The Experts below are selected from a list of 42 Experts worldwide ranked by ideXlab platform
Neil Stewart - One of the best experts on this subject based on the ideXlab platform.
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Universal Generalization and Universal inter item confusability
Behavioral and Brain Sciences, 2001Co-Authors: Nick Chater, Paul M B Vitanyi, Neil StewartAbstract:We argue that confusability between items should be distinguished from Generalization between items. Shepard's data concern confusability, but the theories proposed by Shepard and by Tenenbaum & Griffiths concern Generalization, indicating a gap between theory and data. We consider the empirical and theoretical work involved in bridging this gap.
Thomas L Griffiths - One of the best experts on this subject based on the ideXlab platform.
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Generalization similarity and bayesian inference
Behavioral and Brain Sciences, 2001Co-Authors: Joshua B Tenenbaum, Thomas L GriffithsAbstract: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 single novel stimulus, and for stimuli that can be represented as points in a continuous metric psychological space. Here we recast Shepard's theory in a more general Bayesian framework and show how this naturally extends his approach to the more realistic situation of generalizing from multiple consequential stimuli with arbitrary representational structure. Our framework also subsumes a version of Tversky's set-theoretic model of similarity, which is conventionally thought of as the primary alternative to Shepard's continuous metric space model of similarity and Generalization. This unification allows us not only to draw deep parallels between the set-theoretic and spatial approaches, but also to significantly advance the explanatory power of set-theoretic models.
Douglas Walton - One of the best experts on this subject based on the ideXlab platform.
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Rethinking the Fallacy of Hasty Generalization
Argumentation, 1999Co-Authors: Douglas WaltonAbstract:This paper makes a case for a refined look at the so- called ‘fallacy of hasty Generalization’ by arguing that this expression is an umbrella term for two fallacies already distinguished by Aristotle. One is the fallacy of generalizing in an inappropriate way from a particular instance to a Universal Generalization containing a ‘for all x’ quantification. The other is the secundum quid (‘in a certain respect’) fallacy of moving to a conclusion that is supposed to be a Universal Generalization containing a ‘for all x‘ quantification while overlooking qualifications that have to be added to the more limited kind of Generalization expressed in the premise. It is shown that these two fallacies relate to two different kinds of Generalization.
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Rethinking the Fallacy of Hasty Generalization
Argumentation, 1999Co-Authors: Douglas WaltonAbstract:This paper makes a case for a refined look at the so- called ‘fallacy of hasty Generalization’ by arguing that this expression is an umbrella term for two fallacies already distinguished by Aristotle. One is the fallacy of generalizing in an inappropriate way from a particular instance to a Universal Generalization containing a ‘for all x’ quantification. The other is the secundum quid (‘in a certain respect’) fallacy of moving to a conclusion that is supposed to be a Universal Generalization containing a ‘for all x‘ quantification while overlooking qualifications that have to be added to the more limited kind of Generalization expressed in the premise. It is shown that these two fallacies relate to two different kinds of Generalization. The classification of fallacious Generalizations is based on a new theory of Generalization that distinguishes three kinds of Generalizations – the Universal Generalization of the ‘for all x’ type, used in classical deductive logic, the inductive Generalization, based on probability, and the presumptive Generalization, which is defeasible, and allows for exceptions to a general rule. The resulting classification goes beyond a logic-oriented analysis by taking into account how a respondent may oppose a potentially fallacious generalizing move by falsifying it. Using a dialectical interpretation of premise-conclusion complexes, the paper outline a richer concept of generalizing argument moves embedded in a communicational reconstruction of the strategic uses of such moves in which two parties take part in an orderly dialectical exchange of viewpoints.
Nick Chater - One of the best experts on this subject based on the ideXlab platform.
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Universal Generalization and Universal inter item confusability
Behavioral and Brain Sciences, 2001Co-Authors: Nick Chater, Paul M B Vitanyi, Neil StewartAbstract:We argue that confusability between items should be distinguished from Generalization between items. Shepard's data concern confusability, but the theories proposed by Shepard and by Tenenbaum & Griffiths concern Generalization, indicating a gap between theory and data. We consider the empirical and theoretical work involved in bridging this gap.
Joshua B Tenenbaum - One of the best experts on this subject based on the ideXlab platform.
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Generalization similarity and bayesian inference
Behavioral and Brain Sciences, 2001Co-Authors: Joshua B Tenenbaum, Thomas L GriffithsAbstract: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 single novel stimulus, and for stimuli that can be represented as points in a continuous metric psychological space. Here we recast Shepard's theory in a more general Bayesian framework and show how this naturally extends his approach to the more realistic situation of generalizing from multiple consequential stimuli with arbitrary representational structure. Our framework also subsumes a version of Tversky's set-theoretic model of similarity, which is conventionally thought of as the primary alternative to Shepard's continuous metric space model of similarity and Generalization. This unification allows us not only to draw deep parallels between the set-theoretic and spatial approaches, but also to significantly advance the explanatory power of set-theoretic models.
