Vision and learning have long been considered to be two areas of research linked only distantly. However, recent developments in vision research have changed the conceptual definition of vision from a signal-evaluating process to a goal-oriented interpreting process, and this shift binds learning, together with the resulting internal representations, intimately to vision. In this review, we consider various types of learning (perceptual, statistical, and rule/abstract) associated with vision in the past decades and argue that they represent differently specialized versions of the fundamental learning process, which must be captured in its entirety when applied to complex visual processes. We show why the generalized version of statistical learning can provide the appropriate setup for such a unified treatment of learning in vision, what computational framework best accommodates this kind of statistical learning, and what plausible neural scheme could feasibly implement this framework. Finally, we list the challenges that the field of statistical learning faces in fulfilling the promise of being the right vehicle for advancing our understanding of vision in its entirety.

中文翻译：

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视觉中的统计学习

视觉和学习长期以来一直被认为是联系较远的两个研究领域。然而，视觉研究的最新发展已经将视觉的概念定义从信号评估过程转变为以目标为导向的解释过程，这种转变将学习以及由此产生的内部表征与视觉紧密结合在一起。在这篇综述中，我们考虑了过去几十年中与视觉相关的各种类型的学习（感知、统计和规则/抽象），并认为它们代表了基础学习过程的不同专业版本，在应用时必须完整地捕获这些版本到复杂的视觉过程。我们展示了为什么统计学习的广义版本可以为视觉学习的统一处理提供适当的设置，哪种计算框架最适合这种统计学习，以及哪种合理的神经方案可以切实实现该框架。最后，我们列出了统计学习领域在履行成为促进我们对视觉整体理解的正确工具的承诺方面所面临的挑战。