title: Notions of intuition and attention modeled by a hierarchically arranged generalized regression neural network.
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: 1) to provide an engineering basis for a macroscopic representation of psychology-oriented functions by means of artificial neural networks; 2) 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.
authors.affiliation: Laboratory for Advanced Brain Signal Processing, Saitama 351-0198, Japan. hoya@bsp.brain.riken.go.jp
authors.forename: Tetsuya
authors.initials: T
authors.lastname: Hoya
mesh_terms: D001288:Attention / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D003243:Consciousness / Q000502:physiology; D006801:Humans; D019545:Intuition / Q000502:physiology*; D008959:Models, Neurological; D008960:Models, Psychological; D016571:Neural Networks, Computer; D012044:Regression Analysis