Two very different classes of theoretical models have been proposed to explain visual perception. One class of models assume that there is a point at which we become consciously aware of a stimulus, known as a threshold. This threshold is the foundation of discrete process models all of which describe an all-or-none transition between the mental state of perceiving a stimulus and the state of not perceiving a stimulus. In contrast, the other class of models assume that mental states change continuously. These continuous models are founded in signal detection theory and the more contemporary models in Bayesian inference frameworks. The continuous model is the more widely accepted model of perception, and as such discrete process models were mostly discarded. Nonetheless, there has been a renewed debate on continuous versus discrete perception, and recent work has renewed the idea that perception can be all-or-none. In this dissertation, we developed an experimental platform and modeling framework to test whether visual perception exhibits measurable characteristics consistent with discrete perception. The results of this study revealed a selective influence of stimulus type on the way that a visual stimulus is processed. Moreover this selective influence implied perception can either be discrete or continuous depending on the underlying perceptual processing. These qualitative differences in the way perception occurs even for highly similar stimuli such as motion or orientation have crucial implications for models of perception, as well as our understanding of neurophysiology and conscious perception.