We present a framework in which Perceptual Learning, Statistical Learning and Rule/Abstract learning are not different types of learning but only differently specialized versions of the fundamental learning process, and we argue that this learning process must be captured in its entirety to successfully integrate learning into complex visual processes. First, we demonstrate how recent behavioral and neural results in the literature reveal a convergence across perceptual, statistical, and rule/abstract learning supporting this framework. Next, we show why the generalized version of statistical learning can provide the appropriate setup for such a unified treatment of learning in vision and present a computational approach that best accommodates this kind of statistical learning. We follow up by discussing what plausible neural scheme could feasibly implement this framework and how this scheme can help alleviate the present disconnect between neural measures and their interpretation from the standpoint of learning. We conclude with a case study, “roving” in visual learning, and by listing directions in the field where statistical learning needs to take steps to approach the level of sophistication required for being the method of choice for advancing our understanding of vision and other cognitive processes in their completeness.