9 found
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  1. Probably Good Diagrams for Learning: Representational Epistemic Recodification of Probability Theory.Peter C.-H. Cheng - 2011 - Topics in Cognitive Science 3 (3):475-498.
    The representational epistemic approach to the design of visual displays and notation systems advocates encoding the fundamental conceptual structure of a knowledge domain directly in the structure of a representational system. It is claimed that representations so designed will benefit from greater semantic transparency, which enhances comprehension and ease of learning, and plastic generativity, which makes the meaningful manipulation of the representation easier and less error prone. Epistemic principles for encoding fundamental conceptual structures directly in representational schemes are described. The (...)
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  2.  18
    Electrifying Diagrams for Learning: Principles for Complex Representational Systems.Peter C.-H. Cheng - 2002 - Cognitive Science 26 (6):685-736.
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  3.  13
    Truth Diagrams Versus Extant Notations for Propositional Logic.Peter C.-H. Cheng - 2020 - Journal of Logic, Language and Information 29 (2):121-161.
    Truth diagrams are introduced as a novel graphical representation for propositional logic. To demonstrate their epistemic efficacy a set of 28 concepts are proposed that any comprehensive representation for PL should encompass. TDs address all the criteria whereas seven other existing representations for PL only provide partial coverage. These existing representations are: the linear formula notation, truth tables, a PL specific interpretation of Venn Diagrams, Frege’s conceptual notation, diagrams from Wittgenstein’s Tractatus, Pierce’s alpha graphs and Gardner’s shuttle diagrams. The comparison (...)
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  4. Mechanisms in Human Learning.Fernand Gobet, Peter C. R. Lane, Steve Croker, Peter C.-H. Cheng, Gary Jones, Iain Oliver & Julian M. Pine - 2001 - Trends in Cognitive Sciences 5 (6):236-243.
    Pioneering work in the 1940s and 1950s suggested that the concept of chunking might be important in many processes of perception, learning and cognition in humans and animals. We summarize here the major sources of evidence for chunking mechanisms, and consider how such mechanisms have been implemented in computational models of the learning process. We distinguish two forms of chunking: the first deliberate, under strategic control, and goal-oriented; the second automatic, continuous, and linked to perceptual processes. Recent work with discrimination-network (...)
     
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  5.  20
    What Forms the Chunks in a Subject's Performance? Lessons From the CHREST Computational Model of Learning.Peter C. R. Lane, Fernand Gobet & Peter C.-H. Cheng - 2001 - Behavioral and Brain Sciences 24 (1):128-129.
    Computational models of learning provide an alternative technique for identifying the number and type of chunks used by a subject in a specific task. Results from applying CHREST to chess expertise support the theoretical framework of Cowan and a limit in visual short-term memory capacity of 3–4 looms. An application to learning from diagrams illustrates different identifiable forms of chunk.
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  6.  5
    Combinations of Simple Mechanisms Explain Diverse Strategies in the Freehand Writing of Memorized Sentences.Peter C.-H. Cheng & Erlijn van Genuchten - 2018 - Cognitive Science 42 (4):1070-1109.
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    Interpreting Lines in Graphs: Do Graph Users Construe Fictive Motion?Rossano Barone & Peter C.-H. Cheng - 2004 - In A. Blackwell, K. Marriott & A. Shimojima (eds.), Diagrammatic Representation and Inference. Springer. pp. 333--336.
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  8.  15
    The CHREST Model of Active Perception and its Role in Problem Solving.Peter C. R. Lane, Peter C.-H. Cheng & Fernand Gobet - 2001 - Behavioral and Brain Sciences 24 (5):892-893.
    We discuss the relation of the Theory of Event Coding (TEC) to a computational model of expert perception, CHREST, based on the chunking theory. TEC's status as a verbal theory leaves several questions unanswerable, such as the precise nature of internal representations used, or the degree of learning required to obtain a particular level of competence: CHREST may help answer such questions.
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  9.  6
    Review: The Common Thread of Induction. [REVIEW]Peter C.-H. Cheng - 1991 - British Journal for the Philosophy of Science 42 (2):269 - 272.
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