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  1. Jonathan A. Waskan (forthcoming). A Virtual Solution to the Frame Problem. Proceedings of the First Ieee-Ras International Conference on Humanoid Robots.
    We humans often respond effectively when faced with novel circumstances. This is because we are able to predict how particular alterations to the world will play out. Philosophers, psychologists, and computational modelers have long favored an account of this process that takes its inspiration from the truth-preserving powers of formal deduction techniques. There is, however, an alternative hypothesis that is better able to account for the human capacity to predict the consequences worldly alterations. This alternative takes its inspiration from the (...)
     
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  2. Jonathan A. Waskan (2006). Models and Cognition. A Bradford Book.
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  3. Jonathan A. Waskan (2003). Folk Psychology and the Gauntlet of Irrealism. Southern Journal of Philosophy 41 (4):627-656.
  4. Jonathan A. Waskan (2003). Intrinsic Cognitive Models. Cognitive Science 27 (2):259-283.
    Theories concerning the structure, or format, of mental representation should (1) be formulated in mechanistic, rather than metaphorical terms; (2) do justice to several philosophical intuitions about mental representation; and (3) explain the human capacity to predict the consequences of worldly alterations (i.e., to think before we act). The hypothesis that thinking involves the application of syntax-sensitive inference rules to syntactically structured mental representations has been said to satisfy all three conditions. An alternative hypothesis is that thinking requires the construction (...)
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  5. Jonathan A. Waskan (2001). A Critique of Connectionist Semantics. Connection Science 13 (3):277-292.
  6. Jonathan A. Waskan (2000). Kant's Epistemic and Defining Criteria of Truth. International Studies in Philosophy 32 (4):107-121.
  7. Jonathan A. Waskan & William P. Bechtel (1997). Directions in Connectionist Research: Tractable Computations Without Syntactically Structured Representations. Metaphilosophy 28 (1‐2):31-62.
    Figure 1: A pr ototyp ical exa mple of a three-layer feed forward network, used by Plunkett and M archm an (1 991 ) to simulate learning the past-tense of En glish verbs. The inpu t units encode representations of the three phonemes of the present tense of the artificial words used in this simulation. Th e netwo rk is trained to produce a representation of the phonemes employed in the past tense form and the suffix (/d/, /ed/, or /t/) (...)
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