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On the notion of concept I

Artificial Intelligence 172 (4-5):570-590 (2008)

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  1. Similarity-based categorization and fuzziness of natural categories.James A. Hampton - 1998 - Cognition 65 (2-3):137-165.
    The adequacy of similarity to prototype as an account of categorization in natural concepts was assessed by analyzing the monotonicity of the relation between typicality of an item in a category and the probability of a positive categorization response using data from McCloskey and Glucksberg (1978). The analysis revealed a strong underlying similarity-based threshold curve, with systematic deviations. Further data collection showed that deviations from the curve could be attributed to the effects of unfamiliarity and non-categorial associations on typicality judgments, (...)
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  • A note on formal learning theory.Daniel N. Osherson & Scott Weinstein - 1982 - Cognition 11 (1):77-88.
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  • Fuzzy Sets.Lofti A. Zadeh - 1965 - Information and Control 8 (1):338--53.
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  • Family resemblances: Studies in the internal structure of categories.Eleanor Rosch & Carolyn Mervis - 1975 - Cognitive Psychology 7 (4):573--605.
    Six experiments explored the hypothesis that the members of categories which are considered most prototypical are those with most attributes in common with other members of the category and least attributes in common with other categories. In probabilistic terms, the hypothesis is that prototypicality is a function of the total cue validity of the attributes of items. In Experiments 1 and 3, subjects listed attributes for members of semantic categories which had been previously rated for degree of prototypicality. High positive (...)
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  • Prototype theory and compositionality.H. Kamp - 1995 - Cognition 57 (2):129-191.
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  • Conceptual spaces as a framework for knowledge representation.Peter Gardenfors - 2004 - Mind and Matter 2 (2):9-27.
    The dominating models of information processes have been based on symbolic representations of information and knowledge. During the last decades, a variety of non-symbolic models have been proposed as superior. The prime examples of models within the non-symbolic approach are neural networks. However, to a large extent they lack a higher-level theory of representation. In this paper, conceptual spaces are suggested as an appropriate framework for non- symbolic models. Conceptual spaces consist of a number of 'quality dimensions' that often are (...)
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  • (1 other version)Concepts: a potboiler.Jerry Fodor - 1994 - Cognition 50 (1-3):95-113.
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  • On typicality and vagueness.Daniel Osherson & Edward E. Smith - 1997 - Cognition 64 (2):189-206.
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  • Why stereotypes don’t even make good defaults.Andrew C. Connolly, Jerry A. Fodor, Lila R. Gleitman & Henry Gleitman - 2007 - Cognition 103 (1):1-22.
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  • A note on prototype theory and fuzzy sets.Lofti Zadeh - 1982 - Cognition 12 (1):291--7.
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  • Structure and process in semantic memory: A featural model for semantic decisions.Edward E. Smith, Edward J. Shoben & Lance J. Rips - 1974 - Psychological Review 81 (3):214-241.
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  • Vagueness by numbers.Rosanna Keefe - 1998 - Mind 107 (427):565-579.
    Degree theories of vagueness build on the observation that vague predicates such as 'tall' and 'red' come in degrees. They employ an infinite-valued logic, where the truth values correspond to degrees of truth and are typically represented by the real numbers in the interval [0,1]. In this paper, the success with which the numerical assignments of such theories can capture the phenomenon of vagueness is assessed by drawing an analogy with the measurement of various physical quantities using real numbers. I (...)
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  • Stacks not fuzzy sets: An ordinal basis for prototype theory of concepts.Gregory V. Jones - 1982 - Cognition 12 (3):281-290.
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  • Preferential reasoning in the perspective of Poole default logic.Michael Freund - 1998 - Artificial Intelligence 98 (1-2):209-235.
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