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  1. William J. Clancey (forthcoming). Situated Action: A Neuropsychological Interpretation (Response to Vera and Simon). Philosophical Explorations.
    Symbols in computer programs are not necessarily isomorphic in form or capability to neural processes. Representations in our models are stored descriptions of the world and human behavior, created by a human interpreter; representations in the brain are neither immutable forms nor encoded in some language. Although the term "symbol" can be usefully applied to describe words, smoke signals, neural maps, and graphic icons, a science of symbol processing requires distinguishing between the structural, developmental, and interactive nature of different forms (...)
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  2. William J. Clancey (2012). Working on Mars: Voyages of Scientific Discovery with the Mars Exploration Rovers. The Mit Press.
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  3. William J. Clancey (2011). Relating Modes of Thought. In Thomas Bartscherer (ed.), Switching Codes. Chicago University Press. 161.
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  4. William J. Clancey (2006). How Anchors Allow Reusing Categories in Neural Composition of Sentences. Behavioral and Brain Sciences 29 (1):73-74.
    van der Velde's &de Kamps's neural blackboard architecture is similar to “activation trace diagrams” (Clancey 1999), which represent how categories are temporally related as neural activations in parallel-hierarchical compositions. Examination of other comprehension examples suggests that a given syntactic categorization (structure assembly) can be incorporated in different ways within an open composition by different kinds of anchoring relations (delay assemblies). Anchors are categorizations, too, so they cannot be reused until their containing construction is completed (bindings are resolved).
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  5. William J. Clancey (2005). Modeling the Perceptual Component of Conceptual Learning—a Coordination Perspective. In Peter Gardenfors, Petter Johansson & N. J. Mahwah (eds.), Cognition, Education, and Communication Technology. Erlbaum Associates. 109--146.
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  6. William J. Clancey (2003). The Newell Test Should Commit to Diagnosing Dysfunctions. Behavioral and Brain Sciences 26 (5):604-605.
    “Conceptual coordination” analysis bridges connectionism and symbolic approaches by positing a “process memory” by which categories are physically coordinated (as neural networks) in time. Focusing on dysfunctions and odd behaviors, like slips, reveals the function of consciousness, especially constructive processes that are often taken for granted, which are different from conventional programming constructs. Newell strongly endorsed identifying architectural limits; the heuristic of “diagnose unusual behaviors” will provide targets of opportunity that greatly strengthen the Newell Test.
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  7. William J. Clancey (2000). Conceptual Coordination Bridges Information Processing and Neurophysiology. Behavioral and Brain Sciences 23 (6):919-922.
    Information processing theories of memory and skills can be reformulated in terms of how categories are physically and temporally related, a process called conceptual coordination. Dreaming can then be understood as a story-understanding process in which two mechanisms found in everyday comprehension are missing: conceiving sequences (chunking categories in time as a higher-order categorization) and coordinating across modalities (e.g., relating the sound of a word and the image of its meaning). On this basis, we can readily identify isomorphisms between dream (...)
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  8. James G. Greeno, William J. Clancey, Clayton Lewis, Mark Seidenberg, Sharon Derry, Morton Ann Gernsbacher, Patrick Langley, Michael Shafto, Dedre Gentner, Alan Lesgold & Colleen M. Seifert (1998). Efforts to Encourage Multidisciplinarity in the Cognitive Science Society. Cognitive Science 22 (1):131-132.
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  9. William J. Clancey (1996). Conceptual Coordination: Abstraction Without Description. Philosophical Explorations.
    Conceptual coordination is a learning process that relates multiple perceptual-motor modalities (verbal, visual, gestural, etc.) in time. Lower-order categorizations are thus related by sequence and simultaneity, as shown by neurological dysfunctions. Heretofore, many theories of abstraction have only considered verbal behavior and assumed that the neural mechanism itself consists of manipulation of descriptions (linguistic models of the world and behavior). This broader view better relates physical and intellectual skills.
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  10. William J. Clancey (1995). The Learning Process in the Epistemology of Medical Information. Philosophical Explorations.
    Progress in the past few decades in representing medical knowledge, along with the availability of low-cost, powerful workstation computers, has increased interest in encoding all medical records in electronic form. But despite the advantages of legibility, access, and automated performance reviews, computerization may also restrict what can be recorded and rigidify health care interactions with patients. Balancing the beneficial and negative effects requires an understanding of medical practice, especially the difference between human knowledge and today's computer programs. Human perceptual and (...)
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  11. William J. Clancey (1994). Comment on Disessa. Philosophical Explorations.
    In the predominant symbolic approach of AI in the 1970s and early 80s, a description—such as an expert system rule, frame, script, or natural language grammar—was often called a "knowledge representation." Knowledge was viewed as something that could be inventoried. Human memory was modeled as a repository of knowledge representations. Arguments that "there are no knowledge representations in the brain," were then misinterpreted in this community as "throwing the baby out with the bathwater.".
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  12. William J. Clancey (1993). Notes on "Epistemology of a Rule-Based Expert System". Philosophical Explorations.
    In the 1970s, we conceived of a rule explanation as supplying the causal and social context that justifies a rule, an objective documentation for why a rule is correct. Today we would call such descriptions post-hoc design rationales, not proving the rules? correctness, but providing a means for later interpreting why the rule was written and facilitating later improvements.
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  13. William J. Clancey (1993). Notes on "Heuristic Classification". Philosophical Explorations.
    Knowledge engineers once viewed themselves as priests; they received "The Word" from experts above, added nothing to the content, but codified it accurately into written rules, and passed it down to ordinary folks as commandments to live by.
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  14. William J. Clancey (1993). Situated Action: A Neuropsychological Interpretation Response to Vera and Simon. Cognitive Science 17 (1):87-116.
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  15. William J. Clancey (1992). Representations of Knowing: In Defense of Cognitive Apprenticeship. Philosophical Explorations.
    Sandberg and Wielinga argue in their paper, "Situated Cognition: A paradigm shift?" that "there are no strong reasons to leave the traditional paradigm of cognitive science and AI." They are certainly correct that we should not "disregard evidence and achievements of Cognitive and Instructional Sciences." But they fail to appreciate the implications of the storehouse view of knowledge, which suggests that learning is like putting tools in a shed. Situated cognition arguments against traditional views of learning transfer suggest that human (...)
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  16. J. Roschelle & William J. Clancey (1992). Learning as Social and Neural. Philosophical Explorations.
    Representations are created and given meaning in a shared perceptual space, where they are spoken, written, and drawn in the context of social activity. Consequently, problems in science education cross the boundaries of traditional modularization of the mind into separate perceptual, representation, and communication components that act at distinct times, in distinct domains. We illustrate these issues with a case study of physics learning using a simulation program. The learners are initially uncertain about what aspects of motion to see, where (...)
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  17. William J. Clancey (1991). Situated Cognition: Stepping Out of Representational Flatland. Philosophical Explorations.
    Descriptions of novice-expert differences, reasoning strategies, explanation-based learning, etc. are descriptions of how people create and use models within a representational language, when interacting with their environment in cycles of perceiving and acting. To complement these descriptions, we need to understand how representational languages are created.
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  18. William J. Clancey (1987). Functional Principles and Situated Problem Solving. Behavioral and Brain Sciences 10 (3):479.
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