Title :
Notions of intuition and attention modeled by a hierarchically arranged generalized regression neural network
Author_Institution :
Lab. for Adv. Brain Signal Process., Saitama, Japan
Abstract :
In this paper, two psychological functions, intuition and attention, are modeled by a newly proposed hierarchically arranged generalized regression neural network (HA-GRNN). The main contribution of the paper is two-fold: to provide an engineering basis for a macroscopic representation of psychology-oriented functions by means of artificial neural networks; to propose a concrete model for the two functions, intuition and attention, in terms of the associated interactive and evolutionary processes within an HA-GRNN. In the simulation study, the effectiveness of an HA-GRNN is justified within the context of pattern classification tasks.
Keywords :
Gaussian processes; evolutionary computation; neural nets; pattern classification; psychology; regression analysis; support vector machines; associated interactive process; attention notion; evolutionary process; hierarchically arranged generalized regression; intuition notion; macroscopic representation; neural network; pattern classification; psychological function; Artificial neural networks; Biological neural networks; Biology computing; Concrete; Neural networks; Pattern classification; Psychology; Signal processing; Support vector machine classification; Support vector machines; Attention; Brain; Computer Simulation; Consciousness; Humans; Intuition; Models, Neurological; Models, Psychological; Neural Networks (Computer); Regression Analysis;
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
DOI :
10.1109/TSMCB.2003.811758
