A stochastic version of general recognition theory
A stochastic version of general recognition theory
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DOI：
10.1006/jmps.1999.1249
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发表时间：
2000-06-01
影响因子：
1.8
通讯作者：
Ashby, FG
中科院分区：
文献类型：
--
作者：
Ashby, FG
广义识别理论（General recognition theory， GRT）是信号检测理论的多元推广。过去版本的GRT是静态的，缺乏进程解释。本文提出了一种随机版本的GRT，它通过多元扩散过程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，这驱动了决定受试者反应的单变量扩散过程。建立了随机和静态GRT预测精度相同的条件。这些等价关系表明，传统的感知噪声估计可能经常被决策影响所破坏。(C) 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 lineal 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. (C) 2000 Academic Press.