John R. Vokey [3]J. Vokey [1]
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    Opposition logic and neural network models in artificial grammar learning.J. Vokey - 2004 - Consciousness and Cognition 13 (3):565-578.
    Following neural network simulations of the two experiments of Higham, Vokey, and Pritchard , Tunney and Shanks argued that the opposition logic advocated by Higham et al. was incapable of distinguishing between single and multiple influences on performance of artificial grammar learning and more generally. We show that their simulations do not support their conclusions. We also provide different neural network simulations that do simulate the essential results of Higham et al.
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  2.  53
    Implicit knowledge as automatic, latent knowledge.John R. Vokey & Philip A. Higham - 1999 - Behavioral and Brain Sciences 22 (5):787-788.
    Implicit knowledge is perhaps better understood as latent knowledge so that it is readily apparent that it contrasts with explicit knowledge in terms of the form of the knowledge representation, rather than by definition in terms of consciousness or awareness. We argue that as a practical matter any definition of the distinction between implicit and explicit knowledge further involves the notion of control.
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    Quotidian cognition and the human-nonhuman “divide”: Just more or less of a good thing?Drew Rendall, John R. Vokey & Hugh Notman - 2008 - Behavioral and Brain Sciences 31 (2):144-145.
    We make three points: (1) Overlooked studies of nonhuman communication originally inspired, but no longer support, the blinkered view of mental continuity that Penn et al. critique. (2) Communicative discontinuities between animals and humans might be rooted in social-cognitive discontinuities, reflecting a common lacuna in Penn et al.'s relational reinterpretation mechanism. (3) However, relational reinterpretation need not be a qualitatively new representational process.
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    Statistics without probability: Significance testing as typicality and exchangeability in data analysis.John R. Vokey - 1998 - Behavioral and Brain Sciences 21 (2):225-226.
    Statistical significance is almost universally equated with the attribution to some population of nonchance influences as the source of structure in the data. But statistical significance can be divorced from both parameter estimation and probability as, instead, a statement about the atypicality or lack of exchangeability over some distinction of the data relative to some set. From this perspective, the criticisms of significance tests evaporate.
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