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Author(s): Carpenter, G.A. | Ersoy, B. | Grossberg, S. |
Year: 2005
Citation: Proceedings of the International Joint Conference on Neural Networks (IJCNN?05), Montreal.
Abstract: How do humans and animals learn to recognize objects and events? Two classical views are that exemplars or prototypes are learned. A hybrid view is that a mixture, called rule-plus-exceptions, is learned. None of these models learn their categories. A distributed ARTMAP neural network with self supervised learning incrementally learns categories that match human learning data on a class of thirty diagnostic experiments called the 5-4 category structure. Key predictions of ART models have received behavioral, neurophysiological, and anatomical support.
The ART prediction about what goes wrong during amnesic learning has also been supported: A lesion in its orienting system causes a low vigilance parameter.
Topics:
Biological Learning,
Models:
Distributed ART,
