A sub-symbolic model of the cognitive processes of re-representation and insight

Authors:
Dan Ventura
Affiliations:
Brigham Young University, Provo, UT, USA
Venue:
Proceedings of the seventh ACM conference on Creativity and cognition
Year:
2009

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Abstract

We present a sub-symbolic computational model for effecting knowledge re-representation and insight. Given a set of data, manifold learning is used to automatically organize the data into one or more representational transformations, which are then learned with a set of neural networks. The result is a set of neural filters that can be applied to new data as re-representation operators.