Behavioral and Brain Sciences 20 (1):83-83 (1997)

Authors
Andy Clark
University of Sussex
Abstract
We argue that existing learning algorithms are often poorly equipped to solve problems involving a certain type of important and widespread regularity that we call “type-2 regularity.” The solution in these cases is to trade achieved representation against computational search. We investigate several ways in which such a trade-off may be pursued including simple incremental learning, modular connectionism, and the developmental hypothesis of “representational redescription.”.
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DOI 10.1017/s0140525x97440025
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