Title :
FEBAMSOM-BAM∗: Neural network model of human categorization of the N-bits parity problem
Author :
Morissette, L. ; Chartier, Sebastien
Author_Institution :
Sch. of Psychol., Univ. of Ottawa, Ottawa, ON, Canada
Abstract :
Classification following a non-linearly separable boundary is a cognitive task that is hard to complete for humans and animals. Feedforward neural networks are able to perform the task efficiently but they present little correspondence with human cognition. We present a neural network model of human categorization of the n-bit parity problem, using a modification of the pi-sigma network incorporating bidirectional associative memories and self-organizing maps. The model has good cognitive validity with cognitive human processes and is efficient.
Keywords :
cognition; content-addressable storage; feedforward neural nets; pattern classification; self-organising feature maps; FEBAMSOM-BAM; N-bits parity problem; cognitive human process; cognitive task; cognitive validity; feature extracting bidirectional associative memory with a self-organizing map; feedforward neural network model; human categorization; human cognition; nonlinearly separable boundary; pi-sigma network modification; Associative memory; Brain modeling; Equations; Mathematical model; Neurons; Psychology; Vectors;
Conference_Titel :
Neural Networks (IJCNN), The 2013 International Joint Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4673-6128-6
DOI :
10.1109/IJCNN.2013.6706971
