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情景设定:一种神经网络方法。

Occasion setting: a neural network approach.

作者信息

Schmajuk N A, Lamoureux J A, Holland P C

机构信息

Department of Psychology: Experimental, Duke University, Durham, North Carolina 27708-0086, USA.

出版信息

Psychol Rev. 1998 Jan;105(1):3-32. doi: 10.1037/0033-295x.105.1.3.

DOI:10.1037/0033-295x.105.1.3
PMID:9450370
Abstract

Classical conditioning data show that a conditioned stimulus (CS) can act either as a simple CS--eliciting conditioned responses (CRs) by signaling the occurrence of an unconditioned stimulus (US)--or as an occasion setter--controlling the responses generated by another CS. In this article, the authors apply a simple extension of a network model of conditioning, originally presented by N. A. Schmajuk and J. J. DiCarlo (S-D; 1992), to the description of these 2 different CS functions. In the model, CS inputs are connected to the CR output both directly and indirectly through a hidden unit layer that codes configural stimuli. In this framework, a CS acts as (a) a simple stimulus through its direct connections with the output units and as (b) an occasion setter through its indirect configural connections via the hidden units. Computer simulations demonstrate that the network accounts for a large part of the data on occasion setting.

摘要

经典条件作用数据表明,条件刺激(CS)既可以作为一个简单的条件刺激——通过发出无条件刺激(US)出现的信号来引发条件反应(CR)——也可以作为一个情境设定者——控制由另一个条件刺激产生的反应。在本文中,作者应用了一种对条件作用网络模型的简单扩展,该模型最初由N. A. 施马尤克和J. J. 迪卡洛(S-D;1992)提出,来描述这两种不同的条件刺激功能。在该模型中,条件刺激输入通过一个编码构型刺激的隐藏单元层直接和间接地连接到条件反应输出。在这个框架中,一个条件刺激通过其与输出单元的直接连接作为(a)一个简单刺激,并且通过其经由隐藏单元的间接构型连接作为(b)一个情境设定者。计算机模拟表明,该网络解释了情境设定方面的大部分数据。

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Occasion setting: a neural network approach.情景设定:一种神经网络方法。
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