A Model of Perceptual Learning Integrated with Top-Down Information

Author

Liu, Yun-Hui ; Luo, Si-Wei ; Lv, Zi-Ang ; Zou, Qi

Author_Institution

Beijing Jiaotong Univ., Beijing

Volume

6

fYear

2007

fDate

19-22 Aug. 2007

Firstpage

3538

Lastpage

3542

Abstract

Recent psychological and neurobiological experiments results show that top-down information such as attention and other higher cortical processes play an important role in perceptual learning issues, while current neural network models, mostly concerned with bottom-up information process only, do not combine the top-down information. In this paper, we give a model of perceptual learning that takes top-down information into account, and explain the mechanism of this model in the framework of information geometry.

Keywords

learning (artificial intelligence); statistical distributions; conditional distribution; information geometry; neural network models; neurobiological experiments; perceptual learning; psychological experiments; top-down information; Biological neural networks; Computer science; Cybernetics; Information analysis; Information geometry; Machine learning; Manifolds; Probability distribution; Psychology; Solid modeling; Information geometry; Perceptual learning; Top-down information;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2007 International Conference on

Conference_Location

Hong Kong

Print_ISBN

978-1-4244-0973-0

Electronic_ISBN

978-1-4244-0973-0

Type

conf

DOI

10.1109/ICMLC.2007.4370760

Filename

4370760