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

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. Author Keywords Re-representation, Insight, Cognitive Model ACM Classification Keywords I.2.0 Computing Methodologies: Artificial Intelligence— General—Cognitive Simulation; I.2.4 Computing Methodologies: Artificial Intelligence—Knowledge Representation Formalisms and Methods General Terms Theory, Algorithms

• Type: Conference
• Year: 2009
• Where: CANDC
• Authors: Dan Ventura
• Keywords: ACM Classification Keywords, Author Keywords Re-representation, CANDC 2009, Emerging Technology, Sub-symbolic Computational Model