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  1. The conditional in mental probability logic.Niki Pfeifer & G. D. Kleiter - 2010 - In Mike Oaksford & Nick Chater, Cognition and Conditionals: Probability and Logic in Human Thought. Oxford University Press. pp. 153--173.
    The present chapter describes a probabilistic framework of human reasoning. It is based on probability logic. While there are several approaches to probability logic, we adopt the coherence based approach.
     
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  2. Uncertain deductive reasoning.Niki Pfeifer & G. D. Kleiter - 2010 - In K. Manktelow, D. E. Over & S. Elqayam, The Science of Reason: A Festschrift for Jonathan St B.T. Evans. Psychology Press. pp. 145--166.
    Probabilistic models have started to replace classical logic as the standard reference paradigm in human deductive reasoning. Mental probability logic emphasizes general principles where human reasoning deviates from classical logic, but agrees with a probabilistic approach (like nonmonotonicity or the conditional event interpretation of conditionals). -/- This contribution consists of two parts. In the first part we discuss general features of reasoning systems including consequence relations, how uncertainty may enter argument forms, probability intervals, and probabilistic informativeness. These concepts are of (...)
     
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  3. Towards a mental probability logic.Niki Pfeifer & G. D. Kleiter - 2005 - Psychologica Belgica 45 (1):71--99.
    We propose probability logic as an appropriate standard of reference for evaluating human inferences. Probability logical accounts of nonmonotonic reasoning with system p, and conditional syllogisms (modus ponens, etc.) are explored. Furthermore, we present categorical syllogisms with intermediate quantifiers, like the “most . . . ” quantifier. While most of the paper is theoretical and intended to stimulate psychological studies, we summarize our empirical studies on human nonmonotonic reasoning.
     
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  4. Human reasoning with imprecise probabilities: Modus ponens and Denying the antecedent.Niki Pfeifer & G. D. Kleiter - 2007 - In Niki Pfeifer & G. D. Kleiter, Proceedings of the 5 T H International Symposium on Imprecise Probability: Theories and Applications. pp. 347--356.
    The modus ponens (A -> B, A :. B) is, along with modus tollens and the two logically not valid counterparts denying the antecedent (A -> B, ¬A :. ¬B) and affirming the consequent, the argument form that was most often investigated in the psychology of human reasoning. The present contribution reports the results of three experiments on the probabilistic versions of modus ponens and denying the antecedent. In probability logic these arguments lead to conclusions with imprecise probabilities. In the (...)
     
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  5. Is human reasoning about nonmonotonic conditionals probabilistically coherent?Niki Pfeifer & G. D. Kleiter - 2006 - In Niki Pfeifer & G. D. Kleiter, Proceedings of the 7 T H Workshop on Uncertainty Processing. pp. 138--150.
    Nonmonotonic conditionals (A |∼ B) are formalizations of common sense expressions of the form “if A, normally B”. The nonmonotonic conditional is interpreted by a “high” coherent conditional probability, P(B|A) > .5. Two important properties are closely related to the nonmonotonic conditional: First, A |∼ B allows for exceptions. Second, the rules of the nonmonotonic system p guiding A |∼ B allow for withdrawing conclusions in the light of new premises. This study reports a series of three experiments on reasoning (...)
     
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  6. Nonmonotonicity and human probabilistic reasoning.Niki Pfeifer & G. D. Kleiter - 2003 - In Niki Pfeifer & G. D. Kleiter, Proceedings of the 6 T H Workshop on Uncertainty Processing. pp. 221--234.
    Nonmonotonic logics allow—contrary to classical (monotone) logics— for withdrawing conclusions in the light of new evidence. Nonmonotonic reasoning is often claimed to mimic human common sense reasoning. Only a few studies, though, have investigated this claim empirically. system p is a central, broadly accepted nonmonotonic reasoning system that proposes basic rationality postulates. We previously investigated empirically a probabilistic interpretation of three selected rules of system p. We found a relatively good agreement of human reasoning and principles of nonmonotonic reasoning according (...)
     
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  7. How people interpret an uncertain If.A. J. B. Fugard, Niki Pfeifer, B. Mayerhofer & G. D. Kleiter - 2009 - In T. Kroupa & J. Vejnarova, Proceedings of the 8th Workshop on Uncertainty Processing. pp. 80-91.
    Conditionals are central to inference. Before people can draw inferences about a natural language conditional, they must interpret its meaning. We investigated interpretation of uncertain conditionals using a probabilistic truth table task, focussing on (i) conditional event, (ii) material conditional, and (iii) conjunction interpretations. The order of object (shape) and feature (color) in each conditional's antecedent and consequent was varied between participants. The conditional event was the dominant interpretation, followed by conjunction, and took longer to process than conjunction (mean di (...)
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  8. Experiments on nonmonotonic reasoning. The coherence of human probability judgments.Niki Pfeifer & G. D. Kleiter - 2002 - In H. Leitgeb & G. Schurz, Pre-Proceedings of the 1 s T Salzburg Workshop on Paradigms of Cognition.
    Nonmonotonic reasoning is often claimed to mimic human common sense reasoning. Only a few studies, though, investigated this claim empirically. In the present paper four psychological experiments are reported, that investigate three rules of system p, namely the and, the left logical equivalence, and the or rule. The actual inferences of the subjects are compared with the coherent normative upper and lower probability bounds derived from a non-infinitesimal probability semantics of system p. We found a relatively good agreement of human (...)
     
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  9. (1 other version)Towards a probability logic based on statistical reasoning.Niki Pfeifer & G. D. Kleiter - 2006 - In Niki Pfeifer & G. D. Kleiter, Proceedings of the 11th IPMU Conference (Information Processing and Management of Uncertainty in Knowledge-Based Systems. pp. 9.
    Logical argument forms are investigated by second order probability density functions. When the premises are expressed by beta distributions, the conclusions usually are mixtures of beta distributions. If the shape parameters of the distributions are assumed to be additive (natural sampling), then the lower and upper bounds of the mixing distributions (Polya-Eggenberger distributions) are parallel to the corresponding lower and upper probabilities in conditional probability logic.
     
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  10. (1 other version)Proceedings of the 6 T H Workshop on Uncertainty Processing.Niki Pfeifer & G. D. Kleiter - 2003
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  11. Proceedings of the 5 T H International Symposium on Imprecise Probability: Theories and Applications.Niki Pfeifer & G. D. Kleiter - 2007
     
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  12. Proceedings of the 11th IPMU Conference (Information Processing and Management of Uncertainty in Knowledge-Based Systems.Niki Pfeifer & G. D. Kleiter - 2006
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