Brain categorization:  learning, attention, and consciousness

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,

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