  Measurements of response inhibition components of reactive inhibition and
proactive inhibition within the stop signal paradigm have been of special
interest for researchers since the 1980s. While frequentist nonparametric and
Bayesian parametric methods have been proposed to precisely estimate the entire
distribution of reactive inhibition, quantified by stop signal reaction
times(SSRT), there is no method yet in the stop-signal task literature to
precisely estimate the entire distribution of proactive inhibition. We
introduce an Asymmetric Laplace Gaussian (ALG) model to describe the
distribution of proactive inhibition. The proposed method is based on two
assumptions of independent trial type(go/stop) reaction times, and Ex-Gaussian
(ExG) models for them. Results indicated that the four parametric, ALG model
uniquely describes the proactive inhibition distribution and its key shape
features; and, its hazard function is monotonically increasing as are its three
parametric ExG components. In conclusion, both response inhibition components
can be uniquely modeled via variations of the four parametric ALG model
described with their associated similar distributional features.
