  The distribution of single Stop Signal Reaction Times (SSRT) in the stop
signal task (SST) as a measurement of the latency of the unobservable stopping
process has been modeled with a nonparametric method by Hans Colonius (1990)
and with a Bayesian parametric method by Eric-Jan Wagenmakers and colleagues
(2012). These methods assume equal impact of the preceding trial type (go/stop)
in the SST trials on the SSRT distributional estimation without addressing the
case of the violated assumption. This study presents the required model by
considering two-state mixture model for the SSRT distribution. It then compares
the Bayesian parametric single SSRT and mixture SSRT distributions in the usual
stochastic order at the individual and the population level under the
ex-Gaussian distributional format. It shows that compared to a single SSRT
distribution, the mixture SSRT distribution is more diverse, more positively
skewed, more leptokurtic, and larger in stochastic order. The size of the
disparities in the results also depends on the choice of weights in the mixture
SSRT distribution. This study confirms that mixture SSRT indices as a constant
or distribution are significantly larger than their single SSRT counterparts in
the related order. This offers a vital improvement in the SSRT estimations.
