but also of the redundancy between those cues (i.e., inter-cue correlations or vicarious functioning) and the ability of a perceiver to detect and integrate those cues. Although performance may be low when a subject has access to only one imperfect cue, performance improves when the subject can rely on a number of imperfect but correlated cues. Deductive reasoning tasks, such as the Wason (1966) fourcard selection task, can be analyzed within this framework (Hoch & Tschirgi, 1983). The problem content consists of various cues to an underlying structure. The goal of the problem solver is to perceive the valid cues to logical structure and infer a solution. However, people vary in their abilities to perceive cues and in their own inferential machinery. In this paper, we examine the selection task in terms of cue redundancy and differences in general inferential ability among members of the subject population.Requests for reprints should be sent to Stephen J.

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“…The latter point is curious since "scientists" have not fared well on the standard abstract form of the task (Griggs & Ransdell, 1986;Kern, Mirels, & Hinshaw, 1983;Mahoney & Kimper, 1976;Tolbert, 1983). For example, not one of the 10 scientists with doctoral degrees made the correct selection on the standard abstract selection task in Griggs and Ransdell 's study .…”

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confidence: 99%

Education and the selection task

Jackson

Griggs

1988

Bull. Psychon. Soc.

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The present study was concerned with the effects of education level and area of expertise on performance on the standard abstract selection task. Subjects had received bachelor's or doctoral degrees. Contrary to some recent results reported by Hoch and Tschirgi (1985), no effect of education level was found. However , there was a significant effect for area of expertise. Subjects trained in mathematics performed better than subjects from computer science, electrical engineering, and the social sciences. An explanation in terms of the mathematics subjects' greater likelihood of using a disconfirmation strategy and greater familiarity with the relevant propositional logic is offered .Since Wason's first report on the selection task in 1966, researchers have attempted to explain why performance is so poor on this particular deductive reasoning problem. Typically less than 10% of the subjects make the correct selection. Most research has focused on the effects of problem content and other task variables, with varying results (see Evans, 1982, andGriggs, 1983, for reviews of the selection task literature) . Recently, however, some attention has focused on subject variables . Hoch and Tschirgi (1985) reported data that suggests an education effect; that is, subjects with more education perform better on the task. The present study examined this factor , along with another subject variable, area of training or expertise. Because the standard abstract form of the task was used, we will describe it before we further detail the Hoch and Tschirgi research and the present study.The selection task is based on the logic of a material conditional rule. In its standard form, the problem involves a rule of the form, "If P, then Q," and an array of four cards representing P, NOT P, Q, and NOT Q, where P and Q are usually letters and numbers. For example, the subject may be presented with four cards that display C, E, 5, and 8 and the rule, "If there is a C on one side of a card, then there is a 5 on the other side. " The subject is told that each card has a letter on one side and a number on the other side and is asked to select only the card or cards that need to be turned over in order to determine whether the rule is true or false with respect to these cards .The correct answer according to standard logic is P and NOT Q (C and 8 in the example) since these are the only cards that can possibly falsify the rule . A number other than 5 may be on the other side of the C card , and a C may be on the other side of the 8 card. The most common errors made are choosing only the P card (C in the This article is based on a master's thesis completed by the first author under the second author's supervision. Requests for reprints should be sent to Richard A. Griggs, Department of Psychology , University of Florida, Gainesville, FL 3261 I. example) or choosing the P and Q cards (C and 5 in the example).In stark contrast to the typically observed poor performance on the standard abstract form of the task, Hoch and Tschirgi (1985) found a...

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Scientists' Understanding of Propositional Logic: An Experimental Investigation

Cited by 47 publications

References 2 publications

Positive test bias in software testing among professionals: A review

Positive test bias in software testing among professionals: A review

Logical knowledge and cue redundancy in deductive reasoning

Education and the selection task

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