Sources of error in syllogistic reasoning

Ceraso, John; Provitera, Angela

doi:10.1016/0010-0285(71)90023-5

Cited by 100 publications

(73 citation statements)

References 4 publications

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“…Thus, why does the negative predominate over the affirmative and why does the particular predominate over the universal? Second, in recent years, some studies have failed to support predictions derived from the atmosphere formulation (Ceraso & Provitera, 1971;Dickstein, 1975), and these findings cast some doubt on its validity. Finally, while there may be a response bias against nonpropositional conclusions, such a bias cannot be attributed to an imbalance in the ratio of valid to invalid syllogisms.…”

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confidence: 99%

Error processes in syllogistic reasoning

Dickstein

1978

Memory & Cognition

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confidence: 99%

Error processes in syllogistic reasoning

Dickstein

1978

Memory & Cognition

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“…Braine & O'Brien, 1998;Rips, 1994), though they differ in the way they treat the relations between the sets. For instance, some theorists make use of diagrammatic representations to handle relations (Ceraso & Provitera, 1971;Erickson, 1974;Ford, 1995;Newell, 1981), others rely on formal rules of inference (Geurts, 2003;Guyote & Sternberg, 1981;Politzer, van der Henst, Luche, & Noveck, 2006;Stenning & Yule, 1997); and yet others analyze sets in terms of simulated possibilities, i.e., mental models (Bucciarelli & Johnson-Laird, 1999;Johnson-Laird & Byrne, 1991;Polk & Newell, 1995). The psychological systems can all account for how individuals make valid deductions, however few of them can account for the differences in relative difficulty between various reasoning problems.…”

Section: Mental Models and Quantifiersmentioning

confidence: 99%

A process model of immediate inferences

Khemlani¹,

Trafton²,

Lotstein³

et al. 2012

PsycEXTRA Dataset

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Individuals can make inferences from a single quantified premise. For instance, if you know that all of the Virginians are students, you might infer that some of the students are Virginians. We describe a computational system, mReasoner, of the cognitive processes that underlie these so-called 'immediate' inferences. The account is based on the assumption that when individuals understand discourse, they construct discrete mental simulations, i.e., mental models, of the assertions in the discourse. To draw conclusions, reasoners describe the relation between the individuals in the models and, if they are prudent, they search for alternative models to corroborate or refute a conclusion. We describe an experiment in which participants' carried out a series of immediate inferences, and present a simulation that predicts the accuracy and latency of their responses.

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“…Studies in cognitive psychology, e.g. [2,3], discovered that humans make systematic errors while formulating or interpreting logical descriptions. Results presented in [4,5] confirmed these observations regarding ontology development.…”

Section: Introductionmentioning

confidence: 99%

Query Strategy for Sequential Ontology Debugging

Shchekotykhin

Friedrich

2010

Lecture Notes in Computer Science

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Debugging of ontologies is an important prerequisite for their wide-spread application, especially in areas that rely upon everyday users to create and maintain knowledge bases, as in the case of the Semantic Web. Recent approaches use diagnosis methods to identify causes of inconsistent or incoherent ontologies. However, in most debugging scenarios these methods return many alternative diagnoses, thus placing the burden of fault localization on the user. This paper demonstrates how the target diagnosis can be identified by performing a sequence of observations, that is, by querying an oracle about entailments of the target ontology. We exploit a-priori probabilities of typical user errors to formulate information-theoretic concepts for query selection. Our evaluation showed that the proposed method significantly reduces the number of required queries compared to myopic strategies. We experimented with different probability distributions of user errors and different qualities of the a-priori probabilities. Our measurements showed the advantageousness of information-theoretic approach to query selection even in cases where only a rough estimate of the priors is available.

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Sources of error in syllogistic reasoning

Cited by 100 publications

References 4 publications

Error processes in syllogistic reasoning

Error processes in syllogistic reasoning

A process model of immediate inferences

Query Strategy for Sequential Ontology Debugging

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