14 found
  1.  96
    Framing Human Inference by Coherence Based Probability Logic.Niki Pfeifer & Gernot D. Kleiter - 2009 - Journal of Applied Logic 7 (2):206--217.
    We take coherence based probability logic as the basic reference theory to model human deductive reasoning. The conditional and probabilistic argument forms are explored. We give a brief overview of recent developments of combining logic and probability in psychology. A study on conditional inferences illustrates our approach. First steps towards a process model of conditional inferences conclude the paper.
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  2. How People Interpret Conditionals: Shifts Towards the Conditional Event.A. J. B. Fugard, Niki Pfeifer, B. Mayerhofer & Gernot D. Kleiter - 2011 - Journal of Experimental Psychology 37 (3):635-648.
    We investigated how people interpret conditionals and how stable their interpretation is over a long series of trials. Participants were shown the colored patterns on each side of a six-sided die, and were asked how sure they were that a conditional holds of the side landing upwards when the die is randomly thrown. Participants were presented with 71 trials consisting of all combinations of binary dimensions of shape (e.g., circles and squares) and color (e.g., blue and red) painted onto the (...)
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  3. Coherence and Nonmonotonicity in Human Reasoning.Niki Pfeifer & Gernot D. Kleiter - 2005 - Synthese 146 (1-2):93-109.
    Nonmonotonic reasoning is often claimed to mimic human common sense reasoning. Only a few studies, though, have investigated this claim empirically. We report four experiments which investigate three rules of SYSTEMP, 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 reasoning and principles of nonmonotonic (...)
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  4.  1
    Bayesian Diagnosis in Expert Systems.Gernot D. Kleiter - 1992 - Artificial Intelligence 54 (1-2):1-32.
  5.  18
    A Process Model of the Understanding of Uncertain Conditionals.Gernot D. Kleiter, Andrew J. B. Fugard & Niki Pfeifer - 2018 - Thinking and Reasoning 24 (3):386-422.
    ABSTRACTTo build a process model of the understanding of conditionals we extract a common core of three semantics of if-then sentences: the conditional event interpretation in the coherencebased probability logic, the discourse processingtheory of Hans Kamp, and the game-theoretical approach of Jaakko Hintikka. The empirical part reports three experiments in which each participant assessed the probability of 52 if-then sentencesin a truth table task. Each experiment included a second task: An n-back task relating the interpretation of conditionals to working memory, (...)
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  6.  57
    Mental Probability Logic.Niki Pfeifer & Gernot D. Kleiter - 2009 - Behavioral and Brain Sciences 32 (1):98-99.
    We discuss O&C's probabilistic approach from a probability logical point of view. Specifically, we comment on subjective probability, the indispensability of logic, the Ramsey test, the consequence relation, human nonmonotonic reasoning, intervals, generalized quantifiers, and rational analysis.
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  7.  31
    The Pseudodiagnosticity Trap: Should Participants Consider Alternative Hypotheses?Gernot D. Kleiter, Michael E. Doherty & Ryan D. Tweney - 2010 - Thinking and Reasoning 16 (4):332-345.
  8.  9
    Imprecise Uncertain Reasoning: A Distributional Approach.Gernot D. Kleiter - 2018 - Frontiers in Psychology 9.
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  9.  1
    Propagating Imprecise Probabilities in Bayesian Networks.Gernot D. Kleiter - 1996 - Artificial Intelligence 88 (1-2):143-161.
  10. How People Interpret an Uncertain If.Andrew Jb Fugard, Niki Pfeifer, Bastian Mayerhofer & Gernot D. Kleiter - 2009 - In T. Kroupa & J. Vejnarova (eds.), 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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  11.  39
    Syllogistic Reasoning with Intermediate Quantifiers.Niki Pfeifer & Gernot D. Kleiter - manuscript
    A system of intermediate quantifiers (“Most S are P”, “m/n S are P”) is proposed for evaluating the rationality of human syllogistic reasoning. Some relations between intermediate quantifiers and probabilistic interpretations are discussed. The paper concludes by the generalization of the atmosphere, matching and conversion hypothesis to syllogisms with intermediate quantifiers. Since our experiments are currently still running, most of the paper is theoretical and intended to stimulate psychological studies.
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  12.  22
    Implications of Natural Sampling in Base-Rate Tasks.Gernot D. Kleiter - 2007 - Behavioral and Brain Sciences 30 (3):270-271.
    The hypothesis that structural properties and not frequencies per se improve base-rate sensitivity is supported from the perspective of natural sampling. Natural sampling uses a special frequency format that makes base-rates redundant. Unfortunately, however, it does not allow us to empirically investigate human understanding of essential properties of uncertainty – most importantly, the understanding of conditional probabilities in Bayes' Theorem.
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  13.  13
    Beyond the Answer: Post-Error Processes.Gernot D. Kleiter & Kurt Schwarzenbacher - 1989 - Cognition 32 (3):255-277.
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  14.  10
    Critical and Natural Sensitivity to Base Rates.Gernot D. Kleiter - 1996 - Behavioral and Brain Sciences 19 (1):27-29.