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  1.  27
    Interactive Effects of Explicit Emergent Structure: A Major Challenge for Cognitive Computational Modeling.Robert M. French & Elizabeth Thomas - 2015 - Topics in Cognitive Science 7 (2):206-216.
    David Marr's three-level analysis of computational cognition argues for three distinct levels of cognitive information processing—namely, the computational, representational, and implementational levels. But Marr's levels are—and were meant to be—descriptive, rather than interactive and dynamic. For this reason, we suggest that, had Marr been writing today, he might well have gone even farther in his analysis, including the emergence of structure—in particular, explicit structure at the conceptual level—from lower levels, and the effect of explicit emergent structures on the level that (...)
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  2.  54
    The Dynamical Hypothesis in Cognitive Science: A Review Essay of Mind As Motion[REVIEW]Robert M. French & Elizabeth Thomas - 2001 - Minds and Machines 11 (1):101-111.
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    The Dynamical Hypothesis: One Battle Behind.Robert M. French & Elizabeth Thomas - 1998 - Behavioral and Brain Sciences 21 (5):640-641.
    What new implications does the dynamical hypothesis have for cognitive science? The short answer is: None. The _Behavior and Brain Sciences _target article, “The dynamical hypothesis in cognitive science” by Tim Van Gelder is basically an attack on traditional symbolic AI and differs very little from prior connectionist criticisms of it. For the past ten years, the connectionist community has been well aware of the necessity of using (and understanding) dynamically evolving, recurrent network models of cognition.
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    Ovid, Amores Iii. 9. 35–40.Elizabeth Thomas - 1965 - The Classical Review 15 (02):149-151.
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    Why Localist Connectionist Models Are Inadequate for Categorization.Robert M. French & Elizabeth Thomas - 2000 - Behavioral and Brain Sciences 23 (4):477-477.
    Two categorization arguments pose particular problems for localist connectionist models. The internal representations of localist networks do not reflect the variability within categories in the environment, whereas networks with distributed internal representations do reflect this essential feature of categories. We provide a real biological example of perceptual categorization in the monkey that seems to require population coding (i.e., distributed internal representations).
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