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  1. Précis of bayesian rationality: The probabilistic approach to human reasoning.Mike Oaksford & Nick Chater - 2009 - Behavioral and Brain Sciences 32 (1):69-84.
    According to Aristotle, humans are the rational animal. The borderline between rationality and irrationality is fundamental to many aspects of human life including the law, mental health, and language interpretation. But what is it to be rational? One answer, deeply embedded in the Western intellectual tradition since ancient Greece, is that rationality concerns reasoning according to the rules of logic – the formal theory that specifies the inferential connections that hold with certainty between propositions. Piaget viewed logical reasoning as defining (...)
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  • The uncertain reasoner: Bayes, logic, and rationality.Mike Oaksford & Nick Chater - 2009 - Behavioral and Brain Sciences 32 (1):105-120.
    Human cognition requires coping with a complex and uncertain world. This suggests that dealing with uncertainty may be the central challenge for human reasoning. In Bayesian Rationality we argue that probability theory, the calculus of uncertainty, is the right framework in which to understand everyday reasoning. We also argue that probability theory explains behavior, even on experimental tasks that have been designed to probe people's logical reasoning abilities. Most commentators agree on the centrality of uncertainty; some suggest that there is (...)
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  • 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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  • Does rational analysis stand up to rational analysis?Jonathan St B. T. Evans - 2009 - Behavioral and Brain Sciences 32 (1):88-89.
    I agree with Oaksford & Chater (O&C) that human beings resemble Bayesian reasoners much more closely than ones engaging standard logic. However, I have many problems with their framework, which appears to be rooted in normative rather than ecological rationality. The authors also overstate everyday rationality and neglect to account for much relevant psychological work on reasoning.
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  • On conditionals.Dorothy Edgington - 1995 - Mind 104 (414):235-329.
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  • Let us not put the probabilistic cart before the uncertainty Bull.Guy Politzer & Jean-François Bonnefon - 2009 - Behavioral and Brain Sciences 32 (1):100-101.
    Although we endorse the primacy of uncertainty in reasoning, we argue that a probabilistic framework cannot model the fundamental skill of proof administration. Furthermore, we are skeptical about the assumption that standard probability calculus is the appropriate formalism to represent human uncertainty. There are other models up to this task, so let us not repeat the excesses of the past.
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  • (1 other version)The Foundations of Mathematics and other Logical Essays.Frank Plumpton Ramsey, R. B. Braithwaite & G. E. Moore - 1931 - Mind 40 (160):476-482.
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  • Deduction and degrees of belief.David Over - 1993 - Behavioral and Brain Sciences 16 (2):361-362.
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  • Uncertain premises and Jeffrey's rule.David E. Over & Constantinos Hadjichristidis - 2009 - Behavioral and Brain Sciences 32 (1):97-98.
    Oaksford & Chater (O&C) begin in the halfway Bayesian house of assuming that minor premises in conditional inferences are certain. We demonstrate that this assumption is a serious limitation. They additionally suggest that appealing to Jeffrey's rule could make their approach more general. We present evidence that this rule is not limited enough to account for actual probability judgements.
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  • Reasoning From Inconsistency to Consistency.P. N. Johnson-Laird, Vittorio Girotto & Paolo Legrenzi - 2004 - Psychological Review 111 (3):640-661.
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