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  1. Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - New York: Cambridge University Press.
    Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence, business, epidemiology, social science and economics.
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  • The computational complexity of probabilistic inference using bayesian belief networks.Gregory F. Cooper - 1990 - Artificial Intelligence 42 (2-3):393-405.
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  • The adaptive nature of human categorization.John R. Anderson - 1991 - Psychological Review 98 (3):409-429.
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  • Why are different features central for natural kinds and artifacts?: the role of causal status in determining feature centrality.Woo-Kyoung Ahn - 1998 - Cognition 69 (2):135-178.
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  • Causation, Prediction, and Search.Peter Spirtes, Clark Glymour, Scheines N. & Richard - 1993 - Mit Press: Cambridge.
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  • The role of theories in conceptual coherence.G. L. Murphy & D. L. Medin - 1999 - In Eric Margolis & Stephen Laurence (eds.), Concepts: Core Readings. MIT Press. pp. 289--316.
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  • Concepts, Kinds and Cognitive Development.Frank C. Keil - 1989 - MIT Press.
    In Concepts, Kinds, and Cognitive Development, Frank C. Keil provides a coherent account of how concepts and word meanings develop in children, adding to our understanding of the representational nature of concepts and word meanings at all ages. Keil argues that it is impossible to adequately understand the nature of conceptual representation without also considering the issue of learning. Weaving together issues in cognitive development, philosophy, and cognitive psychology, he reconciles numerous theories, backed by empirical evidence from nominal kinds studies, (...)
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  • Word learning as Bayesian inference.Fei Xu & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):245-272.
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  • Causal models and the acquisition of category structure.Michael R. Waldmann, Keith J. Holyoak & Angela Fratianne - 1995 - Journal of Experimental Psychology: General 124 (2):181.
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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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  • Generalization, similarity, and bayesian inference.Joshua B. Tenenbaum & Thomas L. Griffiths - 2001 - Behavioral and Brain Sciences 24 (4):629-640.
    Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a (...)
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  • Similarity, plausibility, and judgments of probability.E. Smith - 1993 - Cognition 49 (1-2):67-96.
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  • When explanations compete: the role of explanatory coherence on judgements of likelihood.Steven A. Sloman - 1994 - Cognition 52 (1):1-21.
    The likelihood of a statement is often derived by generating an explanation for it and evaluating the plausibility of the explanation. The explanation discounting principle states that people tend to focus on a single explanation; alternative explanations compete with the effect of reducing one another’s credibility. Two experiments tested the hypothesis that this principle applies to inductive inferences concerning the properties of everyday categories. In both experiments, subjects estimated the probability of a series of statements and the conditional probabilities of (...)
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  • Vision: Variations on Some Berkeleian Themes.Robert Schwartz & David Marr - 1985 - Philosophical Review 94 (3):411.
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  • Inductive judgments about natural categories.Lance J. Rips - 1975 - Journal of Verbal Learning and Verbal Behavior 14 (6):665-681.
    The present study examined the effects of semantic structure on simple inductive judgments about category members. For a particular category, subjects were told that one of the species had a given property and were asked to estimate the proportion of instances in the other species that possessed the property. The results indicated that category structure—in particular, the typicality of the species—influenced subjects' judgments. These results were interpreted by models based on the following assumption: When little is known about the underlying (...)
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  • Causal knowledge and categories: The effects of causal beliefs on categorization, induction, and similarity.Bob Rehder & Reid Hastie - 2001 - Journal of Experimental Psychology 130 (3):323-360.
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  • Categorization as causal reasoning⋆.Bob Rehder - 2003 - Cognitive Science 27 (5):709-748.
    A theory of categorization is presented in which knowledge of causal relationships between category features is represented in terms of asymmetric and probabilistic causal mechanisms. According to causal‐model theory, objects are classified as category members to the extent they are likely to have been generated or produced by those mechanisms. The empirical results confirmed that participants rated exemplars good category members to the extent their features manifested the expectations that causal knowledge induces, such as correlations between feature pairs that are (...)
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  • Category-based induction.Daniel N. Osherson, Edward E. Smith, Ormond Wilkie & Alejandro López - 1990 - Psychological Review 97 (2):185-200.
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  • The role of theories in conceptual coherence.Gregory L. Murphy & Douglas L. Medin - 1985 - Psychological Review 92 (3):289-316.
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  • A hypothesis-assessment model of categorical argument strength.John McDonald, Mark Samuels & Janet Rispoli - 1996 - Cognition 59 (2):199-217.
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  • Drinking and driving don't mix: inductive generalization in infancy.Jean M. Mandler & Laraine McDonough - 1996 - Cognition 59 (3):307-335.
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  • Naive probability: A mental model theory of extensional reasoning.Philip Johnson-Laird, Paolo Legrenzi, Vittorio Girotto, Maria Sonino Legrenzi & Jean-Paul Caverni - 1999 - Psychological Review 106 (1):62-88.
    This article outlines a theory of naive probability. According to the theory, individuals who are unfamiliar with the probability calculus can infer the probabilities of events in an extensional way: They construct mental models of what is true in the various possibilities. Each model represents an equiprobable alternative unless individuals have beliefs to the contrary, in which case some models will have higher probabilities than others. The probability of an event depends on the proportion of models in which it occurs. (...)
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  • Causality: Models, Reasoning and Inference.Christopher Hitchcock & Judea Pearl - 2001 - Philosophical Review 110 (4):639.
    Judea Pearl has been at the forefront of research in the burgeoning field of causal modeling, and Causality is the culmination of his work over the last dozen or so years. For philosophers of science with a serious interest in causal modeling, Causality is simply mandatory reading. Chapter 2, in particular, addresses many of the issues familiar from works such as Causation, Prediction and Search by Peter Spirtes, Clark Glymour, and Richard Scheines. But philosophers with a more general interest in (...)
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  • Categories and induction in young children.Susan A. Gelman & Ellen M. Markman - 1986 - Cognition 23 (3):183-209.
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  • Vision.David Marr - 1982 - W. H. Freeman.
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  • Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Tijdschrift Voor Filosofie 64 (1):201-202.
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  • Expertise and Category-Based Induction.Julia Beth Proffitt, John Coley, Medin D. & L. Douglas - 2000 - Journal of Experimental Psychology 26:811-828.
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  • Inductive judgments about natural categories.Lawrence J. Rips - 1975 - Journal of Verbal Learning and Verbal Behavior 14:665-681.
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  • Similarity and Property Effects in Inductive Reasoning.Evan Heit & Joshua Rubinstein - 1994 - Journal of Experimental Psychology 20:411-422.
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  • Review: The Grand Leap; Reviewed Work: Causation, Prediction, and Search. [REVIEW]Peter Spirtes, Clark Glymour & Richard Scheines - 1996 - British Journal for the Philosophy of Science 47 (1):113-123.
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