ISSN 1671-3710
CN 11-4766/R
主办：中国科学院心理研究所
出版：科学出版社

心理科学进展 ›› 2006, Vol. 14 ›› Issue (6): 837-843.

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内隐学习的人工神经网络模型

郭秀艳;朱磊;魏知超   

  1. 华东师范大学心理学系，上海 200062
  • 收稿日期:2006-09-20 修回日期:1900-01-01 出版日期:2006-11-15 发布日期:2006-11-15
  • 通讯作者: 郭秀艳

Artificial Neural Network Model about Implicit Learning

Guo Xiuyan,Zhu Lei;Wei Zhichao   

  1. Department of Psychology, East China Normal University, Shanghai 200062, China
  • Received:2006-09-20 Revised:1900-01-01 Online:2006-11-15 Published:2006-11-15
  • Contact: Guo Xiuyan

PDF (PC)

2010

被引次数

47

摘要： 近年来，人工神经网络模型常被用来模拟各种心理活动，从而为心理学的一些相关理论提供丰富的证据，内隐学习也不例外。基于权重调整来学习正确反应的人工神经网络模型和内隐学习的两大本质特征间有着极为相应的匹配，因此，人工神经网络模型特别适用于内隐学习研究。到目前为止，针对两种较为普遍的内隐学习任务，已经相应地出现了两种使用较为广泛的神经网络模型——自动联系者和简单循环网络

关键词: 人工神经网络模型, 人工语法学习, 自动联系者, 序列学习, 简单循环网络

Abstract: These years, Artificial Neural Networks have been used to simulate many kinds of psychological activities, including implicit learning. Neural networks has some traits which are good for simulating implicit learning, such as learning to response correctly through weight adjustment. There are two types of neural network models named autoassociators and simple recurrent networks which are used broadly in cognitive simulation

Key words: artificial neural network model, artificial grammar learning, autoassociator, serial learning, simple recurrent network

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