A stochastic version of general recognition theory

General recognition theory (GRT) is a multivariate generalization of signal detection theory. Past versions of GRT were static and lacked a process in terpretation. This article presents a stochastic version of GRT that models moment-by-moment fluctuations in the output of perceptual channels via a m ultivariate diffusion process. A decision stage then computes a lineal or q uadratic function of the outputs from the perceptual channels, which drives a univariate diffusion process that determines the subject's response. Con ditions are established under which the stochastic and static versions of G RT make identical accuracy predictions. These equivalence relations show th at traditional estimates of perceptual noise may often be corrupted by deci sional influences. (C) 2000 Academic Press.