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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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  • Autobiographical Notes.Max Black, Albert Einstein & Paul Arthur Schilpp - 1949 - Journal of Symbolic Logic 15 (2):157.
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  • Memory for goals: an activation‐based model.Erik M. Altmann & J. Gregory Trafton - 2002 - Cognitive Science 26 (1):39-83.
    Goal‐directed cognition is often discussed in terms of specialized memory structures like the “goal stack.” The goal‐activation model presented here analyzes goal‐directed cognition in terms of the general memory constructs of activation and associative priming. The model embodies three predictive constraints: (1) the interference level, which arises from residual memory for old goals; (1) the strengthening constraint, which makes predictions about time to encode a new goal; and (3) the priming constraint, which makes predictions about the role of cues in (...)
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  • Structure‐Mapping: A Theoretical Framework for Analogy.Dedre Gentner - 1983 - Cognitive Science 7 (2):155-170.
    A theory of analogy must describe how the meaning of an analogy is derived from the meanings of its parts. In the structure‐mapping theory, the interpretation rules are characterized as implicit rules for mapping knowledge about a base domain into a target domain. Two important features of the theory are (a) the rules depend only on syntactic properties of the knowledge representation, and not on the specific content of the domains; and (b) the theoretical framework allows analogies to be distinguished (...)
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  • Qualitative reasoning about physical systems: A return to roots.Brian C. Williams & Johan de Kleer - 1991 - Artificial Intelligence 51 (1-3):1-9.
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  • The Impact of Goal Specificity on Strategy Use and the Acquisition of Problem Structure.Regina Vollmeyer, Bruce D. Burns & Keith J. Holyoak - 1996 - Cognitive Science 20 (1):75-100.
    Theories of skill acquisition have made radically different predictions about the role of general problem‐solving methods in acquiring rules that promote effective transfer to new problems. Under one view, methods that focus on reaching specific goals, such as means‐ends analysis, are assumed to provide the basis for efficient knowledge compilation (Anderson, 1987), whereas under an alternative view such methods are believed to disrupt rule induction (Sweller, 1988). We suggest that the role of general methods in learning varies with both the (...)
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  • Connecting internal and external representations: Spatial transformations of scientific visualizations. [REVIEW]J. Gregory Trafton, Susan B. Trickett & Farilee E. Mintz - 2005 - Foundations of Science 10 (1):89-106.
    Many scientific discoveries have depended on external diagrams or visualizations. Many scientists also report to use an internal mental representation or mental imagery to help them solve problems and reason. How do scientists connect these internal and external representations? We examined working scientists as they worked on external scientific visualizations. We coded the number and type of spatial transformations (mental operations that scientists used on internal or external representations or images) and found that there were a very large number of (...)
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  • The Generality/Specificity of Expertise in Scientific Reasoning.Christian D. Schunn & John R. Anderson - 1999 - Cognitive Science 23 (3):337-370.
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  • Shuttling Between Depictive Models and Abstract Rules: Induction and Fallback.Daniel L. Schwartz & John B. Black - 1996 - Cognitive Science 20 (4):457-497.
    A productive way to think about imagistic mental models of physical systems is as though they were sources of quasi‐empirical evidence. People depict or imagine events at those points in time when they would experiment with the world if possible. Moreover, just as they would do when observing the world, people induce patterns of behavior from the results depicted in their imaginations. These resulting patterns of behavior can then be cast into symbolic rules to simplify thinking about future problems and (...)
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  • How Experts Solve a Novel Problem in Experimental Design.Jan Maarten Schraagen - 1993 - Cognitive Science 17 (2):285-309.
    Research on expert‐novice differences has mainly focused on how experts solve familiar problems. We know far less about the skills and knowledge used by experts when they are confronted with novel problems within their area of expertise. This article discusses a study in which verbal protocols were taken from subjects of various expertise designing an experiment in an area with which they were unfamiliar. The results showed that even when domain knowledge is lacking, experts solve a novel problem within their (...)
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  • Collaborative discovery in a scientific domain.Takeshi Okada & Herbert A. Simon - 1997 - Cognitive Science 21 (2):109-146.
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  • The Processes of Scientific Discovery: The Strategy of Experimentation.Deepak Kulkarni & Herbert A. Simon - 1988 - Cognitive Science 12 (2):139-175.
    Hans Krebs' discovery, in 1932, of the urea cycle was a major event in biochemistry. This article describes a program, KEKADA, which models the heuristics Hans Krebs used in this discovery. KEKADA reacts to surprises, formulates explanations, and carries out experiments in the same manner as the evidence in the form of laboratory notebooks and interviews indicates Hans Krebs did. Furthermore, we answer a number of questions about the nature of the heuristics used by Krebs, in particular: How domain‐specific are (...)
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  • Dual Space Search During Scientific Reasoning.David Klahr & Kevin Dunbar - 1988 - Cognitive Science 12 (1):1-48.
    The purpose of the two studies reported here was to develop an integrated model of the scientific reasoning process. Subjects were placed in a simulated scientific discovery context by first teaching them how to use an electronic device and then asking them to discover how a hitherto unencountered function worked. To do this task, subjects had to formulate hypotheses based on their prior knowledge, conduct experiments, and evaluate the results of their experiments. In the first study, using 20 adult subjects, (...)
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  • Dual Space Search During Scientific Reasoning.David Klahr & Kevin Dunbar - 1988 - Cognitive Science 12 (1):1-48.
    The purpose of the two studies reported here was to develop an integrated model of the scientific reasoning process. Subjects were placed in a simulated scientific discovery context by first teaching them how to use an electronic device and then asking them to discover how a hitherto unencountered function worked. To do this task, subjects had to formulate hypotheses based on their prior knowledge, conduct experiments, and evaluate the results of their experiments. In the first study, using 20 adult subjects, (...)
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  • The Role of a Mental Model in Learning to Operate a Device.David E. Kieras & Susan Bovair - 1984 - Cognitive Science 8 (3):255-273.
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  • The Role of Mental Knowledge in Learning to Operate a Device.D. E. Kieras & S. Bovair - 1984 - Cognitive Science 8 (3):191-219.
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  • Analogical Mapping by Constraint Satisfaction.Keith J. Holyoak & Paul Thagard - 1989 - Cognitive Science 13 (3):295-355.
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  • The role of deliberate practice in the acquisition of expert performance.K. Anders Ericsson, Ralf T. Krampe & Clemens Tesch-Römer - 1993 - Psychological Review 100 (3):363-406.
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  • Model-based reasoning in conceptual change.Nancy J. Nersessian - 1999 - In L. Magnani, N. J. Nersessian & P. Thagard (eds.), Model-Based Reasoning in Scientific Discovery. Kluwer/Plenum. pp. 5--22.
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  • How scientists think: On-line creativity and conceptual change in science.Kevin Dunbar - 1997 - In T. B. Ward, S. M. Smith & J. Viad (eds.), Creative Thought: An Investigation of Conceptual Structures and Processes. American Psychological Association. pp. 461--493.
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  • The Logic of Scientific Discovery.Karl Popper - 1959 - Studia Logica 9:262-265.
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  • How do Scientists Think? Capturing the Dynamics of Conceptual Change in Science.Nancy Nersessian - 1992 - In R. Giere & H. Feigl (eds.), Cognitive Models of Science. University of Minnesota Press. pp. 3--45.
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