A Layered Network Model for Learning-to-learn and Configuration in Classical Conditioning
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A Layered Network Model for Learning-to-learn and Configuration in Classical Conditioning

Abstract

Networks composed of layers of adaptive elements provide a rigorous explanation for complex associative learning phenomena. In particular, a network composed of three adaptive elements can explain previously intractable phenomena, namely the rapid rate of reacquisitions, learning-to-learn, spontaneous configuration, and negative patterning (the exclusive-OR problem). This paper will compare the results of computer simulations to the behavioral results of classical conditioning experiments using the rabbit's nictitating membrane response.

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