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  1. A logic for default reasoning.Ray Reiter - 1980 - Artificial Intelligence 13 (1-2):81-137.
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  • Logic and Psychology.E. A. Peel - 1955 - British Journal of Educational Studies 4 (1):86-87.
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  • Informal Logic: A Handbook for Critical Argument.Douglas Neil Walton - 1989 - Cambridge, England: Cambridge University Press.
    This is an introductory guidebook to the basic principles of how to construct good arguments and how to criticeze bad ones. It is non-technical in its approach and is based on 150 key examples, each discussed and evaluated in clear, illustrative detail. Professor Walton, a leading authority in the field of informal logic, explains how errors, fallacies, and other key failures of argument occur. He shows how correct uses of argument are based on sound strategies for reasoned persuasion and critical (...)
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  • The Development of Logic.William Kneale & Martha Kneale - 1962 - Oxford, England: Clarendon Press. Edited by Martha Kneale.
    This book traces the development of formal logic from its origins inancient Greece to the present day. The authors first discuss the work oflogicians from Aristotle to Frege, showing how they were influenced by thephilosophical or mathematical ideas of their time. They then examinedevelopments in the present century.
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  • The Structure of Scientific Revolutions.Thomas Samuel Kuhn - 1962 - Chicago: University of Chicago Press. Edited by Otto Neurath.
    A scientific community cannot practice its trade without some set of received beliefs. These beliefs form the foundation of the "educational initiation that prepares and licenses the student for professional practice". The nature of the "rigorous and rigid" preparation helps ensure that the received beliefs are firmly fixed in the student's mind. Scientists take great pains to defend the assumption that scientists know what the world is like...To this end, "normal science" will often suppress novelties which undermine its foundations. Research (...)
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  • Richard C. Jeffrey. Formal logic: Its scope and limits. McGraw-Hill Book Company, New York etc. 1967, xii + 238 pp. - Richard C. Jeffrey. Instructor's manual to accompany Formal logic: Its scope and limits. McGraw-Hill Book Company, New York etc. 1967, iii + 58 pp. [REVIEW] Sibajiban - 1973 - Journal of Symbolic Logic 38 (4):646-647.
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  • Children's causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers.D. Sobel - 2004 - Cognitive Science 28 (3):303-333.
    Previous research suggests that children can infer causal relations from patterns of events. However, what appear to be cases of causal inference may simply reduce to children recognizing relevant associations among events, and responding based on those associations. To examine this claim, in Experiments 1 and 2, children were introduced to a “blicket detector,” a machine that lit up and played music when certain objects were placed upon it. Children observed patterns of contingency between objects and the machine's activation that (...)
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  • Reasoning and Rationality.D. E. Over K. I. Manktelow - 1987 - Mind and Language 2 (3):199-219.
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  • On bad decisions and disconfirmed expectancies: The psychology of regret and disappointment.Marcel Zeelenberg, Wilco W. van Dijk, Antony S. R. Manstead & Joop Vanr de Pligt - 2000 - Cognition and Emotion 14 (4):521-541.
    Decision outcomes sometimes result in negative emotions. This can occur when a decision appears to be wrong in retrospect, and/or when the obtained decision outcome does not live up to expectations. Regret and disappointment are the two emotions that are of central interest in the present article. Although these emotions have a lot in common, they also differ in ways that are relevant to decision making. In this article we review theories and empirical findings concerning regret and disappointment. We first (...)
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  • Fuzzy logic and approximate reasoning.L. A. Zadeh - 1975 - Synthese 30 (3-4):407-428.
    The term fuzzy logic is used in this paper to describe an imprecise logical system, FL, in which the truth-values are fuzzy subsets of the unit interval with linguistic labels such as true, false, not true, very true, quite true, not very true and not very false, etc. The truth-value set, , of FL is assumed to be generated by a context-free grammar, with a semantic rule providing a means of computing the meaning of each linguistic truth-value in as a (...)
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  • Vision as Bayesian inference: analysis by synthesis?Alan Yuille & Daniel Kersten - 2006 - Trends in Cognitive Sciences 10 (7):301-308.
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  • Matching versus optimal data selection in the Wason selection task.Hiroshi Yama - 2001 - Thinking and Reasoning 7 (3):295 – 311.
    It has been reported as a robust effect that people are likely to select a matching case in the Wason selection task. For example, they usually select the 5 case, in the Wason selection task with the conditional "if an E, then a not-5". This was explained by the matching bias account that people are likely to regard a matching case as relevant to the truth of the conditional (Evans, 1998). However, because a positive concept usually constructs a smaller set (...)
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  • Special issue on Combining Probability and Logic.Jon Williamson & Dov Gabbay - 2003 - Journal of Applied Logic 1 (3-4):135-138.
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  • Modus tollens probabilized.Carl G. Wagner - 2004 - British Journal for the Philosophy of Science 55 (4):747-753.
    We establish a probabilized version of modus tollens, deriving from p(E|H)=a and p()=b the best possible bounds on p(). In particular, we show that p() 1 as a, b 1, and also as a, b 0. Introduction Probabilities of conditionals Conditional probabilities 3.1 Adams' thesis 3.2 Modus ponens for conditional probabilities 3.3 Modus tollens for conditional probabilities.
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  • The Scientific Image by Bas C. van Fraassen. [REVIEW]Michael Friedman - 1982 - Journal of Philosophy 79 (5):274-283.
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  • Judgment under Uncertainty: Heuristics and Biases.Amos Tversky & Daniel Kahneman - 1974 - Science 185 (4157):1124-1131.
    This article described three heuristics that are employed in making judgements under uncertainty: representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and adjustment from an anchor, which is usually employed in numerical prediction when a relevant value (...)
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  • Theory-based Bayesian models of inductive learning and reasoning.Joshua B. Tenenbaum, Thomas L. Griffiths & Charles Kemp - 2006 - Trends in Cognitive Sciences 10 (7):309-318.
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  • Conditionalization and observation.Paul Teller - 1973 - Synthese 26 (2):218-258.
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  • Deduction from Uncertain Premises.Rosemary J. Stevenson & David E. Over - 1995 - Quarterly Journal of Experimental Psychology Section A 48 (3):613-643.
    We investigate how the perceived uncertainty of a conditional affects a person's choice of conclusion. We use a novel procedure to introduce uncertainty by manipulating the conditional probability of the consequent given the antecedent. In Experiment 1, we show first that subjects reduce their choice of valid conclusions when a conditional is followed by an additional premise that makes the major premise uncertain. In this we replicate Byrne. These subjects choose, instead, a qualified conclusion expressing uncertainty. If subjects are given (...)
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  • Individual differences in reasoning: Implications for the rationality debate?Keith E. Stanovich & Richard F. West - 2000 - Behavioral and Brain Sciences 23 (5):645-665.
    Much research in the last two decades has demonstrated that human responses deviate from the performance deemed normative according to various models of decision making and rational judgment (e.g., the basic axioms of utility theory). This gap between the normative and the descriptive can be interpreted as indicating systematic irrationalities in human cognition. However, four alternative interpretations preserve the assumption that human behavior and cognition is largely rational. These posit that the gap is due to (1) performance errors, (2) computational (...)
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  • Intelligent design and probability reasoning.Elliott Sober - 2002 - International Journal for Philosophy of Religion 52 (2):65-80.
    This paper defends two theses about probabilistic reasoning. First, although modus ponens has a probabilistic analog, modus tollens does not – the fact that a hypothesis says that an observation is very improbable does not entail that the hypothesis is improbable. Second, the evidence relation is essentially comparative; with respect to hypotheses that confer probabilities on observation statements but do not entail them, an observation O may favor one hypothesis H1 over another hypothesis H2 , but O cannot be said (...)
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  • Tensor product variable binding and the representation of symbolic structures in connectionist systems.Paul Smolensky - 1990 - Artificial Intelligence 46 (1-2):159-216.
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  • The empirical case for two systems of reasoning.Steven A. Sloman - 1996 - Psychological Bulletin 119 (1):3-22.
    Distinctions have been proposed between systems of reasoning for centuries. This article distills properties shared by many of these distinctions and characterizes the resulting systems in light of recent findings and theoretical developments. One system is associative because its computations reflect similarity structure and relations of temporal contiguity. The other is "rule based" because it operates on symbolic structures that have logical content and variables and because its computations have the properties that are normally assigned to rules. The systems serve (...)
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  • Information leakage from logically equivalent frames.Shlomi Sher & Craig R. M. McKenzie - 2006 - Cognition 101 (3):467-494.
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  • The logic of reliable and efficient inquiry.Oliver Schulte - 1999 - Journal of Philosophical Logic 28 (4):399-438.
    This paper pursues a thorough-going instrumentalist, or means-ends, approach to the theory of inductive inference. I consider three epistemic aims: convergence to a correct theory, fast convergence to a correct theory and steady convergence to a correct theory (avoiding retractions). For each of these, two questions arise: (1) What is the structure of inductive problems in which these aims are feasible? (2) When feasible, what are the inference methods that attain them? Formal learning theory provides the tools for a complete (...)
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  • Cognitive processes in propositional reasoning.Lance J. Rips - 1983 - Psychological Review 90 (1):38-71.
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  • Experience and Prediction: An Analysis of the Foundations and the Structure of Knowledge.Hans Reichenbach - 1938 - Chicago, IL, USA: University of Chicago Press.
    First published in 1949 expressly to introduce logical positivism to English speakers. Reichenbach, with Rudolph Carnap, founded logical positivism, a form of epistemofogy that privileged scientific over metaphysical truths.
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  • From a Logical Point of View.Willard Van Orman Quine - 1953 - Cambridge: Harvard University Press.
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  • From a Logical Point of View.Richard M. Martin - 1955 - Philosophy and Phenomenological Research 15 (4):574-575.
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  • Responses to inconsistent premisses cannot count as suppression of valid inferences.Guy Politzer & Martin D. S. Braine - 1991 - Cognition 38 (1):103-108.
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  • Toward a method of selecting among computational models of cognition.Mark A. Pitt, In Jae Myung & Shaobo Zhang - 2002 - Psychological Review 109 (3):472-491.
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  • The Origin of the Idea of Chance in Children.J. Piaget & B. Inhelder - 1976 - British Journal of Educational Studies 24 (3):279-280.
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  • Logic and psychology.Jean Piaget - 1953 - New York,: Basic Books.
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  • Deontic Reasoning With Emotional Content: Evolutionary Psychology or Decision Theory?Nick Perham & Mike Oaksford - 2005 - Cognitive Science 29 (5):681-718.
    Three experiments investigated the contrasting predictions of the evolutionary and decision-theoretic approaches to deontic reasoning. Two experiments embedded a hazard management (HM) rule in a social contract scenario that should lead to competition between innate modules. A 3rd experiment used a pure HM task. Threatening material was also introduced into the antecedent, p, of a deontic rule, if p then must q. According to the evolutionary approach, more HM responses (Cosmides & Tooby, 2000) are predicted when p is threatening, whereas (...)
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  • Hits and misses: Kirby on the selection task.D. E. Over & J. StB. T. Evans - 1994 - Cognition 52 (3):235-243.
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  • Mechanical learners pay a price for Bayesianism.Daniel N. Osherson, Michael Stob & Scott Weinstein - 1988 - Journal of Symbolic Logic 53 (4):1245-1251.
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  • What makes us believe a conditional? The roles of covariation and causality.Klaus Oberauer, Andrea Weidenfeld & Katrin Fischer - 2007 - Thinking and Reasoning 13 (4):340 – 369.
    Two experiments were conducted to investigate the roles of covariation and of causality in people's readiness to believe a conditional. The experiments used a probabilistic truth-table task (Oberauer & Wilhelm, 2003) in which people estimated the probability of a conditional given information about the frequency distribution of truth-table cases. For one group of people, belief in the conditional was determined by the conditional probability of the consequent, given the antecedent, whereas for another group it depended on the probability of the (...)
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  • Bayesian rationality for the Wason selection task? A test of optimal data selection theory.Klaus Oberauer, Oliver Wilhelm & Ricardo Rosas Diaz - 1999 - Thinking and Reasoning 5 (2):115 – 144.
    Oaksford and Chater (1994) proposed to analyse the Wason selection task as an inductive instead of a deductive task. Applying Bayesian statistics, they concluded that the cards that participants tend to select are those with the highest expected information gain. Therefore, their choices seem rational from the perspective of optimal data selection. We tested a central prediction from the theory in three experiments: card selection frequencies should be sensitive to the subjective probability of occurrence for individual cards. In Experiment 1, (...)
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  • Probabilistic effects in data selection.Mike Oaksford, Nick Chater & Becki Grainger - 1999 - Thinking and Reasoning 5 (3):193 – 243.
    Four experiments investigated the effects of probability manipulations on the indicative four card selection task (Wason, 1966, 1968). All looked at the effects of high and low probability antecedents (p) and consequents (q) on participants' data selections when determining the truth or falsity of a conditional rule, if p then q . Experiments 1 and 2 also manipulated believability. In Experiment 1, 128 participants performed the task using rules with varied contents pretested for probability of occurrence. Probabilistic effects were observed (...)
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  • A rational analysis of the selection task as optimal data selection.Mike Oaksford & Nick Chater - 1994 - Psychological Review 101 (4):608-631.
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  • Assessing interactive causal influence.Laura R. Novick & Patricia W. Cheng - 2004 - Psychological Review 111 (2):455-485.
    The discovery of conjunctive causes--factors that act in concert to produce or prevent an effect--has been explained by purely covariational theories. Such theories assume that concomitant variations in observable events directly license causal inferences, without postulating the existence of unobservable causal relations. This article discusses problems with these theories, proposes a causal-power theory that overcomes the problems, and reports empirical evidence favoring the new theory. Unlike earlier models, the new theory derives (a) the conditions under which covariation implies conjunctive causation (...)
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  • Hempel's paradox and Wason's selection task: Logical and psychological puzzles of confirmation.Raymond S. Nickerson - 1996 - Thinking and Reasoning 2 (1):1 – 31.
    Hempel's paradox of the ravens has to do with the question of what constitutes confirmation from a logical point of view; Wason 's selection task has been used extensively to investigate how people go about attempting to confirm or disconfirm conditional claims. This paper presents an argument that the paradox is resolved, and that people's typical performance in the selection task can be explained, by consideration of what constitutes an effective strategy for seeking evidence of the tenability of universal or (...)
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  • Smarter Than We Think When Our Brains Detect That We Are Biased.Wim De Neys, Oshin Vartanian & Vinod Goel - 2008 - Psychological Science 19 (5):483-489.
    Human reasoning is often biased by stereotypical intuitions. The nature of such bias is not clear. Some authors claim that people are mere heuristic thinkers and are not aware that cued stereotypes might be inappropriate. Other authors claim that people always detect the conflict between their stereotypical thinking and normative reasoning, but simply fail to inhibit stereotypical thinking. Hence, it is unclear whether heuristic bias should be attributed to a lack of conflict detection or a failure of inhibition. We introduce (...)
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  • Finding Useful Questions: On Bayesian Diagnosticity, Probability, Impact, and Information Gain.Jonathan D. Nelson - 2005 - Psychological Review 112 (4):979-999.
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  • Experience and Prediction. An Analysis of the Foundations and the Structure of Knowledge. [REVIEW]E. N. & Hans Reichenbach - 1938 - Journal of Philosophy 35 (10):270.
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  • Principia ethica.George Edward Moore - 1903 - Mineola, N.Y.: Dover Publications. Edited by Thomas Baldwin.
    First published in 1903, this volume revolutionized philosophy and forever altered the direction of ethical studies. A philosopher’s philosopher, G. E. Moore was the idol of the Bloomsbury group, and Lytton Strachey declared that Principia Ethica marked the rebirth of the Age of Reason. This work clarifies some of moral philosophy’s most common confusions and redefines the science’s terminology. Six chapters explore: the subject matter of ethics, naturalistic ethics, hedonism, metaphysical ethics, ethics in relation to conduct, and the ideal. Moore's (...)
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  • Log[p(h/eb)/p(h/b)] is the one true measure of confirmation.Peter Milne - 1996 - Philosophy of Science 63 (1):21-26.
    Plausibly, when we adopt a probabilistic standpoint any measure Cb of the degree to which evidence e confirms hypothesis h relative to background knowledge b should meet these five desiderata: Cb > 0 when P > P < 0 when P < P; Cb = 0 when P = P. Cb is some function of the values P and P assume on the at most sixteen truth-functional combinations of e and h. If P < P and P = P then (...)
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  • A Bayesian Defence of Popperian Science?Peter Milne - 1995 - Analysis 55 (3):213 - 215.
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  • Against Logicist Cognitive Science.Mike Oaksford & Nick Chater - 1991 - Mind and Language 6 (1):1-38.
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  • Principia Ethica.Evander Bradley McGilvary - 1904 - Philosophical Review 13 (3):351.
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