 
Summary: Journal of Mathematical Psychology 44, 310 329 (2000)
A Stochastic Version of General
Recognition Theory
F. Gregory Ashby
University of California, Santa Barbara
General recognition theory (GRT) is a multivariate generalization of signal
detection theory. Past versions of GRT were static and lacked a process inter
pretation. This article presents a stochastic version of GRT that models
momentbymoment 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. Condi
tions 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. 2000 Academic Press
General recognition theory (GRT), which was first introduced by Ashby and
Townsend (1986), is a multivariate generalization of signal detection theory (e.g.,
Green 6 Swets, 1966; Tanner 6 Swets, 1954). GRT has been used successfully to
model perceptual and decisional processing in stimulus identification (Ashby 6 Lee,
