I got quite interested in the idea of converting the outputs of different channels to probability. I wondered whether the visual system might possibly represent the correspondence problem in probabilistic terms. We tend to pose the correspondence problem in terms of finding "the" matching feature in the right eye for a given feature in the left eye. However, sometimes there may be two matches for a given feature (Panum's limiting case), and sometimes none (occlusion). So, it might make sense to use a concept like probability -- where it is quite possible for two disparities to be considered likely, or none -- rather than a winner-take-all model which enforces exactly one match. I applied the model developed in the previous paper to various test stimuli, and it generally behaved sensibly. Because it was constructed from V1 neurons with a constant disparity preference across their receptive fields, it had a built-in preference for smoothly-varying disparity fields, so it gave correct percepts for the double-nail stimulus. It produced two probability peaks for Panum's limiting case, but only one for a random-dot stereogram. The question of when the visual system produces two disparity values, as in transparency, and how it handles occlusion, is a very interesting one, and this paper certainly goes nowhere near far enough in explaining this.