System-level learning of sensory information is traditionally divided into two domains: perceptual learning that focuses on acquiring knowledge suitable for fine discrimination between similar sensory inputs, and statistical learning that explores the mechanisms that develop complex representations of unfamiliar sensory experiences. The two domains have been typically treated in complete separation both in terms of the underlying computational mechanisms and the brain areas and processes implementing those computations. However, a number of recent findings in both domains call in question this strict separation. We interpret classical and more recent results in the general framework of probabilistic computation, provide a unifying view of how various aspects of the two domains are interlinked, and suggest how the probabilistic approach can also alleviate the problem of dealing with widely different types of neural correlates of learning. Finally, we outline several directions along which our proposed approach fosters new types of experiments that can promote investigations of natural learning in humans and other species.

中文翻译：

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用于感知和统计学习的通用概率框架。

感官信息的系统级学习传统上分为两个领域：感知学习，侧重于获取适合于对相似感官输入进行精细区分的知识，以及探索不熟悉的感官体验的复杂表征的机制的统计学习。这两个域通常在底层计算机制和大脑区域以及实现这些计算的过程方面完全分开处理。然而，这两个领域最近的一些发现对这种严格的分离提出了质疑。我们在概率计算的一般框架中解释了经典和最近的结果，提供了关于两个域的各个方面如何相互关联的统一观点，并建议概率方法如何还可以缓解处理广泛不同类型的学习神经相关性的问题。最后，我们概述了几个方向，我们提出的方法将沿着这些方向促进新型实验，这些实验可以促进对人类和其他物种的自然学习的研究。