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Z. W. Pylyshyn (1981). Complexity and the Study of Human and Machine Intelligence.

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  1.  17
    ‘Ed Tech in Reverse’: Information Technologies and the Cognitive Revolution.Norm Friesen & Andrew Feenberg - 2007 - Educational Philosophy and Theory 39 (7):720–736.
    As we rapidly approach the 50th year of the much‐celebrated ‘cognitive revolution’, it is worth reflecting on its widespread impact on individual disciplines and areas of multidisciplinary endeavour. Of specific concern in this paper is the example of the influence of cognitivism's equation of mind and computer in education. Within education, this paper focuses on a particular area of concern to which both mind and computer are simultaneously central: educational technology. It examines the profound and lasting effect of cognitive science (...)
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  2.  82
    The Generality Problem, Statistical Relevance and the Tri-Level Hypothesis.James Beebe - 2004 - Noûs 38 (1):177 - 195.
    In this paper I critically examine the Generality Problem and argue that it does not succeed as an objection to reliabilism. Although those who urge the Generality Problem are correct in claiming that any process token can be given indefinitely many descriptions that pick out indefinitely many process types, they are mistaken in thinking that reliabilists have no principled way to distinguish between relevant and irrelevant process types.
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  3.  58
    Confirmation and the Computational Paradigm, or, Why Do You Think They Call It Artificial Intelligence?David J. Buller - 1993 - Minds and Machines 3 (2):155-81.
    The idea that human cognitive capacities are explainable by computational models is often conjoined with the idea that, while the states postulated by such models are in fact realized by brain states, there are no type-type correlations between the states postulated by computational models and brain states (a corollary of token physicalism). I argue that these ideas are not jointly tenable. I discuss the kinds of empirical evidence available to cognitive scientists for (dis)confirming computational models of cognition and argue that (...)
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    Confirmation and the Computational Paradigm (Or: Why Do You Think They Call Itartificial Intelligence?). [REVIEW]David J. Buller - 1993 - Minds and Machines 3 (2):155-181.
    The idea that human cognitive capacities are explainable by computational models is often conjoined with the idea that, while the states postulated by such models are in fact realized by brain states, there are no type-type correlations between the states postulated by computational models and brain states (a corollary of token physicalism). I argue that these ideas are not jointly tenable. I discuss the kinds of empirical evidence available to cognitive scientists for (dis)confirming computational models of cognition and argue that (...)
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