A Stochastic Version of General Recognition Theory.
A Stochastic Version of General Recognition Theory.
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一般识别理论的随机版本。
DOI：
10.1006/jmps.1998.1249
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发表时间：
2000
影响因子：
1.8
通讯作者：
Gregory Ashby
中科院分区：
文献类型：
--
作者：
F. Ashby;Wolfgang Schwarz;J. D. Balakrishnan;W. K. Estes;J. Falmagne;Todd Maddox;R. Ratcliff;Elliot;Gregory Ashby
广义识别理论(GRT)是信号检测理论的多元推广。过去的GRT版本是静态的，缺乏过程解释。本文提出了一个随机版本的GRT，它通过多变量扩散过程来模拟感知通道输出中的时刻波动。然后，决策阶段计算来自感知通道的输出的线性或二次函数，这驱动单变量扩散过程，该单变量扩散过程确定对象的响应。建立了GRT的随机版本和静态版本做出相同精度预测的条件。这些等价关系表明，传统的感知噪声估计可能经常受到决策影响的影响。版权所有2000年学术出版社。
General recognition theory (GRT) is a multivariate generalization of signal detection theory. Past versions of GRT were static and lacked a process interpretation. This article presents a stochastic version of GRT that models moment-by-moment fluctuations in the output of perceptual channels via a multivariate diffusion process. A decision stage then computes a linear or quadratic function of the outputs from the perceptual channels, which drives a univariate diffusion process that determines the subject's response. Conditions are established under which the stochastic and static versions of GRT make identical accuracy predictions. These equivalence relations show that traditional estimates of perceptual noise may often be corrupted by decisional influences. Copyright 2000 Academic Press.
影响因子：
5.4
作者：
Ratcliff, R;Van Zandt, T;McKoon, G
通讯作者：
McKoon, G