Notions of intuition and attention modeled by a hierarchically arranged generalized regression neural network

Author

Hoya, Tetsuya

Author_Institution

Lab. for Adv. Brain Signal Process., Saitama, Japan

Volume

34

Issue

1

fYear

2004

Firstpage

200

Lastpage

209

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;

fLanguage

English

Journal_Title

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

1083-4419

Type

jour

DOI

10.1109/TSMCB.2003.811758

Filename

1262494