Recently, several studies proposed a probabilistic framework for explaining the phenomenon of binocular rivalry, as an alternative to the classic bottom-up or eye-dominant interpretation of it. According to this framework, perception is generated from the observer’s internal model of the visual world, based on sampling-based probabilistic representations and computations in the cortex. To test the validity of this proposal, we trained participants with repeated four patterns of two-Gabor patches corresponding to the four possible perceptions in binocular rivalry settings, with a particular probability distribution of appearance (10%, 40%, 15% and 35%). We also tested participants’ prior and posterior distributions of these four perceptions in both binocular rivalry and non-rivalry situations, where they either made judgments by what was perceived in rivalry or guessed what could possibly be the answers of Gabor orientation pairs when they saw only non-rivalry Gaussian noise. Kullback–Leibler divergence and resampling methods were used to compare the pretest and posttest distributions from each individual participant. For the non-rivalry inference, three out of four participants showed significant difference (ps<0.05) between pre and post distributions of the four possible answers. Compared with the pretest, the post-test distribution shifted towards the target distribution manipulated in the training session. In contrast, for binocular rivalry, none of the participants showed change in the distributions of four perceptions overall from pretest to posttest, suggesting no learning effect transferred from non-rivalry training. Further analysis on the relationship between perception duration time and distribution changes in rivalry showed that with longer perception duration it was more likely to find pre-test and post-test distribution differences. However, the transition from pretest to posttest did not necessarily follow the target distribution from training. These results provided no decisive evidence that binocular rivalry is a visual process based on probabilistic representation.