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To explore some mechanisms of generalization in concept formation, we build a three-layer neural network with feedback and Hebbian learning rules. Using binary sequences as input, we simulate the generalization process from multiple examples to a concept. After tens of training, the outputs of the network will converge to stable states which denote the formation of a concept. We suggest that generalization...
The formation of concepts is a key point in development of human intelligence. There are two kinds of traditional ideas about this problem: hypothesis elimination and associative learning. The Bayesian framework suggested by Tenenbaum absorbs the virtue and avoids the limitations of the two traditional ideas and its results are demonstrated by behavioral experiments. The model simulates statistics...
Acquisition of abstract concept is the key step in human intelligence development, but the neural mechanism of concept formation is not clear yet. Researches on complexity and self organization theory indicate that concept is a result of emergence of neural system and it should be represented by an attractor. Associative learning and hypothesis elimination are considered as the mechanisms of concept...
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