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  1. Categorization and representation of physics problems by experts and novices.Michelene T. H. Chi, Paul J. Feltovich & Robert Glaser - 1981 - Cognitive Science 5 (2):121-52.
    The representation of physics problems in relation to the organization of physics knowledge is investigated in experts and novices. Four experiments examine the existence of problem categories as a basis for representation; differences in the categories used by experts and novices; differences in the knowledge associated with the categories; and features in the problems that contribute to problem categorization and representation. Results from sorting tasks and protocols reveal that experts and novices begin their problem representations with specifiably different problem categories, (...)
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  • Fundamental aspects of cognitive representation.Stephen Palmer - 1978 - In Eleanor Rosch & Barbara Bloom Lloyd (eds.), Cognition and Categorization. Lawrence Elbaum Associates. pp. 259-303.
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  • Problems and projects.Nelson Goodman (ed.) - 1972 - Indianapolis,: Bobbs-Merrill.
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  • (2 other versions)Controlled and automatic human information processing: Perceptual learning, automatic attending, and a general theory.Richard M. Shiffrin & Walter Schneider - 1977 - Psychological Review 84 (2):128-90.
    Tested the 2-process theory of detection, search, and attention presented by the current authors in a series of experiments. The studies demonstrate the qualitative difference between 2 modes of information processing: automatic detection and controlled search; trace the course of the learning of automatic detection, of categories, and of automatic-attention responses; and show the dependence of automatic detection on attending responses and demonstrate how such responses interrupt controlled processing and interfere with the focusing of attention. The learning of categories is (...)
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  • (1 other version)Computer Science as Empirical Inquiry: Symbols and Search.Allen Newell & H. A. Simon - 1976 - Communications of the Acm 19:113-126.
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  • Fostering general transfer with specific simulations.Ji Y. Son & Robert L. Goldstone - 2009 - Pragmatics and Cognition 17 (1):1-42.
    Science education faces the difficult task of helping students understand and appropriately generalize scientific principles across a variety of superficially dissimilar specific phenomena. Can cognitive technologies be adapted to benefit both learning specific domains and generalizable transfer? This issue is examined by teaching students complex adaptive systems with computer-based simulations. With a particular emphasis on fostering understanding that transfers to dissimilar phenomena, the studies reported here examine the influence of different descriptions and perceptual instantiations of the scientific principle of competitive (...)
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  • Perceptual learning: Differentiation or enrichment?James J. Gibson & Eleanor J. Gibson - 1955 - Psychological Review 62 (1):32-41.
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  • Thought in a Hostile World: The Evolution of Human Cognition.Kim Sterelny - 2003 - Wiley-Blackwell.
    (From the Press's Website) -/- Winner of the 2004 Lakatos Prize, Thought in a Hostile World is an exploration of the evolution of cognition, especially human cognition, by one of today's foremost philosophers of biology and of mind. Features an exploration of the evolution of human cognition. Written by one of today’s foremost philosophers of mind and language. Presents a set of analytic tools for thinking about cognition and its evolution. Offers a critique of nativist, modular versions of evolutionary psychology, (...)
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  • Visual routines.Shimon Ullman - 1984 - Cognition 18 (1-3):97-159.
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  • Reuniting perception and conception.Robert L. Goldstone & Lawrence W. Barsalou - 1998 - Cognition 65 (2-3):231-262.
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  • (1 other version)Electrifying diagrams for learning: principles for complex representational systems.Peter C.-H. Cheng - 2002 - Cognitive Science 26 (6):685-736.
    Six characteristics of effective representational systems for conceptual learning in complex domains have been identified. Such representations should: (1) integrate levels of abstraction; (2) combine globally homogeneous with locally heterogeneous representation of concepts; (3) integrate alternative perspectives of the domain; (4) support malleable manipulation of expressions; (5) possess compact procedures; and (6) have uniform procedures. The characteristics were discovered by analysing and evaluating a novel diagrammatic representation that has been invented to support students' comprehension of electricity—AVOW diagrams (Amps, Volts, Ohms, (...)
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  • Feature discovery by competitive learning.David E. Rumelhart & David Zipser - 1985 - Cognitive Science 9 (1):75-112.
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  • Physically distributed learning: Adapting and reinterpreting physical environments in the development of fraction concepts.Taylor Martin & Daniel L. Schwartz - 2005 - Cognitive Science 29 (4):587-625.
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  • Situating vision in the world.Zenon W. Pylyshyn - 2000 - Trends in Cognitive Sciences 4 (5):197-207.
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  • The origin of concepts.Susan Carey - 2009 - New York: Oxford University Press.
    Only human beings have a rich conceptual repertoire with concepts like tort, entropy, Abelian group, mannerism, icon and deconstruction. How have humans constructed these concepts? And once they have been constructed by adults, how do children acquire them? While primarily focusing on the second question, in The Origin of Concepts , Susan Carey shows that the answers to both overlap substantially. Carey begins by characterizing the innate starting point for conceptual development, namely systems of core cognition. Representations of core cognition (...)
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  • (1 other version)Computer science as empirical inquiry: Symbols and search.Allen Newell & Herbert A. Simon - 1981 - Communications of the Association for Computing Machinery 19:113-26.
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  • (1 other version)Electrifying diagrams for learning: principles for complex representational systems.Peter C.-H. Cheng - 2002 - Cognitive Science 26 (6):685-736.
    Six characteristics of effective representational systems for conceptual learning in complex domains have been identified. Such representations should: (1) integrate levels of abstraction; (2) combine globally homogeneous with locally heterogeneous representation of concepts; (3) integrate alternative perspectives of the domain; (4) support malleable manipulation of expressions; (5) possess compact procedures; and (6) have uniform procedures. The characteristics were discovered by analysing and evaluating a novel diagrammatic representation that has been invented to support students' comprehension of electricity—AVOW diagrams (Amps, Volts, Ohms, (...)
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  • Perceptual learning and the technology of expertise.Philip J. Kellman, Christine Massey, Zipora Roth, Timothy Burke, Joel Zucker, Amanda Saw, Katherine E. Aguero & Joseph A. Wise - 2008 - Pragmatics and Cognition 16 (2):356-405.
    Learning in educational settings most often emphasizes declarative and procedural knowledge. Studies of expertise, however, point to other, equally important components of learning, especially improvements produced by experience in the extraction of information: Perceptual learning. Here we describe research that combines principles of perceptual learning with computer technology to address persistent difficulties in mathematics learning. We report three experiments in which we developed and tested perceptual learning modules to address issues of structure extraction and fluency in relation to algebra and (...)
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  • (1 other version)A Theory of Content and Other Essays. [REVIEW]Ruth Garrett Millikan - 1992 - Philosophical Review 101 (4):898-901.
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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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  • The nature of learned categorical perception effects: a psychophysical approach.Leslie A. Notman, Paul T. Sowden & Emre Özgen - 2005 - Cognition 95 (2):B1-B14.
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  • Association theory and perceptual learning.Leo Postman - 1955 - Psychological Review 62 (6):438-446.
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  • Perceptual Learning Modules in Mathematics: Enhancing Students' Pattern Recognition, Structure Extraction, and Fluency.Philip J. Kellman, Christine M. Massey & Ji Y. Son - 2010 - Topics in Cognitive Science 2 (2):285-305.
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  • Human symbol manipulation within an integrated cognitive architecture.John R. Anderson - 2005 - Cognitive Science 29 (3):313-341.
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  • An Odyssey in Learning and Perception.Eleanor J. Gibson - 1994 - Behavior and Philosophy 22 (1):79-83.
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